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tests/data/DRD2/drd2_reg.pkl delete mode 100644 tests/test_json2.py diff --git a/README.md b/README.md index b836e94..853781b 100644 --- a/README.md +++ b/README.md @@ -336,3 +336,132 @@ build_best(buildconfig, "target/best.pkl") # Build (Train) and save the model on the merged train+test data. build_merged(buildconfig, "target/merged.pkl") ``` + + +## Adding descriptors to QSARtuna + + +Add the descriptor code to the optunaz.descriptor.py file like so: + +```python +@dataclass +class YourNewDescriptor(RdkitDescriptor): + """YOUR DESCRIPTION GOES HERE""" + + @apischema.type_name("YourNewDescriptorParams") + @dataclass + class Parameters: + # Any parameters to pass to your descriptor here + exampleOfAParameter: Annotated[ + int, + schema( + min=1, + title="exampleOfAParameter", + description="This is an example int parameter.", + ), + ] = field( + default=1, + ) + + name: Literal["YourNewDescriptor"] + parameters: Parameters + + def calculate_from_smi(self, smi: str): + # Insert your code to calculate from SMILES here + fp = code_to_calculate_fp(smi) + return fp +``` + +Then add the descriptor to the list here: + +```python +AnyUnscaledDescriptor = Union[ + Avalon, + ECFP, + ECFP_counts, + PathFP, + AmorProtDescriptors, + MACCS_keys, + PrecomputedDescriptorFromFile, + UnscaledMAPC, + UnscaledPhyschemDescriptors, + UnscaledJazzyDescriptors, + UnscaledZScalesDescriptors, + YourNewDescriptor, #Ensure your new descriptor added here +] +``` + +and here: + +```python +CompositeCompatibleDescriptor = Union[ + AnyUnscaledDescriptor, + ScaledDescriptor, + MAPC, + PhyschemDescriptors, + JazzyDescriptors, + ZScalesDescriptors, + YourNewDescriptor, #Ensure your new descriptor added here +] +``` + +Then you can use YourNewDescriptor inside your Notebook: +```python +from qptuna.descriptors import YourNewDescriptor + +config = OptimizationConfig( + data=Dataset( + input_column="canonical", + response_column="molwt", + training_dataset_file="tests/data/DRD2/subset-50/train.csv", + ), + descriptors=[YourNewDescriptor.new()], + algorithms=[ + SVR.new(), + ], + settings=OptimizationConfig.Settings( + mode=ModelMode.REGRESSION, + cross_validation=3, + n_trials=100, + direction=OptimizationDirection.MAXIMIZATION, + ), +) +``` + +or in a new config: + +```json +{ + "task": "optimization", + "data": { + "training_dataset_file": "tests/data/DRD2/subset-50/train.csv", + "input_column": "canonical", + "response_column": "molwt" + }, + "settings": { + "mode": "regression", + "cross_validation": 5, + "direction": "maximize", + "n_trials": 100, + "n_startup_trials": 30 + }, + "descriptors": [ + { + "name": "YourNewDescriptor", + "parameters": { + "exampleOfAParameter": 3 + } + } + ], + "algorithms": [ + { + "name": "RandomForestRegressor", + "parameters": { + "max_depth": {"low": 2, "high": 32}, + "n_estimators": {"low": 10, "high": 250}, + "max_features": ["auto"] + } + } + ] +} +``` diff --git a/docs/sphinx-builddir/doctrees/README.doctree 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z7jj0`f6|HCAnuU6nMmG=ux{>Ngr8EkX8dk$J%AOA!s#0if?1=K-1ltbj!g1ZF9mB9m3UJ3Wjzm53NccZu$Lbu^#M)qXx9QaRO^X7u8 z^D{i15`opd4Qn(;aCWyR`e+5L`(QIeYcIwK@2d`;M$s25xFlh-{8N~sVy$r8@Eob_ z)x?TFMB-MsE%hLPv46>e-FYX_~-9WCpQfDtsZs2IGUrF{J{ zLQxHOfQRNS8i@t~H`#`~N`Z!1+iCB{rrw}ig@h+p4^9d)(`>^2!No%SCkVkrn1E#X zvvj&+;hqP>40-@R4;;s@xq;v@PD|Z~*%QhbQw)E3@{q74$dCBZAQO=avW3)A1UQp@ z4T7jQXpQ4puV9p(7c!pSndn*GUVdU@y8t=o;$6YzYV@P7kA&VCpa!99Dq#{TSL3PBJ6?^4CVGa7z|hpDvih)WCK zj3H&$`I+22Km2^ajzI#QG$SQ(F}TJj=+6tTaXgK=hc^iEj3%kXBcycWU~Rb_9D5>t+C81O58-hyI0fu;I|@=Jl! zRsdj&wf6g}XGpJ>3m|Qyo-O6qC^s)_oRSbTS1wZYawak~YTl|abQ{gLPC;SD)oUHx zZnv`gm$O%1e))ds@I2V1i)C;!O1S+*Xhh0(6WSv+HrOIHUD_55k{t~PjnruR%WK|7 zGZP)pt&zU8@T*&6$*pP|&!}aB;tpY`Fg?nfV)Jy?>iVKIefzZs>romEI<*t-cH)N^ zt!;~oH_d=8yzl!V25uXh(0P9Bu3u;92yA!ORV7%nK(++$sr))nAjzo)|GPlm5~)=M zdle1_zA3Nn)1rtozIs97SOZsiOI-n8?8G4sscymoyH8xd@Gw<$K5?L_+FXIKI*_6- zdr-%mwz4l> zTpJFC%Xkj9Aa8}M^U+%P*;hbyDO68GonY}Z!F!8N51y{nTSaW+Wd&cDL5tJqH26yn zKN5TTuJkD(l!TAdC!joL4|e{scnwRB0C4j08VQBd32N@58R?1oExdzh0>(>{HWh!l z-xkf^EuDS*D3gQGfif>SwRw7_zcL$CH9AAi$eEv0)ou%4WmWu6dZKO{sY4G&pGG=3 zOP|{gxFWIrnqL4%0>j#2imMmjTZ^&H+U zC2R+8!NUi^HcFZ1t@9%RD6)-n>K$bSlIM;@Y1F~{EttH;5gaiFg6IZtxTf3rA>Li% zF8u=J*0{rC5@WKBPvRzn>4Pzm-WPG!a`a?cRWhmTvfy>E2WZKU@*G9#^MsYqc0w@uS75fg~MR-c6|WYPlvGV=2iLp33FEEm!bHnK8Y{(jLEO>)_)Dvzg#?Q z=g|Hq>;DSizM6n@$U~1ZIQ>rj-v@BQT3PeJP?rq!U#$NnK);k|x-e$n#b2)fhp1GY zs*>;Ouhjo4YJDkD%OPfv`n~#p0m!c>APY#D8h@+)U!%se#TuOEe@qHLA5*TM4b#N` zQ2(FM#8WX8tN%Ar@WniZ=ho=|sQ&|0f3X0S8(ZUJXBt08U&+trA=%X*YT2`fcj7J@ zPoHT#%c{@V;(XgLoN2sBfKM0zG}-u+eiIMO9LpG2w}_9zX%SDbn7H5h>vW7PWic%- z, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-03 09:16:30,398] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-03 09:16:30,673] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-03 09:16:30,835] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n", - "[I 2024-07-03 09:16:30,984] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,098] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,154] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,208] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,250] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,544] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,671] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,824] Trial 11 finished with value: -5514.404088878843 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:31,960] Trial 12 finished with value: -5431.634989239215 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,015] Trial 13 finished with value: -3530.5496618991288 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,058] Trial 14 finished with value: -3497.6833185436312 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,100] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,142] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,172] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" + "[I 2024-07-09 09:44:28,211] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:44:28,379] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:44:28,836] Trial 0 finished with value: -3594.2228073972638 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 3, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-09 09:44:28,985] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-09 09:44:29,248] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-09 09:44:29,270] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n", + "[I 2024-07-09 09:44:29,422] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:29,443] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:29,476] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:29,630] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:29,648] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:29,764] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:29,878] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,003] Trial 11 finished with value: -5514.404088878843 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,141] Trial 12 finished with value: -5431.634989239215 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,159] Trial 13 finished with value: -3530.5496618991288 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,176] Trial 14 finished with value: -3497.6833185436312 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,194] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,304] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,320] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:32,320] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,362] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,403] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,495] Trial 21 finished with value: -4783.0470154796785 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,538] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,617] Trial 23 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,684] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,736] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,814] Trial 26 finished with value: -4454.143979828407 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,845] Trial 27 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:32,863] Trial 28 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:32,927] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:32,954] Trial 30 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:33,029] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" + "[I 2024-07-09 09:44:30,388] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,405] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,423] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,533] Trial 21 finished with value: -4783.047015479679 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,550] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,616] Trial 23 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,659] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,677] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,733] Trial 26 finished with value: -4454.143979828407 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,738] Trial 27 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:30,742] Trial 28 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:30,784] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,789] Trial 30 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:30,832] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,851] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,916] Trial 33 finished with value: -4863.581760751054 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-09 09:44:30,936] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -325,15 +337,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:33,061] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:33,200] Trial 33 finished with value: -4863.5817607510535 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-03 09:16:33,244] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,298] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,343] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,390] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,418] Trial 38 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:33,489] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,582] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n" + "[I 2024-07-09 09:44:30,980] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,009] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,027] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,033] Trial 38 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:31,076] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,143] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -347,16 +356,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:33,664] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,758] Trial 42 finished with value: -3963.906954658341 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,799] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,883] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,927] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:33,958] Trial 46 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:33,989] Trial 47 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:34,009] Trial 48 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:34,104] Trial 49 finished with value: -3660.9359502556 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,155] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" + "[I 2024-07-09 09:44:31,306] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,426] Trial 42 finished with value: -3963.906954658341 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,446] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,504] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,524] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,531] Trial 46 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:31,538] Trial 47 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:31,544] Trial 48 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:31,683] Trial 49 finished with value: -3660.9359502555994 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,705] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,729] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -372,67 +382,66 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:34,192] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,241] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,290] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,330] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,377] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,428] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,468] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,522] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-03 09:16:34,573] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,611] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,657] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,695] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,733] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,772] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,811] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-03 09:16:34,849] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n", - "[I 2024-07-03 09:16:34,900] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n", - "[I 2024-07-03 09:16:34,958] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n", - "[I 2024-07-03 09:16:34,997] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n", - "[I 2024-07-03 09:16:35,059] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", - "[I 2024-07-03 09:16:35,124] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n" + "[I 2024-07-09 09:44:31,762] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,788] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,810] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,835] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,858] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,882] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,906] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-09 09:44:31,930] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:31,955] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:31,979] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:32,002] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:32,026] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:32,051] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:32,077] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-09 09:44:32,102] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n", + "[I 2024-07-09 09:44:32,126] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n", + "[I 2024-07-09 09:44:32,151] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n", + "[I 2024-07-09 09:44:32,185] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n", + "[I 2024-07-09 09:44:32,222] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", + "[I 2024-07-09 09:44:32,249] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", + "[I 2024-07-09 09:44:32,274] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:35,185] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", - "[I 2024-07-03 09:16:35,235] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-03 09:16:35,284] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-03 09:16:35,336] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-03 09:16:35,373] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", - "[I 2024-07-03 09:16:35,411] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", - "[I 2024-07-03 09:16:35,461] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n", - "[I 2024-07-03 09:16:35,515] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n", - "[I 2024-07-03 09:16:35,577] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n", - "[I 2024-07-03 09:16:35,616] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,656] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,696] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,748] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,789] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,832] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,873] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,926] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:35,965] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:36,004] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:36,046] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-03 09:16:36,099] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n" + "[I 2024-07-09 09:44:32,300] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-09 09:44:32,327] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-09 09:44:32,365] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-09 09:44:32,391] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", + "[I 2024-07-09 09:44:32,416] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", + "[I 2024-07-09 09:44:32,442] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n", + "[I 2024-07-09 09:44:32,469] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n", + "[I 2024-07-09 09:44:32,493] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n", + "[I 2024-07-09 09:44:32,519] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,547] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,573] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,609] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,635] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,662] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,689] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,714] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,742] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,782] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,809] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-09 09:44:32,849] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-09 09:44:32,877] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:36,137] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-03 09:16:36,176] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-03 09:16:36,216] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-03 09:16:36,269] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-03 09:16:36,312] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-03 09:16:36,367] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-03 09:16:36,420] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n" + "[I 2024-07-09 09:44:32,905] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-09 09:44:32,931] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-09 09:44:32,959] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-09 09:44:32,998] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-09 09:44:33,026] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-09 09:44:33,055] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n" ] } ], @@ -452,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -485,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -529,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -546,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -627,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -643,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -652,7 +661,7 @@ "array([ 67.43103985, 177.99850936])" ] }, - "execution_count": 114, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -673,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -687,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -720,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -770,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -833,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -873,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -882,154 +891,185 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:38,118] A new study created in memory with name: my_study_stratified_split\n", - "[I 2024-07-03 09:16:38,159] A new study created in memory with name: study_name_0\n", - "[I 2024-07-03 09:16:38,245] Trial 0 finished with value: -1422.6893033211163 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3506885259152708, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -1422.6893033211163.\n", - "[I 2024-07-03 09:16:38,768] Trial 1 finished with value: -3949.4997737240788 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.114418824594481, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002226268245894711, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -1422.6893033211163.\n", - "[I 2024-07-03 09:16:38,854] Trial 2 finished with value: -228.21268605718763 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9792392258490488, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:38,921] Trial 3 finished with value: -1702.9050979147669 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9025687106861437, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,021] Trial 4 finished with value: -3247.6912883239856 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,084] Trial 5 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.705951912360815, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.02209358064818234, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,187] Trial 6 finished with value: -2983.9453142752113 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 31, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,277] Trial 7 finished with value: -2198.950805957436 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1928709077332853, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,332] Trial 8 finished with value: -3949.2319858272 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00010401866577354432, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.016852011028048477, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,371] Trial 9 finished with value: -282.24592651550216 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.494633149075681, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,411] Trial 10 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.301741221650847, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.47427593386346e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,453] Trial 11 finished with value: -1227.6702954948303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8716291123223312, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,494] Trial 12 finished with value: -3949.499098730656 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0026930781846823872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.9422417677400336e-06, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,587] Trial 13 finished with value: -1931.955535244796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,631] Trial 14 finished with value: -2124.9660426577593 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,721] Trial 15 finished with value: -3286.345885718371 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,764] Trial 16 finished with value: -280.7162909122767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3549486376243653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,818] Trial 17 finished with value: -244.84820223527166 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2758926310849665, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,860] Trial 18 finished with value: -1228.103944004991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8460298330938263, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n" + "[I 2024-07-09 09:44:37,459] A new study created in memory with name: my_study_stratified_split\n", + "[I 2024-07-09 09:44:37,501] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:44:37,635] Trial 0 finished with value: -3057.0737441471415 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3057.0737441471415.\n", + "[I 2024-07-09 09:44:37,723] Trial 1 finished with value: -3949.4997729531806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04335327191051897, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.2876523244079226e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -3057.0737441471415.\n", + "[I 2024-07-09 09:44:37,743] Trial 2 finished with value: -2641.7637473751115 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -2641.7637473751115.\n", + "[I 2024-07-09 09:44:37,762] Trial 3 finished with value: -281.6547433605841 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.80890800406477, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,827] Trial 4 finished with value: -1814.6019641143478 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,830] Trial 5 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:37,851] Trial 6 finished with value: -1299.620534925781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7893604099368685, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,880] Trial 7 finished with value: -3949.4973593091877 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03671864361026323, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00014643260043430825, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,898] Trial 8 finished with value: -2756.046839500092 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,915] Trial 9 finished with value: -282.4634701019795 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3839369222964104, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,966] Trial 10 finished with value: -3455.5180070042607 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:37,983] Trial 11 finished with value: -2695.2514836330784 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -281.6547433605841.\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:39,902] Trial 19 finished with value: -1194.7031625463042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5492512678428934, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,955] Trial 20 finished with value: -1724.557551910545 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.892106924021418, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:39,998] Trial 21 finished with value: -261.13000775604115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.572116315247876, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:40,067] Trial 22 finished with value: -2001.3060405990927 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47902806935499087, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:40,112] Trial 23 finished with value: -281.1146424526534 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1153402525180756, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:40,166] Trial 24 finished with value: -1890.76109666487 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7599101082108883, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:40,243] Trial 25 finished with value: -2175.126598549413 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23455690290740283, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:40,300] Trial 26 finished with value: -3946.8342189209093 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011086475398399418, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013887466467005373, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", - "[I 2024-07-03 09:16:40,351] Trial 27 finished with value: -221.4006022524013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8445709244824666, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,404] Trial 28 finished with value: -3949.499483217993 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.026247815852988552, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.15702072587365e-06, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,458] Trial 29 finished with value: -3949.031574503982 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004448995249821876, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.019893469499202194, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,501] Trial 30 finished with value: -3949.4995119480113 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03676077353551019, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.984117431783922e-06, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,540] Trial 31 finished with value: -281.1485116995759 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0955265235124376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,586] Trial 32 finished with value: -1328.6100724258592 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.6432156192415805, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.529e+01, tolerance: 3.820e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:40,688] Trial 33 finished with value: -359.62121933507666 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05966299879541892, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,732] Trial 34 finished with value: -1230.2160884709206 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9805503701482912, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,778] Trial 35 finished with value: -282.64701779602615 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.29245080833279413, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,867] Trial 36 finished with value: -3551.475476217507 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:40,958] Trial 37 finished with value: -2906.3484169581297 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n" + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-2641.7637473751115]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:41,016] Trial 38 finished with value: -3949.4995127562875 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00046194410462432797, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.029411427548193e-06, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,108] Trial 39 finished with value: -2181.820041426174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,154] Trial 40 finished with value: -1691.4178445876241 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.25660511317441, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,199] Trial 41 finished with value: -2720.793752592223 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,243] Trial 42 finished with value: -3949.499773431924 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00014288445814174256, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.6632131996063853e-07, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,285] Trial 43 finished with value: -2756.046839500092 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,340] Trial 44 finished with value: -3973.451158876369 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 42.628521164232666, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.902365014811196, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,385] Trial 45 finished with value: -1243.123067137538 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3751930712764295, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,428] Trial 46 finished with value: -1693.6468746309602 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3819621852265203, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,460] Trial 47 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:41,505] Trial 48 finished with value: -1693.2195874041652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.357933416798716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,596] Trial 49 finished with value: -3551.4754762175066 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 18, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,655] Trial 50 finished with value: -252.22880044604048 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4074905424870299, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n" + "[I 2024-07-09 09:44:38,048] Trial 12 finished with value: -2572.460374011433 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,066] Trial 13 finished with value: -1689.5805291820304 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1532631811134977, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,106] Trial 14 finished with value: -292.22585940088896 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13291352086555208, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,170] Trial 15 finished with value: -3137.8321917955004 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,186] Trial 16 finished with value: -1605.5382531580788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.28521421962693694, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,217] Trial 17 finished with value: -1773.5125457246183 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.10131141912172, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,234] Trial 18 finished with value: -2733.5772576431627 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,250] Trial 19 finished with value: -2661.2145086075775 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,254] Trial 20 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:38,319] Trial 21 finished with value: -3455.5180070042607 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,385] Trial 22 finished with value: -1931.9555352447962 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,403] Trial 23 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 26.85865133788878, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3473213833800429e-06, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -281.6547433605841.\n", + "[I 2024-07-09 09:44:38,433] Trial 24 finished with value: -266.77789208348054 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7666549949931907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-2124.9660426577593]\n" + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2756.046839500092]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:41,716] Trial 51 finished with value: -255.31635034669202 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4589283601339789, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,778] Trial 52 finished with value: -256.2177495190004 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4731058898850313, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,836] Trial 53 finished with value: -250.8048373346144 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3833942364290719, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,895] Trial 54 finished with value: -247.44361793349344 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3204936132139333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:41,953] Trial 55 finished with value: -237.67743592416863 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672668791669807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,013] Trial 56 finished with value: -237.41469942991935 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.163017148435057, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,075] Trial 57 finished with value: -236.77997190244454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1526366249808588, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,133] Trial 58 finished with value: -232.71036684152705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0734555939518273, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,196] Trial 59 finished with value: -227.49921956230682 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9650384727405148, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,260] Trial 60 finished with value: -228.050800345174 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9760887954474786, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,322] Trial 61 finished with value: -226.5411165412594 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.944315662351203, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,383] Trial 62 finished with value: -223.6882024574651 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8904553119984893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,447] Trial 63 finished with value: -221.90351644528945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8544623841490979, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", - "[I 2024-07-03 09:16:42,512] Trial 64 finished with value: -220.8253198336247 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8345668974354062, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 64 with value: -220.8253198336247.\n", - "[I 2024-07-03 09:16:42,573] Trial 65 finished with value: -217.53232342915194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.776773300543389, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -217.53232342915194.\n", - "[I 2024-07-03 09:16:42,633] Trial 66 finished with value: -218.7389829217058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.801400475195701, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -217.53232342915194.\n", - "[I 2024-07-03 09:16:42,696] Trial 67 finished with value: -217.69056535924156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7804054136584848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -217.53232342915194.\n", - "[I 2024-07-03 09:16:42,758] Trial 68 finished with value: -216.91097015882943 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7602085351484837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -216.91097015882943.\n", - "[I 2024-07-03 09:16:42,822] Trial 69 finished with value: -215.85510851960055 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7039388910745159, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -215.85510851960055.\n", - "[I 2024-07-03 09:16:42,885] Trial 70 finished with value: -215.668920424489 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6832925660429156, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:42,961] Trial 71 finished with value: -215.7329787021866 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6973711773317055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n" + "[I 2024-07-09 09:44:38,496] Trial 25 finished with value: -2129.55317061882 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 16, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,515] Trial 26 finished with value: -3949.4997529569555 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00014127070069599025, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.240919869254693e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,589] Trial 27 finished with value: -2906.3484169581293 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,658] Trial 28 finished with value: -3473.931670829174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,690] Trial 29 finished with value: -3971.087168793673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.14818118238408418, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 10.953513747430605, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,706] Trial 30 finished with value: -3949.4997433637795 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0001228765801311491, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.4591289226971906e-05, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,725] Trial 31 finished with value: -3949.499719300989 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00041409136806164345, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.821542662478444e-06, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,743] Trial 32 finished with value: -1333.1080184319123 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.6244376673476641, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,762] Trial 33 finished with value: -3948.999405683353 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0012006474028372037, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0035193990764782663, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,780] Trial 34 finished with value: -1248.8139648799436 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0525472971081413, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,798] Trial 35 finished with value: -3916.913857912147 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0005803372070091582, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.366143200836595, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,817] Trial 36 finished with value: -1680.3115194221573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.6315712528909487, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,836] Trial 37 finished with value: -1245.7849372758426 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.325031130330673, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,855] Trial 38 finished with value: -2124.9660426577593 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,873] Trial 39 finished with value: -3949.4997740139383 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.017776950266970536, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.910105415664246e-10, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,890] Trial 40 finished with value: -1351.9376331223525 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9911488496798933, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,957] Trial 41 finished with value: -3286.345885718371 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 24, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:38,963] Trial 42 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:38,982] Trial 43 finished with value: -3949.499768537649 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009258297484819936, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5176632615812382e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:39,002] Trial 44 finished with value: -1703.710237258029 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9478479848575685, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:43,026] Trial 72 finished with value: -215.67102006571292 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6844742580756044, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,091] Trial 73 finished with value: -215.81327642163203 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6524345509991505, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,152] Trial 74 finished with value: -216.23233489315405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6397208322054673, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,202] Trial 75 finished with value: -216.9059768853624 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6240525627120483, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,262] Trial 76 finished with value: -216.96916882508208 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6217835876523938, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,321] Trial 77 finished with value: -216.93975408338443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.622943081420681, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,383] Trial 78 finished with value: -218.81062565770313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4843223898274973, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,446] Trial 79 finished with value: -215.73445853349577 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6608907827069467, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,509] Trial 80 finished with value: -216.98523880564207 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6212698211320781, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,574] Trial 81 finished with value: -215.67775335107788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6868039524504028, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", - "[I 2024-07-03 09:16:43,638] Trial 82 finished with value: -215.6684451623601 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.675099077419251, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:43,699] Trial 83 finished with value: -221.50779683862856 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4128411420851856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:43,762] Trial 84 finished with value: -215.88335246660642 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7053781179584059, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:43,826] Trial 85 finished with value: -215.72603469919923 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.696467737188581, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:43,887] Trial 86 finished with value: -218.4875213911104 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49262152423320377, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:43,940] Trial 87 finished with value: -2733.5772576431627 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,003] Trial 88 finished with value: -223.3316330463421 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3583011871980122, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,055] Trial 89 finished with value: -217.6145666264675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5336056878471176, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,120] Trial 90 finished with value: -215.901895553071 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7063667764245258, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,184] Trial 91 finished with value: -215.70489918927308 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6655648428214861, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,249] Trial 92 finished with value: -215.75938837926353 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6991246060107018, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n" + "[I 2024-07-09 09:44:39,032] Trial 45 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.229495999417457, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0003981480629989713, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:39,052] Trial 46 finished with value: -3949.428258980535 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002172302935728222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005480078761284468, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:39,073] Trial 47 finished with value: -3949.4997740653785 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.045484025655845216, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.497866775391571e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:39,093] Trial 48 finished with value: -3949.499732050299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.023210503277436626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.801011645114503e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "[I 2024-07-09 09:44:39,112] Trial 49 finished with value: -1690.902862507046 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.22764084507571, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 24 with value: -266.77789208348054.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2733.5772576431627]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.244e+02, tolerance: 3.824e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.516e+01, tolerance: 3.820e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.669e+02, tolerance: 3.770e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-09 09:44:39,184] Trial 50 finished with value: -355.30043208462075 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0162536050854966, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.148e+02, tolerance: 3.824e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.547e+02, tolerance: 3.770e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.258e+01, tolerance: 3.820e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-09 09:44:39,247] Trial 51 finished with value: -320.18584104924287 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.009281923522716007, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.469e+01, tolerance: 3.820e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-09 09:44:39,321] Trial 52 finished with value: -365.29910648955155 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.044675516154422834, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 24 with value: -266.77789208348054.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.054e+01, tolerance: 3.824e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.475e+01, tolerance: 3.820e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.207e+01, tolerance: 3.770e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-09 09:44:39,403] Trial 53 finished with value: -248.19318681541083 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0039422500466863575, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 53 with value: -248.19318681541083.\n", + "[I 2024-07-09 09:44:39,440] Trial 54 finished with value: -218.95937317940152 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4811212161180066, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 54 with value: -218.95937317940152.\n", + "[I 2024-07-09 09:44:39,475] Trial 55 finished with value: -217.30176287369363 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5762244804442953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -217.30176287369363.\n", + "[I 2024-07-09 09:44:39,511] Trial 56 finished with value: -217.3805952122523 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5610250131672468, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 55 with value: -217.30176287369363.\n", + "[I 2024-07-09 09:44:39,546] Trial 57 finished with value: -217.29075615961696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5965069449860365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 57 with value: -217.29075615961696.\n", + "[I 2024-07-09 09:44:39,585] Trial 58 finished with value: -217.4699295292146 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5529252738361741, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 57 with value: -217.29075615961696.\n", + "[I 2024-07-09 09:44:39,611] Trial 59 finished with value: -217.30403743758362 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.591083812448875, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 57 with value: -217.29075615961696.\n", + "[I 2024-07-09 09:44:39,649] Trial 60 finished with value: -217.29691912499243 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.579274146968425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 57 with value: -217.29075615961696.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:44,312] Trial 93 finished with value: -217.37803497618862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.561405815846269, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,378] Trial 94 finished with value: -215.6889579245111 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.689830092202269, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,442] Trial 95 finished with value: -215.86864957230281 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7047077164804413, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,508] Trial 96 finished with value: -221.35631440135322 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.42672301990746925, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,547] Trial 97 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:44,613] Trial 98 finished with value: -217.35658017290132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5640798240863641, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", - "[I 2024-07-03 09:16:44,717] Trial 99 finished with value: -2295.758226990139 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n" + "[I 2024-07-09 09:44:39,674] Trial 61 finished with value: -217.29663665981653 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5818879247726123, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 57 with value: -217.29075615961696.\n", + "[I 2024-07-09 09:44:39,712] Trial 62 finished with value: -216.7163115473373 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6285658707995572, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 62 with value: -216.7163115473373.\n", + "[I 2024-07-09 09:44:39,749] Trial 63 finished with value: -215.66659620329202 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6810691120560284, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:39,786] Trial 64 finished with value: -216.64678468403795 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7487983002601213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:39,823] Trial 65 finished with value: -219.08469115255278 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8068222390735325, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:39,857] Trial 66 finished with value: -221.67049576857792 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8493407132310065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:39,894] Trial 67 finished with value: -216.6875210742613 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7508314732901358, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:39,931] Trial 68 finished with value: -217.19882373585992 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7685140566823006, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:39,968] Trial 69 finished with value: -221.51231241451993 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8465024803262398, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,004] Trial 70 finished with value: -216.73145983614108 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.753488429838363, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,041] Trial 71 finished with value: -217.03558248626155 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7638703869128992, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,078] Trial 72 finished with value: -217.11133921135888 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7660487384582346, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,116] Trial 73 finished with value: -218.4601677546666 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7969073835731864, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,155] Trial 74 finished with value: -217.11981969515475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7663003495535811, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,191] Trial 75 finished with value: -227.36711291551754 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9623078145433335, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,226] Trial 76 finished with value: -216.21304370814948 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7229769555215682, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,262] Trial 77 finished with value: -215.98815811142376 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7105170980912668, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,300] Trial 78 finished with value: -224.41250562156347 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34993288668380185, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,337] Trial 79 finished with value: -226.4134153897518 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.941382892540422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,375] Trial 80 finished with value: -215.7394814717604 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6602443806918321, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n", + "[I 2024-07-09 09:44:40,413] Trial 81 finished with value: -215.7076312654289 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6935139136439702, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 63 with value: -215.66659620329202.\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2733.5772576431627]\n" + "[I 2024-07-09 09:44:40,450] Trial 82 finished with value: -215.66506672306295 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6787271614177919, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,489] Trial 83 finished with value: -221.5573047221147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4073714297040414, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,527] Trial 84 finished with value: -215.76836831150376 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6569329476441761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,566] Trial 85 finished with value: -215.88742274290686 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.705586415213896, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,606] Trial 86 finished with value: -215.93365211578404 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6474556827629386, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,645] Trial 87 finished with value: -215.677987167027 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.687131792338989, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,682] Trial 88 finished with value: -215.8173049953933 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6520782072904042, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,722] Trial 89 finished with value: -221.374660807297 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4259162442286648, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,749] Trial 90 finished with value: -2726.0476769808097 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,787] Trial 91 finished with value: -215.6970943689421 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6669588932884131, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,825] Trial 92 finished with value: -215.78916275639816 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6547526034378687, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,852] Trial 93 finished with value: -255.9289281641908 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4686418435830522, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,879] Trial 94 finished with value: -219.41516780662832 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4720283573073992, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,919] Trial 95 finished with value: -255.08394889472513 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968941244750797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,959] Trial 96 finished with value: -215.80459560588784 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6532271661553414, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:40,997] Trial 97 finished with value: -223.9455981312185 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8952103276371259, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:41,079] Trial 98 finished with value: -2119.4669766878847 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n", + "[I 2024-07-09 09:44:41,120] Trial 99 finished with value: -241.1616615224319 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2188693608302126, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.66506672306295.\n" ] } ], @@ -1053,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1091,7 +1131,7 @@ " 'concordance_index']" ] }, - "execution_count": 121, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1147,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 21, "metadata": { "scrolled": true }, @@ -1156,39 +1196,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:45,785] A new study created in memory with name: my_study_r2\n", - "[I 2024-07-03 09:16:45,786] A new study created in memory with name: study_name_0\n", - "[I 2024-07-03 09:16:45,908] Trial 0 finished with value: -0.01117186866515977 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n", - "[I 2024-07-03 09:16:46,487] Trial 1 finished with value: -0.08689402230378156 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n", - "[I 2024-07-03 09:16:46,586] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.01117186866515977.\n", - "[I 2024-07-03 09:16:46,670] Trial 3 finished with value: 0.3039309544203818 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.3039309544203818.\n", - "[I 2024-07-03 09:16:46,711] Trial 4 finished with value: 0.20182749628697164 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.3039309544203818.\n", - "[I 2024-07-03 09:16:46,790] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n", - "[I 2024-07-03 09:16:46,867] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n", - "[I 2024-07-03 09:16:46,921] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n", - "[I 2024-07-03 09:16:47,025] Trial 8 finished with value: -0.1276979508290982 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n", - "[I 2024-07-03 09:16:47,119] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,185] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,250] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,290] Trial 12 finished with value: 0.2935582042429075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,343] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,396] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,483] Trial 15 finished with value: 0.12206148974315867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,546] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,590] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,681] Trial 18 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-09 09:44:42,059] A new study created in memory with name: my_study_r2\n", + "[I 2024-07-09 09:44:42,060] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:44:42,185] Trial 0 finished with value: -0.01117186866515992 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515992.\n", + "[I 2024-07-09 09:44:42,250] Trial 1 finished with value: -0.08689402230378156 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515992.\n", + "[I 2024-07-09 09:44:42,340] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.01117186866515992.\n", + "[I 2024-07-09 09:44:42,420] Trial 3 finished with value: 0.3039309544203818 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.3039309544203818.\n", + "[I 2024-07-09 09:44:42,437] Trial 4 finished with value: 0.20182749628697164 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.3039309544203818.\n", + "[I 2024-07-09 09:44:42,456] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n", + "[I 2024-07-09 09:44:42,477] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n", + "[I 2024-07-09 09:44:42,506] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n", + "[I 2024-07-09 09:44:42,572] Trial 8 finished with value: -0.12769795082909816 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n", + "[I 2024-07-09 09:44:42,623] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,652] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,682] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,699] Trial 12 finished with value: 0.2935582042429075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,714] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,731] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,796] Trial 15 finished with value: 0.12206148974315878 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,812] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,830] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,893] Trial 18 finished with value: 0.04595969590698312 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:47,749] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,802] Trial 20 finished with value: 0.3062574078515544 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,868] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:47,897] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:47,937] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,052] Trial 24 finished with value: -0.12769795082909816 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-09 09:44:42,923] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,940] Trial 20 finished with value: 0.3062574078515544 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,970] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:42,974] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:42,992] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,072] Trial 24 finished with value: -0.12769795082909807 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,103] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,121] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,140] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1202,20 +1245,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:48,109] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,162] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,207] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,295] Trial 28 finished with value: 0.04595969590698327 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,361] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,441] Trial 30 finished with value: 0.1193407034334832 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,487] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,542] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,587] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,618] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:48,661] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,705] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,735] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:48,781] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-09 09:44:43,194] Trial 28 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,223] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,286] Trial 30 finished with value: 0.11934070343348317 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,305] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,335] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,354] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,359] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:43,377] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,395] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,400] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:43,419] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,489] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,554] Trial 40 finished with value: 0.4121525485708119 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1223,24 +1265,7 @@ "output_type": "stream", "text": [ "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [0.2935582042429075]\n", - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [0.3062574078515544]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2024-07-03 09:16:48,883] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:48,975] Trial 40 finished with value: 0.41215254857081174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,016] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-03 09:16:49,121] Trial 42 finished with value: -0.004614143721600923 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,167] Trial 43 finished with value: 0.27282533524183633 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [0.3062574078515544]\n", "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [0.3039309544203818]\n" ] }, @@ -1248,152 +1273,155 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:49,273] Trial 44 finished with value: -0.10220127407364969 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,328] Trial 45 finished with value: 0.30323404130582854 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,384] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,438] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,497] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:49,551] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.936e+02, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:43,560] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-09 09:44:43,625] Trial 42 finished with value: -0.00461414372160085 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,646] Trial 43 finished with value: 0.27282533524183633 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,726] Trial 44 finished with value: -0.1022012740736498 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,746] Trial 45 finished with value: 0.30323404130582854 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,776] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,795] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,815] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:43,835] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.936e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:49,645] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.794e+02, tolerance: 4.977e+01\n", + "[I 2024-07-09 09:44:43,908] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.794e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:49,746] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+02, tolerance: 4.977e+01\n", + "[I 2024-07-09 09:44:43,981] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:49,844] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 4.977e+01\n", + "[I 2024-07-09 09:44:44,055] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:49,950] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-09 09:44:44,127] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.243e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.243e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:50,050] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 4.977e+01\n", + "[I 2024-07-09 09:44:44,200] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:50,145] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-03 09:16:50,235] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,297] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,360] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,424] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,499] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,572] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,634] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,709] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,784] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,858] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,935] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:50,996] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-03 09:16:51,085] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n", - "[I 2024-07-03 09:16:51,175] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n" + "[I 2024-07-09 09:44:44,273] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-09 09:44:44,346] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,383] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,425] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,462] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,511] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,559] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,597] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,649] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,698] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,748] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,796] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,835] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-09 09:44:44,894] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n", + "[I 2024-07-09 09:44:44,953] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 09:16:51,263] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n", - "[I 2024-07-03 09:16:51,351] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n", - "[I 2024-07-03 09:16:51,441] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:51,544] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:51,647] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:45,016] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n", + "[I 2024-07-09 09:44:45,079] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n", + "[I 2024-07-09 09:44:45,143] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:45,212] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:45,286] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:51,758] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:51,872] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.446e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:45,361] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:45,436] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.446e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:51,972] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e+02, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:45,512] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:52,074] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.690e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:45,588] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.690e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:52,186] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:52,252] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:52,348] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.405e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:45,663] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:45,701] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:45,776] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.405e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:52,446] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:52,510] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:52,575] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.050e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:45,849] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:45,887] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,011] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.050e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:52,684] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n" + "[I 2024-07-09 09:44:46,086] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:52,784] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:46,162] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:52,882] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:52,951] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:53,014] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+02, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:46,237] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,280] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,319] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:53,109] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:53,196] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:53,286] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:53,336] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:53,388] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e+01, tolerance: 4.977e+01\n", + "[I 2024-07-09 09:44:46,395] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,459] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,525] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,553] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,580] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.082e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.082e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e+01, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:53,524] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-03 09:16:53,707] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.618e+01, tolerance: 4.782e+01\n", + "[I 2024-07-09 09:44:46,656] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-09 09:44:46,744] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.618e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+01, tolerance: 4.906e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+01, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:53,830] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", - "[I 2024-07-03 09:16:53,901] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" + "[I 2024-07-09 09:44:46,836] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", + "[I 2024-07-09 09:44:46,880] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+02, tolerance: 4.782e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+02, tolerance: 4.977e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 09:16:54,006] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" + "[I 2024-07-09 09:44:46,958] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" ] } ], @@ -1403,7 +1431,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1438,7 +1466,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1466,7 +1494,7 @@ " optunaz.config.optconfig.Mapie)" ] }, - "execution_count": 125, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1563,36 +1591,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:03:21,869] A new study created in memory with name: my_study\n", - "[I 2024-07-02 16:03:21,870] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 16:03:24,369] Trial 0 finished with value: -0.08130897134023123 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08130897134023123.\n", - "[I 2024-07-02 16:03:29,080] Trial 1 finished with value: -0.07343360276296992 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07343360276296992.\n", - "[I 2024-07-02 16:03:32,469] Trial 2 finished with value: -0.08824787163512451 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07343360276296992.\n", - "[I 2024-07-02 16:03:36,303] Trial 3 finished with value: -0.06946431925905228 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.06946431925905228.\n", - "[I 2024-07-02 16:03:40,978] Trial 4 finished with value: -0.06871810141928683 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -0.06871810141928683.\n", - "[I 2024-07-02 16:03:52,341] Trial 5 finished with value: -0.05194238309203199 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:03:53,835] Trial 6 pruned. Duplicate parameter set\n" + "[I 2024-07-09 09:44:48,067] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:44:48,068] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:44:55,862] Trial 0 finished with value: -0.08101726438085996 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08101726438085996.\n", + "[I 2024-07-09 09:44:59,375] Trial 1 finished with value: -0.07097960243642848 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07097960243642848.\n", + "[I 2024-07-09 09:45:01,730] Trial 2 finished with value: -0.09052799500705616 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07097960243642848.\n", + "[I 2024-07-09 09:45:04,427] Trial 3 finished with value: -0.07040719823269156 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.07040719823269156.\n", + "[I 2024-07-09 09:45:09,263] Trial 4 finished with value: -0.06911267342763242 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -0.06911267342763242.\n", + "[I 2024-07-09 09:45:21,857] Trial 5 finished with value: -0.05136901123580041 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:21,865] Trial 6 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.06871810141928683]\n" + "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.06911267342763242]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:03:58,626] Trial 7 finished with value: -0.06827558910154698 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:01,350] Trial 8 finished with value: -0.0753121162867017 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:06,268] Trial 9 finished with value: -0.06780438903573768 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:09,960] Trial 10 finished with value: -0.07776700667235492 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:14,207] Trial 11 finished with value: -0.0669981340211799 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:18,747] Trial 12 finished with value: -0.0631730323000491 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:23,231] Trial 13 finished with value: -0.07284403955164376 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05194238309203199.\n", - "[I 2024-07-02 16:04:35,164] Trial 14 finished with value: -0.056585161860849706 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n" + "[I 2024-07-09 09:45:25,385] Trial 7 finished with value: -0.06280695738717797 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:27,997] Trial 8 finished with value: -0.07816842443619718 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:32,077] Trial 9 finished with value: -0.06455534183210111 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:32,409] Trial 10 finished with value: -0.07792537939575431 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:35,832] Trial 11 finished with value: -0.06861406991549013 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:39,594] Trial 12 finished with value: -0.06427836969444865 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:43,035] Trial 13 finished with value: -0.0685253956054604 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05136901123580041.\n", + "[I 2024-07-09 09:45:54,557] Trial 14 finished with value: -0.05052752675844046 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.05052752675844046.\n" ] } ], @@ -1615,7 +1643,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -1665,7 +1693,7 @@ "outputs": [ { "data": { - "image/png": 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sKi74XJKdDcmh4UPXyJFiWs1QxgZMpkTBDiGEkBZHJGIwekiExtVYCvpGYuQsi90nspH22y3IWRb+Xs6YnByLwFYuBl/wFTk0uqaENJE6iVFWVaf3OFOOHAHGB0ymQsEOIYSQFik+0hfvj4tvUmeHy0hMaUUtvtp5BVf+KgIA9Izxw9ghEXCUNFxWzX3BHzMkHJsPXdc5IsR1pMoWUbBDCCGkRZLLWUidJXg+sQNKymogdZbAU6p9JEax51V6diGOnL+Diup62Nsx6P1IABIifSGxtxOkTXx2RW8cmIkYRueIEJecIVtFwQ4hhJAW51xmATYdzNJYOVlTQHAuswAbDlxrkoBcL2Nx9OJdHL1416DKy+q4Lj1P6tkW0e28VAIzfUnCxrSruaNghxBCSIvCd/NMbcdzfTwXilGjsxpq4WjS2sdF66qqTqGtcPh8LgqKq+Dr4YTELkGwt2/ZZfUo2CGEENJi8N08Uy5n8e1e3ZWW1W08mAUniT1Kq2o5JShr2ildHw8XB2WA1DgR+kLW/Sbn2ncmx+gRp+aOgh1CCCEtBpdpIkU9mrBgd6z5+SrKOaxyaqyorAaLvr+o/Luu6S2uo0aNeUkdUFZVhxmpx1Vei7Y9qowZcbIVLXtcixBCSIvCtUJx7oNyLNx4ASeu5Bv9nIpgQ327Br7JyAoJUb5ITbvcJGjTtxnnpoNZkMtZ3s9nCyjYIYQQ0mJwrTPz09GbuJZbArGAuS7qwQbffbC8pA6YnByDU1e55fWo47PXFx9yOYuM7CKcTM9DRnaRVQZUFp/GksvlWLZsGX788UeUlZUhPj4ec+bMQXBwsMbjHz58iI8//hjHjh0Dy7Lo1asXZs2aBT8/PzO3nBBCSHPDtUJxVa0MbfxcMenJGCz6/qIgFY3Vt2vgOsqU2CUQ3SJ8ER7sYfRGoULvvaUp30iIVWlCs/jITkpKCjZu3Ij//Oc/2Lx5M+RyOSZMmIDaWs37i0ybNg13797FN998g2+++QZ3797FlClTzNxqQgghzRHXXb4TuwTig7FdEdDKhfOu4Fw0Dja4jjJ1i/BV7opubLAiZAVlRb6RevClbdrOkiwa7NTW1mLNmjWYOnUq+vfvj8jISCxZsgR5eXnYv39/k+NLS0tx+vRpvPrqq4iKikJ0dDQmTpyIP//8E8XFxeZ/AYQQQpodRT0aL2nTC79ELMLrybEYMzgC4r+LBCqO91Q7njGgPl/jYEMxyqSLetVjY4IVISsoc13VZi1TWhadxsrIyEBFRQV69uypvM3NzQ3R0dE4c+YMkpKSVI53dHSEi4sL0tLSkJCQAADYvn07QkJC4ObmZta2E0IIab66RvgiLtwH3+zJxLFLdwEA7fyleC05Fr4eTpqPV9vkskOgO77elY4zGQXgcklXDza47IOlXvXYmI1ChaygzGdVmzXslWXRYCcvLw8AEBAQoHK7r6+v8r7GJBIJPv30U8yZMwfdunUDwzDw9fXF+vXrIRIZN0hlioJLdnYilf8T06B+Ng/qZ/Ohvja9+0VVWP7Tn7h5txQAMCQhGC8OCIO9nj6PDfVW/nnToSyczuA+VTN6SAQkEtUtJbrH+ENkJ8KGfZmq+3O5OWD04AjERzbNexmjZwNTV0d7lDdamaXrXIbisumo4jh7e5HFP9MWDXaqqqoANAQxjTk4OKCkpKTJ8SzL4urVq4iLi8OECRMgk8mwZMkSvP7669i0aRNcXV0NaodIxMDT08Wgx3Lh5tb0VwIRHvWzeVA/mw/1tbBkchbpNx/i5OV72H8qG9W1Mrg4iTFtRBx6xAboP0EjtfVy7DuZzenYVh5OePXpWPTq2Frj/YN7hmBA93ZIv/kQhaXV8HJzRHR7b9hpGYUZ3DMEri4OWJX2Jx6WVCtvd3ORoH+XIMTH+INhgeLyGr3nMlRwgDvn4xpfXy31mbZosOPo6AigIXdH8WcAqKmpgZNT0w7Zs2cP1q9fjyNHjigDmxUrVuCxxx7Dli1b8PLLLxvUDrmcRWlppUGP1cXOTgQ3NyeUllZBJpMLfn7SgPrZPKifzYf6WnhnMgqwfm8GihrtbWVnx2DcsChEt/FAUVEFr/PtPZkNLukoA7oEYuzQSIhEjM7nkMtZlJVVoaKiFmKGRXFRhc4pp6hgdyye0huZt4tw/tp9HL+ch9KKWuz47SZ2/HYTXlIHjB4SgSBvT5SWCH99a+3pCC+pg8polDovNwe09nREUVGFST7Tbm5OnEeKLBrsKKavCgoK0KZNG+XtBQUFiIiIaHL82bNnERISojKC4+7ujpCQEGRnc4uwtamvN90XikwmN+n5SQPqZ/OgfjYf6mthaKtSLJOxSNl6CXYMENehFa9z5hVyCyBYNAQyuhJ1jVm+XVpRi32nc5rcXlhWg6VbLpm0avJIfflGA8KavHZLfaYtOiEcGRkJV1dXnDp1SnlbaWkp0tPTER8f3+R4f39/ZGdno6bmnw9EZWUlcnNz0a5dO3M0mRBCiJHMWYROLmexdk+GzmM27M/k3QZNScyGHGfM8m1Lr4jStkrNS+pgdVtTWHRkRyKRYMyYMVi0aBG8vLwQGBiIhQsXwt/fH4MHD4ZMJkNhYSGkUikcHR2RnJyM1atXY9q0aXjrrbcAAJ9//jkcHBwwfPhwS74UQgghHJizCF1tnQyp2y/r3UahsJT/qqHELkH4/sh1sDriCIZpOE6b+no5vtOzyWjjTUnVWcOKKE2r1PRtfGoJFk/1nzp1Kp577jnMnj0bI0eOhJ2dHVavXg2xWIx79+6hT58+2L17N4CGVVobN24Ey7IYN24cxo8fD7FYjI0bN0IqlVr4lRBCSPNljtEWcxahu/ewAvO/O4c/rj/kdDzfYn329iIMiddc6V9hSHyw1pW+5zILMH35Mb2rmnRt8cC1zUJXTVYnEjGIbOuJHtH+yuKH1sbi20XY2dlhxowZmDFjRpP7goKCkJmpGvWGhoZixYoV5moeIYTYPHOMtnCdcmk8iiGXswaNGJy4nIfv9mWipk4GZwd7VNboHtkBDCvW90JiQ2XlfWdyVEZ4GKYh0FHcr47vTufaghWubRayanJzZfFghxBCiOVou/AqRluEyr3gO+ViSABWUyfDhgPX8PulewCAyDYemJAUjY/WnNY5leXqZG9wZeEXEsMwvG8oDp/PRUFxFXw9nJDYJUjriI4hO51rC1a4FBgUsmpyc0bBDiGEtFCGjLYYis+UiyEB2J0HFViRdhl3HlSAAfBk73Z4qncIx9YZ99rs7UUYnNBG/4EwbKdzbcGKIRWYWyqL5+wQQgixDD6jLcbiOpXi5izhtcKIZVn8duku/rP2DO48qIC7iwTvjuiM5EfbQyRicC2nWG+CcnlVnSCvkQu++TP6gpXmtCLKkmhkhxBCWihzJrhynXIBC84BWLsAKdbtu4YTVxq2F4pu54lXn4yBu8s/VfmtJYlXgWvQJ3UW46UhEZyCleayIsqSKNghhJAWim+Cq6EJwwD3KZfSqlqt9zd2424J1u3PxL2HlWAYIPnR9niiZ1uABTKyi5RtzHvIrfifuZJ4uQR9UicxFr/em9eejYoVUUQzCnYIIaSF4pPgKsSKLcWUi/p5vKQOGPn3eTKyizida/vvt1AvY+HhKsGkp2IQ0UZzUjMXXm7mS+LlEvS9NDTCJJtTt2QU7BBCSAvFdbTlQtZ9wVZsdY3wRafQVlpXL3EJwACgXsYitr0XJiRFw81Zwns5d2OjB0eYdcqHS9BHhMU72HnppZfw0UcfITQ0tMl9GRkZmDFjBnbu3ClI4wghhJiWvgtvXJgPZqQe13kOPiu2NI2+7DuToxwh4hKAMQzwXL9QDOneBiKGMWg5t8KoIRHoGu6D9FuFZs134ZJnY8y0IVHFKdg5e/Ys2L8rJp0+fRpnzpxBYWFhk+OOHDmCnJymG5IRQgixXrouvBnZRYJtScB1Sbm2AAwAXJ3EmPpsR3QIclfexnc5d2PllXWYvvR3ld27TbV9hTpdeTZ8pw0pMNKNU7Dz448/Yvv27WAYBgzD4N///neTYxTBUFJSkrAtJIQQYnLaLrxCrWbiW9Ona4QvItp4Yvm2P5H597LwTqHeeCUpGq5OYoPaqMmO3242uY3r9Jx6gNEh0B3X75QYHXDwrTNkzv3GmitOwc7s2bPx7LPPKvekmjNnDjp06KByjEgkgpubG8LCNJfHJoQQ0vwItSUB3wrKt+6VIjXtMh6UVMNOxOD5/qEYFB8MhmkaPBi6kophoHMjT13Tc5oCDPXzGRJw8A0KzVUBu7njFOxIpVIkJCQAAL777jtER0fD1dXVpA0jhBBieUJtScB19KWovBr7z+TgxyPXIZOzaOXuiNeejkX71m5GtVETXYEOoH16TluAoX4+QwIOPkFheLCH2SpgN3e8E5QTEhJQVlaGAwcOoLKyUjl91VhycrIQbSOEEGJhQm1JwHX05cj5u7h+pwQA0DXcB+OHRcLZUazzMVza2JiX1AFdI3xw4Gyu3mPVgzRDkqH5BBx8pg35jpa1ZLyDnd9++w1Tp05FdXW1xkCHYRgKdgghxIYIsVSay+iLiAGu3ymBvR2DFxPDkNglUOO0Fd82vjigA6ROEpVcmms5xZyCHfUgzZBkaD4BB59pQ2urDm3NeAc7ixcvRvv27fH+++/Dz88PIhEVPiKEEFtn7JYEXEZf5Czg6+GE15Jj0M5f+7SVEG0MD/aAl9RBZRWWOk3Tc4YGDlwfx2fakOt+XuaqDm3NeAc7N27cQEpKCrp162aK9hBCCLFSxm5JoGtJOQDER/ri5ccj4eRgeL1bTW3Utix79JAILN1ySeu5NE3PGRo4cH0cn2lDofKpWgLen6jWrVujvLzcFG0hhBBi4xSjL4fP52L777dQUV0PezsRRg0MQ7/OrTlPW3Gla1l29xh/vD8uHiu3XVIZ4dE1PWdIMjTfgIPrtKFQ+VQtAe9gZ9KkSVi+fDkeeeQRBAUFmaJNhBBCmjFdBe7kLIs9p7Lx06+3IGdZ+Hk5Y/LTMWjjJ+V1Hi70LcsW2YkwuGcIIgLdOFdQ5psMDRgWcHCdkqOtJ7jhHezs3LkT+fn5GDRoELy8vODo6KhyP8MwOHjwoGANJIQQ0nzoGkkJC/LAV7vSceVWQwX+HjF+GDs4QuO0FddCedoCIi6rpjbsz8SA7u14T89pCzDU6+wYG3BwbZex+VQtAe9gx9/fH/7+/qZoCyGEkGZM30iKs6M9KqvrIbEXYfSgcPTpGKBx2oproTxdAZGLo1j/suzSGqTffIggbyfer1VTgCFUBWVDGJtPZet4BzuffPKJKdpBCCGkGeMyklJZXY8Ab2e8nhyLQB/NhWm5VhCWsyxS0640uU8REA3qxi3NorC02qBgB9AcYFDAYZ0MTnm/ceMGjh07hoKCAowdOxY5OTmIjIykysqEENICca0/82JiB62BDtfzFJbVYN2+azqPOZmer7ctAODl5qj/INLs8Q525HI55syZg61bt4JlWTAMg8cffxwpKSm4ffs21q9fT9NchBDSwnCtI1NZUy/Iecqr6nTeX1ZZB1cnsc7jvNwcEN3eG6UllZyekzRfvCsCpqSkYOfOnZg/fz6OHTumrKI8Y8YMyOVyLFmyRPBGEkIIsW5CbRgqZAG8njF+Ou8fPTgCdjaSxCuXs8jILsLJ9DxkZBdBLtez8VcLw3tkZ+vWrZg6dSqeffZZyGQy5e1RUVGYOnUqFi1aJGgDCSGEWL/wYA+4u0hQUlGr9Rgu9WY6BLrr3Y1c3/0KcWE+ys0yNS3Ljo+0jWXZXFeutWS8g50HDx4gKipK431+fn4oLS01ulGEEEKalyt/FaKmTqbzGC71Zq7fKdEbyLAs9E9RSf9ZDWXLy7K5rlxr6XhPY7Vt2xZHjx7VeN/p06fRtm1boxtFCCGkeaiXyfHjL9ex5Ic/UF0rg9RJDBe1ujleUgfOF12uOTv6pqgaB1aKVVM9ov0R2dbTZgIdzivXaEqL/8jOuHHjMGfOHNTV1eGxxx4DwzDIzs7GqVOnsGbNGsyaNcsU7SSEEGIlFIX8cu+X4+jFu7jzoEJ5X9nfoy2uTmL0jPFTTiUBQEZ2kd7RFa45Oy6Ohu+fZSu4rlzLuF0EEcPY5MgWV7w/Lc8//zwKCwuRmpqKTZs2gWVZTJ8+HWKxGBMmTMDIkSNN0U5CCCFWQFN+iCblVXU4cDYX4cEeuJB1n3NOCZe9pzylDvjl4l2dz7/pYBbiwnxs+qLOdRQsNe0yKqr/WQXXEvN5GJblkubVVHl5Oc6fP4+SkhK4ubmhU6dO8PDwELh55iGTyVFYWKH/QJ7s7UXw9HRBUVEF6uvlgp+fNKB+Ng/qZ/MRqq+N3VtKnbb8EF1cHe1RXq19ubmm6S19z5PcJwRpv9/S+9wzR8bpLPLX3D/TGdlFWLDpgsGPN2c+jyn62svLBXZ23LJxDB4HdHV1Rd++fQ19OCGEEBMSeoWOXM5i3X7dhfw00RXoAJpHYPRtblkn43ax5Dry0VwZsgN7Yy1h9EuBd7BTUlKCL7/8EufPn9e48oo2AiWEEMsyxQqdXcf/QqmOZeWGKiyrwbWc4iYjMF0jfNEptBUOn89FQXEVfD2ckNglCPb2ImRkF3E6t5A1e6yRITuwN6at720R72Dnww8/xKFDh/Doo48iMjLSFG0ihBBiIK4rdLj+oq+rl+OHw9dx6HyuUE1sQtMIjKaRqX1ncjBqYBjiwnz0jmhwqeljC7SNgrk42qvk6Whj66NfCryDnePHj2P27NmUiEwIIVaI6wodLr/o84sqsSLtCrLzy4RsYhPqIzBcRqb0jWh0jWiorWNMnhLXnCehc6P40rQDu1zOYtH3F/U+1tZHvxR4BzsuLi4ICuK2mywhhBDz4vpLXd9xp6/mY+2eDFTXyuDqJMb/DYvEuv3XeOeH6BthUB+B4ToytWByL40jGorqygfO5uLA2VyD85S45jxZS/Vi9R3Y5XKWRr8a4V1UcPTo0Vi9ejUqKoRfvUQIIcQ4xu5RVVsnw3f7MrFi+xVU18rQIcgdc8fHo3OYD0YNDOPcDkUhwZcf153uoF5Vmc/IVNcIXyyc3AszR8ZhULeGH+Hq64sVo0HnMgs4t10xsqTeDvVzcT3OEhT5PLpwqWhtK3iP7IwZMwY//fQT+vXrh5CQEDg5OanczzAMvv32W8EaSAghLYViOqSsqg7BAe5o7enI+xztA9z0HsMwDXtQqbv3sAKpaVeQe78cAPBEz7ZIfjQEdqKG38W6Vkm9OCAMUiexxqkcXSur1Ec/+I5MiUQMwoM98NWudJ3Hc81T4jqy1Cm0laC5Uaagb1VbS6qzwzvYmTNnDm7duoX27dvD0dER6mV6DCzbQwghzYKp8jM0TYfwvSidyyzAd3sz9R7Hsg17UDWe9jhxJQ/f7c1ETZ0MUmcxXk2KRmx77yaP1ZQfoq8P+DzGkJEpIfOUuJ7r8PlcwZ7TlAx5v2wR72Dn8OHDeOedd/Dqq6+aoj2EEGK1TJWfoS0ht5DHUnG+Bf8UIyM1dTJsPHANv126BwCIbOOBV5+MgadUe9Chnh/CBdfHcKkdo55rIlSeEp9zFRRXCfacpmbI+2VreAc7EokEsbGxpmgLIYRYLVPtLi3EUnEu51Dn4eKAOw8qsCLtMu48qAAD4Mne7fBU7xAA3Pax0qbx6JebkwRggNLKWk7nEokYdI/yxd7TOVqPSYjyVTmHsXlKcjmrfL2l5dxqCfl6OOk/iEfbmgtLrzwzFO9g5+mnn8amTZvQvXt3iES885sJIaTZEbp2TWNCTMFwOUdjXlIH3C+uwudb/kBtnRxuLhJMejIaUe28jB690rd3lr5zyeUsTl3Vndh7+moBnuvfQdnXhowGKRy/dBcrt11CoYYVXbrOldglCPvO5LSo1U7WsvLMELyDHalUii1btiAxMREdO3aEi4uLyv0Mw+Djjz8WrIGEEGJpQuaEqBNiCobvcvBWHk74Zk8GAKCtnxRTn+sIT6mD0aNXXKbS9J3LkL7mUklY08qjMxkFWLrlUpNj9aWejhwYBnt7kUHP2VyZamTTXHgHO9u2bYO7e0MW/+XLTV84w9jGG0sIIQpC5oSoM3YKBgDKKrlNvTiIRbC3E+FaTrHytuz8Msz/7ixGDAjD5kOGj17xnUrbqOVchvY135VHcjmLDft0J3Orj/Con0vbczaX0Q6uTDmyaS4GJSgTQkhLIkRAoo0xUzAKri5iTs9VVy9HTV3TTTSLymqQmqY/uVnX6BXfqbSishrsOn4LT/Vpr3K7MX3NZ+XRtZxilakrTVgWGJHYAW6uEp3nsvVVyaYc2TQXg5Nu5HI5MjIy8Ouvv6K8vBzFxcUCNosQQqyHIiDRxdD8DCGKv3m5cqvHIxfgGqxt5MWQUa203/9qUnjP2L5WrDzqEe2PyLaeWvuNa3vdXCVaz6WY2ilWS2ouLq+1eFFBIZlyZNNcDAp2tm/fjv79+yM5ORmTJk1CdnY2Zs2ahTfffBO1tcLviksIIZbEJSDp2ynA4PMrpkPUL/Jebg6cciG4BAhC0TbyYuiqo00HsyBvFIWZq/KvECu4uEztyIWIMC3MlCOb5sI72Nm9ezfee+899OjRA0uWLFEO1w0aNAhHjx5FSkqK4I0khBBL0xaQKKT9/hdmpB43+Nd81whffDapJ0YkdsDAbkGY8FQsFr3em1PeB5cAIT7Cx6B2Naat8jJgeMClmP5oTGvwJ+UW/HERHuwBLyNGkPhM7VgzxbL7k+l5yMgu0hicmXJk01x45+ysWLECI0aMwNy5cyGTyZS3P/vssygsLMQPP/yAadOmCdlGQgixCoqckF3H/0La77ea3G/MypRzmQXYcOBaoymRXGw9LMGoQeGczqUIEFTPAYjtRHhpSDi83Z1wJvM+rzapY1ngWm4xItt4asyL0bc6SZviipom9VviwnxMWvlXJGIwekiExtVYCrpGkGxhaofrUnJDV7tZE97Bzq1bt/Dee+9pvK9Tp05YunSp0Y0ihBBrdvSPuzrv57syReuy3r9zPyYnx0DqJGly0VcPEDxdHSC2bxiwZxigf+dAjBoYBjs7EaddsLlYuvUS7O1EKjuZN75AalqdpE9BYSVmpB43+4qm+EhfvD8uvkmdHS7bdDT3qR2+S8mb+z5bvIMdb29v3LhxA717925y340bN+Dt3XQvFUIIsRVCr0yRy1ms/bvmjTapaVdU/u4pdUD3KF+culqgsS3ebo54LTkGoa3/mXIyZuSlsZq6piu6FBfI5D4hSOrVTjkiU1RWg40Hr6kERupcncRI+/2vJrebq35Lr46tERHohvRbhbxGkIRYRWcphi4lb877bPHO2Rk2bBi+/PJL7N27V5mMzDAMLl++jJSUFAwdOlTwRhJCiLUQevoiI7tIZzCgSVFZDfae1l69N/nREJVAR0Hx69zDVcLr+bhK+/0W3k05hgtZ9xHZ1hM9Y/3x8uOROh+jb5m2OZJ8ua7gUn+MORKpTcGYfCND+soa8A52pk2bhs6dO2PatGno2rUrAGDs2LF4/vnn0a5dO7z11luCN5IQQqyF0NMXGTlFxjRHo59+vak1QOga4YsFr/WCo8RO8OcFmi671pVsnNwnRG+gZ81JvuZIpDYFW8g34sugjUC//vprHDt2DCdPnkRxcTGkUikSEhLQr18/qqBMCLFpgk9fmGDQQhEghAd7aJxyuH6nBNW1Mv0nMkLjaRBt0x+nM/I5ncuaL7rNcWqnuecbGYJ3sPPKK69gwoQJ6N27t8a8HUIIsWVCr0yJbOuJXSeyhWqe0oWs+/hqV7rGpN86WdMqykLTtH+Veg6TrVx0Nb02a9ac840MxXsa6/z58zR6Qwhp0QyZvtBWzySyjSdcHHn/7tTrwNncJhczRdJvQWGl4M+nib4RGVuo39IcNed8I0Px/hf26KOPYseOHejatSvEYm77segil8uxbNky/PjjjygrK0N8fDzmzJmD4OBgjcfX1dXhyy+/RFpaGsrKyhAbG4sPPvgAUVFRRreFEEK44jN9oa+eycuPRxq9Sqox9Q0s1R29eFfvL3tXJzHKq+qMaoe+ERlbqN/SXDX3peR8MSzPHctmzJiBPXv2QCwWIzQ0FM7OzqonZBh8++23nM+3bNkyrF+/Hp9++in8/f2xcOFC5ObmYufOnZBImq4Y+OCDD/DLL7/g008/RevWrfHFF1/g/Pnz2LNnD6RSKZ+XoiSTyVFYWGHQY3WxtxfB09MFRUUVqK83/bCxtVCv/WHq+euW2s/mRv1sGG31TBQUI0HnMguw8UAWisrNk5+S3CdEY2HExu26cacE+87kqARODACJWKRxQ9HGvKQOWDC5F6d/+5qCQXNcdM3xmTb39yFfhraP7+NM0ddeXi6ws+M2QcV7ZCcvLw9xcXHKvxuz22ttbS3WrFmDd999F/379wcALFmyBI8++ij279+PpKQkleNzcnKwdetWrFixAo8++igAYP78+UhOTsbly5fRs2dPvi+HCIxrRU5CbIG+L3w+9Uy6RvjCSWKPRd9fNLg9nq4SdIv0xYGzuXqP9fVy0vnLHgD2ns5p8jgWDbV2EiJ9cDpDe0VmPiMyzTHJl4vm8H1oSL5Rc3hd6ngHO+vWrRPsyTMyMlBRUaESpLi5uSE6OhpnzpxpEuwcO3YMUqkUffv2VTn+8OHDgrWJGI5vRU5CmjMuX/h8CxCWVhm5kTLDwMWRW3qBh4sDItt6agwyAGBG6nGdj79+pxSTk2Ow+dB1QUZkmluSrz62+n3YXF+X8FlxPOTl5QEAAgJUdwv29fVV3tfYrVu3EBwcjP3792PVqlXIz89HdHQ0Zs2ahdDQUKPaYm9v0AbwOimG17gOszVncjmLTfp+wR7KQnyUn+C/1lpSP1sS9fM/zmTo/sJ/87mOiI/0RRnHnJeyqjrY24vg7eZoVLuKymqQ9vstuDrao1xH/RovNwdEh3gp/y3GhqpWvr/6VyGnIM3D1QFL3uyDzNtFKC6vhYerBBFtmk+hOVN9pi35fWhKxrwuS39/8A52EhMT9a7GOnToEKdzVVVVAUCT3BwHBweUlJQ0Ob68vBzZ2dlISUnBzJkz4ebmhtTUVIwaNQq7d+82eKsKkYiBp6eLQY/lws3NyWTnthZ/Xn+gsreMJoWlNbhbVI1HOrQySRtaQj9bg5bezzI5i40Hruk8ZtPBLAzo3g7BAZp3CFcXHOAOT08XiPPK9SYXc8GIdF9QJj3TEd7erlrvr7vFrdBhHcvA29sVvXScqzkQ+jNtDd+HpiDE67LU9wfvYCchIaFJsFNRUYE///wTNTU1GDduHOdzOTo2/Iqpra1V/hkAampq4OTUtEPs7e1RXl6OJUuWKEdylixZgn79+uGnn37ChAkT+L4cAA3Rammp8Esx7exEcHNzQmlpFWRmqGthSTn3mgan2o4L8hb2w96S+tmSqJ8bXP2rEA9LqnUe86C4Cqf+yEVpVZ3e4MXLzQF+7hLM++oEzmQUCNLGsspaPNO3PX45n4uiRjuge0olGDMkElHB7igq0r4oQ8xwi7bEDKvzPNbOVJ9pS34fmpIxr8sUfe3m5mS6BOVPP/1U4+11dXV4/fXXlaM1XCimrwoKCtCmTRvl7QUFBYiIiGhyvL+/P+zt7VWmrBwdHREcHIzcXP0JebqYcnWJTCa3+dUrUidueQJSJ7HJ+qIl9LM1aOn9/LBUd6CjcDajgFOi8JO92uHDr07h7kNhf3CVV9Y2rEFXwUDO4f0Lbe3OqehcaGt3m/gsCP2ZtobvQ1MQ4nVZ6vtDsMkzsViMl156CVu2bOH8mMjISLi6uuLUqVPK20pLS5Geno74+Pgmx8fHx6O+vh5//vmn8rbq6mrk5OSgbdu2xr0AYhQqDkZaCq7VfE9c0b0VAsMAT/Rsi61Hb+oNdFyd7PHOC50w8alojEjswOn5dRUVVOxbpQ2XonN9OwXovL8ls9Xvw+b8ugTNFCopKUFFBfchTYlEgjFjxmDRokU4dOgQMjIy8Pbbb8Pf3x+DBw+GTCbD/fv3UV3d8EuqW7du6NWrF9577z2cPXsW169fx8yZM2FnZ4enn35ayJdCeGqJFTlJy8TlC1/qrL8gH8sCP5/I5lS4r7yqHnceNHy3Bvm4wsHITTyF2Ek87fe/MCP1uN7AyRjaqk5bO1v9PmzOr4t3UcG0tLQmt8lkMuTl5WH9+vWIjY3FV199xfl8MpkM//vf/7Bt2zZUV1crKygHBQUhNzcXAwYMwCeffILhw4cDaEhSXrRoEfbu3Yvq6mp06dIF//rXv9ChA7dfO5rbQEUFhWKJ4mAtsZ8tgfr5H/oKBQ7qFsRpCgsAott5Iv0v4Xc+12fmyDitS73lchYzUo/rXZGlYIrlxuao5WLqz7SliiWamiGvy9JFBXkHO5GRkVrvi4uLw2effaaSf9McULAjLKqgbJuon1Xp+sJ3cRRjwaYLes/xVO92iGzjyelYoU18Kho9ov013peRXcSrTXyqJXPBteq0saiCsuFsvoKypmXlDMPA1dUVbm5ufE9HbJCtFQcjRBNdVX/lclZvgq+7iwRP9Q4BAL3HmoKu3CN9G3iqU9/h3Bh8qk43h6DBVr8Pm9vr4p2zExgY2OQ/sViMnJwcyGQyU7SREEKskuILv0e0PyLb/lNMj0tuw5jB4RCJGE7HCk1fEinXJOzG+AZI2vCpOk0IV7yDnfLycrz//vvYsGEDAGDPnj147LHH8NxzzyEpKQn37t0TvJGEENLcdI3wxePd20B97MFL6tBkGkaxA7W+xGeu9I136Esi5ZKErc6QAEkTrkGTUMEVaRl4T2MtXrwY+/btQ+/evQEAixYtQmRkJCZPnozPP/8cixYtwuLFiwVvKCGENJf8h7p6OX48ch0HzzUkKQd4O6N/XGsE+0i1trnxtFhZVR3q5MDXO7TnregyJCEYoYHuBifHKkabdOXNNCbkcmOuQZNQwRVpGQzK2Zk1axaSkpJw+fJl3LlzBzNnzsSAAQNQX1+Pjz76yBTtJIS0cNa+07IiELtdUIYjF+4gv7ChwOqQhGA82y8U9hwSKRXTYvb2Iri5O2PbkSy95fkbYxhgSHwwXkhsmBYzZidxxWiTep9rIuRyY8Wokr6ChtZYy4VYL97BTnFxMdq3bw8AOHr0KOzt7ZWjPO7u7qipoaFFQoiwrH2nZU2BGANgWI82eLa/YWUx7EQMRg+JwNItl7QeM+npaJSU1aKguAq+Hk5I7BKksqmxEEmkuhbsmmIZNZdRJWut5UKsF+9gJzAwEJmZmejWrRsOHjyIzp07w9W1YRO4o0ePIigoSPBGEkJaLlOszhFyOkxbIMYC+PnkbbQLcDM4GIiP1Dy6Yo5aLfqWfyf3aYekXiEmCTq0jSrZQo0aYhm8g50RI0bg008/xYYNG3Dz5k3873//AwC88cYbOHToEGbPni14IwkhLRef1TlcRjGEnA6Ty1ms25+p8xhjl0nrWuIuBE2BHwC9Aeahc3fg4+EMT6lpcqdM/bpJy8I72Bk3bhy8vb1x5swZvPHGGxg2bBiAhr2x5s6dixdffFHwRhJCWi4hV+cIPR2W9ttNlFbo3u5BiBo0pqppoi3w69eptd4As6yqDl/tSlc+xhS5U82tlguxXryDHQBISkpCUlKSym1LliwRpEGEENKYUKtzhJwOq6mTYdPBa/j1D26lNqxxmbSuwC/t91u8zmUtuVOEaGNQsHPp0iWcOnUKtbW1yuQ1lmVRWVmJc+fO4YcffhC0kYToIpez+PP6A+TcK4HUSUxD3TYmPNgDLo72qKiu13qMq6O93tU5Qk2H3X1QgdTtl3HnPvctZqxtmTSXwM8Q3+3NRKfQVipJ0taMvjtaDt7BzoYNGzB//nyNGfoikQh9+vQRpGGEcHEuswCbDqouz7Wm5cjETBj9FyghpsOO/XkP6/ZnorZODjcXCV5NisKK7VeMDsTMjUvgZ4iyqjq8s/wYXhoaYfX//ui7o2XhHX6vX78effv2xalTp/B///d/eOGFF3Dx4kV88cUXcHBwwFNPPWWKdhLShGIYXr0OiWJI/VxmgYVaRoR0LadYZzABAOVVdXq3DzBmOqymVobVu9Kx+uerqK2TI6qtJ/49Ph5Rbb30n5BDIGZuppxWK6uqs/p/f/Td0fLwDnZyc3MxatQouLu7IzY2FufOnYOjoyOGDBmCiRMn4rvvvjNFOwlRwTX/Qi7XXiOENA9CJShz2QJB6ixGh0B3ldtyC8ox79szOHY5DwwDJD8agnde7Ax3VwfBAjFDyeUsMrKLcDI9DxnZRZw/71wDv+Q+7QzewsJa//3Rd0fLxHsaSywWw9HREQDQtm1bZGdno66uDmKxGF27dsU333wjeCMJUSf0cmRivYRKUOZSrK6ssg7vrTyBUQPD0CXcB79duocNB66hrl4OD1cJJj0Vg4g2/3yeLLmPkzFL6LlWKU7qFYKkXiG4llOMwvJqfH/wOsqqdK8+U1D/92ctW33Qd0fLxDvYiYqKwpEjR9C9e3eEhIRALpfjjz/+QLdu3ZCXl2eKNhLSBG0W2HIIuX0Aly0QFFMZYUHuyMotAQDEhnhhwpPRcHOWqBxrqX2cjF1Cz7dKseKi72Bvx3m/LOCff3/WtNUHfXe0TLynscaPH4+1a9fiX//6F5ydnTFgwADMnDkTn376KT777DN07drVFO0kRAVtFthyKC7MuvDZPqBrhC8+m9QTrk5incdl5ZZAxDB4tl97THuhU5NAB+A2NSb0Pk5cpmG+25eJ+nq5zmO07bSuaVd29cfo6zsFDxcHZWCmHlxaKj+GvjtaJt7BzsCBA7FixQqEhoYCAObNm4d27dph8+bNaN++PebMmSN4IwlRZ4mLDLGcrhG+GJoQ3CTXl2GAoQnBvEcHrt8pQTmH6ZgRAzrgiZ7tINKSZCx0IMYFl2mYsso6TF9+TG8g0TXCFwsn98LMkXGY+FQ0Zo6Mw4LJvXT2Z9cIX/xvSm9I9QQ8XlIHdPh753VdzJ0fQ98dLZNBxRD69++PV155BQDg6emJNWvW4OLFi1i3bh0CAgIEbSAhmljiImPLDE10NZdzmQXYezoH6hUvWBbYezqH9+gA1ykKV2f9IxiGjJAYg2vbyzmuilJUKe4R7Y/Itp6c/s3Y24vw0tAInceMHBiG63dKOOfHmAt9d7RMBhUVBBo2/Tx+/DgKCgowffp0XL16FTExMQgMDBSyfYRopbjIqNfKoM0C+bGmfApNTLERqNBTGebcx4nv9Iqxe3Npw2WzzpPp3PI4zZ0fQ98dLQ/vYKeqqgpTpkzB8ePH4erqioqKCkyYMAGbNm1Ceno61q9fj7Aw3VEzIULpGuGL+Cg/3C2qpiqoBhB6ryhTMMXqmfBgD3i4SlBcXqv1GC5TGZZYYcQlYbsxU64s0hfkWXN+DH13tCy8g53//e9/uHLlCtauXYtu3bohNjYWAPDZZ59hwoQJ+OKLL7Bs2TLBG0qINiIRg0c6tEKQt5PepEzyD1OMmJiCKVbPVNXWw0vqqDPY0TeVoWtEzJSjPFxWUqm7kHXfZMuodW3WKeRKOlOg746Wg3ews2fPHkyfPh09evSATCZT3u7r64vJkydj3rx5gjaQEGIazaXeiNCjAzfulmBF2hU8LK2GiGHgIBGhquaf7zLFVEZcmA8ysos0Biz6RsTU9/ISelpQMQ3z3d5MTnVvDpzNRXiwh9lH6fgucSfEVHgHO6WlpVrzctzd3VFZWWl0owghptdc6o0INTrAsiz2nc7B1qM3IJOz8PFwxGtPx6Ktn7TJKMyFrPuYkXpc66iNvhEx9arKppgW7Brhi06hrTB9+TFOK8u+22eZTTq55PYQYmq8g52wsDDs3LlT44afhw8fpnwdQpoJa86naEyI0YHyqjqs3pWOP248BAB0i/TFy0Mj4ezY8BXYeORK36hNcp92Bm+iKfS0oL29COOGRnCa0lIsRx9ngU06zZnATYgmvIOdyZMn44033kBxcTEee+wxMAyDM2fOYNu2bdi8eTMWL15sinYSQgRm7fkUjRkzOpCVW4wV26+gqKwG9nYijBzQAf3jAsFoqJ3DJY/pwNlcg1+HKaYFu0b4YlC3IE7tUixHt0Tiua7cHkJMjXewM3DgQCxcuBCLFy/G0aNHAQCffvopvL29MXfuXAwdOlTwRhJChNN4BVG/TgFI+/0vrcdaUz4F39EBOctiz8ls/PTrLchZFn6eTpicHIs2flKtz8Elj0nfxp/6mGJaMC7Mh1cQZg2J54SYk0F1dp588kk8+eSTuHnzJoqLi+Hm5ob27dtDJDLvXDAhhB9NK4hc/p7KaXwRt9Z8Cq6jA6WVtfh6Vzou3ywEAPSI9sPYIRFwctD9lcc1EFFPQObDFNOC1rQcnRBrZHBRQQBo3769UO0ghJiYtlwUxUU7uU8IfL2cmn0+RebtIqzccQXF5bUQ24swelA4Hu0YoHHaSh3XQGRQt2Ck/X6Ld9tMNS1oyHJ0SyeeE2JOnIKdAQMGcD4hwzA4ePCgwQ0ihAhPLmexdk+GzmMOnsvF52/2MWuQI2RRPrmcxa4Tf2H777fAskCAtzMmPx2LIF9XzufoEOgOhkGTbSkaYxhgWI+2CPRxaTJK5uok1rkyypTTgnyXo1s68ZwQc+IU7Ny5cwcMwyAqKgoREbr3QyGEWJ+M20V6p13Kq+qQcbsI0e28zNImIbepKCmvwaqd6biaXQQA6B3rjzGDI+AgseN1nut3SnQGOkBDIHT9TonGHKKyqlqkpl3h9ZxC4roc3VoSzwkxF07BzkcffYTdu3fj3LlzqK2txRNPPIGkpCQEBwebun2EEAFk/B0EcDnOHMGOkNtUpP9ViFU701FaUQuJWISxgyPQ+xHDNiTmW3uocQ6RXM5iRupxnY8zR2Iwl+Xo1pR4Tog5cMooHjlyJNatW4dffvkFL7zwAo4ePYohQ4bg+eefx9q1a1FQwG/HYUKImXG9rgl4/dO2kzrXbSr07bwul7NI++0mFm++iNKKWgT6uODDcfEGBzqAcbWH+FSkNjVz78ROiLXjlaDs6+uLl156CS+99BLu3LmDn3/+GTt27MCCBQvQtWtXJCUlYciQIfDw8DBRcwkhhogM9sQuZHM6Tgi6pqhcHMVGb1NRVFaDVTuuIPPvwKFvpwCMHBgOBzG3aSttuULG1B6ytorUVMiPkH8YvBorMDAQEydOxMSJE5GdnY0tW7Zg/vz5mD9/Pv78808h20iIzTH3btmRbT31Lpd2dbQXZCmyvimqQd2COJ1HW1Bw+eZDrNqZjvKqOjhI7DBuSAR6xPjzap+uXCFDqzVbY0VqKuRHSAOjlp5XVFTgyJEj2Lt3L3777TcAQO/evQVpGCG2SC5nsev4XzhwNsekG0WqE4kYvPx4pM6L+LjHI40OuLhMUZ1Mz+d0LvWgQCaX46dfb2H3yYYRqmBfV0xOjoW/lzPn9nHNFTKkWnNzqkhNSEvDO9hRBDh79uzB77//DplMhh49euCjjz7CoEGDIJVqr05KiDUz9WjLucwCrN2ToXF0xRQbRapTXMQ3HLiG4vJa5e2erhKMGhQuyPNyyVspq6zTu0RbPSgoLK3Gih1XcD23BADwWFwgRgzoALE999VWXHOF4sJ8DJoCEokYdI/yxd7TOVqPSYjypWkkQiyAU7DTOMD57bffIJPJEB8fjw8++ACDBg2CpycNk5LmTchl0NrOz6Xgm6lX65g6j4NrPkrPGD+d2xs0nir64/oDfL0rHRXV9XCU2OHlxyOREOXHu218Eogj23ryngKSy1mcuqp7scbpqwV4rn8HCngIMTNOwU6vXr1QX1+PLl26YNasWRg6dCi8vMxTi4MQUxNyGbQmXEYUFMxRxt+UeRxc81HiwnwQHuyhc6qoXibH1qM3sO/vkZK2/lJMfjoGvp7cp60aM3UCMd9gihBiPpyCnZqahn/AZ86cwdmzZ/Gf//xH67EMwyA9PV2Y1hFiYnymNgz9Nc7lIthYcy7jzydvRSRitI4yPSiuwoodV3DzbikAYEDXILzwWAeI7fntv9d4arKY43vg5iTh9RwK1rYaixDyD07BzhtvvGHqdhBiEeb4Nc734tacy/hz2aOp8RSVplGm89fuY83PV1FZUw9nB3uMHxaFrhE+vNuiaWqSEwNnmKxxNRYhpAEFO6RFM8evcT4XN1tYrWPoaqa6ejl+/OU6Dv6dyxMS4IbJT8eglYcT7zZwzZHSpLSyVv9BGtBqLEKsl1FLzwlp7szxa5zLRVDBVsr4802ELiiqROr2K8jOKwMADEkIxrP9QmFvx2/aCuCXI6WJoe8131EtQoj5ULBDWjRz/BrnchF0dRJj3NAImyrjzzUR+kxGAdbuuYqqGhlcHO3xyhPR6BzWSuOxXMoD8M2RaszY99rQUS1CiGlRsENaNHP9Gtd2EXRxtMegbkFI6hXS4n7x19XLsPnQdRy5cAcA0CHQHa89HQMvN0eNx3MtD2DMlKNQ7zVt00CIdaFgh7R45vo1ThfBf+QXViI17TJuF5QDAIb1aIvkR0O0TlvxKQ9gyDSU0O81bdNAiHWhYIcQmC8QoYsgcDI9D9/uzURNrQyuTmIMTWgDL3cHXM8t0djnfMsDcJma9HSV4JWkaJRW1rbooJOQloJTsJOWlsbrpMnJyQY0hRDLokDEtGrrZNh48Bp+/eMeAKC1tzMqquux5egN5TGapqUMqXysb2py1KBwRLejwqiEtBScgp1Zs2ap/J1hGn4BsSzb5DaAgh1CiKq7DyqQuv0y7tyvAAOga4QPzmbeb3KcpmkpQ8oDaJuaNPWGq4QQ68Qp2Dl06JDyz1evXsWMGTPw+uuv4/HHH4evry+Kiopw+PBhLF26FJ988onJGksIaX6O/XkP6/ZnorZODjcXCSY8EYVv9mTofEzjaSljygM0/kGm6e+EkJaBU7ATGBio/PObb76J119/Ha+++qryNj8/P4wcORK1tbVYuHAh+vXrJ3xLCSHNSk2tDOsPZOLYn3kAgKi2npj4ZDTuPazkNS1lSHkAbQnNxeW1Jt9dnhBifXhX7Lpx4waio6M13te+fXvk5mrfyZgQYnn19XLsP30b6/dnYv/p26ivlwv+HLn3yzHv2zM49mceGAZIfjQE77zYGe6uDrynpRQ5OLo0XjLONaFZLqdRHkJaCt6rsdq1a4edO3eid+/eTe77/vvvER4eLkjDCCHC++FwFvadyUHj2Zzvj1zHkPhgvJCoO6DggmVZ/HbpHjYeuIbaejncXSWY9GSMSuK3IdNSXSN8MTQhuEnbGQYYEh9sVEIzIcT28Q52pkyZgrfeegt//fUXHnvsMXh6euLBgwfYv38/rl+/jq+++soU7SSEGOmHw1nYezqnye0sC+XtxgQ8VTX1WLc/Eyev5AMAYkK88GpSNNxcVHcRN3RaSlfbQwPdjUpoJoTYNt7BzuDBg7F8+XIsX74cn3/+OViWhUgkQlxcHNauXYtu3bqZop2EECPU18ux70zTYKGxfWdyMLxvKOzt+e9HdTu/DKnbryC/sBIihsEzfUPweI+2EDFNa9fwrVrNt84O7T5OCFFnUFHBxMREJCYmoqamBiUlJfDw8IBEItH/QEJaOC57O5nC4fO50LcQiWUbjhuc0IbzeVmWxS8X72LTwSzUy+TwlDpg0lMxeveX4lO1mu+0FO0+TghRZ3AF5Rs3buDYsWO4f/8+xowZg5ycHERGRsLV1ZXXeeRyOZYtW4Yff/wRZWVliI+Px5w5cxAcHKz3sTt27MCMGTNw6NAhBAUFGfpSCDELrns7mUJBcZWgxwFAZXU9vt2bgTMZBQCAjqHeeOWJKEiduf3w6Rrhi06hrXD4fC4Kiqvg6+GExC5BTUaWDE1opt3HCSEKvIMduVyOOXPmYOvWrWBZFgzDYOjQoUhJScHt27exfv16+Pv7cz5fSkoKNm7ciE8//RT+/v5YuHAhJkyYgJ07d+ocLbpz5w7mzZvHt/mEWASfvZ1MoZW75s01DT3ur7xSrEi7goLiKtiJGDzbLxSDE4I1Tltpoyn423cmp0nwZ2hCM+0+TghR4D05n5KSgp07d2L+/Pk4duyYskjXjBkzIJfLsWTJEs7nqq2txZo1azB16lT0798fkZGRWLJkCfLy8rB//36tj5PL5ZgxYwZiYmL4Np8Qs7OGpdBBPtxGXPUdx7Is9p+5jY/XnUNBcRW83Rwxa3QXDO3ehnegs/yny02mmhTB37nMAuVtimkpXTRNS3WN8MXCyb0wc2QcJj4VjZkj47Bgci+dgY5cziIjuwgn0/OQkV1Ey9MJsRG8R3a2bt2KqVOn4tlnn4VMJlPeHhUVhalTp2LRokWcz5WRkYGKigr07NlTeZubmxuio6Nx5swZJCUlaXzcihUrUFdXhzfeeAMnT57k+xKIjZHLWfx5/QFy7pVA6iTWmQdjiZwZa1gKXV5dZ/RxFVV1SN1+Bif+bNjbKi6sFf7viSi4OIp5tYVvwrEx01J89juz5DQjIcS0eAc7Dx48QFRUlMb7/Pz8UFpayvlceXkNlVUDAgJUbvf19VXep+7SpUtYs2YNtmzZgvz8fM7PpY8hK1D0sbMTqfyfCO9MRgE27M9EYanqVMXoIRGIj/Rteuy+TBSW6T9WSGVV3AKNsqo6k3wOAcDbjdv0lLebo8Y23LhTgpSf/sT94mrYiRiMGBiGwfHBKnvicXX1r0JOwd+NuyWI+nuzzu4x/hDZiZq+f24OGD3Y+PfvTIbuacY3n+to0s+IOvruMA/qZ/OxdF/zDnbatm2Lo0ePolevXk3uO336NNq2bcv5XFVVDcmQ6rk5Dg4OKCkpaXJ8ZWUl3n33Xbz77rto166dYMGOSMTA09NFkHNp4ubmZLJzt2THL93F0i2XmtxeWFaDpVsu4f1x8ejVsTXvY4UWHODO+ThTfQ67uzvDe2c6HpZUaz2mlYcTuncKgl2jERKWZbH91xtYuysdMjkLf29nzBzbDWHBho9A1d0q4nYcq/rvcnDPEAzo3g7pNx+isLQaXm6OiG7vrdJeQ8jkLDYeuKbzmE0HszCgezujn4sv+u4wD+pn87FUX/MOdsaNG4c5c+agrq4Ojz32GBiGQXZ2Nk6dOoU1a9Y02SFdF0fHhl+btbW1yj8DQE1NDZycmnbI/PnzERISghEjRvBttk5yOYvS0kpBzwk0RLBubk4oLa2CTCZ8Sf6WTC5nsXJb0+ClsZU/XUJEoFvDnzkea4oprdaejvCSOqiMSKjzcnNAa09HFBVVCP78CqMGhWsM+BRGDgxDack//w7KKmvx1c50XMx6AABIiPbD9FFdIaurN6qdYoZbHoyYYTU+T5C3E4K8G74fGrfXUFf/KtQZBALAg+IqnPojVznSZGr03WEe1M/mY4q+dnNz4jxSxDvYef7551FYWIjU1FRs2rQJLMti+vTpEIvFmDBhAkaOHMn5XIrpq4KCArRp809tj4KCAkRERDQ5fuvWrZBIJIiLiwMAZc5QUlISXnvtNbz22mt8X46SKfYHUpDJ5CY9f0uUkV2kM3gAgMLSGqTfKmz4M8djTZUzM1JfzsmAMMjlrEkTYuM6tNK5QimuQyvl5/R6bglW7LiMwtIa2NuJMHJABwyMD4aLkxhF1bVGfZ5DW7tzqoMT2trdLP9uHpbqDnQaH2fuf8f03WEe1M/mY6m+NqjOzqRJkzB69GhcuHABxcXFcHNzQ6dOneDh4cHrPIq6PKdOnVIGO6WlpUhPT8eYMWOaHK++QuuPP/7AjBkzsGrVKtqTq4UxxZYAptw+wFqWQneN8EVcmE+TJG2gIYAsKq/Gtdsl+O3SPchZFn6eTpicHIs2flLe+TnaksFFIgbdo3w1bv+gkBDla7Y6OFRxmRDbxzvYef/99/H6668jODgYjz76qMp9N2/exIIFC7BixQpO55JIJBgzZgwWLVoELy8vBAYGYuHChfD398fgwYMhk8lQWFgIqVQKR0fHJvlAiiTm1q1b8w60SPNmiguUqS9m2gINPhd1IVaTqa9Q0rQKCQDCgtwx7flOcHLg/5tI18qmuDAfnLpaoOPRwOmrBXiufwezBDxUcZkQ28fpW+zu3bvKP6elpWHgwIGws7Nrctyvv/6K48eP82rA1KlTUV9fj9mzZ6O6uhrx8fFYvXo1xGIxcnNzMWDAAHzyyScYPnw4r/MS28b3AmXoxUzopep8lkKrM8XSaG3FDgEgK7cE6X8V8j63vgKKyX3aWXwpfmNUcZkQ28ewrL4dcxqmrX799Ve9J2NZFr1798bq1asFaZy5yGRyFBYKnxhqby+Cp6cLiooqaD5YAOqBR1lVHVLTtF+gGlcl1nVRVz9WwZrqrnBtP5/gTC5n8W7KMRSX12o9r5fUAQsm94JIxHD6PMvlLGakHtcZzLg42qOiul7r/QoTn4pGj2ju1diNpen9tlTFZfruMA/qZ/MxRV97ebkIm6A8b948HD9+HCzL4l//+hcmT56sklAMACKRCG5ubujevTv/FhOih7bAY2hCME5fLWhSO0f9AsU3Z8bS2zs0xrkCMwtsPqQ/OFMERBeu3dcZ6AD8R1i4FFDkEugA5s+REWKakRBinTgFO35+fnjmmWcAAAzDoH///nBzc1NOZVVXV6Ourg5SqdR0LSUtlq7AY+/pHEx59hEE+rrpraDM9WLGt8KvqXGtwKxplEs9ONOWn6OLKZK89Y3uWCpHxphpRkKI9eJdyjApKQmff/45XnjhBeVt58+fR8+ePfHZZ59BLqehQCIcToHHgWuIbu+NnrH+iGzrqTMAUVzMekRrP5bP9g7mIMQqsU0Hs3AmI1/jflT6mCLJe1C3YJ33U44MIURIvIOdpUuXYseOHSr7VkVHR+Pdd9/FDz/8gK+//lrQBpLmR8jNFDkFHqU1SL/50ODnUGeKZe3GEGI6p7CsBt/uzeT9OL4jLFw37Uzq1Q5TnoltcqyX1MGsU4SEkJaB95rSnTt34r333lOpYuzh4YGXX34Z9vb2+O677zBx4kRBG0maD6GTerkGFIWl1cqqurpwSeA1d90VfW3isvKMi0qOuTKN8R1h4bOyiXJkCCHmwjvYKSoqQnCw5iHo9u3ba93Ak9g+UyT1cg0ovDhsdMk1EDNn3RUubeISQAjNmFVIfJLBKUeGEGIOvIOd9u3bY9++fejdu3eT+w4fPsxrI1BiO0yV1Msp8HBzQHR7bxQXVSAju0jjKAGfQMyUFX4bj+IUFFYi7fe/OLVJVwDx4oAO2HzoutEjPwCQ1Kstott6GT3CQqM2hBBrwjvYeemllzBr1iwUFxdj4MCB8Pb2RmFhIY4cOYI9e/bgk08+MUU7iZXjk9TL55c8l1GN0YMjcOryPazcdkllCXrjir18AjG5nDVJhV++K6HUg0NdAYSIYXT20aD4YJzNKNA7WpXcp71gAQmN2hBCrAXvYCc5ORkVFRVISUlR2avK09MTH374IZKTk4VsX7Mml7P48/oDvUuibYEpk3r1TYsAwCffnmnyuH8q9obwCsRMEbjpKwrI9Tm0BRCKPtpw4JpK7Rw7EYMRAzpgQNdghAe5U5VgQkiLZNBGoKNHj8aoUaNw69Yt5Uag7du3h0jEe3GXzTqXWYBNB7M0jjTY4koTUyf16trAcmaq7i1KDpzVPh3VmCIQEzpw4zLFZ+xzAEBbfym83ByVwU6XcB+8mhQNB0lDPSxr2YyUEELMzaBgB2goLti+fXsh22IzrKn6rrmYI6lX06hGRnaRSkCpCd+KvUIHblxGiox9jgvX7mP1z1dRWVMPJwd7/N+wSI2fMcqlIYS0RJyCnaioKHz//ffo2LEjIiMjwTDavxgZhkF6erpgDWxurK36rrlYajNFU1TsFTpwM7Qej6ujvd7nqJfJ8cOR6zh4NhcAEBLghteejoGPh/Zl+JRLQwhpaTgFO1OmTIGfn5/yz7qCnZbOVIm6zYElpkm4V+wN0rjySaFxICZ04GZwPR49/84KiquwIu0y/sorAwAMjg/Gc/1DYc9xYzxCCGkpOAU7b7zxhvLPb775pskaYwusrfquuZl7miQ82ANeUgedU1kNFXtDEOjjyjkQEzJwM7QoYHlVndag+GxGAb7ZfRVVtTI4iEV4omc7DOvR1qZGCwkhRCicgp27d+/yOmnr1q0NaowtMHf1XWuka5qESwVjvs81ekgElm65pPUYfRV7AWisz8PneABaX5cxRQHVg+K6ehk2H76OI+fvKG+rqZNj2683ceTCHZtNgCeEEGNwCnYSExN5TV1dvXrV4AY1d+asvtvcCL2VhEJ8pC/eHxffpM4Ol4q9+trE5XgXx4Z/Ro1zgtRfl7aRIn0aB8X5hZVITbuM2wXlGo+15QR4QggxBsOyrN5dGrdt26YMdkpKSrBo0SL07NkTjz/+OHx8fFBcXIzDhw/jl19+waxZs/Diiy+avOFCksnkKCysEOx8+mqqWOpiJPSoCh+m7BN7exE8PV3w8GE50m8Vcn59fNtkSK0c9XMo3oPC8mp8f/A6yqrqtD7WS+qABZN7QSRicDI9D9/uzURNrQwMA+j6V9v4cUJS9HNRUQXq6+WCnpuoor42D+pn8zFFX3t5ucCOY44ip5Gd4cOHK/88ZcoUJCcnY/78+SrHPPnkk/jvf/+LPXv2NLtgR2jafsVbss6OqUZVuDDXCjUuq4zUgw2ubTK0Vo7662rcRgd7O71J0PUyOTbuy8KvfzRMJQf5uCD3vu7A3FYT4AkhxFC8l20cO3YMjz/+uMb7+vfvjwsXLhjdKJuh9vObwyCaSShGJNSnTxTTHucydW+NYCw+K9RM6VxmAWakHseCTRfw9c6rOkdV1NtkaK0cXa9LERR7SlXzt7ykDpjyTCxat3LB/O/O4tc/7oIBkNSrHYZ2b8PpeW01AZ4QQgzBu6igp6cnLl26pHEj0JMnTyqXqLdk2qY7istrzZ5TYQ11f6xhhZohU1AA/6rKus6hibYk6BNX8vDvtWdQWyeHm7MYrz4Zg5gQL2RkF3F6TltOgCeEEL54BzvPP/88li9fjurqavTv3x+enp548OAB9u7di02bNuFf//qXKdrZbFhDcNGYNdT9sfQKNWO2a+BbVVkTN2eJzvsbT23V1MrwzZ6rOPZnHgAgso0HJj4VAw/XhuenBHhCCOGPd7AzefJklJWVYfXq1Vi1ahWAhukZR0dHvPXWWxg9erTgjWxOrCG4aMwaRlUsfYE2dAqKb1Vlba7+VQQRw+hNmL5zvxyp26/g7oMKMAzwdO8QJPVqp/IYS1WqJoSQ5ox3sMMwDN577z28/vrruHjxIkpKSuDp6Ym4uDg4Ozuboo3NiimDC0NWU5lqVIVPWyx9gTY0kONbVVmbn09m4+eT2VoTwlmWxe+X7mHDgWuorZfD3VWCV5OiIWIYnM7Ib9K/tKEnIYTwY/BGoC4uLvDx8QHLsujUqRNqa2sp2IHpggtDV1OZYlTFkLZY8gLNt6/5VlXmSlMdnOraeny3LxMnr+QDAGJCvJAQ5YvVP1/V2b+0oSchhHDHqc6Ouu3bt2Px4sW4f/8+GIbBjz/+iKVLl0IsFmPx4sWQSHTnKFgbIevsyOUsZqQe1xtc8KmDYmyNGiFr3Bh7LlPU+tFXv4HLeyJ1EmPEgDB4SvW3SfEaispqsOlQFsr1rOpSp3j/c/+etsovrISIYfBM3xD4eTohJe2K1sdasmAg1SQxH+pr86B+Nh9L19nhvfR89+7deO+999CjRw/873//g1ze0OhBgwbh6NGjSElJ4XtKm6KY7tCFz5QN14RnuVx7zKpviTPXi6cQbVEk4/aI9kdkW0+zjERcyLqPmjqZzmNeGhqBnrH82sSIgIFdA3m3p7CsBj8cuY75351DfmElPKUOmDkqDo93b4tNh/TX/tHVv4QQQpriPY21YsUKjBgxAnPnzoVM9s8F5Nlnn0VhYSF++OEHTJs2Tcg2NjuK4GLTwSy92xfoI1TCsxDTHtaSfK0+OhQd4qX1WH0jUa5OYowbGsFrZIvLdhH67D+TAwDoGOqNV56IgtRZgozsIqvoX0IIsTW8g51bt27hvffe03hfp06dsHTpUqMbZQu6RvgiPsoPd4uqkXOvBFInsUFTNkImPHOpMGyuthhKU7DhJXXApOEdERXsrnKsXM5i7Z4Mnedj5XLEhflwfm5NgZMiyEnuE4J6uRy7jmfrPZeIAZ7r3wGDE4Ih+nsrFmvoX0IIsUW8p7G8vb1x48YNjffduHED3t7eRjfKVohEDB7p0Ir39Ehj+mq08D1OLmeRkV2Ek+l5yMgu4jUlYul6OdoqQReW1eCTb8/gTIZqJeiM7CK9oy0VNTJOhfq4TOH9+sddPNUrpMl0oToRA7w3qguGdm+jDHQAy/evJRjzeSSEEK54j+wMGzYMX375JXx9fdGvXz8ADcvRL1++jJSUFCQlJQneyBaN63c/h+OM3R/LkvVyuAQbG/ZnolN7b2VQmZHDrdpwRk6RzqkwgPsU3vU7JXqXqI8fFokwDX1k6XpE5mbJ/doIIS0L75GdadOmoXPnzpg2bRq6du0KABg7diyef/55tGvXDm+99ZbgjWzJSqtqBTlOiP2x+CRfG/KLXddjOAUbpTU4eDbnn8cJGCjymWJS5GxJncUq9zk72OH15Bj0fqS1xscKndxuzSy9XxshpGXhPbIjkUjw9ddf49ixYzh58iSKi4shlUqRkJCAfv36gWGa/xexNRFiakPILSy41MvR9YtdW5K0vl/5XIONzYevY9+ZHIwaGIbItp7YdUJ//gyXPCY+7wPLsnhYUo3Kv6fQpM5iJPcJQb/OgYL0b3NnbVuqEEJsH+9g55VXXsGECRPQu3dvjZuBEmEJMbUh9CoqXSu7tCXxKn6xuzjaq+TReEod0D3KF3tP52h9zJRnYnnlqSgeNzk5tsnzqXN1EiOyjf7XzPV9aN3KBUu3/omL1x8AALpG+GD845FwdhRrfZw6Wy8YaC2r+gghLQfvaazz58/T6I0ZCTG1YYpVPprq5XD5xa4eeBSV1WgMdBrbdDALHQLd9Sb+qvv+UBZeGhqh85hxQyM4BRFc3of+cYGY9+0ZXLz+APZ2DMYMDsfrybG8Ap3Gz2fuekTmQqvOCCHmxjvYefTRR7Fjxw7U1fGrGksMZ2xRQHOt8jF0w019Gif+8n3cvQeVgrVD2/vg6SpBzxg/pP12C4WlNfD1dMIHY7shsUsQ/TDQoCWuOiOEWBbvaSwHBwfs2LEDe/bsQWhoaJP9sBiGwbfffitYA0kDY6Y2zLXKx5S/xIsratAj2p/33lQHzuofNeKTG6L+PkjsRPjlj7s48ffeVglRvhg3NBJODgZvO2fzWtqqM0KI5fEe2cnLy0NcXBxiY2Ph5OQElmVV/lNsH0GEZ+jUhrlW+Zjyl7ji3F0jfLFwci+MSOzA6XH66uwockP4ULwPnq4OWH/gGi7fLITYXoRxQyMw6akYCnT0aEmrzggh1oH3t/K6detM0Q6bI5ezuPpXIepuFUHMsAht7W7RL29TrfJpvHWDm5NE7y92Q3i6SlR+5YtEDAZ2C8a+Mzk6n0tfcrIC3/bKWRY/n8hG2m83wbKAv5czJifHItjXldd5WrKWsOqMEGI9eAU7ly5dwp07d9C2bVtER0ebqk3NnrUWSxN6lY+ufaKEVFsvx4Ws+yp9pxgd0FW8r3OHVjh2OU/v+csqudUyAoCSilp8vfMKrvzVULCwZ4w/xg4Jh6OERnP4svVVZ4QQ68GwLKu3pFppaSkmTZqEixcvgmVZMAyDuLg4LF68GAEBAeZop0nJZHIUFlYIci59G0/y2WXcmqhvvllWVYvUtCtaj1cfVXF1tEe5jlGWoQnB+O3SPZ0jMZr67lxmgdYNV2vqZfh651W9r23Ck1HoFaP/c3z1r0Ks2pmOkopaSOxFGD04HH0eCWgxScj29iJ4erqgqKgC9fU0XW1K1NfmQf1sPqboay8vF9jZccvG4fRz9PPPP0d6ejrefPNNxMbG4ubNm1ixYgXmzJmDr776yqjG2hJbLZamaQRH3/VdYi/C5BGdUVpZq/zFfiHrvtZpi7gwH5y6WqAz2NHUd7o2XOWy5xUAeLk66rxfLmex49gt7Dz2F1gArVu5YHJyLAJbuXA6PyGEEMviFOwcOXIE06dPx7hx4wAAffv2hZ+fH959911UVlY2WZHVUtlisTRtI1X6xgOLymshYhj0iPZX3qZr2iIju8jgvlNsuBrk7aTyi0GIVT/F5TVYteMKMm4XAwD6dAzA6EHhcBDb6WwrIYQQ68Ep2Ll//z5iYmJUbuvevTtkMhnu3buH0NBQkzSuubG1YmlcRqp00fQ6FSuZuBzL9ZzacMnr0bXq5/Kth/hqZzrKKuvgILbDS0Mi0DPWX+OxhBBCrBenYKe+vh4SiUTlNnd3dwBATU3zuHCbg60VSzO2SCCf12mqvusa4YuhCQ0rtxqPRjEMMCQ+WGP+lEwuR9pvt7D7RDZYAEE+rpicHIMAb5q2IoSQ5sjoJSQc8ptbjOZaLE09+VgxtWTMCBTf12mqvjuXWaBxOwqWBfaezkFooLtKwFNYWo1VO67gWm4JAKB/59YYMSAMkmYybaXtvSSEkJbM6GCnpaxE4cLYaRNT0Hfx07VM3pgRKL6v0xR9xzdh/NKNB/h611WUV9XBUWKHlx+PREKUH+fnszRrLXlACCGWxjnYmTt3Llxd/ymaphjR+fDDD+Hi8s/wPm0XYT30Xfz07VA+OTmGd5FAY4rCCV1ojmvC+NXsIlz5qxB7T90GALT1k+K15Bj4eTafxHt972VzLXlACCFC4BTsxMfHA2g6ZaXp9pY8rWVNS8/1BzKx2HxId1u/P3QdIwaEITVN+2gLACT3CYGvl5Mg0yZCFprjOg23bn8mCoqqAAADugThhcQOENvz3knFYqzpc0cIIdaIU7BDW0RwY4ml55qmqQDovfit35+JskrdO9cXltXAxcFe77YLv/5xFwsm97K6CynXabiCoio4Odhj/OOR6BbZ/EY/bLHkASGECIlq3AvI3EvPtU1T9esUoPfipy/QUcjIKeK8maaxF1K5nMWu47dw4GyuynMamnfCJekZANr5SzE5ORY+Hk4GtdsU+CQa21rJA0IIERoFOwIy59JzXdNUab//ZfT5lTjOShp7IT2XWYC1ezI0BlaG5p1wSXruFOqNKcMfgT3HkuPmwDfR2NZKHhBCiNCs5xveBoQHe+jdCNP17+0MjGFssb/GbdHFS+rAebRG24VULmeRkV2Ek+l5yMguglzeNHpSBG76RpA2HczS+HhdFEnPnlLV9jEAhvVog7ee72R1gc7yny43GY1SBHznMguaPEYxgqWLNZY8IIQQc6GRHXMTIIHb2GJ/QMPF78UBHXRu5jlyYBgi23gaXP+GywgFn8DN0OmyjqHeSP+rCEcu3AHQsLfVW891tKppK8DwRGNrLHlACCHWxHp+0tqAaznFekcnyqvrcS2n2KjnESL3YuTAMMRH+mFyciykaiM8XlIH5ZSR4kKq71zqF1KuIxR8Aze+rz2/qBL/XXdOGeg83r0N5o6Pt7pAB+CXaKxO2whW4/eSEEJaKhrZEZC5EkW55l4k9wnB0T/uaq1Zcy6zAJsPZaGs6p9kZVcnMV4c0EHl4si3/g2fEQq+fcEn7+RUej6+3ZuB6loZXJ3EmJAUhY6hrXg9nzkZ+/kRctk+IYTYEosHO3K5HMuWLcOPP/6IsrIyxMfHY86cOQgODtZ4fFZWFhYuXIg//vgDIpEI8fHxmDVrFlq3bm3mljdlrkRRrlsrJPVqh6Re7TRe/LQlOJdX1SE17QpEzzBNAh6uF1I+IxR8+oJr3kltnQybDmXh6MW7AIDwIHdMfCoGXm6OnJ/LEoT4/GjbaJUQQloyi09jpaSkYOPGjfjPf/6DzZs3Qy6XY8KECaitrW1ybFFREcaPHw9HR0esW7cOX331FQoLCzFhwgSr2JDUXImifKaWFBe/HtH+iGzrCZGI4Tzyop4MrOlcmvAZoeDSZwoJUb56RynuPazA/O/O4ujFu2AAJPVqixmj4qw+0AEo0ZgQQkzFosFObW0t1qxZg6lTp6J///6IjIzEkiVLkJeXh/379zc5/uDBg6isrMSCBQsQHh6O2NhYLFy4EDdu3MD58+ct8ApUGZrfYghjcjSMyQ3hgs8IBZc+Uzh9tUDnaqxjl+5h3tqzyL1fATdnMaa/2BnD+4bCTmTxmJ4Tc35+CCGkJbHoNFZGRgYqKirQs2dP5W1ubm6Ijo7GmTNnkJSUpHJ8z549kZKSAkfHf36li/6+kJWWlpqn0XoIvb+TNnI5CxdHMZ7t3x7lFXWQOkvgKf1naklXUTpT5xbx3cG8a4QvkvuEIO33WzrPq201Vk2tDF9svoCDZxr2tops44GJT8XAw7X51ZUx1+eHEEJaEosGO3l5eQCAgIAAldt9fX2V9zUWFBSEoKAgldtWrVoFR0dH5T5dhrIXcC+k7jH+iI/yQ9adEtTUs3CwZxAW6C7YL/IzGQXYsC8ThWoXw9FDIiCR2Om8Pz7SF94cp3S83RwN7pcxQyKwdMslrfcr2qoQ0MpF67GNlVXVqbQp9345lm/7E3fuV4ABkNy3PZ7uE9KsRz8Un5/M20UoLq+Fh6sEEW20Txuai93f9YjsrKguka2ivjYP6mfzsXRfWzTYqapq2HxRIpGo3O7g4ICSkhK9j1+3bh3Wr1+P2bNnw8vLy+B2iEQMPD25XWz58PZ21X8QT8cv3dUYRBSW1WDplksY3j8U2365ofX+98fFo3unIHjvTMfDkmqtz9PKwwndOwXBzsAL7OCeIXB1ccCqtD9VnqeVhxNefToWvTqqJpQHB7hzOm9wgDs8PV3AsiwOnbmN1G1/orZOBqmzGI/3DEHncB94eLoY3G5r0ssEnx8huLlZ37J9W0V9bR7Uz+Zjqb62aLCjmI6qra1VmZqqqamBk5P2DmFZFl988QVSU1MxefJkjB071qh2yOUsSksrjTqHJnZ2Iri5OaG0tAoymdzo88nlLFZu0z5aAgA/HW0a6DS28qdLiAh0w6hB4TpHXkYODENpiXF9EhXsjsVTemscoSgqqlA5trWnI7ykDiqjUeq83BzQ2tMR9/JLsHZ3Bo5fbhj9s7djUFZZhx8OXcMPh66pjGIR4Qj9eSbaUV+bB/Wz+Ziir93cnDiPFFk02FFMXxUUFKBNmzbK2wsKChAREaHxMXV1dXj//fexa9cuvP/++3j55ZcFaUt9vek+6DKZXJDzZ2QX6QwGAP0FmgtLa5B+qxBxHVrpzA2J69BKsD4JC/JQ/lkuZ7UmGY/UVwV4QBiy88qQmnYZeYWVYNCwdVe9TPV8ilEsKqZnGkJ9nol+1NfmQf1sPpbqa4sGO5GRkXB1dcWpU6eUwU5paSnS09MxZswYjY+ZOXMmDhw4gMWLF+OJJ54wZ3MFxWdXawWhdq1WnMfaitDpSs4dMaADyirrsHLHWdTL5PB0dUCdTI7yKu27t2vaWoEQQkjLY9FgRyKRYMyYMVi0aBG8vLwQGBiIhQsXwt/fH4MHD4ZMJkNhYSGkUikcHR2xbds27N69GzNnzkRCQgLu37+vPJfimOaA767WCkLtWt34PI2L0BkSgAlNUwAW7OuKdfszcfpqwxYTj7T3Rr/OrbFs2586z2XoXlqEEEJsi8UrKE+dOhX19fWYPXs2qqurER8fj9WrV0MsFiM3NxcDBgzAJ598guHDh2PXrl0AgAULFmDBggUq51EcY+20VS5W7Bmla+qFy5JuhtE9ldV4yXfj4KagsKrJ1hJcAjBTaByAZeeV4T/fnUVBURVEDINn+7fHkIQ2OH01n9O5hBoNI4QQ0nxZPNixs7PDjBkzMGPGjCb3BQUFITMzU/n3NWvWmLNpgpPLWazdk6HzmG/3ZmqdehGJGHSP8sXe0zlaH98p1BsXrz/Uer+iKJ2m0SV1XAIwU2FZFofP38H3h7NQL2Ph7eaASU/HokNgw6otc23NQQghpPmzeLDTkmTcLtK/K3pVHTJuFyG6XcNS+sajL25OEpz6eypHm9v55Zj0dAw27L+mks+ivgGorkRgdebOfamsrsM3ezJwLrNhmrJzh1b4vyei4Npod3a+hQsJIYS0XBTsmIFcziIjuwiHzuVyOj4juyHY4TL6oq6wrAYb1QIdqZMYLw5oCHS47Iul6Zzmyn25da8UqWmX8aCkGnYiBs8/1gGDugWBYVQDLcXWCjpXb9HWCoQQQkDBjsnI5Syu/lWIK0dv4si5HJRVal811ASjPbeHizK1FUplVXVITbsM0TOxcHEU8wqeFEyd+8KyLA6czcWPR65DJmfRyt0Rk5NjERLgpvUxitVbmw5mNakWTVsrEEIIUaBgxwQMGZFpjAF4j75wselgFob3b2/QY02Z+1JeVYc1P1/FxesPAABdw30wflgknB3Feh7ZEPDER/nhblE1cu6VQOoktujyeUIIIdaHgh2BGTMio7DzeLZArVFVWFaD8goeI0x/M2Xuy/U7JVi5/TIeltbA3o7Bi4lhSOwS2GTaSheRiMEjHVohyNuJCoMRQghpgoIdARmSD2Nuit3R+Yw6mSL3Rc6y2Hf6NrYdvQmZnIWvhxMmJ8eirb9U0OchhBBCKNgR0LWcYoOnrowldRI3ydXRRFE7h8vok6lyX8oqa7H656u4dKNhiXxClC/GDY2EkwN9HAkhhAiPri4CMmcBO09XCV5JikZpZS08XBzQIdAd7608wWkptkjEaNyWwdNVgn6dW8PXy9lkFZSv5RRj5Y4rKCqrgb2dCKMGhaFfp9a8pq0IIYQQPijYEZCbk8RszzVqULiyFo/yNh5Lsc29L5acZfHziWyk/XYTLAv4ezljcnIsgn1dTfJ8hBBCiAK3vdEJJ6y+Lcd5kDqLMSQhGOoDHgwDDE0I1ji1pFiK7SlVXTnlJXXQWAVZsS1DQqQfAOB0Rj4ysou07kpuqJKKWiz5/iJ++rUh0OkZ44c5L3ejQIcQQohZ0MiOgDJuFwl2rl6x/tinYVsIlgX2ns5BaKC71oCHz4iNoZuScnU1uwirdlxBSUUtJPYijB4cjj6PBNC0FSGEELOhYEdADzkmJ4cFuSErt1Tr/UPig5Q7fGujawuHxhtp6mLMpqT6yOUsdh7/CzuO3QLLAq1buWDy0zEI9KHRHEIIIeZF01gC8pY6cjouPMhT43ST1EmMyckx6NTBR++qLsUWDobiskx+08Esg6a0istrsGjzBWz/vSHQ6fNIAD58qRsFOoQQQiyCRnYEFNXOEz+f1F8QMKqdJ6LbeWmdbjqZnsfp+YxZ/cVlmbwhe2JduVWIr3ZeQWllHRzEdhg7JBy9YgMMbichhBBiLAp2BBQe5AEGgK6xEObv4wDt001ct2YwZgsHroES1+Nkcjm2/34LPx/PBgsgyMcFk5NjEeDtYnAbCSGEECFQsCOg63dKdAY6QEMgdP1Oic7RkvBgD71Vjo3dwkHIgKqwtBqrdlzBtdwSAED/zq0xYkAYJGI7g9tHCCGECIVydgTEdRQk/a9CnbkwIhGDUQPDdJ5D1xYOcjmLjOwinEzP07qUXBFQ6cIloLp04wHmfnMG13JL4Cixw6SnYvDS0EgKdAghhFgNGtkRENfRkl0nsnHsch5GDQzTmrejqJmjvixcfQsHuZxVeXxZVR02H9K/lFwRUHEtQqiuXibHtl9vYu+p2wCANn6umPx0LPy8nDn1ASGEEGIuDCtkJbxmSiaTo7CwwujzyOUsZqQe57U/loujPSqq65V/Vw9M1IOZxjVzNNXI0UXTUnJN59C3J9bDkmqs2HEZN+40LJ9P7BKIFxM7QGxvmdEce3sRPD1dUFRUQbuemxD1s/lQX5sH9bP5mKKvvbxcYGfHbYKKRnYEJBIx6B7li70aigFq0zjQAZrWuNGWxKytRo4ummrz8C1CeCHrPtb8fBUV1fVwcrDH+Mcj0S1S2I1CCSGEECFRsCMguZzFKT3FALnSVTSQS40cTbQtJedShLBeJseWX25g/5mGQC4kQIpJT8fC18OJdzsIIYQQc6JgR0BcatdwpavGjTHPY0htnvvFVVix/TJu3SsDAAzqFoznHwuFPcfhQ0IIIcSSKNgRkDFF/vicz5jn4Vub51xmAdbszkBVTT2cHezxyhNRiAv3Mfj5CSGEEHOjYEdAbs4SQc+nLTAxtJggn9o8dfVy/HD4Og6dzwUAhLZ2w6SnY9DKnaatCCGENC8U7AhJwHVtugITLkUHNdG1lLyx/KJKrEi7guz8hmmrx7u3wTN929O0FSGEkGaJgh0BlVbVCnYuXYEJlxo5jelbSt7Y6av5WLsnA9W1Mrg6iTEhKQodQ1vxajshhBBiTSjYEZAxe1U1ltwnRG9goqvo4IsDOkDqJOG0lFyhtk6GzYey8MvFuwCAsCB3THoqBl5u3HZyJ4QQQqwVBTsCMnR6qTFPVwmSerXjdCzfGjna3HtYgdS0K8i9Xw4GwLCebZH8aAjsRDRtRQghpPmjYEdAhhQVVDdqUDivYIVLjRxdTlzOw3f7MlFTJ4PUWYxXn4xGbIi3wecjhBBCrA0FOwKSy1n8dumeQY/lk1cjhJo6GTYcuIbf/25vZBsPTHwqBh6uwkzFEUIIIdaCgh0BZWQXNdn+QZ+knm0R3c7LoOknQ915UIHUtMu4+6ACDIAne7fDU71DzPb8hBBCiDlRsCOgjJwi3o/xb+Vs1DQUHyzL4vc/72HD/muorZfD3UWCiU9GI6qdl1menxBCCLEECnaEZECdnfKKOuHboUF1bT3W7buGE1fyAADR7Tzx6pMxcHcRthAiIYQQYm0o2BFQeLAHcCKb12OkalWX5XLW6NVV6nIKyrFi+2Xce1gJhgGSH22PJ3q2hYihaStCCCG2j4IdARkSlHhK/0kIPpdZ0KRujqfUAaMMTFxmWRZH/7iLTQezUFcvh4erBJOeikFEG/NMmxFCCCHWgIIdAZVW8qug3HhLiHOZBRorIheV1WD5T5cx5ZlYXgFPVU09vt2bgdNXCwAAj7T3xitJUYLv30UIIYRYOwp2BMS3grJiSwi5nMXGg1k6j910MAtxYT6cRo+y88qQuv0yCoqqIGIYPNuvPYZ0b0PTVoQQQlokCnYExLWCsmJqKi7MBxnZRUj/q1DvYwrLanAtp1jnyi2WZXH4/B18fzgL9TIWXm4OeO2pWHQIcjfo9RBCCCG2gIIdAXHZoDO5Tzsk9QrBhaz7mJF6nNfWEsUV2o+trK7D2j0ZOJt5HwDQuUMr/N8TUXB1EnN/AYQQQogNomBHYLo26FRUSNaWn6OPtmmyW/dKkZp2GQ9KqmEnYvB8/1AMig8GQ9NWhBBCCAU7pqDYoDPrTgn+yi9HVVUtwoM8ENnWk1N+jiaNk5kVWJbFgbO5+PHIdcjkLFq5O+K1p2PRvrWbQK+EEEIIaf4o2DGRC1n3selgFgrVlpH36xRg0K7oimRmhfKqOnyz+youZD0AAHQN98H4YZFwdqRpK0IIIaQxCnZMQNcy8rTf/+J1Lk0bhN64U4IV2y/jYWkN7O0YvJgYhsQugTRtRQghhGhAwY7ADJ2mUpfUqy2i26puECpnWew7fRvbjt6ETM7C18MJryXHoJ0/TVsRQggh2lCwI7BrOcUGTVM15iV1QHKf9irTVmWVtVj981VcuvEQAJAQ5YtxQyPh5EBvISGEEKILXSkFpmt5OFfq+TnXcoqxcscVFJXVwN5OhFEDw9Cvc2uatiKEEEI4oGBHYFyrKCdE+uJMZgHYRjulMwwwJD5YmZ8jZ1nsOZmNn369BTnLws/LGZOfjkEbP6kpmk4IIYTYJAp2BMalirKjWITTGQVNbmdZYO/pHIQGuiMsyANf7UrHlVuFAICeMX4YOyQCjhJ6ywghhBA+6MopMC5VlKvr5DrP8d3eTIhEDEoqaiGxF2H0oHD06RhA01aEEEKIASjYMYG4MB84SOxQUysz6PFlVXUAgABvZ7yeHItAH1chm0cIIYS0KBTsmMCu47cMDnQUwoM98PbzneAgsROoVYQQQkjLJLJ0A2yNXN6whYOxkvuEUKBDCCGECICCHYFdyylGRXW9UefQtA8WIYQQQgxDwY7ATFFnhxBCCCGGo5wdgXGts2Nvx6BexqrcpmkfLEIIIYQYx+LBjlwux7Jly/Djjz+irKwM8fHxmDNnDoKDgzUeX1RUhPnz5+PXX38FwzB44oknMHPmTDg5OZm55ZpxqbMDAPUyFsG+rhiSEAyRiIGHi4PKPliEEEIIEYbFp7FSUlKwceNG/Oc//8HmzZshl8sxYcIE1NbWajx+6tSpyM7Oxtq1a/HFF1/g6NGjmDt3rnkbrYOizo4+iV0CMfulrugVG4Ae0f6IbOtJgQ4hhBBiAhYNdmpra7FmzRpMnToV/fv3R2RkJJYsWYK8vDzs37+/yfEXLlzA6dOn8dlnnyEmJgY9e/bEvHnzsH37duTn51vgFWjWNcIXU56JhZe06ZSWxF6E15NjMWZwBMT2tNqKEEIIMTWLBjsZGRmoqKhAz549lbe5ubkhOjoaZ86caXL82bNn4ePjg9DQUOVtCQkJYBgG586dM0ubueoa4YsFr/dCn06tlbe185di3oTu6BZJOTmEEEKIuVg0ZycvLw8AEBAQoHK7r6+v8r7G8vPzmxwrkUjg4eGBe/fuGdUWe3th477q2np8tuE8btwpBQAMSQjGC4lhEAv8PASwsxOp/J+YBvWz+VBfmwf1s/lYuq8tGuxUVVUBaAhYGnNwcEBJSYnG49WPVRxfU2P4km+RiIGnp4vBj9fkQmYBbtwphYuTGNNGxKFHbID+BxGjuLlZR5K6raN+Nh/qa/OgfjYfS/W1RYMdR0dHAA25O4o/A0BNTY3G1VWOjo4aE5dramrg7OxscDvkchalpZUGP16TNj7OeHdkHGLDfCBmgKKiCkHPT/5hZyeCm5sTSkurIJPp3mSVGI762Xyor82D+tl8TNHXbm5OnEeKLBrsKKakCgoK0KZNG+XtBQUFiIiIaHK8v78/Dh48qHJbbW0tiouL4etrXB5Mfb3wH/SOod7wdHdCUVGFSc5PVMlkcupnM6B+Nh/qa/OgfjYfS/W1RScqIyMj4erqilOnTilvKy0tRXp6OuLj45scHx8fj7y8PGRnZytvO336NACga9eupm8wIYQQQpodi47sSCQSjBkzBosWLYKXlxcCAwOxcOFC+Pv7Y/DgwZDJZCgsLIRUKoWjoyM6deqELl264O2338bcuXNRWVmJOXPmIDk5GX5+fpZ8KYQQQgixUhZPQZ86dSqee+45zJ49GyNHjoSdnR1Wr14NsViMe/fuoU+fPti9ezcAgGEYLFu2DEFBQRg3bhymTZuGvn37WlVRQUIIIYRYF4ZlWVb/YbZNJpOjsFD4BGJ7exE8PV0oZ8fEqJ/Ng/rZfKivzYP62XxM0ddeXi6cE5QtPrJDCCGEEGJKFOwQQgghxKZRsEMIIYQQm0bBDiGEEEJsGgU7hBBCCLFpFOwQQgghxKZRsEMIIYQQm0bBDiGEEEJsGgU7hBBCCLFpVEEZAMuykMtN0w12diLBtrMn2lE/mwf1s/lQX5sH9bP5CN3XIhEDhmE4HUvBDiGEEEJsGk1jEUIIIcSmUbBDCCGEEJtGwQ4hhBBCbBoFO4QQQgixaRTsEEIIIcSmUbBDCCGEEJtGwQ4hhBBCbBoFO4QQQgixaRTsEEIIIcSmUbBDCCGEEJtGwQ4hhBBCbBoFO4QQQgixaRTsEEIIIcSmUbBjBLlcji+//BKPPvooOnfujFdffRU5OTlajy8qKsI777yD+Ph4JCQk4N///jeqqqrM2OLmiW8/Z2VlYeLEiejevTt69uyJqVOn4u7du2ZscfPEt58b27FjByIiIpCbm2viVtoGvn1dV1eHxYsXK48fM2YMrl69asYWN098+/nhw4d455130KNHD3Tv3h1vv/028vPzzdhi27By5UqMHTtW5zHmvh5SsGOElJQUbNy4Ef/5z3+wefNmyOVyTJgwAbW1tRqPnzp1KrKzs7F27Vp88cUXOHr0KObOnWveRjdDfPq5qKgI48ePh6OjI9atW4evvvoKhYWFmDBhAmpqaizQ+uaD7+dZ4c6dO5g3b56ZWmkb+Pb13LlzsW3bNnz88cfYunUrvLy88Oqrr6KsrMzMLW9e+PbztGnTcPfuXXzzzTf45ptvcPfuXUyZMsXMrW7eNmzYgM8//1zvcWa/HrLEIDU1NWxcXBy7YcMG5W0lJSVsx44d2Z07dzY5/vz582x4eDh7/fp15W2//fYbGxERwebl5Zmlzc0R337+4Ycf2Li4OLaqqkp52927d9nw8HD2+PHjZmlzc8S3nxVkMhk7cuRI9qWXXmLDw8PZnJwcczS3WePb17dv32YjIiLYI0eOqBz/2GOP0WdaB779XFJSwoaHh7OHDh1S3nbw4EE2PDycLSoqMkeTm7W8vDx20qRJbOfOndmhQ4eyY8aM0XqsJa6HNLJjoIyMDFRUVKBnz57K29zc3BAdHY0zZ840Of7s2bPw8fFBaGio8raEhAQwDINz586Zpc3NEd9+7tmzJ1JSUuDo6Ki8TSRq+JiXlpaavsHNFN9+VlixYgXq6uowadIkczTTJvDt62PHjkEqlaJv374qxx8+fFjlHEQV3352dHSEi4sL0tLSUF5ejvLycmzfvh0hISFwc3MzZ9ObpStXrkAsFmPHjh3o1KmTzmMtcT20N8lZW4C8vDwAQEBAgMrtvr6+yvsay8/Pb3KsRCKBh4cH7t27Z7qGNnN8+zkoKAhBQUEqt61atQqOjo6Ij483XUObOb79DACXLl3CmjVrsGXLFspr4IFvX9+6dQvBwcHYv38/Vq1ahfz8fERHR2PWrFkqFwuiim8/SyQSfPrpp5gzZw66desGhmHg6+uL9evXK38wEe0SExORmJjI6VhLXA/pHTSQIpFKIpGo3O7g4KAxN6SqqqrJsbqOJw349rO6devWYf369Xj33Xfh5eVlkjbaAr79XFlZiXfffRfvvvsu2rVrZ44m2gy+fV1eXo7s7GykpKRg+vTpSE1Nhb29PUaNGoWHDx+apc3NEd9+ZlkWV69eRVxcHDZs2IBvv/0WrVu3xuuvv47y8nKztLmlsMT1kIIdAymmSdQT3WpqauDk5KTxeE1JcTU1NXB2djZNI20A335WYFkWn3/+OebPn4/JkyfrXRnQ0vHt5/nz5yMkJAQjRowwS/tsCd++tre3R3l5OZYsWYI+ffqgY8eOWLJkCQDgp59+Mn2Dmym+/bxnzx6sX78eCxcuRNeuXZGQkIAVK1bgzp072LJli1na3FJY4npIwY6BFENwBQUFKrcXFBTAz8+vyfH+/v5Njq2trUVxcTF8fX1N19Bmjm8/Aw3LdGfMmIEVK1bg/fffx7Rp00zdzGaPbz9v3boVx48fR1xcHOLi4vDqq68CAJKSkrBixQrTN7gZM+S7w97eXmXKytHREcHBwbTUXwe+/Xz27FmEhITA1dVVeZu7uztCQkKQnZ1t2sa2MJa4HlKwY6DIyEi4urri1KlTyttKS0uRnp6uMTckPj4eeXl5Kv9oTp8+DQDo2rWr6RvcTPHtZwCYOXMm9u7di8WLF+Pll182U0ubN779vH//fuzatQtpaWlIS0vD/PnzATTkR9Foj26GfHfU19fjzz//VN5WXV2NnJwctG3b1ixtbo749rO/vz+ys7NVplEqKyuRm5tLU7UCs8T1kBKUDSSRSDBmzBgsWrQIXl5eCAwMxMKFC+Hv74/BgwdDJpOhsLAQUqkUjo6O6NSpE7p06YK3334bc+fORWVlJebMmYPk5GStIxSEfz9v27YNu3fvxsyZM5GQkID79+8rz6U4hjTFt5/VL7KKhM/WrVvDw8PDAq+g+eDb1926dUOvXr3w3nvvYd68efDw8MCXX34JOzs7PP3005Z+OVaLbz8nJydj9erVmDZtGt566y0AwOeffw4HBwcMHz7cwq+mebOK66FJFrS3EPX19eyCBQvYHj16sJ07d2ZfffVVZZ2RnJwcNjw8nN26davy+AcPHrBvvvkm27lzZ7Z79+7sRx99xFZXV1uq+c0Gn34eP348Gx4ervG/xu8FaYrv57mxkydPUp0dHvj2dVlZGfvRRx+x3bt3Zzt16sSOHz+ezcrKslTzmw2+/Xz9+nV20qRJbEJCAtujRw/2jTfeoM+0Ad577z2VOjvWcD1kWJZlTRNGEUIIIYRYHuXsEEIIIcSmUbBDCCGEEJtGwQ4hhBBCbBoFO4QQQgixaRTsEEIIIcSmUbBDCCGEEJtGwQ4hNo6qS7RM9L4T8g8KdgjhYNasWYiIiND6X+/evS3dRI2ysrIwcuRIQc516tQpREREqJTfV6fop759+2q92C5atAgRERG0OauJ5OXlYeLEibhz547eY+vq6jB8+HAcP34cgObPeUxMDPr06YMZM2bg3r17AIBt27bp/Peg+E/XsR07dkRiYiLmzZunsqv4F198gblz5wrfMaRFo+0iCOHIx8cHy5Yt03ifWCw2c2u42bt3Ly5cuGDW5xSJRMjPz8f58+c17nOze/dus7anpTl+/DiOHj3K6dgVK1bA398fvXr1Ut6m/jmvr6/HrVu3sGjRIly4cAG7du1C//798f333yuP+eWXX5Camoply5bBx8dH43Op31dSUoLffvsN69atQ2FhIT7//HMAwMSJEzFkyBAMGTIEPXv25PPSCdGKgh1COJJIJOjcubOlm2H1AgICwLIs9uzZ0yTYuXjxIvLz8xEeHm6h1hGFgoICrFq1Cps2bVK5XdPnvFu3bhCLxXjvvfdw6NAhPPHEE/Dy8lLef/PmTQBAVFQUgoKCND6fpvv69euHhw8fYs+ePaioqICLiwucnJwwbtw4fPLJJ9ixY4cAr5QQmsYiRFCXL19GTEwMZs2apbzt4cOH6NmzJ8aPHw+WZZXD+n/88QeeeeYZdOzYEU8++ST27t2rcq6amhosWLAA/fr1Q2xsLJ588skmoyIsy2Lt2rV4/PHH0bFjRwwaNAirV68Gy7JYunSp8hd6REQEli5dCgCQy+VYtWoVBg0ahNjYWAwZMgTr1q1r8lo2b96MIUOGoGPHjhgzZgzu3r3LuR+GDh2K/fv3N5nK2r17N3r16qVxs9Aff/wRTzzxBGJjY9G/f38sXboUMpmsyTHDhw9H586d0bFjRzz99NPYs2eP8n65XI4lS5YgMTERsbGxSExMxOLFi1FXVwdA+1Tc2LFjVabVEhMT8fHHH2PcuHHo2LEjPvjgAwBAcXEx5syZg169euGRRx7BCy+8gBMnTqicKyIiAps2bcKsWbPQtWtXJCQkYP78+aiursZnn32GHj16oHv37vjggw9Udtjm8r6MHTsWH3zwAVatWoX+/fvjkUcewYgRI3Dp0iUADVNG77//PgBgwIABKp9Ddd988w1at26N2NhYrcc09sgjjwAAp+kxPqRSKRiGAcMwytuSkpKQlZWFX375RdDnIi0XBTuE8FBfX6/xP8VFPTY2Fq+++ip++ukn5UVwzpw5kMvl+PTTT1W+0CdNmoQBAwZg2bJlCAkJwbRp05TTDyzLYsqUKdi8eTPGjx+P1NRUxMXF4e2330ZaWpryHAsWLMCCBQuQmJiIFStW4LnnnsOiRYuwatUqPP/883juuecAAN9//z2ef/55AMDcuXPx5Zdf4qmnnsKKFSswdOhQfPzxx1i+fLnyvOvXr8dHH32Efv36ISUlBZ06dcKHH37IuZ+GDRumnMpSkMvl2Lt3L5544okmx69cuRIffvghevbsiRUrVmD06NH46quvVJ5zw4YNmDNnDgYOHIiVK1di0aJFkEgkePfdd5W7rn/11VfYtGkTpkyZgjVr1mDkyJFYvXo1UlNTObe98fM98sgjSElJwXPPPYeamhqMGzcOhw4dwttvv41ly5bB398fEyZMaBLwLFy4EBKJBMuWLUNycjLWrVuH5ORk3Lt3D4sWLcLYsWOxZcsWlWCGy/sCAPv27cOhQ4cwe/Zs/O9//8ODBw/w5ptvQiaToX///pg8eTKAhmmj119/Xevr27lzJ4YMGcK5P27dugUAaNOmDefHNCaXy5X/Xurq6vDw4UNs2bIFP/30EwYNGgRnZ2flsX5+fujcuTN27txp0HMRoo6msQjh6M6dO4iJidF438yZM/HKK68AAKZMmYLDhw/j3//+NyZOnIiDBw/iiy++gJ+fn8pjxo4diylTpgAAHn30UTzzzDNYvnw5+vXrh+PHj+O3337DkiVLMGzYMOUxVVVVWLRoEZKSklBZWYnvvvsOY8aMwYwZMwAAvXr1wv3793HmzBlMmjQJ/v7+AKCclrh16xZ++OEHTJ8+HRMnTgQA9OnTBwzDYOXKlRg1ahQ8PDyQkpKCYcOG4V//+pfymPLycmzevJlTXz3yyCMIDg5Wmco6e/YsiouLMXDgQGzdulV5bFlZGVJSUvDiiy9i9uzZyufz8PDA7NmzMX78eISFhSEnJwevvPKKygU8MDAQw4cPx7lz5/DEE0/g9OnTiI2NxbPPPgsASEhIgJOTE6RSKad2N9a6dWu8++67yr//8MMPyMjIwA8//IBOnToBAPr27YuxY8di0aJFKq+pQ4cOmDdvnrINP/74I+rq6rBo0SLY29ujT58+2LdvnzIY5PK+eHp6AmgIuFevXg1XV1cAQEVFBd577z1cvXoVsbGxymBE15TSjRs3cP/+fXTs2FHj/fX19co/l5eX488//8Qnn3yCoKAg9O/fn3dfAsCgQYOa3NaqVSuMGjUKU6dObXLfI488gl27dhn0XISoo2CHEI58fHy0jhAEBAQo/ywWi/HZZ5/h+eefxwcffIBnnnkGQ4cObfKYZ555RvlnhmEwaNAgLF26FNXV1Thx4gQYhkG/fv1ULjyJiYnYsWMHsrKycP/+fdTX12Pw4MEq51UEDJqcPHkSLMsiMTGxyXlTU1Nx7tw5hISE4OHDh3jsscdUHvv4449zDnaAhtGdtLQ0fPDBB2AYBj///DP69++vvEgrXLhwAdXV1RrbBADHjh1DWFiYckqmtLQUN2/eRHZ2tnI6qra2FgDQvXt3LF68GKNGjUJiYiL69++PMWPGcG5zY1FRUSp/P3HiBHx8fBATE6PSzsceewwLFixASUkJ3N3dAQBxcXHK++3s7ODp6YmYmBjY2//zlevh4YGysjIA3N6XgQMHAmgIpBr3oSKIrqqq4vzacnJyAEBjMKQtqO/UqRPmzZsHR0dHzs/TWGpqKnx8fFBXV4dt27YhLS0NU6dOxYsvvqjx+MDAQDx8+BBVVVVwcnIy6DkJUaBghxCOJBKJMm9Bn6ioKERERODy5ctNggYFX19flb97e3uDZVmUlpaiuLgYLMuiS5cuGh9bUFCAkpISAFBJFNWnuLgYADROJQFAfn6+8nyKkQQFbatstBk2bBhWrlyJ8+fPo3Pnzti/f7/GJcWKNilGNNQVFBQAAG7fvo05c+bgxIkTEIvFaN++PSIjIwH8U1NmwoQJcHFxwdatW7Fo0SIsXLgQYWFhmD17Nnr06MGr/Y2nVRTtvH//vtbRvfv37yuDHfWATtP51M8N6H5fFNQv/CJRQzaCXC7Xen51iiBLUxChHtRLJBL4+/srX5uhwsPDlcFVly5dUF9fjzlz5sDV1VXj61b0V1lZGQU7xGgU7BBiAt9//z0uX76MyMhI/Pe//0XPnj3h5uamckxxcTFatWql/PuDBw9gZ2cHDw8PSKVSODs747vvvtN4/rZt2yqnQAoLC9G+fXvlfXfv3sXt27c1LvtWtOHbb7+Fi4tLk/tbt26N0tJSAA2J1ert5SMyMhIhISHYu3cvqqurUVNTo3EKRNGmRYsWoV27dk3ub9WqFeRyOSZOnAixWIwtW7YgKioK9vb2uH79OrZv3648ViQSYfTo0Rg9ejQePnyIo0ePYsWKFXjzzTdx7NgxZc6UemCgWAmki1QqRbt27bBo0SKN92ubMuKCy/siJEUgq3ivG+MT1Btj9uzZOHbsGObOnYvu3bur/FsAGpamMwyjMZmdEL4oQZkQgd25cwefffYZnnvuOaxYsQJlZWX473//2+S4gwcPKv/Msiz279+Prl27QiKRICEhAZWVlWBZFo888ojyv2vXrmH58uWor69Hx44dIRaLceTIEZXzrlmzBtOnT4ednZ3yV79Ct27dAABFRUUq5y0sLMQXX3yB4uJitGvXDgEBAU1Wh6k/DxfDhg3D/v37sXv3bgwaNAgODg5NjunUqRPEYjHy8/NV2mRvb4///e9/yM3NRVFREW7duoXnnntOeR8A/PrrrwD+CV5GjBiB+fPnA2gYKRs+fDhGjx6N0tJSlJeXK0dcFAnNQMNF9caNG3pfS0JCAu7duwdvb2+Vdh47dgxff/017OzsePePApf3hSv191wTRfDUuB/MzdXVFe+//z5KS0uxePHiJvfn5eWhVatWkEgkFmgdsTU0skMIR7W1tbh48aLW+yMiIuDo6IgPPvgATk5OmDlzJtzd3TFt2jR8/PHHGDJkiDIPBWhYSVVTU4OQkBD8+OOPuHHjBr799lsADfVH4uPj8frrr+P1119HaGgoLl26hC+//BKPPvqocqrppZdewtq1a5UB0h9//IFNmzZh5syZEIlEyhGDXbt2oVOnToiIiMBTTz2FDz/8EHfu3EFsbCxu3bqFJUuWICgoCO3atQPDMHj33XfxzjvvYPbs2Rg6dCguXrzYpB4LF8OGDcPy5cuxfft2pKSkaDzG09MTEyZMwBdffIHy8nJ0794d+fn5+OKLL8AwDCIjIyGVShEYGIgNGzbA398fbm5u+O2335QjX4p8lfj4eKxZswatWrVCXFwc8vPz8c033yAhIQFeXl5wd3dHQEAAli9fDldXV2UCMJdpkuHDh2P9+vUYP348XnvtNQQEBOD48eP46quvMGbMGKMKS3J5X7hSvOcHDhxA3759ERoa2uSY9u3bo3Xr1jh37pzGxGFzGTZsGDZu3IiffvoJI0eOVEmYPn/+PB599FGLtY3YFgp2COHo/v37WpMpASAtLQ3nz5/HiRMn8PnnnytzHMaOHYudO3dizpw5Kjk4c+fOxcqVK5GTk4Po6GisWbNG+QtfJBJh1apV+OKLL7By5Uo8fPgQfn5+GD9+vHIFFwDMmDED3t7e2Lx5M77++msEBQXhww8/xIgRIwAAgwcPxvbt2zFr1iw899xzmDt3Lj755BOsXLkSmzdvRl5eHry9vTFs2DBMmzZNOTqRlJQEkUiElJQUbN++HeHh4Zg3bx6mT5/Oq886dOiA8PBw3L9/X6VKr7pp06bBx8cHGzduxNdffw13d3f07NkT06dPV66kSklJwX//+1/MmjULEokEHTp0QGpqKj7++GOcPXsWY8eOxVtvvQWJRIKtW7di+fLlkEqlSExMxDvvvAOgIVn4yy+/xMcff4zp06ejVatWGDduHG7evKlcWq2Ns7MzNmzYgMWLF2PhwoUoKytDYGAg3nnnHfzf//0fr37RhMv7wkX37t3Rq1cvLF68GCdOnMCqVas0HjdkyBD8+uuvOmvxmMPs2bMxfPhwzJs3Dz/++CMYhkFBQQEyMjLw1ltvWbRtxHYwLO0WR4hZKQq/HTp0yKg8D0KMkZ+fj4EDB2LNmjWIj4+3dHNULF++HAcOHMBPP/2kUpuKEENRzg4hhLRAfn5+ePnll/HVV19ZuikqKioqsGnTJkyfPp0CHSIYCnYIIaSFevPNN5Gfn4/ff//d0k1RWrVqFRITE9G3b19LN4XYEJrGIoQQQohNo5EdQgghhNg0CnYIIYQQYtMo2CGEEEKITaNghxBCCCE2jYIdQgghhNg0CnYIIYQQYtMo2CGEEEKITaNghxBCCCE2jYIdQgghhNi0/we2rQoJVf/MfgAAAABJRU5ErkJggg==", + "image/png": 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    " ] @@ -1711,18 +1739,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:04:43,555] A new study created in memory with name: my_study\n", - "[I 2024-07-02 16:04:43,595] A new study created in memory with name: study_name_0\n", - "[W 2024-07-02 16:04:43,596] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n", + "[I 2024-07-09 09:46:03,945] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:46:03,989] A new study created in memory with name: study_name_0\n", + "[W 2024-07-09 09:46:03,990] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n", "Traceback (most recent call last):\n", - " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n", + " File \"/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n", " value_or_values = func(trial)\n", - " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/objective.py\", line 128, in __call__\n", + " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/objective.py\", line 128, in __call__\n", " self._validate_algos()\n", - " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/objective.py\", line 264, in _validate_algos\n", + " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/objective.py\", line 270, in _validate_algos\n", " raise ValueError(\n", "ValueError: PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n", - "[W 2024-07-02 16:04:43,603] Trial 0 failed with value None.\n" + "[W 2024-07-09 09:46:03,994] Trial 0 failed with value None.\n" ] }, { @@ -1841,11 +1869,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:04:43,669] A new study created in memory with name: my_study\n", - "[I 2024-07-02 16:04:43,670] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:46:04,046] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:46:04,048] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n", - "[I 2024-07-02 16:05:32,125] Trial 0 finished with value: -6833.034983241957 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n", - "[I 2024-07-02 16:06:17,937] Trial 1 finished with value: -6793.886041846807 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 61.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.12, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 900.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': 0, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 800.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -6, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 1 with value: -6793.886041846807.\n" + "[I 2024-07-09 09:46:50,317] Trial 0 finished with value: -6833.034983241957 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n", + "[I 2024-07-09 09:47:38,277] Trial 1 finished with value: -8093.803069372614 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 180.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 10.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 5.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.08, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 1.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 2100.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': 0, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -5, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n" ] } ], @@ -2141,55 +2169,55 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:06:19,217] A new study created in memory with name: my_study\n", - "[I 2024-07-02 16:06:19,219] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 16:06:21,170] Trial 0 finished with value: -5817.944430632202 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5817.944430632202.\n", - "[I 2024-07-02 16:06:21,224] Trial 1 pruned. Duplicate parameter set\n" + "[I 2024-07-09 09:47:39,820] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:47:39,822] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:47:41,747] Trial 0 finished with value: -5817.944009132488 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5817.944009132488.\n", + "[I 2024-07-09 09:47:41,757] Trial 1 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5817.944430632202]\n" + "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5817.944009132488]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:06:23,195] Trial 2 finished with value: -5796.343328806865 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5796.343328806865.\n", - "[I 2024-07-02 16:06:25,119] Trial 3 finished with value: -5795.086720713623 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5795.086720713623.\n", - "[I 2024-07-02 16:06:25,151] Trial 4 pruned. Duplicate parameter set\n" + "[I 2024-07-09 09:47:43,445] Trial 2 finished with value: -5796.34421890567 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5796.34421890567.\n", + "[I 2024-07-09 09:47:45,258] Trial 3 finished with value: -5795.086276167766 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5795.086276167766.\n", + "[I 2024-07-09 09:47:45,265] Trial 4 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}, return [-5795.086720713623]\n" + "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}, return [-5795.086276167766]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:06:27,065] Trial 5 finished with value: -5820.227555999914 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5795.086720713623.\n", - "[I 2024-07-02 16:06:27,094] Trial 6 pruned. Duplicate parameter set\n" + "[I 2024-07-09 09:47:46,888] Trial 5 finished with value: -5820.227555999914 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5795.086276167766.\n", + "[I 2024-07-09 09:47:46,895] Trial 6 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}, return [-5795.086720713623]\n" + "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}, return [-5795.086276167766]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:06:29,109] Trial 7 finished with value: -5852.16017644995 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5795.086720713623.\n" + "[I 2024-07-09 09:47:48,730] Trial 7 finished with value: -5852.159469428255 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5795.086276167766.\n" ] } ], @@ -2346,40 +2374,40 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:06:29,404] A new study created in memory with name: my_study\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/_store_backends.py:215: CacheWarning: Unable to cache to disk. Possibly a race condition in the creation of the directory. Exception: [Errno 2] No such file or directory: '/var/folders/1v/9y_z128d7gvcp8mf8q0pz3ch0000gq/T/tmp036btnoj/joblib/optunaz/descriptors/SmilesAndSideInfoFromFile/calculate_from_smi/5c35a12d8bcea8acbc6d5682447dac4e/output.pkl.thread-140297255347648-pid-10944' -> '/var/folders/1v/9y_z128d7gvcp8mf8q0pz3ch0000gq/T/tmp036btnoj/joblib/optunaz/descriptors/SmilesAndSideInfoFromFile/calculate_from_smi/5c35a12d8bcea8acbc6d5682447dac4e/output.pkl'.\n", - " warnings.warn(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "[I 2024-07-02 16:06:29,481] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n", - "[I 2024-07-02 16:06:29,643] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n", - "[I 2024-07-02 16:06:29,696] Trial 2 pruned. Incompatible subspace\n", - "[I 2024-07-02 16:06:29,725] Trial 3 pruned. Incompatible subspace\n", - "[I 2024-07-02 16:06:30,207] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n", - "[I 2024-07-02 16:06:30,254] Trial 5 pruned. Incompatible subspace\n", - "[I 2024-07-02 16:06:30,399] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n", - "[I 2024-07-02 16:06:30,428] Trial 7 pruned. Incompatible subspace\n", - "[I 2024-07-02 16:06:30,457] Trial 8 pruned. Incompatible subspace\n", - "[I 2024-07-02 16:06:30,500] Trial 9 pruned. Incompatible subspace\n", - "[I 2024-07-02 16:06:30,781] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n", - "[I 2024-07-02 16:06:30,815] Trial 11 pruned. Duplicate parameter set\n", - "[I 2024-07-02 16:06:30,980] Trial 12 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n", - "[I 2024-07-02 16:06:31,013] Trial 13 pruned. Duplicate parameter set\n" + "[I 2024-07-09 09:47:49,005] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:47:49,071] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n", + "[I 2024-07-09 09:47:49,250] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n", + "[I 2024-07-09 09:47:49,257] Trial 2 pruned. Incompatible subspace\n", + "[I 2024-07-09 09:47:49,260] Trial 3 pruned. Incompatible subspace\n", + "[I 2024-07-09 09:47:49,349] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n", + "[I 2024-07-09 09:47:49,353] Trial 5 pruned. Incompatible subspace\n", + "[I 2024-07-09 09:47:49,505] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n", + "[I 2024-07-09 09:47:49,510] Trial 7 pruned. Incompatible subspace\n", + "[I 2024-07-09 09:47:49,513] Trial 8 pruned. Incompatible subspace\n", + "[I 2024-07-09 09:47:49,520] Trial 9 pruned. Incompatible subspace\n", + "[I 2024-07-09 09:47:49,727] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n", + "[I 2024-07-09 09:47:49,736] Trial 11 pruned. Duplicate parameter set\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4396.722635068717]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-07-09 09:47:49,966] Trial 12 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n", + "[I 2024-07-09 09:47:49,977] Trial 13 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4396.722635068717]\n", "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4030.4577379164707]\n" ] }, @@ -2387,7 +2415,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:06:31,229] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" + "[I 2024-07-09 09:47:50,204] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" ] } ], @@ -2487,7 +2515,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 42, "metadata": { "scrolled": true }, @@ -2496,10 +2524,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 12:07:35,764] A new study created in memory with name: my_study\n", - "[I 2024-07-03 12:07:35,766] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:47:50,491] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:47:50,492] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-03 12:08:30,402] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", + "[I 2024-07-09 09:48:33,788] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", " \r" ] } @@ -2538,7 +2566,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 43, "metadata": { "scrolled": true }, @@ -2547,9 +2575,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 12:14:50,109] A new study created in memory with name: my_study\n", - "[I 2024-07-03 12:14:50,157] A new study created in memory with name: study_name_0\n", - "[I 2024-07-03 12:15:34,865] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n" + "[I 2024-07-09 09:49:37,680] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:49:37,724] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:50:21,175] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n" ] } ], @@ -2588,7 +2616,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 44, "metadata": { "scrolled": true }, @@ -2597,14 +2625,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 12:15:34,986] A new study created in memory with name: my_study\n", - "[I 2024-07-03 12:15:34,988] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:50:21,284] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:50:21,286] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-03 12:15:56,468] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", - "[I 2024-07-03 12:16:18,120] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 105.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 60.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", - "[I 2024-07-03 12:16:40,381] Trial 2 finished with value: -5846.868596513772 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 14.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 10.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 2.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.24, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 1600.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 900.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -1, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -2, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n", - "[I 2024-07-03 12:17:02,053] Trial 3 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n", - "[I 2024-07-03 12:17:24,942] Trial 4 finished with value: -5890.881210303758 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n" + "[I 2024-07-09 09:50:43,189] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", + "[I 2024-07-09 09:51:05,358] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 105.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 60.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", + "[I 2024-07-09 09:51:30,139] Trial 2 finished with value: -5846.868596513772 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 14.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 10.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 2.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.24, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 1600.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 900.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -1, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -2, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n", + "[I 2024-07-09 09:51:51,787] Trial 3 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n", + "[I 2024-07-09 09:52:14,070] Trial 4 finished with value: -5890.881210303758 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n" ] } ], @@ -2655,7 +2683,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -2815,9 +2843,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:10:27,089] A new study created in memory with name: calibrated_rf\n", - "[I 2024-07-02 16:10:27,092] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 16:10:28,093] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n" + "[I 2024-07-09 09:52:36,078] A new study created in memory with name: calibrated_rf\n", + "[I 2024-07-09 09:52:36,080] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:52:36,948] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n" ] } ], @@ -2892,9 +2920,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:10:30,400] A new study created in memory with name: uncalibrated_rf\n", - "[I 2024-07-02 16:10:30,442] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 16:10:30,739] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n" + "[I 2024-07-09 09:52:39,409] A new study created in memory with name: uncalibrated_rf\n", + "[I 2024-07-09 09:52:39,457] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:52:39,766] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n" ] } ], @@ -3219,9 +3247,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:10:32,303] A new study created in memory with name: calibrated_rf\n", - "[I 2024-07-02 16:10:32,345] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 16:10:33,181] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n" + "[I 2024-07-09 09:52:41,510] A new study created in memory with name: calibrated_rf\n", + "[I 2024-07-09 09:52:41,556] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:52:42,461] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n" ] } ], @@ -3327,7 +3355,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -3376,11 +3404,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:10:37,199] A new study created in memory with name: my_study\n", - "[I 2024-07-02 16:10:37,240] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 09:52:46,701] A new study created in memory with name: my_study\n", + "[I 2024-07-09 09:52:46,748] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__fd833c2dde0b7147e6516ea5eebb2657': 'ReLU', 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': 'mean', 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': 'none', 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657'}\n", - "[I 2024-07-02 16:17:22,733] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", - " \r" + "[I 2024-07-09 09:59:50,943] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", + " \r" ] } ], @@ -3438,7 +3466,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -3491,11 +3519,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 16:45:35,973] A new study created in memory with name: my_study\n", - "[I 2024-07-02 16:45:36,018] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:03:46,674] A new study created in memory with name: my_study\n", + "[I 2024-07-09 11:03:46,722] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__c73885c5d5a4182168b8b002d321965a': 'ReLU', 'aggregation__c73885c5d5a4182168b8b002d321965a': 'mean', 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50, 'depth__c73885c5d5a4182168b8b002d321965a': 3, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'features_generator__c73885c5d5a4182168b8b002d321965a': 'none', 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a'}\n", - "[I 2024-07-02 16:47:00,208] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': , 'aggregation__c73885c5d5a4182168b8b002d321965a': , 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': , 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n", - " \r" + "[I 2024-07-09 11:05:35,737] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': , 'aggregation__c73885c5d5a4182168b8b002d321965a': , 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': , 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n", + " \r" ] } ], @@ -3538,7 +3566,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " \r" + " \r" ] } ], @@ -3561,7 +3589,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
    " ] @@ -3658,9 +3686,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:07:15,971] A new study created in memory with name: my_study\n", - "[I 2024-07-02 17:07:16,012] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 17:07:17,576] Trial 0 finished with value: -4342.479127043105 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 12, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4342.479127043105.\n" + "[I 2024-07-09 11:28:43,526] A new study created in memory with name: my_study\n", + "[I 2024-07-09 11:28:43,578] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:28:45,864] Trial 0 finished with value: -4305.8949093393 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 25, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4305.8949093393.\n" ] } ], @@ -3738,7 +3766,7 @@ "outputs": [ { "data": { - "image/png": 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6c7XH001rOnHjFRtyj5FM2xCCoa87SGM2IQ2umc+3qKBi85GQmEgadelnPRsKrknNnLa2E0/95ig4ZxCcgXM25aQyezK5bnlbRdMCi0nxeccpXRV/voSQ5lSLwHfe9OeUjRUNmP5cq5TJTWs6sWF1R1OufhGyWDV7SjUVVGwujAHRRGPss87XOt3CScNbtSyMniUBmLYLXRNTTlqzQXNPZwBresK4+rzV8GkC43ETpuXAlRKm5WA8bi4oLXB6io+mCnDmBfXtIQ1p08G2nYfgyvJmvFwpcXAwitfeOomDg9Gy748QUj/ZwDeRtiGnfZbzx6pyAt9s+nOhce5k1KhI+nOlx6VajadZnDGs6Yng7euWYE1PhAJrQhpYrceHasifWC2kkTOKFhvGgKRhI2049T6UGWjlmtTMQvbSVSotsNgUn0ODMSzpDJX0vJo5BYoQMlOt9v0WGucUwbCmN1J2n+tqjEuUMkkImU0rjA9UULF52LZEPGU15GQNBdekpqafTCbSNgCgPaTh4jOXY8Pqjim3LTctsNgUn1iJKT7NngJFCCmslAm+Uor4TB/n2sI6ztzYg4mJZMn9Rqs1LlHKJCFkNq0wPlBBxeYRbbB91vkouCY1lz2ZfOa/juKZXccwHjcwHjOwbcch/Hrv8JQT12xaYKmK3TsZLiHFh6pKEtLaFjLBV85Kcf44pygcnJeXCl6tcamZi7ARQqqrVcYHKqjY2BhjiCVNmCVOPtcCBdekLvYeGsMTLx6ZXFlRqrPiW2yKz+qehaf4tEIKFCFkbsVM8DVSBks1xyVKmSSEzKaVxgcqqNi4DMtG0rDRgNngOVTQjNRcLYtezFU0aKHF0abLpUApc/SpdWRDp0ARQsrTaEV8qjkuVXM8JYQ0t1YbH6igYuORAGLJxulnPRsKrknNLWRlpRKyKT593UEYloNo3IRhOejrDpa1okRVJQkhtR7P5lPtcala4ykhpPnR+ECqxWu7ZcJq4HTwLEoLJxVTbDGfehS9qEaKTyulQBFCStNoRXyy49KhwRiCPgVC8Nz+x0qNS5QySQiZDY0PpNJybbdMu96HUhQKrklFLKSYT72KXpRbHK3Q/VFVSUIWt0Yr4rP30BgSKQspw0YybYMxQFU4gj4VtisrNi5VejwlhLSOaowPrpR488g4jg5FEdAFBewtoNhFOdvx2m418j7rfBRck7IttJhPK634UlVJQha3RhrP8sfi9rCORNqGZbswbRdW3MTKZSH83uZ+GpcIIU1l98FRPPb8YQyNpWBazoK6MUxXSstEUnkLWZSLJs2GbbtVCAXXpCyltH1ptRVfSoEiZPFqlPGs0Fgc8qswbReO4yKZthHQBTas7qjqcRBCSCVlJw0N00EkpMHvE7Cs0roxlNMykVROsYtyubZbllPvQ14QKmhGylJqMZ9WK3pBVSUJWbwaYTwrNBYzxqCrAgGfikhQw9BYqmaF1QghpFxTJg3DGvQyujFkA7qB4Th0VSAS8u4vG9DtPjhag2dEiu2wISFhWDZSDd52qxBauSZlKaeYD634EkJaRb3Hs0YrrEYIIeVayALOXHu8S8myJNVRzN90eDyFoycS8OsKnAZvu1UIBdekLOUW86GiOISQVlHP8azRCqsRQki5KjVpWKkgnZRvvr+pqnD4dQUnxpNY3hWq8dFVBqWFk7Jki/kk0jbktLyNbDGfns5AUxQnI4SQZkVjMSGk1eRPGhZS7KRhLqBTZg/SHUdSZk8NzPc39esK1Fn+Ts2iuY+e1F22mI9PExiPe0UHXClhWg7G42bTFScjhJBmRGMxIaTVFDtp2Lc0hIODUbz21kkcHIzO2INdqSCdlG+uv6mucjgSXqG5JcE6HWH5KLgmZWuEYj6EELLY0VhMCGklUyYNYyYM04HjukikLIyMpyE4w9vWdODvfrQL//CTV/HdbbvxDz95Ff/nhy9PKVBGmT2NY7aJYMdx4UrAsR2cv2lZTSaCR8ZT+H/PHsB/vTFc0fulPdekIupdzIcQQgiNxYSQ1pKdNHzs+cM4ciKOeMqClBKMe2PaI88ehKowtAX1WVs6NUrLROLJ/k2zbdGSaRvdHT50hHWcvrYT/X3tVT+GE2Mp/N//91ukDBvPvTqIr//xe9Ae0ity3xRck4qh4mSEEFJ/NBYTQlrJpjWdYILh3m17oNsuAj4Ffl1gaDQFy3YhJYPjSmiZlk6FKoAXCuiEYOjrDlKf6zrInwg2TAdcMIT8KhiqP8ExHjdw3/bdSBk2AEBVORRRuWTupguu/+Iv/gKmaeJrX/valMt37NiBLVu2YP/+/ejt7cWf/Mmf4Oqrr67TURJCSG3R2EgIIaQVuVJi+3OHYNsuujt8ABhMy4HjSiicwZUS0YRXW4IxNmsFcMrsaSycMaztjSBlOogmTNiOi2MjcSTTNgI+Bb1dwYr/beIpC/du242JhAkAYAA+cdUmhPyV22/fNHuuXdfF//k//wc//OEPZ1y3f/9+/NEf/REuvPBC/OQnP8H111+P22+/HTt27KjDkRJCSO3Q2EgIIaSVHR6K4fjJBMLByVZabqb/MeMMnDFYtgszr2DZbBXAs5k9b1+3BGt6IhRY15njSiSSFvYdHsN923fjX57ci397ej/+5cm9uG/7buwfGK/YYxmmg62P78HIRDp32e9euBZnb1pasccAmmTlev/+/fjiF7+IQ4cOYfny5TOu37p1KzZs2IDPfe5zAID+/n68/vrruOeee3D++efX+nBJi3GlpFlO0pBobJwbfXYJIaT5xZMWbEdCFTxXCZxn9lxDequPLiYDboAqgDeLaMLEnkOjePjZAzAsBwFdgRAcjuNicDSFh589gGsvWFv2PmzbcfGvP3sDR4cTucsu+50+nP/2njKfwUxNEVzv3LkT/f39+Md//Ed89rOfnXH9Sy+9hPe+971TLjvvvPPwla98xSt6QCdTTaXQCTGAupwk7z44mtuf4zjSaw/QGaD9OaQh0Ng4u3p+dhs5qG/kYyOEkEJCARWKYLAcFyITVGf3Vpu2C868ADsbcGcrgPd1B6kCeINijCGWNJEybTy96xgMy0EkoOXOS7giEBEc0aSFp3cdw9oVbSV/V7muxI+eehNvHp3IXXbe25bh0rNWVOS5TNcUwfVHP/rROa8fHBxET8/UmYelS5cilUphbGwMnZ2lnUjN1my+EJHZCC8quCG+kdTq+b1+YBQ/fe4gjp9MwHYkFMEQDmgAgFjSzF3WuySIa969BqetrcxJcqHn9/qBUXz/ib1Imw6C/snKkkeHE/j+E3tx81WbKvb41dbK70/GGGzXxcnxFGzbhbaAz22zq9fY2Ojvp3p+dguNYQsZr6r52pZ7bM2s0d+zhJDZrVoWRu+SIAZGEmgLqkCm6FVbUMPIRAq2A2gqhyK8vdhUAbzxGZaNpGHj2HACI+MpBHRlxoQ/YwwBXWBkPIXjIwms6A4t+HGklPh//3kArx2YbM12ev8SXPPuNVVbYKh7cD0wMIDLLrts1ut37Ngx7wlgOp2GpmlTLsv+bJpmScfFOUNHx8IbmEci/pIer1lU8/nt2jeMrU/sRSptIxxUoQqOWMrC4aEYAGBJuw9LQjosx8XAiNdm4X9+5Aycsb67YseQfX6uK/H4i7tgWC662n25D6CmCPh1BSejBh5/8QjOP7NvMjWpCbTa+zNlWEikbJguIB0HbUEdoYA2/y82gUYeG7Pvo0Z8P9Xzs1toDCt1vKr0a1vJY2tmjfieJYTMjTOGa969xmulFTMRyLTS4pzBpyowHRe6KhBLWFQBvAm4EoglLbiuRDJtw3Fnn/gUgsMxHCTTdkmP9fOXBvDC7hO5n9f3teEjm/urOulS9+B62bJl2L59+6zXt7W1zXsfuq7POFHM/uz3l/ZF6roS0Why/ttJiUODMSTTNnq6w+iKaJCunPf3mo0QHJGIH9FoCo7jzv8LC+RKiQef2INkykJ72EsLcV2JWKaaHxgQS1jeXgzO0BZUMR4z8eATe9C3xF/2h2T68ztwPIojg1EEfAKOKwFM/ZsGdIEjg1G8vGcQa3sbv+VNtf9+tea6ErGkibTlwHUlhOAIhXxIJAxYhjXv70ci/oZfwWrksTGRSDfc+yk7Fr85MI4DR8cR8Ck1/ewWHMOkXPB4VY3PaqWOrZm12hhYTc0wPpLF57S1nfifHzkDDz6xB8dOJnKttFb3hnHleasR9Cm03aUZMCAWN2Blis8FfAoEBxzHBVfEjJs7jgvBvdst1HOvHccv/+to7ueVS0P46PtOrWjbrULqHlyrqor+/v6y7qO3txcnTpyYctmJEycQCAQQDpe+18K25/4Cnr6nT1MFlnX4ceW5q1p2tsxx3Hlfl1IcHIzi2MlE5sPDICVgWg4s24XgHBISlu3CsLzZSYAh4FNw7GQCbx2dqFhP1+zzm4gZsB2JgOCQBeZKhOCwHdu7XXfznKhV6+9XK4wBKcNGImXDmnKC7P3bcV3YpU1uNpxGHhuzwUmjvJ/yx+K06SBl2EhbLtqCgF+f+jVXrc9uoTFs0sLHq0q+tpU+tmbWKO9ZQsjCnbG+G31L/Hjr6AQF0k2IMa8VVtpycpf1dgXR1e7H4GgKEcGnpGpLKZE0HPR0+tHbtbBs4pf3jeCnzx3K/by0w48b378RmjozgK+0lpiafNe73oUXXnhhymU7d+7EWWedBc6r8xR3HxzF1if2YmA4Dl0VaAtr8OkCR07EsfWJvdh9cHT+OyE58aQFx5FT9rnnqj5mClVITK0EOVubhUoIBVQIwWY9CaMqlLUnAYzHTUQT1rTAmsymHmNjrU0fiyMBFQyAZTkYjaaRMqbOtlTrs1toDMtXzfFqPo18bIQQshDUSqt5mbaLZNqeMsHLGcPmM5ZDV73iZZbtwJUSlu0gmrSgqxybz1i+oL/z3sNj+PHT+3M/t4c03HzVppJWv0vREmdXH/vYx/DKK6/grrvuwv79+3Hvvffi8ccfxy233FKVx3OlxLadh5A2bbSHdGiqAGcMuirQHtaQNh1s23ko1y6AzK9QMJvfZkFiaiVIoLoB7qplYfR0BpBI25DT/o7ZKpQ9nQGqQlkDjDEY9mSgRJ+r4tV6bKy1QmOxrinQVJGbjIsmzNxnuJqf3UaekGvkYyOEENL6JCSiSTOzXWuq/r52XHvBWvR0+mFaDuJJC6blrVgvtA3X4aEYHvjZvty5YsCn4BNXbUJbsHb1eOqeFl4J69evx913340tW7Zg69at6Ovrw5YtW6rWx/XwUAyDo0kEfWrBynZBn4LB0SQOD8VaPsWuUrLB7MBwAqripYVk2ywYlgMGr+1CthJ0tdsscMZw9XmrveIZcRNB32TFYapCWUsS0aTlBdUtWMug2mo9NtbabGNxW1DDyWgajith2i4MywFnrKqf3UJjWFa928I08rERQghpbYwBE3ELljV71mF/XzvWrmjD8RFvP33Ap6C3K7ig7+qh0SS2Pr4nl92oqRw3X7kRXe21LWTZdMH1/fffX/Dyiy66CBdddFFNjiGXYuefPcUumbYpxW4BZgtmA7oCw3Ig4c0+SXjpnrUIcDet6cSNV2zI7eXMFs+gKpTVxxiDaTuIJU2YcwzGZFIjjI21NttY7NMVLIn4MB43YNouYgkLPl1U9bPbyBNyjXxshBBCWhdjQDJtI23OXxCHM1ZSuy0AGIulcd/23UgZ3n5uwRk+dvmGku+vHE0XXDeC/BS7QhvjKcWuNLMFs6uWhQEpEUtZiMbNmga4m9Z0YsPqDhweilHxjBpgzEv1jWdWqwulDxGSNddY7NMVdHJvtfoD569Gf19b1T+7jTwh18jHRgghpDXZtkQ8bRUsDlwp8ZSFe7fvQTSzqMkY8PuXnoL+FfN3VakGCq5LQCl21TNbMAugbgFutngGqb6kYSM5oxI4IYUVOxZf8jt9NRsvGnlCrpGPjRBCSKuRiCYNOE71Iuu0aeN7j+3ByYl07rIPXbAWb1+3pGqPOR8KrktQKMVOVTkM00E0YVKKXZlmC2YpwG1NjAEp00EybcGy3arObpLW0qjpzo08IdfIx0YIIaQ1MMYQTVR3a59lu/iXJ9/AsZFE7rLLz16Jszctq9pjFqMlqoXXQzbFrq87CMNyMBEzkTYdrFwawo1XbKAUuwbjSomDg1G89tZJHByMUsXpBsCY15ZhNGYgGvcGYPqzkIWaPhZH4yYMy0Ffd5DGYrLo0XcfIaTWvEUTGynTq5lUDa4r8aOn3sRbx6K5y97z9h5cfObyKj1i8Wjlugz5KXZJw8GKZRF0BBW4VUx/IAu3++Bobp+h40gIwdDTGaB9hnWSLVaWSFkwbZeqgLc4xtiMdnaVRunOhMxE332EkHqwHK92TrXO76SUeOTZA/jtwdHcZWee0oUrz189o4tTPdDKdZmyKXan9y/BKSvb6WSuwew+OIqtT+zFwHAcuioQCWnQVYGB4QS2PrEXu/M+mKR6GPN6HKYtB+OxNMZiBtKmQ4F1i2OMIZo0YdqO16i+irJj8dvXLcGangiNxWRRo+8+Qkh9SMSSJuwq1s752YtH8NKeE7mfN6xqx3Wb1zXM9z4F16RluVJi285DSJs22kM6NFWAZ/pnt4c0pE0H23YeojS5amNAIm3j5ISBibiBFAXVi4rtuBiPmRiNppEybUhINMj3HyEtib77CCH1wBhDLGnBNJ2qPcZ/vnocT798LPfz6mVh/Lf3rofgjRPSNs6REFJhh4diGBxNIuhTZ6SJMMYQ9CkYHE3i8FCsTkfY2hhjsB0XYzEjN4tJ53KLkyslTMtFNGHi5ISBeMqCK2VDpG8R0mrou48QUmuMASnDQsqo3j7r/3pjGNt2HMr93NMZwMffvwGaMrMtcj3RnmvSsuJJC44jofgLzyEpCkcybSOe6YtHKoN6VZPZSOmtZMeSLpKGDV0R8OkKNJWDZa4nhJSHvvsIIbVm2xKxzMT5bFwpcXwkgWTaRsCnoLcrWHQq955DY3jomf25nzvCOm66ciP8euOFso13RIRUSCigQggG23ahqTNntWzbhRAMoYBah6NrXcm0jWSaelWTuTmORNKxkbYcCMHgUwV8qtfWkLYNEFI6+u4jhNTW/P2s9w+M4+ldxzAynoLjAoIDXe1+bD5jOfr72ue894ODUTzw8zeQPTUI+lV84qpNiAS1Cj6HyqG0cNKyVi0Lo6czgETanlGtWEqJRNpGT2cAq5aF63SELYQBacvByVga0aRJgTUpmutKWJaLWNLCyVgaJ6NppK3q7dcipNXRdx8hpFY4z+yznqOf9f6BcTz87AEMjiahqQKhgApNFRgcTeHhZw9g/8D4rL97/GQC3398L+xM4K6rAjdfuRFL2nyVfioVQ8E1KagVemNyxnD1eavh0wTG4yZMy8ns/XQwHjfh0wSuPm91w1QXbEqZoHosmsZE3IBFvapJGVxXwjAdTMQNnIylkTbteh8SaVGt8B03G/ruI4TUAmNAMj33PmtXSjy96xgMy0EkoEFVvAKLqiIQCagwLBdP7zpWcAwejabxve17kM4USFMEw8eu2IDlXcEqPqvyUVo4maFQb8zlS4L4gys2YlVXoN6HtyCb1nTixis25J5PMm1DCIa+7iD1+iwRY97e2LTlIJW2YNmypU5MSf1JCViWiwnbRNKwEfCp8GkCVauSQhaVxdD/mb77CCHVZjsS8ZQ95zng8ZEERsZTCOhKwQKLAV1gZDyF4yMJrOgO5a6LJU3cu303Yikrc1vgDy5bj3XLI9V5MhVEwTWZItsbM23aCPpUKH4O23Zx5EQc//jjXbjxig04dWV7vQ9zQTat6cSG1R04PBRDPGkhFFCxalmYZu0XKFuoLJl2kDJsqv5Nqk5KwLRcWLaBpMIR0BX4NCU3wUPIQr1+oPB3XLb/841XbGiZwJO++wgh1RRNGPP2s06mbW+PtSicLC0Eh2M4SKYnM9XSpo3vPbYHo1Ejd9mHL1qH05pkbKbgmuRM742ZnWHSVAFN5ZhIWPjpcwfx2d87o+m+nDljWNPT+LNdjYgxBstxkTZspE0HjktBNamtySDbRCJtw68r8Oteahm9F0mxXFfip88dLPgdpyoc43ET23YewobVHU33HTcb+u4jhCyUK+Wck3KMMUST5pz7rLMCPgWCA47jghdomeU4LgT3bgcAlu3i+0/sxfGTydxt3n/OKvzOhqUVeGa1QcE1yZmvN2bYr+L4yQQOD8Vq/mU93wedVB5jgGm7SKZtmJZDLbXIgrhS4thwHENjSUBiQS03ZiOl98Vr2yaSaQ6fJuDXFSiCzyjcRMh0bx2dwPGTiaL6P1NASghZjObbNsMYkDJtpAy7qJ1avV1BdLX7MTiaQkTwKWOvlBJJw0FPpx+9XUE4rsSDv9iHg8djudtceHovLjpzeRWeafVQcE1y5uuNqSrc219R496Yi2F/XKVNn4xYt6KtqN+bsp/asGHZLrVFIguW/cwm0jY484qSFNtyoxgSXr/seMpFyrChqV6Qrat8Ua5k0+RjcaIJE7YjEaD+z4QQMsN822Zuev8GrF/ZgXjSKvrckDOGzWcsx8PPHkA0aSGgCy8V3HGRNBzoKsfmM5aDAXj4V29h96Gx3O+edWoXrjh3VZWebfVQcE1y5uuNadkulBJ7Y9quixdeH8JoNI3OiA/nnLYMCp+/WP1se8BbcX9cpZRSkI72U5NKyf/M9nQGoCoC8ZSVa7lx7QVrKxJgZzmuRMqwYVgOVMHg1xX4NAFWw5Txega3i2nysdzXORLUoFD/Z0IImaGYbTP/8epxLO0MzLvPerr+vnZce8HayT7XhgPBgZ7OyUn3x3Yewq/fGM79zqbVHbj2ov6mnCim4LrJVPMkLtsbc2A4AVWZmboRS1no6wouuDfm488fwrYdh3IpJAzAD36+D1efvxrvP3f1rL831x7wVt0fV66FFqSj/dSkkqZ/ZlVVgCHTckNwRJMWnt51DGtXtFX8M+u6EoYrYdomEikOn+6tZgte3ZTxWgW3hcb+vYfGFs3kYyVe53Ur2tC7JIjDJ+IFv+MSaRt93Qv/jiOEkGY337aZniUBjEYNHDwexfKu0Cz3Mrv+vnasXdGG4yMJJNM2Aj4lt13sV7uO4T9eOZ677bIOPy46oxfNempPwXUTqfZJXLY35tYn9mI8biLoU6Ao3slaMm0j6FdxzbvXLOik+PHnD+GhZ96C40oonHmRtQQSaRsPPfMWAMwaYM+3B5z2x021kIJ0gjPaT00qbr7P7GwtNypJSsByXFhJF0nDhi+TMq4qouJBdq0yawqN/cs6/EgazqKYfKzU68w5wzXvXoN7t++e8R2XSNvU/5kQsmjNtW3Gpwn4fSqOnogjkbIL/HZxOGMzvvt/vfcEHn/+8ORtOBBPWXjwF/squp2slubPyyXzq8H3cPbkYmA4Dl0ViIQ06KrInVzsPjhakcfJ9sbs6w7CsBxE4yYMy8HKpSH8j4+cgdPWFn+iaLsutu04BMeVUAUD5wycef9XBYPjSmzbcQi2Wzi9JLcHXJl9f5xThz3gjWq+wKa7zQfDsnF4KIbRmIGxmIGUYVNgTSpmvs+sEByOiyktN6rJcbzVSO/9nkbacgDIisyGT5/M0lSvermmCrSHNKRNB9t2Hiq7B/xsY//hoTiODMWhTCsQA8ycfGxmlX6dT1tb+DuurzvYUiv9hBCyEPnbZvIpgiEYUBFLGDAtO1fVuxJePziKn/zqrdzPjAEdIR3hoAZNFbntZPsHxiv2mLVAK9cV4LoSo4k0uKrAcd3MXr/KBSy1To8u1Btz3Yo2LOkMYWwsUfT9vPD6EFKGDYWzgid/CgdSho0XXh/Cu9/eO+P359sDTvvjppqtIJ1fVxDweSekQ6MpHBtOwKfRR59U3nyf2ektN2rFdSXSpgPDchDnHJrKoakCusrBWWlp47XIrJlr7A/4FCQNG4mUhXBAm/G7rVKcqxqvM/V/JoSQqQptm2HMC7oNw8HgaCpX1bsS3joWxYO/2JfbisgAdLX5oGbadfEabCerFlq5rhDLcRFPWTg5kcZ43IBpOwBDRVZIFnJyUSnZ3phvX7cEa3oiJb2hR6Npr0z/bL/KvKq/o9F0wauze8ATaXvGyW92f1xPZ4D2x2XkBzYMXlDdGdER0BWkDAeDJ5OIJQzo2sygh5BKmO8zmzQcdLVX7st5oaT0qown0zYm4gZGJtIYi6WRMm3IBa5o1yKzZq6xX2RWrC3bhWk5M363VSYfq/U6V+I7jhBCWkV224xPExiPmzAtB0G/CsNycOREPFfVuxJj5bGRBO5/Yi9sZ/I8oT2s5QLrrOnbyZoFBdcVlq1cOx4zcXIijXjKgu3KGSdGC9Gs6dGdEZ8XV8+2KCS9uLsz4it4dXYPeP4H3ZUSpuVgPG42zP44V0ocHIzitbdO4uBgtOw00FJlAxsJoCOiw68LpAwHw+MpTMQNTCRNdHcE6hbYVJOUwOHBKN4ciOLQUP3+Bovd9M+sZTmQUsKyHUSTVkW/nMslpZc2njYdRBMmTk4YiCUtOEWO1/mTWYVUIrida+zXFA5V4ZnnMfUYWmnysZjXWQIYGk3WdfxdiEb5zmgVBw4cwDvf+U785Cc/yV12xx13YMOGDVP+u/TSS3PXu66Lb37zm7jwwgtx5pln4pOf/CSOHDlSj8MnpGHkb5tRFA7TdDEylkJ3u69inT5OTqRx32N7YGQmhQVn8GsC+iwZlbXeTlYJlBtaJa6UcG0JK1M0SlM4dF2BTxW5XsLFatb06HNOW4Yf/HwfEmkbKsOMyqy2KxH0KTjntGWz3kd2D3i2mE8ybUMIhr7uYEO0mmmkNjiCM3zogrV45D8PYmg0Bdd1AcbgOC5ShoOAT8El71zREIFNJe0fGMevXjmO8biB9rAPE/E0utr8DfH+WIzyP7OJtJ1bWc1vudFosiva+X2zQz4VijJ7yvh83RUqUXl6rrE/m7VkWQ4SaRtC8JYszjXX65xKWzgZNcAY8OhzB6EovOHbkDXSd0YrsCwLn//855FMJqdcvnfvXnz605/GDTfckLtMiMnP0N13340HHngAX/va19DT04MtW7bglltuwaOPPgpNm7nNgpBmVEqHoQ2rO+D3qRgYjiEaM7G004/l3aGKfJdEkybu3b4biZS3GMgZ8P5zVuLZV4/DcVxwpXG2k5WjeY60iTmuRMp0kLYcJDiHrgn4NQWq6p24zRdo1+IkrhoUznH1+avx0DNvwXIkFI5ctXDblRCc4erzV8/b77pR98c1VA9uBiQNGx1hH9592lL88uVjGBk34biA4EDvkgCuvmAdejt8cJzWWSXZPzCOh589ANNy0R7WEAqoSBtWS7YiaibZz+yxkQSGxpKARK7lRqPLZh+ZlgO/riDoV8AL9Myeq7tCpYLb+cZ+25FYuTSEoF9tyMnHSpjtdU4kLYzFDQBeAZygX234NmQN9Z3RIr71rW8hFJpafVhKiTfffBOf+tSn0N3dPeN3TNPEvffei89//vPYvHkzAOAb3/gGLrzwQjz55JO45ppranHohFRVKRN5rx8YxU93HITluEimbEQTRsUqdqcMG9/bvgdjMSN32Ycv7seZ67uw58g4BkdTiIiZ33NJw6noXu9aoOC6hnItYjKrI4rCc6l9quIV1gFmBtu1OImrlmybrVyf60wqeNCnzNvnOl92f1yjaJQe3IwBhuUikTJh2l6P6nUr2rFm+dRegiuXhdHeHsDERHL+O20SrpR4etcxGJaDtqAGTeHgDJkqwqylWhE1I84YVi4Ne3u2zJl7ghud40rEUxbSppf1EdAF2LTiZ9XOrClm7P+9S05pyMnHSpr+OifSNpJpC5wxLGnT4de9rK1GbkPWKN8ZreTFF1/ED3/4QzzyyCO5IBkADh8+jGQyiXXr1hX8vT179iCRSOD888/PXRaJRHDaaafhxRdfpOCaNL1SJvJ27RvGfdt3I+BT4NMV2I47pWJ3OWnhpu3g+4/vxeDo5DnoVeetxlmnepNfm89YjoefPYBo0kJAF14quOMiaTgNtZ2sWBRc14njSjimA8N0wBgy7am8YFsoHArnUAQD4FUeb/T06Lm8/9zVeO/ZK/HC60MYjabRGfHhnNOWzbti3cjq3YPbK2TkIJGyYNgu3GnttKb3EmyiMalox0cSGBlPIaAr1AedVI3tuIglTSTTHKritYDSFAFF8NzYXM3gttixv9Xf4/mv85tHJ/DT5w4ioCsz9uk16me/3t8ZrSYajeL222/HHXfcgd7eqd1G3njjDQDA/fffj1/96lfgnOOiiy7C5z73OYTDYQwODgLAjN9bunRp7jpCmlUpE3mulPjxU/ugawJtIR2j0TQYY1ArULHbcV08+PN9OJRXdPniM5fjgtMnP3/9fe249oK1eHrXMYyMp+AYDgRHQ28nmwsF1w0gW1jHcRyks8E283pCq8pk25jT1i5p2hUKhfOC7baa1Wxtr7Kq1QaHMQbbcZFImzBMZ1H3qE6mbS/tXdT2b0AWn+yebNsB0oYDzhkUhUPPnKwogmFtbxsKZR5VQqNujam1bAZTPGmBgUEtUIMEaMzPfr2+M1rVX/7lX+Kd73wnPvCBD8y47o033gDnHEuXLsW3v/1tHD58GH/7t3+Lffv2YevWrUilUgAwY2+1ruuYmJgo67iyH8nZvpdI6bKvKb22cztwPIqh0SRCfhWcz5zIC/kVDI0mcXQkgbW93kTe4aE4RifS6Aj7EE2amcKek78T9AmcHE9haCyJvu7Q9IeclSslHn76Lew5PJ677OyNS3HleatmTDKeuroD61d14OhIHImUjaBfwYquUNUXhwRnEIJX9PuUgusGJCXgSAkHEpbjImUiF2j7dAWre8IF9wCS2qllkTmWaVmWLY5nWos7qM4K+BQIni120TyF/khzk5iaecQZA+OAwr1Zfl0T0DJVvCup0bbG1FMzFvms1zGzTDFRx3Vb5rzhkUcewUsvvYRHH3204PWf+cxn8Id/+Ifo6OgAAJx66qno7u7G7/3e7+HVV1+Fz+d1KDFNM/dvADAMA36/v6xjywYMkUh590NmR6/t3A4MJeBKwKcpM4JrwPsuSRkOwAU6Orx9zAdPJODXBQzL9sai6UG5KpA2HYAJtLUFijoOmVkN/80bI7nLzjy1Gzd98G0Qc2SutrcXd/+VwhkQiQSgztKRqRQUXDeBXLBtOjAsB4Jz6CqHrilQFdYyX5jNpNpF5hhjcKULy3ZhWG4uoJ6e/r2Y9XYF0dXux+Boasag2MiF/khrcaUEHC/7yLBcJA0bqmDw+9SSukOQ+TVjkc9qHvPkXTEw5qVhOq6XbWFZDkzHhcI52kM6Zu+N2TweeughnDx5cso+awD40pe+hO3bt+Oee+7JBdZZ69evBwAMDg7m0sFPnDiBVatW5W5z4sQJbNiwoaxjk9Jr5ReNpma0yCPlEYIjEvHTazsf1wFnQNq0C07kmZZ3PVwHY2Ne72iFed9h0aRZMMi0bMfr3Sydomv3/PI3R/GLFyfb261bHsFHLl6HeCxdyrOqGsEZNI6iVq4jEX9RmRMUXDeZyfREr70S5yyXNq6K7D5tOpmrtmoUmfNWFyRM21sRs2wXjuvS33IWnLHJIhgJr7iRK70vjomE1dCF/kjrcl0Jw5UwbQMJwRHQFfh0QZOgFdSMRT4rccz5q9Cui8yEq1dzw8n7T0pv0ie/G4miNc5rUa677roL6fTUE/TLL78ct956Kz74wQ/i9ttvx4kTJ/C9730vd/2rr74KADjllFOwcuVKhEIhPP/887ngOhqN4vXXX5/SuqsU2dfbcdxZe7OT8tTqtS2ljVUjWNEVxLLMRF57gerb8ZQ3kbeiK+i9jgwI+AQ0jSM5aiMcUAtM/nkVu5d1BIrqOPPinhN4/PnDuZ+XdwVxw+WngjPWkB1rHMeFC0oLJ8j00nZkJtC2vX3aPFNwR+WZ6smFK5CT8pVbZC57omQ7LkzbhWHYMB3vRIn+XsXJFsHI9rmOJy0YltMUhf5Ia5PS28oRdbxiaD5dwKcpuUJopDzNWORzIcfMGIOE9OqxuC6czHe95bi5ADr7Plpsb6dly5YVvHzJkiVYtmwZrrjiCvyP//E/8A//8A/44Ac/iAMHDuDLX/4yrrnmGvT39wMAbrjhBtx1113o7OzEihUrsGXLFvT09ODyyy+v5VMhDaqZ+9EvZCKPMYZY0oRpObj83DW4f/vrZVfs/u2BUTzyH2/lfl7S5sNNV26ET1s8IefieaYtLpc67srM3lyvArkiOHSFQ1UFFMHAGZ3YVVIphYa8tD0glfYCQceRmVWGGh54C+nva8f6VR2Ipm1YpoOAzrFyaXPMMJPWl2vBmPRSxjVFwK97FccpZbw8zVjoLf+YkykboaCKlUtDEJzDcV3YjvcdbtkOLFvOWIEm87vsssvwd3/3d/jOd76Df/7nf0Y4HMYHPvABfPazn83d5tZbb4Vt27jjjjuQTqdx9tln47vf/S5UtXH26ZP6aIV+9MVM5DEGpEwbScMGY8DGNZ247uJ+/OI3AyVX7N5/bAIP/mJfbryKBFR84qqNCPkX1+eKSYq0CnIcF6OjiaJu60qJsbiBcNiPiYlkQ6Y8cJ6pPi68lW1V5VAEBwNDMSvbisLR0RHE2FiiJVOdavH8GPOK2SRNG2nTrun7RAiGtrZAw74/y5V9fsx1oBbR4q2zM0gVR0vkOC6i0VRRnxfGGEZj6absc11NnDEIwaCrItPai+V6aLf6WFsvjfK6ZgtUOraElZfSbdsObFdCupl9/BXm1wTaw76iJtdpfCyd47gQgtf9fdaKavEZdqXE//nhyxgYjk9pYwV4mSLjcRN93UH82e+f2dCTeFlzpbY7rsRYzIDtuFPOES3bxfGRBJJpGwGfgt6uYFHP9ehwHPf8dDcMy/u+9+sCn/zA29DTWdsCZQslBENXmy8TD82t2LGRVq4XCdeVcCG9VjKmA5YXbCsKhxDcK0efudwbUGi2vFzZwmSGlUn7tl2q9E3IIudKCdf2Vih52gbnkxOffq5SdlEL8c5JCxeopBVpQhpLq/Wjn73LhFe8zC5QGI4zhhULaLcFACPjKXzvsT25wFoVHB+/YmPDB9bVQsH1IiQByLxgG5ne2ixTaZQxBsGR2bvtFUprggm6hjEjoKZ91ISQWUzWzvAmPpOmDYcxWGkLgnstGBkofbyZTHZ7kLBsB6btFSGl7wFCGtti6EfPGEM0acGsUEbZRMLEvdt3I5G2AXjB+R++bz1W9zROt4Zao+CaAPBO3CRkpkuHd6JnWC44tyEYQ8CvQPVpcFw3NxPv/R6dKQCZVD8pkbZcqvRNCCmJhJdl5DgSsaQJKTNtQlQBXeVQFU5VxxvUZHHKTLcHx61aijchpDrq1Y++VrL7rFOGXZGmfMm0jfu278Z43Mxd9pHN/diwqmOO32p9TbGp5vjx4/izP/szvOc978HZZ5+N//7f/zv27ds35TaPPfYYrrrqKpx++un40Ic+hB07dtTpaFuL60pYjot4ysJEzMDJiTSGx9M4GU1hPJ5G0rC9kwhIcJ5d+a73UVdf9nm6cvL1OTlhYCJuIGXYsB0KrEn10djY2txMgcpEysJ4zMTJCQPRhAnT9lYcFsNY26gY82qZOK5EyrQxGktjNJpGNGEibU4WqiSENI9sP/pE2p6xeJTtR9/TGSipH30jsB2JeNKCW4Htiabl4PtP7MGJsVTusmvevRpnru8q+76bXcMH16Zp4lOf+hSGh4fx7W9/Gw888ACCwSBuvPFGjI6OAgB27tyJ2267DX/wB3+Ahx9+GOeffz4+9alPYf/+/XU++tYi4RVAsB0XpuX12Y4mTIzFDIxMpDEykcJE3EQiZSFtZVLh3EzbEEggk3LerAF49rgdVyKRsjASTePkRBpjUQOxpOVNMtC5FKkRGhsXF1d6Y28ibWM8ZmJkIu2l9tne5Ob0/YGkfNltUmCA40ivZaLtIGXaiCYtnJxI5QJqw3RyLbIIIc0p28bKpwmMx70WVa6UMC0H43GzqH70jUsimjAK7rNeKNtx8cDP38DhoXjuskveuQLvfntv2ffdCho+Lfyll17CG2+8gV/96le53oZbtmzBueeei6eeegof+chH8M///M9473vfi49//OMAgC984Qv4r//6L2zduhVf/vKX63n4i0J2BsxxJEx4H9rsuJM94cvu6eYcUDiHIhiEwqFwDs6RGai8Imr5yj1RmatS4mwm094l7EyPUduRsG2vII0jZUVm/QgpB42Ni1dun3bKRSpbEE3JdILIjKuMsZps2ylljG1E2XFf5o37ju31lc5viQUAuR1UhJCWs5B+9M2CMeZlPVnlB9aulHjomf1448hE7rKzNy7Fe9/VV/Z9t4qGD67Xr1+P73znO7mTRwDgmVY70WgUruviN7/5Df78z/98yu+de+65ePLJJ2t6rGRS7hxkysmdBBxMCcBzK9nwquVywXO/zDnL9erOVjJfSAC+++AoHnv+EE6MpwB4hYF6OgO4/OyV2LCqwzt5cr1jtF2JaNxEPGXBspxMOrx3MkU9qEkjorGRADMLojHmdXzQVA6/rkBTvPdENcaw3QdHcyegjiMhBENPZ6ChT0Dzv3dyE6eOCyfzf9uh3tKELHb5/ehbYeIwZVhIGU7Zk4JSSmx77hB2vXkyd9nb13bidy9YS9lTeRo+uO7u7sbFF1885bL7778f6XQa73nPexCNRpFMJtHT0zPlNkuXLsXg4GAtD5UskJT5wXemcjmmVi9kyJwIZVa3FcFyPeakzOx7Yww8s/8t++E+eCyK7S8chmm76OkMggsGx3YxHjfxk1+9hfe9ayVWLgt5J08S4AJwOUcsac7ZB9qVsqT+f4RUGo2NZLrsmOp1gnCRNp1cP209r/NDOUFjdqX69QOj+MVvjsJ2HIT8GhQ/h227GBhOYOsTe3HjFRtqFmBPP6mTkLnK3G5mgtR0XLC4gYlMqqfjTk6cSlfSSjQhZIrZ21g1F9uRiKfsitSA+OV/HcWO306eP/SviOD3Lj0FnNN5cL66B9cDAwO47LLLZr1+x44d6Oyc/IL+2c9+hq9//eu46aabsGHDhtxJoqZpU35P13UYhlHWsSlKcVvSHdfNBXze/6vT3L6eGuH5uVLCtCVgz3z8bCY3g1dJ99EdBzF0MolQQIFpTVbullIimrDwxAuH8d+vOQ3ZbPTsit9cz2//wAR++V9HMTyWhO0CCge6OgJ4+7ol6AzrCPoVrOgKNeR+8kb4+1VT7vlxXvTnttE18tg49f00O8YAIRiEaMAPRYOq9GfVdSVShg3DdCAEh656nxHBOQTPZAgxjvyMoLmygbbvOISjIwmMxw04roSuCrhSetlFmoCmcozHTDz2/GG8rX9JRScfc6nbUuYyjyzb686QPWbXlXC8wT7b/CJTcJMDQiBtOXAyew6lBI6OxJFI2Q09flcCVzgUhUHKFn2ChJACKrfP+vnXh/DzlwZyP6/oDuKG922AMs95wGJU9+B62bJl2L59+6zXt7W15f79gx/8AH/1V3+FD37wg7j99tsBeCeKgFfcJ59hGPD7/SUfF+cMHR3Bom5rWg5M6QV1oZCv5MdsBs3w/A4dj+LocAK6JuC6ACC9E7zMiVnIr2JkIo2JlI3VvVNnJWd7fnsOjuLhZw8gbdgI+lUogiGRtnHg2AT2H52AX1OgawLLlgTw/vPWYGODpkQ2w9+vHMGgjlBAm/+GTaCRx8ZIxPv97P/nYkoGX4EJMTK3an5WJQCHAW7mH4zJTJDN8ro+ZOplwFvBeXNgDNt2HkbactCzJABV4ZkClxKm6SKge6noUgIdYYaRiTTGEjZOWdle1DFlg2QAmS1A3oSq40q4DuBCwsmmbbvekUkGMEWBtoAzmezruufgKB7f6U3COq4LwXnDj9/l0FSB9vbSP/eEkCbDgIm4BaMC+6xffesk/t+zB3I/d7X5cNOVG6FrM9uVkQYIrlVVRX9//7y327JlC+655x7cfPPN+MIXvpD74m9vb0cgEMCJEyem3P7EiRNT9iIulOtKRKPJom7ruC4SCROBgI54PJ2bFW8lQnCEQr45n1+jrAIMjcRh2S58uoBToPAY44BluxgaiaM94H0E5np+UgLbnn0LqbSNSFAFYwxpw0E0bmRO8gDLdREUAgNDcdy//XVcd3E/+vvapj903RTz92tm2eeXSBiwDGve20ci/nlXXeutkcfGRCKNSMSPaDQ15/uJMSAWS8MwnVlvQ6bKvpejsRSODMXqPp4CACTw4BN7cXw0iXBARSLlIpm24dMEdJUBjENCIuhXMy0KVSSSFgZHYmjzK5m7kLltONkkbFd6hSJN24XjuF6wn1/5XOb9Xpnyx8A3Do3hoWf2w7BcBHwCPqHCcdyGHb8rwacrUFFcR4tmGB8JIbNjDJm2gHbZ9/XmwAR+9NSbudymtqCGT1y9CUFfc/b6roW6B9fFyJ48fuELX8AnPvGJKdcxxnDWWWfhhRdewPXXX5+7/Pnnn8e73vWush7XLnK1xZUyd4LpZAqjtJ65n9/+gXE8vesYRsZTcFxAcKCr3Y/NZyxHf197TY/UrwkI7v39VGXmrJptuxDcu93kc5n9+R0djuPEWBJ+XQCZtPNo0oQrAUUw7+9vu5BgCAdURJMWfvGbAazqaaTiF4vk/em6sMv/Lmka9Rob88e7ucZJxhgcR7boe65aXOw5OIptz76FE2PJuo+ngDcGHh2JQ1MFbMfrvR1LmkikWCaYlmDwsrhURWRWoR2k0hZOjOVNUueKbc+Xgl6N94v3PrVtF7/4zQDSloNIQMsF8ooQCAd4g47f5XNtF7adV/GcENKSGGOIp0wkDbvsickjJ+L4lyf35haq/LqCm6/ahPaQXoEjbQwMlR/nG35q8vnnn8c999yDj33sY/jABz6A4eHh3H+JRAIAcPPNN2Pbtm247777sH//fvzt3/4tdu/ejRtvvLHOR7847B8Yx8PPHsDgaBKaKhAKqOCM4eiJBH78zH7sGxiv6fH0dgXR1e5H0nBmnEhIKZE0HHS1+9HbNTXt33UlBobj2HdkHEeH47niD8m07Z3gZmbyLdvxAvS8tEmZ+X3GGAK6wMh4CsdHElV/riT7N7UxeDKBlLF4ImsaG1vTvoFxbN32Oo4MxcEZQyigQlMFBkdTePjZA9hf4fHUlRJHC4x7+aaPgaoioCjc29sMbwx0pFet3LQcTCQsRII6lnYE4Lpy8r+87gvZ/2rt6EgcI+MpBHRlRiE0Gr8JIc2MMSBt2Uikyw+sT4ylsPWxPTAzE+iawnHTlRuwtKN1tpeoCkc4qGbqjlROw69c//SnPwXgVcG9//77p1z3x3/8x/iTP/kTXHDBBfjqV7+Ku+++G9/4xjdwyimn4Nvf/nZRKZWkPK6UeHrXMRiZVQDvxCoNO1NgJm05ePDn+/Df3rsep9RoxYUzhs1nLMfDzx5ANGkhoAsIweE4LpKGA13l2HzGcgDeikwiZeHgUAxvHo1iPGZAIlOsLLNSFPApENxbpeOKmOxxPZm56O1LzOwTFILDMRwk04sn0KsG23ERT1mIJS3EkyaiSSvzs5m7PPtvO7MyqgiG2//wLJyyorVSOgtp9LHxwPEofvbiEViOi7BfRSSkoT2ooy2koS2owV8guFns3hwYxw9+vg8p0/FWgm0HimF7r18mK+bpXcewdkVbRVZVi804mj4GAkDYr2IsZsB2JLIlLaQrEU1auTG2Fiu/C+3gkEhNnSiYjsZvQkizsh2JeMKaPE8t0XjcwH3bdyOZWbAQnOGjl5+KlUvDlTjMuuOcwa8rCPmVTCHkys70Mkk5QgU5jovR0eJmrl0pMRY3EA77MTGRbMkUSCEY2toCM57f0WEvZURTvaBzPG7AlfBWdZl3suVIIBJUcf3F/TVNaZzrxBEAnt51DIMnk0im7SlJiKrCENAV2C6gqxwfumAtntl1DIOjKUQCKmzHxcmJdOYEznt+qsKxJOIDYwyW7a3e3HD5BqzoDtXs+c5ltr9frUkpkTIcxJImYtlAOWkhlrIy/zczQbNV8ir0hy9ah2vevWbO23R2BmlPYYkcx0U0mkJHRxBjY4lZ08L/v+8+j6PDs4+hquCIZALt9pCGSFBHW1DLBd/tIR0+TSyaAHz/wDj+7Zn9iCYsr4o387agOFKCM6A9pINzVrGxJZtxZFgOAroyYwLy2gvW5sZrV0rct313bgw0LQexlDWlSjcABHSB3q5gzdLXF7IdKTsG/vbNE/j+Y3ugqaLgtqFGHL8rwa8JtId9RZ1E0vhYOsfxusfMNTaS0igKn/d7Z7Ebixsl1TjJP0eMJkx85//9FsPjaQDexOnvX3YKTu/vqvDR1h5j3gp8KKBBU8SCg+pix8aGX7kmjS0/XXAikfb2Ief3u8usaJimW9EVl2L097Vj7Yq2GasaB45O4OFnDyCZtmCY7ozdfbYtEXcttAU1GJaLZ3Ydw0VnLMe/T1kJZ7BsmVmxBkJ+r9BZNu28p3Nm2nkrs2wX8UxgHE9ZiGaD5rzV5uy/CxWZqwTOGdb2hvGed/RW5f7JwnSGfXMG11ZmkurkRHrW26gKzwu4J1e9vcu8YLwVAvBsBpBpOmAMue4GDIDCGGxXIpay0BnxVWRVdXrGUfb144pARPAZK+T52UDjUQOG7WQmUTM7mZmXNaIoHBfXMLAuNDmQTZ/PnxzIt6IrhK52vzdRIPiU985iHb8JIfXjSonDQzHEkxZCARWrli283gNjDNGkCdMqr3ioYTnY+tieXGANANe8Z01LBNZCeAtn2UJs1VxbpuCalCWbLmgY9pR9yFnZlGl/3j62Wq4GcMamPF7upNK04cjCZXMkvBY18bSNtqCW25937QVrc6skCuew4Z0Ih/zenkjLdqaknTd7MRxXev1xc+nXmRXm/EA5e126itWg/bpAyK8hHFARDqgI+VWEAxrCmf+HAirawxp6lkYgpAuV04pLI/gfH3o7Xto7jENDUZycSGMiYXr/xY1cGv98LNvFyEQaI3ME4JqaCcBnCb7bQhp8C+nVVAfHRxIYGU/BryswbTM3bmYJxmDbLgzDhuDeuFuJxytm33F2/Ozva8fvXrAWD/58H1zprQBIAKrKEfKr0FWBaNLCM7uOYV2VJ1EXOjkw9fmhqG1DzT5+E0Ia3+6Do9i28xAGR72sQiEYejoDuPq81dhUZEtAxoCUaSNVZgEz23Fx/+N7MZA3KX7pWStw/tt6Sr/TBsAYoKsCYb8GRWE1qfXR2GccpOFli4cdPZHw3rB5cY2UEm4mZVrTBBIpu+772LInlZoikDJM7wSxwAeNM2T2jUs4rrdCv37l1JXw0Wgarx44iZMTacSTXipnT2f9KvoWK1vpN5ZLx57279w+Z6tgcaNKEJxlgmUtEyznBc2ZIDoc0BD0qVCV+YNlIbzVtaoUGSYl0TWBC07vxWmxjilpatmtARMJAxNxE+MJA9G4mRd8m5hIFB+Am5aL4fH0lJn2GceiCkQy6ef5gXckE3y3B/W69uvMZgD5/AKKwWHbLjjPq2LKAOkCKcPBiqXBsldVpxcom262fccBXYGqci9bgHs9sdW81d9CQXk1lDI5kK+/r33KZKljOE0zfhNCGk8pq8+7D45i6xN7kTZtBH0qFL839g8MJ7D1ib248YoNRQXYti2987UysgJdV+J7P/0t9g1M5C4777RluOx3+kq+z3pjDFAER8CnIKArNS2iScE1KUs2XfDHz+xH2nIgXemdCMJb/eWZlV3XlRVZcSlX9qSSK94xcgBzrblambZd2eOevhJ+9mnLFlRMp1pcKZFM29NWlSf3LyfSFhJpG+Ml7scpll9XJoPj3Gqzt7qcDaLDfg1+vflTeUlpGGPel51PQe+SwkFitgL8RHxytTs/8M7+u9gtBoblYHg8heHx1Ky30VXhBdr5+7+zKemZYFxXqxOAZzOAXFciHFAxEfOeG2fe1hOZybLRtcqsqhYqUJbPcdyC43UybcN1gUBAKXgMtSoGVurkQL7Ztg3RijUhZCFKWX12pcS2nYeQNm20h/Tc+ZBXC4JjPG5i285D2LC6Y84xyZXARNKA7ZS+B11KiUf/8yBe2n0id9k71i3BNe9Z05TnaZwzqAqHX1egqzxXv6SWKLgmZevva8eHL+7Hg3lVbhm8Fev8dMFG2MeWPamEnGyhNRspvVWx5d2zrxRND7YrLVs4KBc0Z9KwcwXAMv9OpCxUaSszFMGmrCpPXWGeuvKsUBEcUgGMMQR9KoI+Fctn+exJKZFI25PBdyYQjya81fCJuPfvhQTgJ8ZSODE2ewDu08SMlPMpKekhr0jKQmUzgAZHU2gLquhs82E8bsC2XbjwxiK/JvDhChWFzH+8hew7LjUor7RKHUe1x29CSGsrdfX58FAMg6NJBH1qweyboE/B4GgSh4diWNMTmeXRJSYSJkyrvOJuv/j1AHb8dij38/q+Nlx/SX9TTTQyAFww+FQBn65AU3guoK5H2W4KrklFrO9rx39773r8+Jn9ME0Xfl1A07wK4rVuzTKX3EnlySSEkkm/ZJgRmDqut+ru9ykVP27XlUikp7aSiuXtZ87f21zuoDmXgE9BJC849gLkTAAdmNzX3AoFo0jrYcyb9An5VayYJQB3pUQiZU0G3fG8VfDMZQsJwNOmg7SZwtAcAbhfF16wPUshtkiBAHxK+8CEhZBfRWdEh2E4SBkONI3jIxf3V6ydYbHtCqePe6UG5ZXWKMdBCFm8yll9jictOI6E4i+8IKEoHMm0jXjSmuXRJcbjZtmZiDt+O4infnM09/PKpSH84ftObZqFEs4YhPDaavk0BYJ7hYXr3QeLgmtSMaf0teP6i/tz+9gSKbvh9rHln1Q6aQkH3qzW9L3XDEDPkiCuPGdl0cdtmM6UVlLxWf6dSFtV++Crgk8Gx3lp2ZGQimVdYQi4COgqQn4Fggp/kRbHGctkWWhAd+Hb5ALwuInxhIlowshLR/fS0KMJs+jMkJThIGUkMTianPU2fl3J7f+O5K18n7tpKV4/NIaJuAnb8bbSrFhandZWpew7LjUor7RGOQ5CyOJVzupzKKBCCK9QpVZgu5FtuxCCIRRQZ1wnITERn1lI1pVyQdtcdr05gp/+58Hczz1LArj56o1V2/5USSKb+u1ToKticvtUvaPqDAquSUU1wz62/JPKwZPJXIVFnqko2B7Wcc7GpTj7tGWQEogmpvVkzvRjnv5vs0p9FxmAoF+dssI8mY7t/T+SCah1tfAqc6P0uSak0eQH4LOVbnFdiXhmBXy2/d+x5EICcK+y6/GTswfguirgC6hQBcdrB8dwZDgxuQIe0hEJaEUV+5tLKeN1oxQDa5TjIIQsTuWsPq9aFkZPZwADwwmoyszsm0TaRl93EKuWhWf8bqHAev/A+ORY6AKCA13ts4+FbxwZx7/9cn9ua2RbSMOtv/9OCOk27DkiY4DgXkFNv6ZAUbgXTM/S+aeeKLgmFdfo+9iklFixNIRr3r0GhwZjGI2mEU1a4JwhmbZg2xLP7z6Bn//mKJIpq2ofWk3hU/Ysh6elaGd/DvpVCN44kxOELDacM0Qyq8wrlxYe25xsAD5L8B1NmIgmzaKzVgzLgTHhzNmGLOhTJvd/F0hDjwS1edP7ShmvG2UStVGOgxCy+JSz+swZw9XnrcbWJ/ZiPG4i6POCRdt2kUjb8GkCV5+3espYxhgQTVowrJmB9cPPHoBhOQjoSi6LZ3A0hYefPYBrL1g7JcA+PBTDv/7sjVw3mICu4JZrTkNnxIeJidknfOvF60rB4NMV+DSRK1DWKKvUhVBwTRrGQlNaprMdF4mUNaPY1/S9zfGkBauMyopzyfa9ntqLWUUov8WUX8utMhNCWoPgLBfkzsZxJWLJvP3fiamF2CbiBmLJ4if0EmkbibSNYyOJWW8T9KtozwTekaDXdiySCb7bQxqCfhXDY6kFj7uNMonaKMdBCFlcyll9BoBNazpx4xUbcpXGk2kbQjD0dQdnVBpnzOuSkEpP7WXtSomndx2DYTmIBLTcMXBFICI4okkLT+86hrUr2sAZw9BoElsf3wMrk2mpKRw3XbkRSzv8VXiFyiMEg6aIXNXvehYoWygKrklDmC2l5eLTe7FiaTiXfj2lYva0llNJo3rtX3RVZIp8TQbOkQLVsoM+FZxWmQkhBQjO0B7S0R7SsWrZbLeSkEJg4PgERqMGosnMKnjeSnh8IQF4ppvA0TkCcMa8ntqcA0GfilNXtmHd8ja0h/TMir1KNRoIISRPKavP021a04kNqzvm7JHNmJfJFE9ZudXmrOMjCYyMpxDQlYL7vgO6wMh4CsdHEgj4VNz32B6kDG/lW3CGGy7fgL5ZsrHqgTFA4Rw+XcCnKVAEb4gCZQtFwTWpC9txcyvJ+46M4bnfDmV6SnvpHmnXRfRYFPuPRqt2DNke3KGAd/K4pC0AXWG5/c2RTH/msF8tmPJDCCGVJgRHW5sfCiRWLi18RuG4LqIJa8rK9/T94PHUbFVmZ/K2rUm4DjCRMPHinmG8uGc4dz2DlwI5VxuycECj7SuEkEVlIavPs+GMzdFuC7BtiWjCKtjVIpm2vQWpWbb/CMHhGA5GJ9L44VNvIpowAXhj+u9degpO6Wsr7olWGYO3Rz3oU3Idaho99XsuFFyTipFSImXYM9tK5a86ZwLq1CyrzMWfDs7Op4lpvZjVgn2aAz4lNztIBb8IIc1CcI6OsI6OsD7rbWzH9VLNs23H4pP9vycSJoZGk0W3IJNArtvBwHDhFXDGgHBAm3X/d1vQC8Aps4cQ0kqKWX0ulYTERNKAPctWxoBPgeCA47jgysxFIMdxwZnEz38zMKV+xwcvWIt3rFtS9vFVguAMAZ+CoE+BF2Y3R+r3XCi4JvOybBfRpI2TcQuDw7FcZdzpbabiqcIza5XgVfSdGihPpml7KdrZy8utoEsIIc1OERydER86I74Z1x0djuNfntwLVeHgnMNxJRxHwnFdOK6EbbuwXReaImZUpZ1NtrNCNGHiyCy34dkAPG//d+7fmWA85KetNYSQ5jLf6nNJGBCNmTCt2WsE9XYF0dXux+BoChFReN+3lBLR8cnA+r3v6sO5p826L6mmVMERDmpT9lS3AgquFyk3f5U5mbeXuUBv5mJPrkqhqRyW7UIVHEIwcM4gGPf+zxnAgLRh4/rN/Th1VUfVjoMQQhaLbCqhX/EqryoCQF5BW1dKxJMWrt/cjzW9kcwK+Owp6MXWu3Alcr8/G84YIkE11/+7PZTtBZ5JSQ95dS6oIjghpFUxxhBLmkhbc59/c8aw+YzlePjZA4gmLQR0kasWnkjbsGw3V7wMAM5/ew8ueeeKah9+UXSVIxzUoWS2g7YSCq5bjGW7k6vK+anZKQvxvMvjyZmFESpFcDalvdT0tOz8NlNDo0n8y5N7oakCaoGUFst2oAhvHzQhhJDyFZNKKLh3O1XhWNLmw5K2mSvgWZbtpaDn0s7ziq9lK6PPthVoOldKjMe93wHiBW+TDcCnpJ1n09Az/w5SAE4IaUKMMSQNr0hvMafp/X3tuPaCtZNFgQ0HnEkonCGZF1ifeUoXrj5/9YzCZ7XGGODTFESCKhhac4ym4LoJuK5EIj25Xzm/Qna23VR2b/P0/neVFPSrCPmUOXoze//266LoD+98KS1Jw0FPpx+9XcFqPS1CCFlUKj3uFhOAm5aTCcCzAbeRaT/mrWSPx42is6SmBOBDhW8jMr3Js8F2e0hHT3cIumAI+1W0hXQEfTMr7BJCSL0wBqRML6vUXcA2y/6+dqxd0ZZrZ/vKWyfx672TRSlPXdmO6zavq/uEI+cMAV3xen+32Gp1Pgqu68i0nNzq8mRadn4hMC9wTqQsVGkrMxTBZqwuFwqc20IalnSGKl7wa66UlqThQFc5Np+xvO4DAiGEtIp6jLuaKtDV7kdX++z9VI1MAJ5d+R6Pm5NF2TJp6MUG4I4rMRYzMBYzZr1Ntjd5JDRt/3dQQyRTFZ0CcEJIKVwpF1TkzAusHcQS5oIC6yzOGFZ0h/Cfrx6fElivWhbCH753fd3bKaoKRzigQleVpq0CXiwKrivMdeXUVeW8Ctmx1NTq2XMVKShXwKd4raT8BaplZ9O1/Wqu5P18hKjeyUWhlBbBgZ5OPzafsRz9fe1Ve2xCCFmMGnHc1VWB7nY/uucKwE0ns2979j3gxWZwOa7EaMzAaMwAECt4G0Ww3P7vXPr5tBT0Qj1mCSGL1+6Do7n2XI4jIQRDT2dgzvZcZmZbZzmFgV/eN4JtOw7lfl7a4cfHr9hY13ay2TTwcEADZ83bXmshKLgu03++ehzP7x7CaNTIpWtX632jCj65opzpv5xfNTv7c9Cv1H2GaqGmp7QEfAp6u4K0Yk0IIVXSjOOurgks1fxY2jF7AJ427VygnQ28o0kTScPByHgK43Gj6Mlt25EYjRoYjc6+Aq4Kjkgovw3ZzF7gC9kuRQhpXrsPjmLrE3uRNm0EfSoUP4dtuxgYTmDrE3tx4xUbZgTYjisRTZhlZYbuPTyGHz+9P/dze0jDzVdtQsBXv1BPCIagT0XQp7Rc0bK5UHBdhuMnE/jutt1l3QeDt5d5cnU5Px3b+38ks9qsq6395ZxNaSGEEFIbrTju+jQFPk3Bso5A7jIhGNraApiYSMK2XaSzK+CZvd/jmWA8fz+4aRcXgFuOi5MTaZzM6yM7narwGQH3ZDE2LxgvNpOMENKYXCmxbechpE0b7SE993n2ivZyjMdNbNt5CBtWd+QmMV0JRBPGlKreC3VoMIYHfrYvV6g46FPwias2oS2olf+kSsA5g64KhPwqFNF61cDnQ8F1Gfy6Al0VBVPQNIVPW1XWZgbQARVBn+q1nCKEEEJI1THG4NcV+HUFPZ2BgreRUk4JwCf3f0+mn0/ETVhOkQG47WJkIo2ROQJwTeFTUs69veBe8J3tBe7T6LSNkEZ1eCiGwdEkgj51xkQZYwxBn4LB0SQOD8WwpicCV0pMxA0YZWwTHRxNYuvje3Jjka4K3HTVpjnrW1STIjjCQRW+zN7qxRZYAxRcl6U9pOOOG9+FN46MQ0JiWVcIXLoI6N4qMyGEEEKaT7EBeMqw8/Z95+39zivMZheZ6mnaLobH0xgenz0A11WRC7Qj09PQMyvjFIATsvCCYpUQT1pwHAnFX3hrpqJwJNM2kikbjisxHi9vxXo0msZ923fnCj0KznDDFadiRZ067GgqR1tAh6KwRbG3ejY0ApdpRVcQvUsCGIsbCIf9Fa+mTQghhJDGwxhDwKci4FPRu6TwyazX2syesv97yn7whJeCXmwAblgOhsdTGB5PzXobXRV56ede8L18SQAXnrEcimiueiyElKKUgmKVEAqoEILBtt2CRcS8yznCIRUTZaaCx5Im7tu+B7GkBcArHPYHl61H//K2ku+zHJrK0R7yZYqW1eUQGgYF14QskCtlUxUAIoSQWqHxcSovFdTbArZ8ltUkKSUSaW8FPBo3cr3Asyvf2UC82CrChuXgxFgKJ8amBuBvHJnAH/3u28p+ToQ0slIKilXKqmVh9HQGMDCcgKrwKanhUkqkTAenrelA0KeW1TEobdrY+tgenIxOZrl86MJ1eNva6k0czMWrKaGDdrl6KLgmZAH2D4xPtq5xAcGBrnZqGUYIITQ+loYxhpDfq8kyWzqnKyWSaXvayvfUXuDReQLwkYnZV7sJaQWlFBSrJM4Yrj5vNbY+sRfjcRNBnwJF8YL7pGGjd0kA52xYWlZgbdku7n/iDRw7mcxddsU5K3H2xqWVeAoLwgCoqhdYU/2oSRRcE1Kk/QPjePjZAzAsBwFdgRAcjuNicDSFh589gGsvWEsnkISQRYnGx+ri+QF4d+HbuFIikbKmpJxn/y04w4cuXFfbgyakxhZaUKwaNq3pxMeu2ICHf7Ufw+NpSCmhKQKbVrfj/Lf1YmmHv+S0aceV+OFT+3DgeDR32QWn9+KiM5ZX6OiLJ7hXlyLkV+CF2SSLgmtSN82UPuhKiad3HYNhOYgEtNygzRWBiOCIJi08vesY1q5oa9jnQAgpTzONWbVE42Nj4IwhHNAQDmjom3adXxNoD/sWdZEh0vqKLSgWz+xTrobdB0fx2M5DGIsZkK4EOEPf0hDe/bZedJcRWEsp8e//8RZePziWu+yd67vw/nNX1byFnyo4IiENmsIX/f7qQii4JnXRbOmDx0cSGBlPIaArBWdDA7rAyHgKx0cSLdczlhDSfGNWLdH4SAhpBMUUFBOCIRRQq/L40/d7hwIcuiqQNB08+NQ+XHnOqpK/L5544Qhe2juc+3njqnZ8+OJ1NZ+w1FWOSCYNnALrwqhsJCmbKyWODsex78g4jg7Hc03sZ5NNHxwcTUJTBUIBFZoqcumD+wfGa3PgC5BM294JdabSqmU7MEwblp1pfyA4HNe7HSGkvhY6Js2nGcesrEq/FoVMHx+no/GREFIL2YJiibQ9I0sjWziwpzOAVcvCFX/s6fu9NVUgoCuIBDVI18VEwsvgKWUM/o9XjuFXu47lfl7dE8Z/e++pELx2YRxj2QwY2l89H1q5JmVZ6GpOs6YPBnwKFAGk0hZSpgM7r32ConD4NQHBvdsRQuqn0ivMzTpmAbVbbQ/4FAgOOI4LrsxcLXIcl8ZHQkjVzVVQLJG24dMErj5vdVXG6un7vTWVI+hXkUzbSBlOyRk8v3ljGI/tPJz7uaczgI9fsQGqUtvAOqArCAe0mj1mM6OVa1KyUlZzFpI+WEmcMwjBoAgORXDoKodf92YVgz4FQZ+aKcygIhxQEcn8Fwp4P29Y3YF1y9sR8GtoD+nobvdjSZsPbUHvZDuWtBD0a+idpdIrIaT6qrHCXK8xq1y1XG3v7Qqiq92PpOEUXC1KGg662v00PhJCqm7Tmk7ceMUG9HUHYVgOonEThuWgrztY1TZcuf3eCvd6WQc0pAwb0YQJoLQMnt2HxvCTZ/bnfu4M67jpqo3w67WbqOScIexXEQlSYF0smkYmJSl1Naeo9EHDKSt9kDFvMGDwAmpN4dBUAUVkjpExTFY29E4EFYWjPeIDd71VaSm9+8l/vrGkiVjShMIZVIWDMyDgVxDwqZAAlrTpUBUOTXAIhUO6Ek7mP1dKSOmdaM6VEUQFkwgpTbVWmCs5ZnHGwLj3f8EZGADbzYwNmbFIcAbOMCXtjoGB5/2elIAjJRxHQiJvTMncjysldrw+BFXh6GrzwXEB23EhFW//33jCrOhqO2cMm89YjoefPYBo0kJAF7lq4UnDga5ybD5j+aIdy2hcJ6R0rpQ4PBRD0nCwYpmFjuD8ocumNZ3YsLoDh4diiCcthAIqVi0Ll/y5yx7DXPeV3e/NAYQDGtKGg4m4mbt+oRk8B45H8YOfv4Fsd72QX8XNV29CpIarx0J4RRL9mkLFEBeAgmtSklIL2FQrfZAzBs4ZVMGg6wo0xUvT9oLoqQGt9+/pqyuT/8//d9ahwRhORtPQMpUmY0kLMnPvPk2gLaQhmrBgWg6Wtgfy7t97fFdKuA7gSBeOI2HaDixbesE2vKD7zSNUMImQUlWrqFY5Y1Z2ok9wBk0R0FQOVeEQfLLCKmOA405uM+Gco63ND5UBbuZyxhg4Y9PGrryxxUVuH5+UwPGROCQklrb7IQQHn7Y/riPig2k5mEiY6G7zTwnQpRfpY6GnUf197bj2grWTY5jhQHCgp3Nxj2FUCI+Q0u0+OIptOw9hcDQJx5HQVIFlHX5cee6qeVegOWMVabc1/RiEYOjpDODq81ZPOYZVy8Lo6w4iaThIGfaUwDqbwdPTWVwGz/GTCdz/xF7YjjcS66rATVduxJKIr+znU6ypFcEpsF4ICq5JSUpdzcmmDw6OphARfMpJ8EIHH8YAwTl8moCuCaiCgbHJQaBQEF2qbLpPJKQh5Fdh2i5cV4Jzb2VcAhiPGRiLGljRlX/i7j2+t4oOCAhAgbfaLb0TY8eV2D8wgR27T8CyXbSFdFiOC9umHrGEFKtaWTHZMWtozAvcFUVAZLaZcMaQSNtYt8KH/hVtUzp9CsGhqhxqZitKftaK606OS1J640Pu9ziDmnkM6WYulyhQBCdvbOGAyLuPeMrC8ZEkwkEtt7oiOANjLJeRk0hZ4AC62325AF1Cws1m22T+s11vhdzLvpk786a/rx1rV7TRKm0G9f4mpHTTK2+rAW9S8siJOLY+sbeqKd6zHYPi9/ZvDwwnZhwDZwzXnL8GP3p6P46ciHu1eErI4DkZTeO+7XuQNr2CuYpg+Pj7N2B5DbfVaCpHG1UELxkF16Qkpa7mlJs+yDmDIryTT1Uw6JrIW9FB1WbXprd30Ke1eLAsZ0HtHbLHyRkDOPD4C4cwGk2hq80PXRVwpYRpu7AsByMTRsMWTCKkUVQyK4Yx5Cb+FMZxzfmr8bNfH4Vlu/BpAozBW/lNWtBUjkvP7ENHePqKQuFguhaCfhWcT21H47gS2YDctBwYlgNdE7ngPpfpk5mbmBxqGFzpwnUAy/Um/RzHhZMXkOdveeGMUbstNHchPELqbXrl7ezEoKJwtIc1jMVMbNt5CBtWd1Tt81PoGABAUwVUhWM8PvUYXCmxpM2Hi0/vLTmDJ5Y0cd+23YinvD7cjAF/cNl6rO0tfwW+GNmK4JGQNmXSlywMBdekJOWsQC80fTCbWqkpAgGfAi1TIbFQ+na1ZNs7DAwnoCozn28ibaOvO1hSe4fDQzEMDCegqwKxpIU4s6CrAqoqEPSr8GkKkoaNkfEUlnUGaBaRkAIWOiZlA2gGTJ64cQZF4RCCZ/Y+e3udu9v98GsKtu08hKPD8RmpgetXtjdU2lwlxqvJpyMnM2+EANME8mtWZLe82JktL5blwMqudLuyQrlDzYd6fxNSuumVt/MxxhD0KRgcTeLwUKwiqd/lHMPAcBwrloQwnjRgWW7JGTwpw8b3HtuD0ZiRu+zDF63DaVVenc/iDAj5Nfg0Uamkz0WLgmtSknJXoOcbfHL7FFVvn6KmcHDG501LrJZqtnfIVZj0T04apE0nlxLEOYPjeCtQ7WEdtu3Csl3YTiZ1s4FO6gmpl9nGJOlKWI5EZ1jHFWevzKR280zngEwAnanZkN1KMv0jJaXEqas6cMrK9ooVyKmmao5X07fbTN/ywjJbXmzXG6dMy6sv4bjuopoYrEXxTkJa1fTzoumUTP2beNKq6zEAQCJlYzxuwHLyamcsMIPHsl3c/8ReHD+ZzF125bmr8DsblpZ49MVjDPDrCjojPqQyEwSkPE3Riuvw4cP4zGc+g3e9611417vehT/7sz/D0NDQlNvs2LEDH/7wh3HGGWfg/e9/P7Zt21ano108sivQPZ1+mJaDeNIr6NXT6S9qL1l28Fm/sh0rukO5fYxBn4KOsI4lbT5EAip0RYCB1X1lqFrtHfJTzgtJGzbiKcvbW64IhPwqOsI+LGnT0R7WEfSrXpGkTKBAFg8aG6c6pa8d11+8Dv3LIwj5VQR9KjoiOjatbscNl5+Kd21aho6wD2G/Cp8qoHCe+8y47vx7irMFct6+bgnW9EQa+vNWr3Y02XFa4Rx+TUF7aHKsCugKFMHRwC9bxeRvUyiEen8TMrv5zots213QVrxqHINPFWgLa7BsZ0pgvVCOK/GDn+/DwcFY7rKLzujFhWcsL/k+i8W5Vw28PaRD15RFNQFaTQ0/qpumiZtuugnr16/HAw88AMdx8JWvfAWf/vSn8ZOf/ASMMezfvx9/9Ed/hJtvvhlbtmzB008/jdtvvx2dnZ04//zz6/0UWlq5BWwYMoV/Mn0BdVVkKumWVrG2kgq1Xqh0ewdg4Smc2ZUjBgZNYdBVDkDNtf2ybNcb7G1a2W5li3FszNZcEIwhfzsYAwMXXrbLWRuX4Xc29WDwZBzxlDcm9XWHvM9oXsurSiimPUs9VWO8WigpvbFKVwR8qgJXujBMBynTgZUpDNmKKlm8k5DFpppb8SpxDD5NwJHeJGJXu7/kx3ClxMO/2o89h8dyl/3Ohm5ccc6qso69GIrgCAVU+LWGDwWbTsO/osePH8c73vEOfOlLX0JnpzfTftNNN+F//s//ibGxMXR2dmLr1q3YsGEDPve5zwEA+vv78frrr+Oee+5pyhPIZlNKARtvfyOH36fAr4tcyjdQvaJkCzFf64VK7vEpN4Uz+3Jx5gUYquBgPhWudGHZEnZmRlUILzOgmH7bpPG16tjIWKYfdGYftJfRwqFktol4/erz+9QzZKfh8t/Tfd3VO+kCim/PUm+VakdTCdlA26cpCPhUWJaLlGkjbTotNxFIvb8JKV2h8yJV5TBMB9GEWdbWlnKOQVG8ehyuBEzTxsXv6Cv5GKSUePz5w/jNGyO5yzat7sCHLlw3Y493pWkqRySgQc212aJxqJIaPi189erV+Pu///vcyeOxY8fwgx/8AG9729vQ0dEBAHjppZdmnCied955+PWvf90QgRrxMOYFeH7N2zu8pM2HoE9piJTvfNnWCwPDceiqVzVRV0Wu9cLug6MVf8xKp3BmT2I1hSOgK2gLaejpDKK73Y8lER86wjraghqCPgW66u0/5ZwtinTNVtFKY2P+2NAW1NAZ8aGr3Yfudj86wt72EL+u5NqCyLy2UJP/r93x1mOMaDWu601IhAMqutp86IjoCAdU6Kq3xaUVxqJyt06R4hw4cADvfOc78ZOf/CR32e7du3HDDTfgzDPPxKWXXorvf//7U37HdV1885vfxIUXXogzzzwTn/zkJ3HkyJFaHzqZw/TzoomYibTpYOXSUE3acBU6hlRmwSPkV/C+3+kr6zP8q13H8Owrx3M/r+0N4w8uWw/Bqzf4ZfdXd4T1TIvIqj3UotbwK9f5PvGJT+A///M/0dbWhq1bt+ZmdgYHB9HT0zPltkuXLkUqlcqt4JD64YxBVTn8ulfpO5f2jdqeEBdjoa0XKqmaKZzZ19lrucMgeKb4EPMudaWbSynPFiAqpq8taQzNODbyTDVuXeXQNDGlaCGAWXo71189x4hWlP0Tq8IrXMkCGmxHwrRspAzbqzzexKnj1Pu7uizLwuc//3kkk5OFoMbGxnDzzTfj0ksvxZ133omXX34Zd955J4LBIK677joAwN13340HHngAX/va19DT04MtW7bglltuwaOPPgpN0+r1dMg0+edFScPBimURdAQVuE7txoTsMRw5EUPacOC4LtrDvrI+wy/tOYEnXpiczOldEsDHrtgAVanemifnDAFdQTig0nldldU9uB4YGMBll1026/U7duzInQDedttt+NM//VP84z/+I2666SY88sgj6O3tRTqdnjEYZn82TbPkY1OKfJM7rpurCOr9v/Uq7ZXy/Djz2tqEfCp0bXrf2cY6scg+v4ETCQyNJhHK9InNxxhDyK9gaDSJoyOJqvUdPKUKqxlT/34FroeACsCneQG3K2Wuwq9peQF3I6dt5p4f50V/bhtdI4+N872fsrIr0kJMdgFQOIeuCa8TgOIVt5r6tmqssWG6A8ejVR0jin1tW5nGGTRVQyigwczVkPDGIafEQLue39ECDKt6qrtNYT5c4VAUBikb+/O1UN/61rcQCk3dlvajH/0Iqqriy1/+MhRFQX9/Pw4dOoTvfOc7uO6662CaJu699158/vOfx+bNmwEA3/jGN3DhhRfiySefxDXXXFOHZ0Jmk93aoigcHR1BjI0l4Na4Ig9nDKuWhTEeM2CUWU37twdG8fB/vJX7eUnEh5uu3AhfFfc+C8EQ8qsI6GpDZa21qroH18uWLcP27dtnvb6trS33702bNgEA/u7v/g6XXHIJHnroIfzxH/8xdF2fcaKY/dnvL63QAOcMHR3FFRoxLQem9NIVQyFfSY/XLIp5fowh15PaX6DHZyNz4O2l8WnKjBNnwBtgU4YDcFH0+6ORRCKlfR4cV8IwvZUk03IhC7QragTBoI5QoDVWHRp5bMy+j4p5P1mZk3ldVaBrHJoqmmpMmO7AUKImY0Spn9VWZloOkoaNtGnnemgvdBxq9e/o2WiqQHsZhZca0Ysvvogf/vCHeOSRR3JBMuBthznnnHOgKJOnuOeddx7+7//9vxgZGcGxY8eQSCSmbJmJRCI47bTT8OKLL1JwTaZgDLAdiVjSLDuwfuvYBH741L7cuBUOqLj5qo0IV/G8RVM5wgENmsIpsK6RugfXqqqiv79/1uuPHz+OXbt24f3vf3/uskAggL6+Ppw4cQIA0Nvbm/t31okTJxAIBBAOlzZb7LoS0Why/hvCW7lOJEwEAjri8fSsrTeamRAcoZBv1ufnFR7i8OleQK3AhZEyYaRKXx2rJSE4IhE/BCQ4A9KmDU2dvtrundxxBsB1MDaWqP2Blij7/KLRVFnvT8EAjctcj23TcpC27Ewbowoe8EKPK/P+TCQMWMb8fS8jEX/Drww28tiYSKSLej8xBnApITiDY1lIWkBxo2oDc52qjhGV+qy2Mh9ncOCNQ7bj9dM25hmH5vsOa3U+XYGK4nqNN8P4GI1Gcfvtt+OOO+5Ab2/vlOsGBwdx6qmnTrls6VKvX/Dx48cxODgIADN+b+nSpbnrCPFIxJI2UqYNp8xU9GMjCdz/xBuwM/fj0wRuvmoTOiPVmfDjjMGnC4QDaqa2UVUehhRQVnA9OjqaS0uMRqM4ceIETjnllIocWNaePXvwp3/6p3jsscewbt263GMdOHAAH/zgBwEA73rXu/DCCy9M+b2dO3firLPOAuelf0HM1ttuOlfK3Je147hlfwAb08znx5i3iqUKL6j2qV6rAulKWE2yRy7bSie7l2d5dwDLMq0X2gu0T4mnvPYPK7qCRb8/GonjuBU5bo7M3z5T7T1tOkjXdX9k5v3purDtOjx8AdUeH+s5NuaPd8W8n2q5P67aVnQFazJGVOqz2so4AE1w6IpAQBdei69Zx6HG+Y52paz5HmzXdmHbsmVWrv7yL/8S73znO/GBD3xgxnWFtsPoug4AMAwDqVQKAAreZmJioqzjyv4ZG31yohnVesuMKyUm4hbSpp153NI/oyPjKdy3fTcMywEAqArHzVdtxIru6mRACsERXkCbLdqOVFklBdexWAyf+9zncPToUTz22GMAgJdffhmf+tSncPnll+Nv//Zv4fNVZibmPe95DzZu3IgvfOEL+NKXvgTGGLZs2YKOjo5cYYqPfexjuPbaa3HXXXfh2muvxTPPPIPHH38c99xzT0WOgUzFmNcfL+BToKvZCr6NWaBsLtNb6WiqwLIOP96+thMjE+mS2mItNlJ6RdL8moKArmRWkBwYlgPHyRZFq/dR1latxkcaG+uj3NZ5pPLyW3z5dQWG5SKVtmDabsl7tKtl/8A4nt51DCPjKTguIDjQ1e7H5jOWU/XwIj3yyCN46aWX8Oijjxa83ufzzdgOYxgGAC+7Jzv+mqY5ZSw2DKPk7TJZ2ck22tZRPbV4bW3HxVg0Dd2vQfeXl7I9HjNw7/Y9SKS9IJ0zhk996B14xyldlTjUGYRg6Ajp0EvYw03v28ooKbi+6667sHv3bnzxi1/MXXbeeefhW9/6Fu68805861vfwm233VaRA9Q0Df/8z/+Mv/mbv8Ett9wC0zRxwQUX4F/+5V9yRSzWr1+Pu+++G1u2bMHWrVvR19eHLVu2NGwf12amCA7d560SMDbZFqfZZFvppE0bQZ8KNeC1JDhyIo4T4ylsPnM5XjswisHRJJJpG0Iw9HUHG66HbSOR0nt/qApHyK/CdiQsx4VpOjBsp+6p47VSq/GRxsb6ybZnyU7O0RjROKQENIVDD/tgOy5ShtdHW9a4AFIh+wfG8fCzB2BYDgK6kut7PTiawsPPHqD2XEV66KGHcPLkySn7rAHgS1/6ErZv346enp6C22EAr5aFnUlvOnHiBFatWjXlNhs2bCjr2KSUYIzRto4qqNWWGcd1MZEwYZhO2feVNGz830d+i5MT6dxl11/Sj1XdAUxMVH6TlKYKdIR0JBMGkgmj6N+j7UjFKXbLTEnB9VNPPYUvfOELuOqqq3KXaZqG973vfYjFYhUNrgFvH8zXv/71OW9z0UUX4aKLLqrYY5KphGAI+BT4hM8rJFPjvrKVVKiVDmNedfj2sIaxmInXDozis793BgZOxCveFqvVZd8X2ZZffk2BK11vFcmRcFwJ03bgtujKdi3HRxob66earfNI+WRmr384oCLgU2FYdl3/Nq6UeHrXMRiWg0hAy61wckUgIjiiSQtP7zqGtSva6D00j7vuugvpdHrKZZdffjluvfVWfPCDH8S///u/48EHH4TjOBDCq4uwc+dOrF27FkuWLEE4HEYoFMLzzz+fC66j0Shef/113HDDDWUdW/b7jLZ1VE+1XlvGAMNyEUuYsCoQYJq2g+9t24PB0ckg+urzV+OMU7oqvjUlf3+165a+PY/et5VRUnAdj8enVKrN193djdHR0bIOijQOIbxUu0hQRUfYh7GxBOwmj4YOD8UwOJpE0KfOqFrMGEPQp2BwNImBE3Gs6alOu61SZPeHN9uJfDZlU1fye2urXpBtOUibDizHbZmVbRofF49si5h6atZxoVakBDgDIkENwZAPlmkhkbTguMUV96qU4yMJjIynECjQQYMxhoAuMDKewvGRBFZ0h2a5FwJ4q8+FLFmyBMuWLcN1112He+65B1/84hdxyy234JVXXsH3vvc93HnnnQC8yc4bbrgBd911Fzo7O7FixQps2bIFPT09uPzyy2v5VEiDYIwhmbYQT1kV2UriuC5+8PN9ODQUy1128ZnL8Z539M7xW6URgiHsV+HXlZY4h2oFJQXXGzduxEMPPYSLL754xnWPPPJI2Wk1pP44Y175fr8GReEQBVrONKt40oLjSCj+wqkdisKRTNuIJ+evOl0r0/eHC8HQ0xloyhTU7ODPWXaPpBdoG5aXvmk34D7JhaDxkdRKK40L1SYloCoCkYAKv8qRtlwYpg3L9jJoql2IMZm2vT3Ws6QUCsHhGA6S6QapyNjElixZgnvuuQdf+cpXcO2116K7uxu33347rr322txtbr31Vti2jTvuuAPpdBpnn302vvvd70JV1ToeOakLBsSSJpJpG24FolNXSjz09FvYe3g8d9nZG5fi8rNXln3f0ymCoy2UbbNV8bsnJSopuP70pz+NT3/60/jwhz+M973vfViyZAlGR0fxy1/+Eq+++ir+6Z/+qdLHSWqEcwZV4fDrCvyayNtT3TrBdSigQggG23YLttKxbRdCMIQCjfElO31/uOL3iicNDCew9Ym9uPGKDU19Ii2l1/7MrykI+tQpRdFsx4V0UZEvvFqh8ZHUQquPC9XiDSUMPtXbsiKl184rbTowTAd2lVa0Az4Fgntpl1yZ+b3jOC4E925HFm7v3r1Tfj799NPxwx/+cNbbCyFw2223VXQLI2k+EhLRTEXwSnzupZTYvuMQXn5zJHfZ29Z04ncvWDsjY6VcFFg3rpJG8Ysvvhh33303vvWtb+Gb3/xmroDDpk2bcPfddxdcsWlF2XQ8w3Rgui7WB6vTq67acj2qNQGfJqCpomVSdAtZtSyMnkwrHVWZ2UonkfZa6axaVlof4EoqtD8c8IpWqArHeNzEtp2HsGF1R0ukgrqut08y6PMCbVe6sOxsL1sHdnaVqYH3atP4SKqtUuPCYk8pzxbjVARHOOAVYkxbmXZetgtZwXGmtyuIrnY/BkdTiBRo4ZY0HPR0+tHbVZ3WPISQqbxWWwYMq3J7jJ95+Riee22yV/q65RH83qWngFc4+1NVOCJBCqwbVclTpJdccgkuueQSGIaB8fFxhMNhBAKBSh5bQ8tPx+sM+5AyLIRDh3HRO3qxprdx9unORWRWqaf0qJaoU5/i2inUSkdVOQzTQTRhNlQrnWL3hx8eitV972cleV8W3l5tTWHQFA7mUwFI2I6E7XpFNyzbhe1IMD7ZX7QRLPbxkVRXJcYFSimfKnuC6q1oC9i2hOU4MG0XhumUvVWFM4bNZyzHw88eQDRpIaCLXLXwpOFAVzk2n7G8Ib53CGl1rpQYjxswKxhYv7B7CE++eCT38/KuIG64/FSoSmV7R+sqRySoZ9rgVvSuSYWUlX80MTGBVCoF13UxPj6O8fHx3HXLly8v99ga1vR0vFBQheO6ODocx0PP7MeHGridBucMqmDQNQW6KqAI3pQ9qstVqJWOpgqsXBrCleeuapiTy2bcH14t2fdptgq5TxUAMj3WGYM/oCGZNNAAHXcALN7xkVRfueMCpZTPTUqvSJAQCvw6g+1zkUxbSFtOWVV++/vace0Fayf7XBsOBAd6OqnPNSG14kpUPLB+7a2T+PdnD+R+XtLmw01XboSvhF7Ts2EAdE2gLaSBtdBWzVZU0l/90KFD+MIXvoBdu3bNepvdu3eXfFCNrFA6HmfMa+MU9No4NVo7DQaAC69as9+nQMvMojVrj+pKyW+lkzQcrFgWQUdQgVvhFgnlaLb94bWUXd0GAEUwhAIaLMOqexuJxTw+ktooZ1xYbFtNypVt6RUJ6gg4k0G265TWObu/rx1rV7Th+EgCybSNgE9Bb1eQXmtCaiCbCl7JwHr/0Qn88Kk3cwtUkYCKT1y1CSF/5c7LGAP8uoJIQKvYfZLqKSm4/qu/+iscPHgQf/zHf4yenh5wXtmUh0Y2XzpewNc47TQY8/aS+XUFPk1AcF7RPWStINtKR1E4OjqCGBtLwG2UpU801/5w4lnM4yOpjXLGhcW61aRc+UF20JUwTBsp04btLLzSOGes7ucHhCw2jisxkahsYD0wHMf9T+7NbRvx6wI3X7UJHWG9Yo/BGBD0qQgHVDp/bxIlBdcvvvgivvKVr+Caa66p9PE0vPnS8YTgcNz6t9NQFY6AT4FfU8AYrVJXUzWLAhXaH64oXgpnIm031P5w4lnM4yOpjXLGhUbfatLoRday3Q0CPgUBn5Lbk502HThOI03NEkKyHEdiPGHAqmBm2/B4Ct97bE8uWFcVjhvfvxHLOitXX4VnCryG/BRYN5OSgutQKIS2trZKH0tTmC8dr97tNDhj8OleL89sgTL6QFZPLYoCFdofLgRDX3dw0RYfamSLeXwktVPquNDIW02aqcha9ntVFRxagCPgU5E2LKQMB5ZT360phNRao06KMQaYtoto3Kzo53IibuC+7btzC2mcMXz0fafmsoVcKcve+sE5Q8ivIuhTaXGsyZQUAf7u7/4u/vVf/xUXXHBBxfu2Nbr50vGSaQfL6tROQxEcwcxsOgXV1VfLokD5+8Mb7cuLTLWYx0dSW6WMC4261aSZi6xJCXAGBP0q/D4FiZSXMl5O8TNCmkWjTooxBhiWi2jChF3BwDqZtnHfY3swHje9xwFw/SX9OHVlOwBg/8D4ZNFCFxAc6GpfWNFCCqybW0nBtd/vx69//Wu8733vwzve8Q74fFP7OzPG8NWvfrUiB9hoCqXjuVLCtl2MJ8y6tNMQgsGneX2BBaeguhbqURQouz+cNLbFPD6S2lvouNCIW01apcialAADQzigIuBTkEhZXrp4i7e3JItXo06KMQakLa+9aiUnuUzLwdbH9+DEWCp32dXvXoMzTukC4AXWDz97AIblIKAruXZ7g6MpPPzsAVxbRDchCqybX0nB9cMPP4xwOAzXdQtWxG311Zrp6XjxhAXTcrCiO1TTPtecMegqR8ivQVGoWFktUVEgMpvFPj6SxtdoW01abTz1VrIzFcZ1F/GUCcN2F1z4jJBG1qiTYowxJA3Lqy9Rwc+c7bj415+9gSMn4rnLLjlrBd799h4A3uvx9K5jMCwHkYCWez24IhARHNGkNW83Ic4Zwn4VAQqsm1pJwfVTTz1V6eNoOvnpeIbpwHRdrF+1BLFYquqpYIx5hROCfhU+VVCxsjpo9KJApH5ofCTNoJG2mrTqeCqllyLbHvbBsBwk0hYsy4VL39ekBTTqpFgsaSJp2BWdzHKlxI+f3o99AxO5y87ZtBTv/Z2+3M/HRxIYGU8hoCuFuwnpc3cTosC6ddSn6laLyKbjuVJiLG6A8+qelGRba2WrgAOUAl4vjVwUiBBCitEoW01afTyVUkJTOPSwjpTpIJW2YNoufX+TptZok2ISEhNxE4blVPSzJaXET587iFf2n8xd9o51nfjge9ZOCaKTadvbYy3m6CZkFO4mJARDOKDBrykUWLeAkoLrj3/84/Pe5vvf/34pd01moQiOgC4Q8Cm5KuCkfhq1KBCpPxofCVmYxTKeSgn4VAGfJpBehEH2sWPH0N3dDVWdOUliGAZ++9vf4qyzzqrDkZFSNNKkmCuBiYQJw3Qqft9P/eYodv52KPfzKSvacP0lp8xYUAv4FAjudQ3iSvHdhBTBEQmq0FVBgXWLKDy9Mg9vb+/U/xKJBF555RW8+eabWLduXaWPc9HinMGvK+iM6Aj6VQAUWDeCbFEgnyYwHjdhWg5cKWFaDsbjJvWfXsRofCRkYRbdeJoJsjsiPkQCGhTB0SLPbE6XXXYZdu/eXfC6V155BTfffHONj4iUIzsplkjbM4LC7KRYT2eg6pNijisxHk9XJbDe+fogfvHrgdzPfd1BfPTyU6EUWJ3u7Qqiq92PpOEUfD2ShoOu9qndhFSFoz2kZQLrih8+qZOSVq7vv//+gpdPTEzgk5/8JJ08VoiqcIQCKnyqQsXKGlCjFQUijYHGR0IWblGOp9JbxfLpAmnDQcq0YduypfZk/83f/A3Gx8cBeAHG3XffjY6Ojhm32717N8Lh5s5MWGzq3Xkg22orlqhsD+usV/afxKPPHsz93N3uw41XboReYJUe8F6PzWcsx8PPHkA0aSGgi1y18KThzOgmpKscbSEdnLJRW05F91y3tbXhU5/6FL761a8WlRpJCuOMQdcEwgENnFGxsmpzpcSB41EcGEoAroMVXcGivwwaqSgQaWw0PpJm50pZ1bFuMY6n2fZdAZ8Cv09B2vQKn9ktki6+bt06/NM//VPu59deew2apk25jRAC4XAY/+t//a9aHx4pU70mxRjz9jjHU6VXBHelxPGRBJJpGwGfgt68c799A+P4t1++iew9twU13HzVJgR9c6e49/e149oL1k72uTYcCA70dE7tc61rAu0hDWxR5KwsPlUpaHby5Mn5b0RmYMwrABH0qfBrlCJSC7sPjmLbzkMYGk3ClQBnwLLOwIK+FBqlKBBpDjQ+kmaUHSsHR5NwHK8Kds8Cx8piLNbxNPt971MFfCpHIm0jadhV7z5Sbddffz2uv/56AMCll16Ku+++Gxs3bqzzUZFKqsekWDRhImU4JWd5vDkwjidfPILRmAG4EorK0d3uBcCaJvCvT76RC9oDuoKbr9qE9pBe1H3397Vj7Yq2goE7A+DTBNpCGkCBdcsqKbh+8cUXZ1zmOA4GBwdx9913421ve1vZB7aYcMagKN7ear8mQPuqa2P3wVFsfWIv0qaNkF+FT1OQNm0MDCew9Ym9uPGKDa2ZikiqisZH0mryx8qgT4Xi91I/aaysFuZ9J6kKYikTZou077rgggswNDSEdevWzVi9Js2tVpNipuVgNJZGKm2j1E/Ef+w6ip+9NADbkWDwFraE6+LocAI/fmY/0qYL0/bSzDWF48YrN2Jph39Bj8EZm9FuiwHw6QragvTeb3UlBdcf+9jHZvRwA7z05d7eXvzv//2/yz6wxUBwBk3h8PsUr9Ji8393Ng1XSmzbeQhp00Z7SAfnDJwzaKpAu+AYj5vYtvMQNqzuaOmURFJ5ND6SVjJ9rMy+tzVVQFVorKwWKb32PB2Z9l3JtAWryVPFX3nlFfzoRz+C3+/Hueeei0svvRSbN2/G0qVL631opMExBpi2CyPqFS4r9WOwb2A8F1gL7gW8EoDtSDiOg3ReUTTBGT56+alYuXRmT+oFHz8osF5MSgquC7WRYYwhFAphw4YN4LykIuSLhhAMPk1BQFegCO7tqW7iL8xmdHgohsHRJII+dUYgxBhD0KdgcDSJw0OxRZmiSEpH4yNpJTRW1leufZcqkDJsJNM2bMdtylOGRx55BMPDw/jVr36F//iP/8DXv/51fOlLX8KmTZtwySWXYPPmzXjHO95R78MkDSa7vzpl2giFF7aCnM+VEj976YgXWDPkJgMZAEiJ6Tswrr+kH+sze6TLwRjg1xVEAhRYLxYlBdfnnHNOUbdzXRfve9/78O1vfxvr168v5aFaihAMfk3J9MLzUr+pWFl9xJMWHEdC8RcOdBSFe8UyklaNj4w0OxofSSuhsbJxZCuLp9I2UoYD222+lezu7m5cd911uO666+C6Ll544QV861vfwj/+4z/i7rvvnrVVF1msJCYSFtKGA1bmvPTxkQTGooaXCp7Xo1pKiek10c4/bRlO7+8q7wHhtdMN+hSE/GrTfVZJ6apS0CxLSomjR4/CNM1qPkxDYwwQnMOniWlBdb2PbHELBVQIwWDbrpeSP41tuxCCIRSYuzIkIaWi8ZE0AxorG0e2snjQr3qVxQ0HSSOzkt0k5xSmaWLXrl144YUX8OKLL2LXrl1IpVLo7+/HueeeW+/DIw2CMcCyXcSSJgzL2/8syiwAlkzbk7ULJIBMNx7HnZo8qiocZ23oLuuxAC+wDvtVBHwqLaQtMlUNrhezbOXvgObNNGf72NHnqzGsWhZGT2cAA8MJqAqfku4opUQibaOvO4hVy6jvJiFk8aKxsvFMad+lK0hbDlKGDct24ZbYlqgWbrjhBrz66qswTROrV6/GOeecg+uvvx7nnnsuurrKXyUkrSHXZittVbRafsCnQMv04bZdCQHAKbArc2m7D71dwbIeizEg6FMosF6kaPNfhXHu9ahuC+lYEvEh4FPAqPp3w+GM4erzVsOnCYzHTZiWA9eVMC0H43ETPk3g6vNWU4EeQsiiVnCslDRWNoL89l2dYR2dYT2XIdeIXn31VRiGgdNOOw3XXXcdPvKRj+DKK6+kwJrkSEhMJEzEkpUNrAGgtyuIrnY/FM4BCdjuzAUvwYErzllV1njGGBDwqZlUcDr5X4xo5bpCBGfwaQJK2AfBMh9YqlPW0Dat6cSNV2zI9blOGQ44A/q6gxXv3UoIIc0qf6wcHE0imbYhBKOxsoFICSiCoy2ow9QdJFMWjAZbyX7xxRfx8ssvY8eOHfjFL36Bv//7v4eu6zjrrLNwzjnn4Nxzz8Xpp59e78MkdZCtBh7PSwOvNM4YNp+xHA8/ewCW461e5xMcuPzslTiljCJm2eJl4QDtsV7MKLiuAM6AjrCOJW1+jI0lYNvVGRhI5W1a04kNqztwdCQBcAG4DlZ0BWkVhhBC8mTHysNDMcSTFkIBFauWhWmsbDBSSqiCoz3sg2l7LbzMKgUrC6VpGs455xycc845+NM//VMkEgm89NJL+Ld/+zd8/etfB2OMCpotRgxIpG0kKpwGXkh/XztO7WvDztdP5C7jDOjpCuCKs1eVXR3cpwqvKjgF1osaBdcVwSCovU7TcKWccYK4tjeCjo4gTY4Q0kAKfVYpmKsfzhi122oSuSA75INtuzBtZ/5fqpGRkRE899xz2LFjB3bs2IHBwUEsX74cF198cb0PjdSYKyVicRNpy6nJSu/Lb45MCazbQxquu3gd1i5vK/u7xacJtIWo3Rah4JosMrsPjuZSGx1HQgiGns4APvietXhPR3kFLAghlTPbZ5XSkAkpnpTeZyegKHXf//nVr34VO3bswJtvvgnOOd75znfiox/9KDZv3kztCBcZxhjSlo14woLl1GZB440j4/jxL/fnfm4PafijD74NbSG97Pv2aQLtIQ0os6I5aQ0UXJNFY/fBUWx9Yi/Spo2gT4Xi96pGDgwncN/23QiFfVjVFaj3YRKy6M31Wd36xF7ceMUGCrAJWYBG2P/56KOP4sILL8RnPvMZXHDBBYhEKAtisYolTSQNu2Y1AQ4PxfCvT76Ra8UV8Cm4+apNFQmsdZWjLaTBlcDhoShlWpHqBteMMZx99tkIBmlFkNSXKyW27TyEtGmjPaTn2sloqoCqcEzETfz4qX347PVUTIXUBo2Phc33WR2Pm9i28xA2rO6gExdCmshzzz03pZUbWVwYYzBtB/GUBbNGaeAAMDSaxNbH9+RWyDWV4+YrN6K73V/2fQvBEAno2HtoDD/dQZlWxFNycG2aJn784x/jueeew/DwML761a/ihRdewNve9rZctUfOOe6///6KHSwhpTo8FMPgaBJBnzrjy50xhqBfwdETcRwa/P/bu/P4Jur8f+Cvmcnk7gmlBcplsQWUU46igoCuKLiriLuuLsixiq6K+0UF72NVcFdAXWU9EJVLVv0BuioseCKrghwqipSCQAsV2lJ6pEmaazK/P2oipS2kae6+no8HD83MJPl8JpN3857PVYsuGeYolZISBeNj8M74XdVrUFppx+GyWo7/JYpxN9xwQ8DHCoKAZcuWhbE0FC0qVNTaXHC4FCgRnMG+qtaB19cXoM5ZP+eAJAqYfGkeOofgd54gAGa9jANHq7F0A3ta0a+CmoWrsrISEydOxNy5c1FcXIzvv/8eDocDmzZtwuTJk/Htt9+GupxErWL9Zc1EjabpS16jEeFRvKi1uyNcMko0jI+tE8h3VVFUWPldJYp5qqoG/M/r5WSiiaa+tdqLylonbA5PRBNra50br63fC8svfysEAbh2TE/kdE4JyevrtRrodRLe+7LI39NKK0sQBQFauX4MtsOlYN3WYn93dGobgmq5fuqpp2Cz2bB+/Xp07twZ5557LgDgueeew5///Gc899xzeP3110NaUKLWMBtlSJIAj8cLrSw12u/xeKGRRCQZ5SiUjhIJ42PrBPJdlSQBZn5XiWIee+e0YQJgrXPBHuGkGgAcLg+W/ncvTtQ4/NuuvLAHzj2rXUheXyuLSDbJKC5lTytqLKjk+rPPPsP999+Pbt26QVF+Xd5Bp9Nh+vTpuPfee0NWQEpckVxmp2tmErLSjSg5boOsERsEQVVVYavzoEfnFHTLSoI3zOssUmJjfGydM35XHR5kZ5jQNTMpiqUkgEulUXA2b96Mbdu2wWKxID09Heeddx5GjBgR7WJRCHlVFRarC84Ijq32cXu8WPnhPhytsPm3DevTAWlmHX4+bkXH9qZWxSmNJCLFqIMA4deeVobme1rZHR72tGpjgkqunU4nUlNTm9wnSRLcbl5EdHqRXmZHFASMz++GZRsLUW11waTX1HcF93hhc3hg0Eq4ZszZEAUBXjC5puAxPrbOmb6req2E8fndmMRFGZdKo5ZyuVy49dZb8cUXX0CSJKSlpaGqqgovv/wy8vPz8fLLL0Or5TrB8UwQBNS5PLDZI7fE1sm8XhVvf/oTDh61+LelmLQoKKrEj4eqIIlA+1QDRvXvhJzs1Ba/viQKSDLJ0GgEqCp7WlHTghpz3bdvX6xatarJfe+//76/GyRRU3zL7JQct0InS0g2a6GTJf/kDwVFlWF5397d0zFlbB6yM0xwuhX/XdXsDBOmjeuN/mdnhOV9qW1hfGy9031XOTlM9EUrhlN8e/7557Fz50489dRT+P777/HFF19g165dePLJJ/Hdd9/hxRdfjHYRqZVq7S5YbK6oJNaqquLdLw7hx5Pij1YW4VYU6LQamI0ytLKE0so6vPPFIRwoqW7R64tifZKslyV/a7yvp5XN4Wm0jryvp1VWupE9rdqYoFqu//rXv2Lq1Km48sorcdFFF0EQBHzwwQd4/vnn8cUXX2DJkiWhLqffjh07MHnyZCxduhTDhg3zb9+yZQvmz5+PAwcOoGPHjpg5cybGjx8ftnJQcKK9zE7v7unI65bWqCtjU3cciYIRrfiYaLGxue8qW6yjK9oxnOLXBx98gNtvvx2/+93v/Ns0Gg2uuuoqnDhxAv/+97/x17/+NYolpGAIAuBRVFjsrogusXWqD7cfwY695f7HRp0GEFSkmH6NU6JGQrIkwmJ3Y9Ouo+jROSWgOCUKAswGGUad3CCJZk8rakpQLdeDBw/G66+/DoPBgCVLlkBVVSxduhTHjx/3d+8Jh9raWsyZM6fRjJIHDhzAzTffjBEjRmDt2rX4/e9/jzlz5mDLli1hKQcFryXL7ISLKAjonpWMc89qh+5ZyQx6FFLRiI+JGhv5XY09sRDDKT5VVlaiT58+Te7r06cPysrKIlwiaq36buAKqmqdcLqil1h/8f0xfP7dUf/jjulGaCQ0G6eMOgkV1XU4dtK47OYIAmDUa2DSy41ap4HGPa1qrC7YHB6kmrUYO7QL8rqltb6CFFeCXud6yJAhePPNN+FwOFBTUwOz2QyTyRTKsjXy6KOPokuXLvj5558bbF+2bBny8vIwa9YsAEBOTg727NmDJUuWYPjw4WEtE7UMJ3+gtiDS8ZGxkSKFMZyC1bVrV+zcubPJ2LN9+3Z07NgxCqWi1qi1u2B3euCN8GzgJ/tm33Gs31rsf5yVbsToQZ3x3pdFkKSm45QkiVCcCuwOz2lfWwBg0GmQZGw6sfbx9bT6/Nuf8fmuo6i2OlFd68S6LcXYWXicc1G0MQG3XB89etQ/Ec/Ro0f9/yorK6EoCmpqahpsD7X//Oc/+Pbbb3H//fc32rdjx45GwTo/Px87d+487ZeBwsurqigqtWD3wRMoKrXAq6oNJn9oCid/oHgUzfjI2JhYvKqKn45U4/sDv8bNWMIYTsH64x//iJdffhlLlizBsWPH4Ha7cezYMbzyyit45ZVXMHHixGgXkQLkVYFqqxM2hzuqifXe4iqs/fyA/3Fakg5TL++FtCQdJBFQmhn7rSheSGJ9i/Tp6LQSko3agFrkC4ursHH7EVRbnTDpZaQk6TgXRRsVcMv1xRdfjLfeegv9+vXDmDFjGnWzOFVBQUGrC+dTUlKCuXPn4oUXXmiy9ae0tBRZWVkNtnXo0AF1dXWoqqpCenpwd4s0msB7zfvujjV3lyzetbR+ew5V4oOvinDshA0eRYVGEtCxnQnjh3dDp3YmHCm3Qis3XmbH7vCgSwczzjplHIxXVVFcWotauxtJRhndskI79pKfX3yLdv2iFR+jFRujfb4T1Z5DlfhgSxHKKuvg8ijQiPVx84rzu6NPj9ho9Tirc0pMxvAz4TUbfddddx327NmDBQsWYOHChf7tqqpiwoQJuOmmm6JYOgqEIAAuj7d+0rJmbrBFSlGpBas+3gdfbm8yyJg+rjeSTVqYjTLapxpQWlmHZKmJOOVUkJVuQMf2zfcok2URySZdQGXhXBR0soCT63nz5qFLly7+/z/Tj8dAlZSU4OKLL252/5dffonZs2fj2muvxeDBg1FSUtLoGIfD0Wj5Bt9jl8sVVLlEUUBaWsu7cSYnG4J6v3gRSP127T+OZRsLUefwIMkkQ5ZEuBUvSipsWP7hPlw2vDtObClCjc2NJIMMWSPC7fGits4Nk0HGH8f2Qrt0c4PXW/3pfvxcboVH8UIjiejcwYxrxpwd8hm++fnFt2jVLxzxMZZjo+88J/r1FEmnxs0ko+yPm8s2FuK2a/rHzIoGfxzbC/9avSsmY/iZ8JqNHlEUMXfuXEyfPh3btm1DTU0NUlJSMHToUOTk5ES7eHQmAmCrc8Pm9EBRotuj5tgJG5ZvKITnl3LoZAnTLu+Fdil6APXzdYzq3wnvfHEIFrsbRp1U3xVc8cLuVKCTRYzq36nZRFeSBKQYtRAD/FPekrkoumclB19xigsBJ9cTJkzw///VV18dsgJkZmZi/fr1ze5/8803UVdXh5kzZzZ7jE6na/RD0ffYYAjuD6nXq8JisQd8vCSJSE42wGKpa7YbSjwLtH5eVcWbG/fCXudGapIWgiDAq6qQRAEpJhnVtS5s330Mk8fmYv1XxQ1atrPb17fQdG1vRFVV/SQTew5V4vX1BXC4FJgMGhj0MjweLw79XIPn3/oW08b1DkmLDj+/+NbS+iUnG0LaghWO+BjLsdFmcyT09RRpJ8fNtCQdNBqxfu3ok+Lmmxv3IrudISZaPbq2N2LK2LxGvZOiGcPPJNFjYCiFOj761NTU4LnnnsM333wDi+XXdYh9KygIgoCPP/445O9LreObDbzWXr8kYrRHqpywOLB0/V44XAoAQCMJmDw2F51OaYXOyU7FhAt7YNOuo6ioroPiVCCJQFb66de5FkUBSUYtZI0YcF05FwWdLOgJzSoqKrB8+XL/3cd27dph+PDhmDx5MpKTA78rI8vyae9Yrl27FuXl5f6lZXzjBG+66SZcddVVeOyxx9CxY0eUl5c3eF55eTmMRiOSkoJfW665MWWnoyjeoJ4XL85Uv6JSC46esP0yjkU4JTAJMOo1OHrCBr0s4f/+0L/JZXZ8r+9VVbz35SHUndLNRtZISDHXd7N578tD6Jkd2FIKoahfvGP9IiMU8TGWY6MvOYmV8x3vGsTNX0KZCvWX+Plr3Dz4c03MtHrkdkmNyRh+Jrxmo+ehhx7CJ598ghEjRqBXr17RLg4FQgDsDg9sDg88MXBTymJz4tX396C2rj5JFQTgjxefjbM6pTR5fE52Knp0TsGxChvsDg+Meg06tjc1G28EoX4JL4NW06J5SU6ei6KppV05F0XbElRyvXfvXtxwww1wOp0YOHAgOnfujIqKCrz88st4++238e9//xudOnUKSQFXrFgBj+fX2fzKysowefJkPPHEE7jgggsA1C99s23btgbP27p1KwYNGgRR5PiqSGrJ3TvfMjvNYTcbikeRio+MjYkjXls9GMOpJb766is8+OCDuO6666JdFAqAVwVqrU44YqC1GgDqnB68uuYHnLA4/dsmjDgLfc4wC7coCOicYT7tMT56rQbmM8wM3pSumUnISjei5LgNsqbxGG+bw4PsDBO6ZgZ/U5viR1DJ9d///nd07NgRS5YsQUbGr+OlysrKcOONN+If//gH/vnPf4akgJ07d27wWJLq7whlZmaiXbt2AIDJkydjwoQJWLBgASZMmIDPP/8cGzZs8Hc1osgJ5d27eP3BSW1bpOIjY2PiODluStrEafVgDKeTmUwmZGdnR7sYUedV1SZ7fMQKQRDgdHtQa3dHfdIyH7fHi+UbCnGk3OrfNnZoFwzu1SFk76HTSkg2yUAQNxJEQcD4/G5YtrEQ1VYXTHoNNBoRHo8XNocHeq2E8/IysOdQZUx+5hRaQSXXu3btwoIFCxr8cATqf9TdfvvtePDBB0NSuECdffbZeOGFFzB//nwsW7YM2dnZmD9/PtdxjYJQ3r1jNxuKR7EUHxkb48PJcVMrN0xE47nVgzGcTvanP/0Jr776KgYNGtTk6gZtQUFRJdZtLUZppb1+XgVJQFa6MabWQY6FtatPpnhVvPnJfhw8+us4/RH9OmJk/9D0kAUAnSzWD11pxWv07p6OKWPz/J+v3eGBJAlIS9IBqop1W4pj9jOn0AoquU5LS0NtbW2T+xRFgV6vb1WhTic7OxuFhYWNto8cORIjR44M2/tSYAK5ezc+v1tAd+zYzYbiUbTiI2Nj/GoQN2tdSDZpIYiA293yuBlLGMPpZJMmTcI777yDiy66CD169Gg0qaIgCFi2bFmUShd+BUWVWLaxEA6XBya9DI2h/reRbx3kKWPzopZs+SYts9hdcMVIN3CgPk68+7+DKCiu8m87Ly8Dlw3rGrJVi2RZREorE2uf3t3Tkdctzd8z4Xh1HTZsOwynW4m5z5zCJ6hBd7fddhsWLFiAb775psH2gwcP4p///Cduv/32kBSO4pPv7l12hglOtwKLtX6GyewMU4sCie8Hp14rodpaH/C9qgqXW0G11RW3PzgpsTE+UjB8cbNLBzMcLgU1tcHFzVjCGE4ne/jhh3Ho0CF06NABer0eqqo2+Of1xkYX5HA4dR1krSxBFARoZQmpZi0cLgXrthbDG4WsVhAE1LkUVNU64XTFTmINABu3HcbOwuP+x/16tsfEUTkhS6w1kohUoy6kMcg3F0WfHunYue84nG4l5j5zCi9BbemofdSP49u7dy+sViuys7ORmZmJqqoqFBUVwev1NpisJ16XVlAULyorbQEfr9GISEszoarKlpAzkQZTv1CNK4pENyp+fvGtpfVLTzeFZakZIPHjo6J4YbHUJfT1FE2iJKDK5sHPZRYYdVJCjM2Lha6wiR4DQylc8XHQoEH4y1/+gptuuinkrx0rFMULSRIbXWdFpRYsWvsDdLLU5BAJl1uB063g9qv7RnhyPxW1dR7UxVA3cJ/Nu45iw9eH/Y97dEzCrOvPQ53dGZJ1tiVJQIpJB50c+JJbLRG7n3ljjI+BCTQ2BtUtPDs7u9GkFF26dEG/fv2CeTlKUGeaSTZQp3az4WQQFMsYH6k1REFAzy6paGeWE+ZHDmM4AYBWq8W5554b7WJERaxN7ufvBm5zwuX2BjOHV1jtLCxvkFh3bGfElMt7QStLqAvB6/vWsg5XYg3E3mdOkRNUcv3kk0+GuhyUIMI1C2aoEnWicGN8pGiLxdmIGcPpyiuvxL///W8MGzaszS0FGEuT+wlC/bJW1rrYWLv6VHuKKrF280H/4/RkHaZe3gsGXVApSyOiKCDJILd4LeuWiqXPnCKrVVfqiRMn4HK5/Ben1+tFXV0dduzYwXUM26BY6PpHFCsYHykaGIcpViUlJWH16tUYM2YM+vXr12jGcEEQMG/evCiVLrxiZXI/FSosNjccTiUmx/oePGrBm5/s97cmJxlkTB/XG0lGbUheXxAAk14Do77la1m3VKx85hR5QSXXe/fuxd13340DBw40uV8QBP54bGNieRZMokhifKRoYRymWLZ27VqkpKQAAHbv3t1of6gmqYpFoVxJJRiCALg8XtTaXXC5Y6+1GgCOVtiwYmMhPL+Mp9ZrJUwd1wvpyaFZYUMQAKNOA7Mh/Ik1EP3PnKInqOT6qaeeQk1NDe655x589tln0Gq1GD16NDZv3ozNmzdj+fLloS4nxbBTZ8H0/YHUyhJkjYhqqwvrthYjr1sagwglPMZHigbGYYp1n376abSLEFXNrYOcnWEKa88SQQBsDg9sDndIJgILhxM1Diz971443QoAQCMJmDw2Dx3bhW49dJ0sIcmojehs6NH6zCm6gkqud+3ahfvuuw/XXHMNDAYD3n//fVx//fW4/vrrcccdd2DFihUYPHhwqMtKMepwWS1KK+0w6eVGd54FQYBJr0FppR2Hy2o55o4SHuMjRQPjMFHsi/TkfipU1FjdcLg8MbXE1sksdhdeW18Aa139xF6iAFx/SS56dAxdnNL+spZ1NHBCx7YnqBklXC4XunfvDgDo3r079u7d69939dVX47vvvgtF2ShO+GdE1DQ/I6KiqJwRkdoExkeKBsZhamtOnDiB2bNnIz8/HwMHDsSMGTMaDMd58MEHkZeX1+DfmDFj/Pu9Xi+ee+45jBgxAgMGDMBNN92EI0eOhL3cvsn9zj2rHbpnJYclyRIEwK14UVXrRJ0zdhPrOqcHS9fvRVWt07/t6oty0KtbWsjeQ5IEJBu1iGYqG4nPnGJHUMl1p06d/AGoe/fusFqtKCkpAVC/1EJNTU3oSkgx7+QZEZvCGRGpLWF8pGhgHKa25rbbbkNxcTEWL16M1atXQ6/XY+rUqairq1+sqbCwELfccgu++OIL/7/Vq1f7n//CCy9g1apVePzxx/Hmm2/C6/XixhtvhMvlilaVQkIQALvDg2qrM2bHVwOAy6Ng+YZClFba/dvG5XfDoNyMkL2Hb8ktuZmbjonMq6ooKrVg98ETKCq1xOQEdokqqG7hl156KRYuXAij0YixY8firLPOwrPPPoubbroJr732Grp06RLqclIM44yIRL9ifKRoYBymtqSmpgadO3fGzTffjNzcXADArbfeiiuvvBL79+9H37598dNPP2HGjBnIyGicrLlcLrz22mu4++67MWrUKADAM888gxEjRuDDDz/EFVdcEcnqhFD9bOB1MTobuI/i9eLNj/ejuKzWv+2iAZ1wYb+OIXsP3wRm4V5yKxZx1YjoCupWzu23345Bgwb57wDed999+Oijj3DllVdi69atmDlzZkgLSbHNNyOiXiuh2uqCy10f1F1uBdVWF2dEpDaF8ZGigXGY2pKUlBQsXLjQn1hXVlZi6dKlyMrKQs+ePXH48GHY7XacddZZTT5/7969sNlsGD58uH9bcnIy+vTpg+3bt0ekDqEkCICiqKiqdcLu8MR0Yu1VVaz9/CD2Hq72bxvcqwMuHRK6G88CAINOgyRjZGYGjyW+VSNKjluhkyUkm7XQyZJ/1YiCospoFzHhBdVyrdPp8Nxzz8Htrh+7NWLECHzwwQfYvXs3zj33XLbMtEGcEZGoHuMjRQvjMLVFDz30EN5++21otVq8+OKLMBqN2LdvHwBgxYoV2Lx5M0RRxMiRIzFr1iwkJSWhtLQUANCxY8OW0g4dOvj3xY04mA3cR1VVbNh6GN/ur/Bv69M9DVde2COkS7FptRKSjXLMjjUPF64aERuCSq5ramrw3HPP4ZtvvoHFYmm0XxAEfPzxx60uHMUXzohIxPhI0cU4TG3NlClTcO211+KNN97AbbfdhlWrVmHfvn0QRREdOnTASy+9hMOHD+Opp57C/v37sWzZMv+4bK1W2+C1dDpdq+bF8H3NJCkyY3wVrxe19vrZwOvfN7a/5599cxRf/HDM/zinUzKu/01uQGOifee0/r/NjyWXNRLSk3QQxdg+F+Fw6JgFZZV2mA1yo/oLggCzQYOySjt+rrA1mI294bml1goquX7ooYfwySefYMSIEejVq1eoy0RxzDcjIlFbxfhI0cY4TG1Jz549AQBz587Frl27sHLlSsydOxfXX3890tLqZ53Ozc1FRkYG/vCHP+CHH36AXq8HUD/22vf/AOB0OmEwGIIui6+lMDk5+NcIlLXOBafdDa1eC61ee+YnRNkXu37Ghq8P+x93yUzC7dcOhEHXslTEbNY3u08QgFSzDkZ925y48VCZDV4V0Gs1Td5cEAUBdU4FECWkpTVeQzwS121bEFRy/dVXX+HBBx/EddddF+ryEBHFNcZHIqLwqqysxJYtWzB27FhoNPU/ZUVRRM+ePVFeXg5RFP2Jtc/ZZ58NACgtLfV3By8vL0fXrl39x5SXlyMvLy/ocqmqCkEQYLHUQVHCM1O3ChW1NjfqnLE9tvpkuw+ewBsf7vM/bpeix5TLcuFyuOByBDY7uySJMJv1sFodTZ5bQQDMRi1cDgHOuvie8T1oXgWiADhcHmhlqdFul7t+P7wKqqps/u2SJCI52RDW6zYRJCcbAmrdDyq5NplMyM7ODuapREQJjfGRiCi8KioqcOedd2LJkiUYMWIEAMDtdmPPnj0YM2YM5syZg/LycixdutT/nB9++AFAfUt3ly5dYDab8fXXX/uTa4vFgj179mDSpElBl8uX6yqKt9ll8YIlCIDb40Wt3QVnDC+xdaoDR2uw6qP9/nOTZJQx7fJeMOrkFo4Rr6+zonibfJ5BK0EvS3DH0bkJtc7tTcj8ZdWIVKnxqhHWuvpVIzq3NzV5fYbjum2Lgupc/6c//QmvvvoqbDbbmQ8mImpDGB+JiMIrNzcXI0eOxBNPPIHt27dj3759uPfee2GxWDB16lSMHTsWW7ZswaJFi3D48GF8/vnnuP/++3HFFVcgJycHWq0WkyZNwoIFC/DJJ59g7969mDVrFrKysnDppZdGu3qNCAJgd3pQZXXGVWL983ErVmwshOKtT4b1WgnTxvVGenLzXbuDIWtEJJl0QHw05IcNV42IDYIaxBz1drsdEydOxPHjx9GjR49G41MEQcCyZctCVshoUBQvKisD/3Gs0YhISzOhqsqWkHd9WL/4xvo1lJ5uCtvEHYkeHxXFC4ulLqGvp2hK9O9qtPC8Bi6c8TGUamtrsXDhQnz88ceora3F4MGDce+99/q7f//3v//F4sWLcfDgQSQlJeG3v/0t/u///g86nQ4AoCgKnn76aaxduxYOhwNDhgzBww8/3KqeR4rihSSJIb3OVKj1k5bF+NrVp6qorsPL7/0Im6N+sjVZEjF9fG90y0oK6vUkSUBKihE1NfYGLdeSKCDFrIM2gEnR2oqWrnPN+BiYQGNjUN3CH374YRw6dAhnnXUW9Hp9ozXk2tqackREPoyPREThl5SUhEcffRSPPvpok/svv/xyXH755c0+X5IkzJ49G7Nnzw5TCVtHEADXL93AXXHUWg0ANTYXXltf4E+sRUHA9b85O+jEujmCAJj0GuhkiX9bT8JVI6IrqOT6008/xV133YWbbrop1OUhIoprjI9ERNQaQgytXe1VVRyrsMHu8MCo16Bje9NpkzS7w4PX1xeg2vrrpGLXjMpBXte0Zp8TDAGAQaeBySAzsW4CV42InqCSa61Wi3PPPTfUZSEiinuMj0REFCyvCtRanXC4FUQ7ZzxQUo1Nu46ioroOiheQRKB9qgGj+ndCTnZqo+NdbgXLN+5FeVWdf9sV53fDgLPbh7xsWq2EZKMc9XNEdKqgBihceeWV+Pe//w2vN766qRARhRvjIxERtZQgCHB5vKiqdaDOFRuJ9TtfHEJppR1aWYLZKEMrSyitrMM7XxzCgZLqBsd7FC9WfbwPh8us/m2jB3bG+ed2DHnZNJKIZKMW9e3XbYNXVVFUasHugydQVGqJq/H3bU1QLddJSUlYvXo1xowZg379+sFkargQuSAImDdvXkgKSEQUTxgfiYioRQSg1u6C3emB1xv9pMmrqti06yicbgXJRq1/SSdRIyFZEmGxu7Fp11H06JwCURDgVVWs3nQA+47U+F9jaO8OuGRw6JelFAQBJr0GGkmI+g2ISGnpBGUUXUEl12vXrkVKSgoAYPfu3Y32CxwwT0RtFOMjEREFQhAAj6L+snZ19FurfY5V2FBRXQejTtPob5YgCDDqJFRU1+FYhQ2d2puw7qtifH/ghP+Yc3uk43cX9AjL3zuDVoJRp4mZcxVuBUWVWLaxEA6XBya9DI1BhMfjRclxG5ZtLMSUsXlMsGNM0BOaERFRY4yPREQUiFiZtOxUdoenfox1M8sOSZIIxanA7vDgs29/xpYfS/37cjon4w9jekIUQ59YS6IAvUkLb4ydr3DxqirWbS2Gw+VBqlnnv1mhlSXIGhHVVhfWbS1GXrc0zgQeQ7goHBERERFRBNW5PKi1u2IusQYAo14DSaxft7spiuKFJAIHj1nw8Y4S//bOGSZM+k0eNGFYJ10UBSSbtG0qiTxcVovSSjtMernJHgQmvQallXYcLquNUgmpKUyuiYiIiIgiLfbyagBAx/YmtE81wO5UGi1zpaoq7E4Fep0Gm7876t/ePkWPqZf3gk4rhbw89etZyzDq5ZC/diyz2ut7NWg0TadrGo0IRVFhtbsjXDI6HSbXREREREQEoH6N5FH9O0En109e5vYo8Koq3B4FFrsbogAcr6rz3xtIMWkxfXxvmMKU/Op+ma28rTEbZUiSAI+n6R4EHo8XkiS0yXMTy5hcExERERGRX052KiZc2ANZ6Qa43AqsdjdcbgWpZi3qnB74JjU36DSYOq4XUs26sJRDI4lIMmpjtpU/nLpmJiEr3Qibw9NkDwKbw4OsdCO6ZiZFqYTUlKAmNCMiIiIiosSVk52KHp1TcKzCBrvDA6dbwbv/OwTPL+PEZY2IqZfnITPNGJb3F4X6VlmN1HbGWZ9MFASMz++GZRsLUW111S9BpqmfLdzm8ECvlTA+v1ubGoceD9hyTUREREREjYiCgM4ZZmSkGbBuSzHsTg+A+pm7//SbXHTpEL5WU72ubS271ZTe3dMxZWwesjNMcLoVWKz1y7ZlZ5i4DFeMYss1ERERERE1ye5w4/X1e1FjcwEABADXjMpBbpfUsL2nVhaRZJTh9bbhzPoXvbunI69bGg6X1cJqd8NslNE1M4kt1jGKyTURERERETXidCtY+t+9OF5d59/22wu6o3/P9mF7T0kUkGTUQgCTRx9RENA9KznaxaAAsFs4ERERERE14FG8eOPDfSg5bvNvGzOoM/LPyQrbe9Yvu6WBtpnlp4hiHa9cIiIiIiLy83pV/L/PDuCnn2v82/L7ZOLi87LD+r5aWYLRILfpcdYU35hcExERERERgPplnt7/qgg/HDzh39b3rHa44oLuEMI4zleSBCQb2uayW5Q4OOa6jfCqKidCICJKQIzvRBRKn+wswdd7yvyPz85Owe9H54Q1rggCYNJpoNEIUWm1ZhylUImL5Hrnzp24/vrrG21fvnw5hg0bBgDYsmUL5s+fjwMHDqBjx46YOXMmxo8fH+mixqSCokqs21qM0ko7FEWFJAnISjdifH43TuEfJQziFAqMjcT4HnsY3ymebdldik+/+dn/ODvDhOt/kwuNFN7OrroodgdnHKVQiovkurCwEF27dsWqVasabE9JSQEAHDhwADfffDOmTZuG+fPnY9OmTZgzZw7S09MxfPjwaBQ5ZhQUVWLZxkI4XB6Y9DI0hvrF50uO27BsYyHXyIsCBnEKFcbGto3xPfYwvlM82/VTBT74qsj/OCPVgCmX94JOlsL6vrIkItkUne7gjKMUanGRXO/btw89e/ZERkZGk/uXLVuGvLw8zJo1CwCQk5ODPXv2YMmSJW36B6RXVbFuazEcLg9SzTr/OBmtLEHWiKi2urBuazHyuqXxrnqEMIhTKDE2tl2M77GH8Z3i2b4j1fh/nx3w57cpJi2mjesFk14O6/uKooAkkxyVOMU4SuEQFxOaFRYWIicnp9n9O3bsaPRDMT8/Hzt37oTahqcbPFxWi9JKO0x6udEEFIIgwKTXoLTSjsNltVEqYdtyahDXyhJEQYBWlpBq1sLhUrBuazG8bfiapZZhbGy7GN9jC+M7xbPDZbV446N9/uvTqNNg2vjeSDXrwvq+glD/Xjo5Om19jKMUDnGRXO/fvx8HDx7E1VdfjQsuuADTpk3D999/799fWlqKrKyGa+516NABdXV1qKqqinRxY4bV7oaiqNA0s1agRiNCUVRY7e4Il6xtYhCnUGNsbLsY32ML4zvFq7IqO5ZtKITb4wUAaDUipl7eCx1SDWF/b61GhNkgR+1mL+MohUPUu4WXlJTg4osvbnb/pk2bUFtbC7vdjgcffBCSJGHlypWYNGkS1q5di549e8LhcECr1TZ4nu+xy+UKumzNfdmaIv0y0YMU5gkfWiIlSQeNJEBRvJCaGC+jeLzQSEL9cWeoayzWL5QiUT+7U4GiqJCNIprqXSTLIuwOD+xOpUXXXiD4+cWfWI6NiXi+Y0Wg5zaU8b0tCPc1G834ThSsqlonXl+/F3VODwBAEgVMujQP2R3MYX9vSRKQbAxvy/iZmI0yJEmAx+OFtok46vF4IUkCzMbwdo2nxBL15DozMxPr169vdn+HDh2wfft2GAwGyHL9xd23b1/s2bMHK1aswN/+9jfodLpGPxR9jw2G4O68iaKAtDRTi5+XnBz+O32BSkkxokvWIRQds8Cg0zS4m66qKuxOBd07JmNAryyIYmBjSWKpfuEQzvp1znRDK0tQ1aZv3DhdCrSyhM6ZyUFde4Hg5xc/Yjk2+s5zIp3vWHOmcxuO+N4WhOuajYX4TtQS1jo3Xl9fAIut/m+CAOAPY3qiZ3ZK2N9bFASY9XLUlt3y6ZqZhKx0I0qO2yBrxEZx1ObwIDvDhK6ZSdErJMWdqCfXsiyfdswgACQnJzd4LIoicnJyUFZWvwZfx44dUV5e3uCY8vJyGI1GJCUF94XwelVYLPaAj5ckEcnJBlgsdVAUb1DvGQ6XDemC19cXoKLaAZNBA42mfoIVW50Heq2Ey4Z0QU3NmesZq/ULlUjUL82kQWaaAUfKrUhN0jYK4habC106mJFm0qCqyhbS9+bn11BysiHmW11jOTbabI6Evp6iqSXXcqjie1sQ7hgYzfgeavEQH6l1nC4FyzbsRUWNw7/tdxf2QN+z2oX9vQUABp0Eo14T1cQaqE/yx+d3w7KNhai2umDSnxRHHfVxdHx+N05mRi0S9eT6TDZv3oy//vWveO+999ClSxcAgMfjwd69e3HppZcCAAYPHoxt27Y1eN7WrVsxaNAgiGLwfyA8npb/AVYUb1DPC5fcLqm4YWzer0uD1HkgSQI6Z5gwPr8bcruktqi8sVa/UAt3/S4f1hXLNhaiqrbpIH75sK7wKiq8YVqPgp9f4ohmbPQlJ23pfEdaIOc21PG9LQjnNRvt+E4UCI/ixcqPCvHz8V9v8lwyOBvD+mRG5P21WglJRm3UE2uf3t3TMeWkOGp31MfR7F/iaKhm+PeqKg6X1cJqd8NslNE1M4lJe4KK+eR60KBBSEtLwz333IP7778fsixj8eLFqK6uxtSpUwEAkydPxoQJE7BgwQJMmDABn3/+OTZs2IAlS5ZEt/Axonf3dOR1S+OXOgZEKohT4mNsJIDxPZYwvlOs83pVvPXpTzjws8W/bfi5WRg9sHNE3l/WiEgxRXecdVPCHUcLiip/vQmqqJAkAVnpRsaFBCWocbAey+HDh7FgwQJ8/fXXcDqdOO+883DPPfcgNzfXf8zmzZsxf/58FBUVITs7GzNnzsS4ceOCfk9F8aKyMvCuWxqNiLQ0E6qqbAnZUsD6hVak72Dy82soPd2UEN0eoxUbLZa6hL6eoinRv6vREsnzGu8tVIkSH6NBUbyQJDGg66zO5YHF6opYPwZVVfGfLw5hW8GvQ4UG9GyPa0bnROT6lCQBqWYd5CCvrXiNjQVFlVi2sRAOlwcmvdyoR8uUsXlRT7Dj9dxGWqCxMS6S62hgct1QqOsXaz8++PnFt7aaXEdDoibXsRKTEv27Gi08r4FjfAxeLCfXH24/gk3f/ux/nNslFZPH5kJqxRChQImigGSjDIMu+HHW8fgd9qoqnn7rO5QctyLVrGs0F0O11YXsDBPuvHYAfwPHgUBjY8x3C6fEw+4xRBRLGJOIKJF9+cOxBol110wzrr/k7Igk1gBg0Eow6KK3nnW0HC6rRWmlHSa93CCxBgBBEGDSa1Baacfhslp0z0pu5lUo3vDWJEWUr3tMyXErdLKEZLMWOllCyXEblm0sREFRZbSLSERtCGMSESWy7/ZXYN2WYv/jDmkG3DC2V5PrOoeDVhZhNra9xBoArHY3FEVtdm17jUaEoqiw2t0RLhmFE5NrihivqmLd1mI4XB6kmnXQyhJEQYBWlpBq1sLhUrBuazG8bTAAE1HkMSYRUSIrPFyF1ZsO+B+nmrWYNq43jPrIdFwVRQFmgxYC4mfOgVAyG2VIktBsV2uPxwtJEmA2yhEuGYUTk2uKmJZ0jyEiCjfGJCJKVMWltVj10X7/zUGTXoPp43sjxaSNyPsLAIw6DXQRaiGPRV0zk5CVboTN4WnUcq+qKmwOD7LSjeiamRSlElI4MLmmiGH3GCKKJYxJRJSISivtWLZhL9xKfYupTpYwdVxvtE8xRKwMWlmE2aBpk93BfURBwPj8btBrJVRbXXC5FXhVFS63gmqrC3qthPH53eJqNQE6MybXFDHsHkNEsYQxiYgSTaXFgdfXF8DhUgAAkihg0thcdG5vilgZJElAslEHtNHu4Cfr3T0dU8bmITvDBKdbgcXqgtOtIDvDFBPLcFHocbZwihhf95iS4zbIGrHRkgQ2hwfZGSZ2jyGiiGBMIqJEUmt34fX1e1H7S28bQQD+ePHZyOmUErEyCAJ+Wc9ZCHrZrUTTu3s68rqlxcRyjxR+bLmmiGH3GCKKJYxJRJQoHC4Plv13L05YHP5tV404C+f0iGzLqEGrgUkf/HrWiUoUBHTPSsa5Z7VD96xk/l1JYEyuKaLYPYaIYgljEhHFO7fHixUb9+HoCbt/29ihXTCkV4eIlkMni0g2yU0m1l5VRVGpBbsPnkBRqYWrMFDCYrdwijh2jyGiWMKYRETxSvGqeOvT/Th0zOLfdmG/jhjZv1NEy6GRRCSbmh5nXVBUiXVbi1FaaYeiqJAkAVnpRozP78YbmJRwmFxTVPi6xxARxQLGJCKKN6qq4j//O4g9RVX+bYNy2+OyYV0bLS/o41VVHKuwwe7wwKjXoGN7U6tvJIpi/cSPGqnxOOuCokos21gIh8tTPxbbIMLj8aLkuA3LNhayhxAlHCbXRERERERxZuO2I9hReNz/uFfXVEwYmdNssnygpBqbdh1FRXUdFC8giUD7VANG9e+EnOzUoMogCPXrWRu0jZfd8qoq1m0thsPlQapZ50/4tbIEWSOi2urCuq3FyOuWFlM9hbyqyp5MFDQm10REREREceR/3x/F5l1H/Y+7ZSXhuktyIYnNJ9bvfHEITrcCo04DSRKhKF6UVtbhnS8OYcKFPYJKsHWyBLNRbnI968NltSittMOklxu1pAuCAJNeg9JKOw6X1Z6x55BXVfHTkWr8XGaBUSeFLeFlF3ZqLSbXRERERERx4pt9x/HfrYf9j7PSjbhhbB5kTdPzFHtVFZt2HYXTrSDZqPUnuqJGQrIkwmJ3Y9Ouo+jROaVFCatGEpFk1ALNzE1mtbuhKCo0hqbLpdGIsDs8sP6ydFhzCooq8d+vD6Osqg4utxK2hJdd2CkUOFs4EREREVEcKCiuwtrPD/gfpyfpMHVcLxh0zbeXHauwoaK6DkadpskWZKNOQkV1HY5V2AIuhygIMBs00EjNJ+NmowxJEuDxeJvc7/F4IUn147Wb40t4j5RboddJSEnSQidL/oS3oKiy2ee2ZIbyU7uwa2UJoiBAK0tINWvhcClYt7WYs5zTGbHlmoiIiIgoxh06ZsG/P94H7y/5ndkgY9r43kg2ak/7PLvDUz/GWmq6TU2SRChOBXaHJ+Cy6LQSDLrTr2fdNTMJWelGlBy3QdaIDRJ7VVVhc3iQnWFC18ykJp9/csKblqSDrJHgUbwBjdluaffuUHZhp7aNLddERERERDHs2Akblm8ohEepz2Z1soSpl/dCu2T9GZ9r1GsgiYCiNN2CrCheSGL9cYGQf+kOfqZGXFEQMD6/G/RaCdVWF1xuBV5VhcutoNrqgl4rYXx+t2a7orck4T2Zr7W75LgVOllCsvnMrd3+LuzNdK3XaEQoinrGLuxETK6JiIiIiGLUCYsDr6/fC6dbAQBoJAE3XJaHTu1NAT2/Y3sT2qcaYHcqjSYeU1UVdqeC9qkGdAzg9URBgMkoo5l50xrp3T0dU8bmITvDBKdbgcXqgtOtIDvDdMYxzMEkvMF27w5FF3YigN3CiYiIiIhiksXuwuvrCmCtq08gRQG47uKz0aNj4F2TRUHAqP6d8M4Xh2Cxu2HUSf7Zwu1OBTpZxKj+nQKazEynlWDQSmdstT5Z7+7pyOuW1uLlrU5OeCWt1Gh/UwlvsN27W9uFnciHLddERERERDGmzunB0vV7UVnr9G+bMPKsoGaszslOxYQLeyAr3QCXW4HV7obLrSAr3RDwMlyBdgdviigI6J6VjHPPaofuWckBJfK+hNfm8DTZ4m5zeJCVbmyQ8Abbvbu1XdiJfNhyTUREREQUQ9weL1ZsLERppd2/7fJhXXFeXoegXzMnOxU9OqfgWIUNdocHRr0GHdubAkoYRUGAyVA/djtSE2b7Et5lGwtRXetCskkLQQTcbi9sDk+TCe/Jrd1aObDWbh9fF3bfRGh2hweSJCA7w8R1rilgTK6JiIiIiGKE4lXx74/3o6j014m6RvbviBH9O7X6tUVBQOcMc4ufp5PFM84OHgivqraoe7gv4T11nevmEt7Wdu8Otgs7kQ+TayIiIiKiGOBVVbyz+QD2Hq7ybzsvLwNjh3aNWpk0kghzkN3BT9bS5bF8endPxzk57VBl8+DnMguMOqnZhLdBa7fVBZNeA41GhMfTfGt3U6/B5bYoWBxzTUREREQUZaqqYsPXh/HNvgr/tj7d03DViLMaTc4VKaIgwGzQQCO17v2DWR7r1HL07JKKfjlnHrPdmhnKiVqLLddERERERFG2eddRfPH9Mf/jHh2TcO2YsyEFuu5ViAkADDoJBp3caEKxljh1eSzfjQKtLEHWiKi2urBuazHyuqWFrPs1u3dTtDC5JiIiIiKKoh17y7Fx2xH/447tjJg8Ng9yM7NeR4JWFpFkbF1iDQS/PFZrsXs3RQO7hRMRERFRXDlx4gRmz56N/Px8DBw4EDNmzMCBAwf8+wsKCjBp0iQMGDAAY8aMwfLlyxs83+v14rnnnsOIESMwYMAA3HTTTThy5MipbxMRPx6qxDv/O+h/3C5Zj6mX94JeG702MI0kItmkQ337desEuzwWUTxick1EREREceW2225DcXExFi9ejNWrV0Ov12Pq1Kmoq6tDVVUVpk2bhq5du2LNmjW47bbbsGDBAqxZs8b//BdeeAGrVq3C448/jjfffBNerxc33ngjXC5XROtx8GgN3vp0v3+ysCSjjGnjeiHJqI1oOU4WqnHWPicvj9WU0y2PRRRvmFwTERERUdyoqalB586d8cQTT6Bfv37IycnBrbfeivLycuzfvx9vv/02ZFnGY489hpycHEycOBFTp07F4sWLAQAulwuvvfYa7rjjDowaNQq9evXCM888g9LSUnz44YcRq8fPFTas2LgPHqU+s9ZrJUwb1xvpyfqIlaEp9eOsW7/slo9veSybw9Ooi7lveaysdGOzy2MRxRMm10REREQUN1JSUrBw4ULk5uYCACorK7F06VJkZWWhZ8+e2LFjB4YOHQqN5tdu1fn5+SgqKkJFRQX27t0Lm82G4cOH+/cnJyejT58+2L59e0TqUF5Vh6X/3QunWwEAyJKIKZf1Qla6MSLv3xytLMJslEOWWAO/Lo+l10qotrrgcivwqipcbgXVVldAy2MRxQtOaEZEREREcemhhx7C22+/Da1WixdffBFGoxGlpaX+xNunQ4cOAIBjx46htLQUANCxY8dGx/j2hVNVrROL1nwPa139GGNRAK77zdnolhXdlltJFJBk1EIIwTjrU/mWx/Ktc213eCBJArIzTGdc55oonjC5JiIiIqK4NGXKFFx77bV44403cNttt2HVqlVwOBzQahuOWdbpdAAAp9OJuro6AGjymJqamqDL4mt4laTmO4baHR488/Z3OGFx+rf9fnRPnNMjusmlAMBk1MKoD1138FP17dke5+S0Q3FpLWrtbiQZZXTLCmx5LN85Pd25peDw3IYWk2siIiIiiks9e/YEAMydOxe7du3CypUrodfrG01M5nTWJ7NGoxF6ff2YZpfL5f9/3zEGgyHosviWmUpObv41Pv5kH0qO2/yPf3/x2Rg9pGvQ7xkqWo2I9BRDRNbUbpduDvq5pzu31Do8t6HB5JqIiIiI4kZlZSW2bNmCsWPH+sdVi6KInj17ory8HFlZWSgvL2/wHN/jzMxMeDwe/7auXbs2OCYvLy/ocqmqCkEQYLHUQVGanhlbc1LuOnpQZwzObY+aGnvQ7xkKkiQgPdkAS5TLcTqSJCI52XDac0vB4bkNTHKyIaDWfSbXRERERBQ3KioqcOedd2LJkiUYMWIEAMDtdmPPnj0YM2YM2rdvjzfffBOKokCSJADA1q1b0aNHD7Rr1w5JSUkwm834+uuv/cm1xWLBnj17MGnSpKDL5etOrSjeZpeduuDcLJj0GqgAOqUboShh6oMdIEEAzHoZgqrC44luWQJxunNLrcNzGxrsXE9EREREcSM3NxcjR47EE088ge3bt2Pfvn249957YbFYMHXqVEycOBFWqxUPPPAAfvrpJ6xduxZLly7FzTffDKB+rPWkSZOwYMECfPLJJ9i7dy9mzZqFrKwsXHrppWEtuyAIGHh2Bnp3S/N3I48mvVYT1nHWRG0NW66JiIiIKK48/fTTWLhwIWbNmoXa2loMHjwYb7zxBjp16gQAWLJkCebOnYsJEyYgIyMDc+bMwYQJE/zPv+OOO+DxePDggw/C4XBgyJAhePXVVyHLcrSqFHGyRkSyScvEmiiEBPXU1dwJQH3XiMpK25kP/IVGIyItzYSqKltCdqlg/eIb69dQerqJs2IGSVG8sFjqEvp6iqZE/65GC89r4Bgfg6coXkiSGNB1VufywGJ1IVo/wkVRQKpZB60mPj5rfofDh+c2MIHGxvj4RgF49dVXcfHFF6Nfv364+uqrsXXr1gb7CwoKMGnSJAwYMABjxozB8uXLo1RSIqLIYWwkIqKWEATAqNNAJ0vRLgpRwomL5PqFF17AokWLcNddd+G9997DgAED8Je//AVHjhwBAFRVVWHatGno2rUr1qxZg9tuuw0LFizAmjVrolxyIqLwYWwkIqKW0skSzEYZ7LxKFHoxP+babrfjlVdewd13341x48YBAB544AHs2LEDO3fuRJcuXfD2229DlmU89thj0Gg0yMnJQXFxMRYvXoyJEydGuQZERKHH2EhERC0lSyKSjFpErT86UYKL+ZbrnTt3oq6uDuPHj/dvkyQJ7733Hq666ioAwI4dOzB06FD/WocAkJ+fj6KiIlRUVES6yEREYcfYSERELSGKAkxGGRop+rOUEyWqmE+uDx06hJSUFBQWFuK6667D8OHDMXnyZHzzzTf+Y0pLS5GVldXgeR06dAAAHDt2LKLlJSKKBMZGIiIKlADAoNPAoOWyW0ThFPVu4SUlJbj44oub3f/Xv/4VDocDDz/8MO666y506tQJb731FqZMmYJ3330XOTk5cDgc0Gq1DZ6n0+kAAE6nM+iyaVowg6Jv9rhEnWGT9YtvrF/8ieXYmIjnO1bw3IYHzyu1dVpZRJJBw3HWRGEW9eQ6MzMT69evb3b/J598AofDgfvvvx8XXXQRAOCcc87Bt99+i5UrV+KRRx6BXq+Hy+Vq8DzfD0ej0RhUuURRQFqaqcXPS042BPV+8YL1i2+sX/yI5djoO8+JdL5jDc9tePC8UlskSQKSjTrUt18TUThFPbmWZRk5OTnN7v/xxx8BAHl5ef5tgiAgJycHJSUlAICsrCyUl5c3eJ7vcWZmZlDl8npVWCz2gI+XJBHJyQZYLHVQlMRbI471i2+sX0PJyYaYb8GK5dhoszkS+nqKpkT/rkYLz2vg4iE+UuAEATDrZWg0AruDE0VA1JPrMxk8eDAEQcB3332Hyy67DACgqip++uknDB8+HAAwZMgQvPnmm1AUBZJUv2bf1q1b0aNHD7Rr1y7o9w5mIXVF8Sb0AuysX3xj/RJHNGOjLzlpS+c70nhuw4PnldoavVYDo57jrIkiJeZvTXbq1AkTJ07EE088gc8//xyHDh3C448/jpKSElx//fUAgIkTJ8JqteKBBx7ATz/9hLVr12Lp0qW4+eabo1x6IqLwYGwkIqLTkTX1y24xsSaKHEGNg5kN3G43Fi1ahLVr16KmpgZ9+vTB7Nmzcd555/mP+f777zF37lzs2bMHGRkZmD59OiZNmhT0eyqKF5WVtoCP12hEpKWZUFVlS8i74qxffGP9GkpPNyVEt8doxUaLpS6hr6doSvTvarTwvAYuUeJjNCiKF5IkBnSd1bk8sFhdYVtuWhQFpJi10GmkML1DZPE7HD48t4EJNDbGRXIdDUyuG2L94hvr1xB/PAaPyXV4Jfp3NVp4XgPH+Bi8WEqujXoNUky6hJkdnN/h8OG5DUygsZHRk4iIiIgoQdR3B5cTJrEmiidMromIiIiIEoAoCjAbZQhcdosoKphcExERERElAL1Wgl6O+cWAiBIWk2siIiIiojjH7uBE0cfkmoiIiIgojrE7OFFsYL8RIiIiIqI4ZvilOzhbrROfV1VxuKwWVrsbZqOMrplJEAXeVIkVTK6JiIiIiOKULIswszt4m1BQVIl1W4tRWmmHoqiQJAFZ6UaMz++G3t3To108AruFExERERHFJVEUkGTQsjt4G1BQVIllGwtRctwKnSwh2ayFTpZQctyGZRsLUVBUGe0iEphcExERERHFHUEAjDoNdLIU7aJQmHlVFeu2FsPh8iDVrINWliAKArSyhFSzFg6XgnVbi+Fl74WoY3JNRERERBRntBoRZgO7g7cFh8tqUVpph0kvQzhlfLUgCDDpNSittONwWW2USkg+TK6JiIiIiOKIJAlINuqiXQyKEKvdDUVRodE0nbppNCIURYXV7o5wyehUTK6JiIiIiOKEIABmvQyNhuOs2wqzUYYkCfB4vE3u93i8kKT65dgouphcExERERHFCb1WA6NeA/YGbzu6ZiYhK90Im8PTaBiAqqqwOTzISjeia2ZSlEpIPkyuiYiIiIjigFYWkWySYyKx9qoqikot2H3wBIpKLZxMK4xEQcD4/G7QayVUW11wuRV4VRUut4Jqqwt6rYTx+d243nUM4DrXREREREQxTpIEJBljY9ktrrcceb27p2PK2Dz/ebc7PJAkAdkZJp73GMLkmoiIiIgohvmW3dJqxKi3WvvWW3a4PDDpZWgMIjwer3+95Slj85johUnv7unI65aGw2W1sNrdMBtldM1MYot1DGFyTUREREQUw7QaESZ99LuDn7resm9ZKK0sQdaIqLa6sG5rMfK6pTHhCxNRENA9KznaxaBmcMw1EREREVGM8nUHD6Vgx0tzvWWi02PLNRERERFRDPJ1B5dP6Q7uVdWguwa3Zry0f71lQ/PrLdsdHq63TG0Wk2siIiIiohjUVHfw1iTHrR0vffJ6y1pZarSf6y1TW8du4UREREREMUYSG3cH9yXHJcet0MkSks1a6GTJnxwXFFU2+3qnjpfWyhJEQYBWlpBq1sLhUrBua/Fpu4hzvWWi02NyTUREREQUQwQBMOrru4P7BJsc+8ZXf/pNCUqOW2HUaYIeL831lolOj93CiYiIiIhiSFPdwVsymZhvNumTu5A7XArqnB443V6kmACDrmEaEOh4aa63TNQ8JtdERERERDFCEgWYm5gdvKWTiZ06vlqrEeFweuB2K6i0OJCerG+QYLdkvDTXWyZqGpNrIiIiIqIYIAj1LcpajdRoTHNLJhM73XrUTrcCr1eFxVbfjVsQBP946ewMU8DjpbneMlFjHHNNRERERBQDtBoRZoPcKLEGWjaZWHNdyFNMWkiiABWAy+OF061wvDRRCDG5JiIiIiKKMklqPDv4yVoymZi/C7mm4U99vU6Ddsl6aDUiVFVFrc0Np1tBdobpjMtwEdGZsVs4EREREVEUCQJg1NXPDn6albACnkzsdF3I9ToN0kUBNocHvx3eDTnZKRwvTRQiTK6JiIiIiKJIK0uNZgdvTiCTifm6kJcct0HWiA26hp88vnr0edlMqolCiN3CiYiIiIiiRJIEJBua7w7eFN9kYuee1Q7ds5IbJchcj5ooOphcExERERFFgSAAJp0GGk3ok1xfF/LsDBOcbgUWq4vjq4nCjN3CiYiIiIgiTajvDm40BNYdPBhcj5oosphcExERERFFmCSJSDZqgTAl1j5cj5ooctgtnIiIiIgowsx6DTQSW5CJEglbromIiIiIIkgjib+sNR3tkhBRKLHlmoiIiIgogrQaiYk1UQJick1EREREFEEqM2uihMTkmoiIiIiIiKiVmFwTERERERERtVLMJ9dr165FXl5ek/9uuOEG/3EFBQWYNGkSBgwYgDFjxmD58uVRLDURUXgxNhIRERHFlpifLXzcuHEYMWJEg20bNmzAk08+iVtuuQUAUFVVhWnTpmHMmDH429/+hu+++w5/+9vfYDKZMHHixGgUm4gorBgbiYiIiGJLzCfXer0eer3e/7i0tBT//Oc/ceutt+L8888HALz99tuQZRmPPfYYNBoNcnJyUFxcjMWLF/MHJBElJMZGIiKilvGqKg6X1cJqd8NslNE1MwmiwLXGKXRiPrk+1fz589GhQwfMmDHDv23Hjh0YOnQoNJpfq5Ofn4+XX34ZFRUVaN++fTSKSkQUMYyNREREzSsoqsS6rcUorbRDUVRIkoCsdCPG53dD7+7p0S4eJYi4Sq4LCwvxwQcf4F//+he0Wq1/e2lpKXJzcxsc26FDBwDAsWPHgv4BqdEEPiRdksQG/000rF98Y/0SW6RjY1s/3+HEcxsePK9EbVtBUSWWbSyEw+WBSS9DYxDh8XhRctyGZRsLMWVsHhNsComoJ9clJSW4+OKLm92/ZcsWpKfXX+xLly5FXl5eo+MdDkeDH5QAoNPpAABOpzOocomigLQ0U4ufl5xsCOr94gXrF99Yv/gRy7HRd54T6XzHGp7b8OB5TQzV1dV4+umnsWnTJlitVuTl5eGuu+7C4MGDAQDTpk3DV1991eA5Q4cOxYoVKwDUx7+///3v2LBhAxwOB8aMGYMHHnjAH1MpsXhVFeu2FsPh8iDVrIPwSzdwrSxB1oiotrqwbmsx8rqlsYs4tVrUk+vMzEysX7++2f0pKSkA6n8kbtiwAbNnz/Z/KXz0ej1cLleDbb4fjkajMahyeb0qLBZ7wMdLkojkZAMsljooijeo94xlrF98Y/0aSk42xHwLVizHRpvNkdDXUzQl+nc1WnheAxcP8fHOO+/E8ePH8fTTT6Ndu3ZYsWIF/vznP+Odd97BWWedhcLCQjz66KO45JJL/M+RZdn//48++ih27NiB559/HlqtFo888gjuuOMOrFy5MhrVoTA7XFaL0ko7THq50d9JQRBg0mtQWmnH4bJadM9KjlIpKVFEPbmWZRk5OTlnPO7LL7+E2+3G5Zdf3mhfVlYWysvLG2zzPc7MzAy6bB5Py/8AK4o3qOfFC9YvvrF+8SOWY6MvOUmk8x1reG7Dg+c1/hUXF+PLL7/EqlWrcN555wEAHnroIfzvf//D+++/j0mTJuHEiRPo378/MjIyGj2/rKwM7777Ll566SV/S/fTTz+Nyy67DN9++y0GDhwY0fpQ+FntbiiKCo2h6ZtGGo0Iu8MDq90d4ZJRIortW5Mn2bFjB3r16oW0tLRG+4YMGYKdO3dCURT/tq1bt6JHjx5o165dJItJRBRRjI1E1JakpaVh8eLF6Nu3r3+bIAgQBAEWiwWFhYUQBAE9evRo8vk7d+4EUD+5o0+PHj2QmZmJ7du3h7fwFBVmowxJEpq9sebxeCFJAsxGucn9RC0RN8n1nj170KtXryb3TZw4EVarFQ888AB++uknrF27FkuXLsXNN98c4VISEUUWYyMRtSXJycm46KKLGswnsXHjRhQXF2PEiBHYt28fkpKS8Nhjj2HkyJG47LLL8Oyzz/qHyJSVlSEtLc0//4RPhw4dUFpaGtG6UGR0zUxCVroRNocHqqo22KeqKmwOD7LSjeiamRSlElIiiXq38EAdP368wV3Kk7Vr1w5LlizB3LlzMWHCBGRkZGDOnDmYMGFChEtJRBRZjI1E1JZ98803uO+++3DppZdi1KhRuP/+++F0OtGvXz9MmzYNBQUFeOqpp3D06FE89dRTqKurazTRI1A/2WOwEz36+IbzxvqY9XjU2hn/f3dBD7y+vgA1VhdMBg00mvrZwm11Hhi0En53QQ9oZSmURY4bXE0htOImuT7dxD4A0K9fP7z11lsRKg0RUWxgbCSiturjjz/G3XffjUGDBmHBggUAgMceewz33HOPf9LH3NxcyLKMWbNmYc6cOU1O9AjUT/ZoMLRuNnnfZFmclT58gj23F6SZYE7SY/Wn+/FzuRV1Tjc0kogenVNwzZiz0f/sxuPz2xpet6ERN8k1EREREREArFy5EnPnzsVll12Gf/zjH/7WaI1G40+sfc4++2wAQGlpKbKyslBdXQ2Xy9WgBbu8vLxVEz0C9V2M68d+c1b6UAvFjP9d2xvxf7/vh+LSWtTa3UgyyuiWlQRREFBVZQtxieMHV1MITKArKTC5JiIiIqK4sWrVKjz++OOYPHkyHnjggQbLK02ePBnZ2dl48skn/dt++OEHyLKM7t27IyMjA16vFzt37sTw4cMBAIcOHUJZWRmGDBnSqnL5hvNyVvrwCcW57ZJh9v+/V1HhhXqao9sOXrehweSaiIiIiOLCoUOHMG/ePPzmN7/BzTffjIqKCv8+vV6PsWPHYt68eejXrx8uvPBC/PDDD3jqqafw5z//GWazGWazGePHj8eDDz6IefPmwWAw4JFHHsHQoUMxYMCA6FWMiBICk2siIiIiigsbN26E2+3GRx99hI8++qjBvgkTJuDvf/87BEHAihUrMG/ePGRkZGDq1KmYMWOG/7jHH38c8+bNw+233w4AGDlyJB588MGI1oOIEpOgnjonPQGo7xpRWRn4+AuNRkRamglVVbaE7FLB+sU31q+h9HQTZ8UMkqJ4YbHUJfT1FE2J/l2NFp7XwDE+Bk9RvJAkkddZGPA7HD48t4EJNDYyehIRERERERG1EpNrIiIiIiIiolZick1ERERERETUSkyuiYiIiIiIiFqJyTURERERERFRKzG5JiIiIiIiImolJtdERERERERErcTkmoiIiIiIiKiVmFwTERERERERtRKTayIiIiIiIqJWYnJNRERERERE1EqaaBeA2havquJwWS2sdjfMRhldM5MgCkK0i0VERK3A2E5ETWFsoLaGyTVFTEFRJdZtLUZppR2KokKSBGSlGzE+vxt6d0+PdvGIiCgIjO1E1BTGBmqL2C2cIqKgqBLLNhai5LgVOllCslkLnSyh5LgNyzYWoqCoMtpFJCKiFmJsJ6KmMDZQW8XkmsLOq6pYt7UYDpcHqWYdtLIEURCglSWkmrVwuBSs21oMr6pGu6hERBQgxnYiagpjA7VlTK4p7A6X1aK00g6TXoZwyjgbQRBg0mtQWmnH4bLaKJWQiIhairGdiJrC2EBtGZNrCjur3Q1FUaHRNH25aTQiFEWF1e6OcMmIiChYjO1E1BTGBmrLmFxT2JmNMiRJgMfjbXK/x+OFJAkwG+UIl4yIiILF2E5ETWFsoLaMyTWFXdfMJGSlG2FzeKCeMr5GVVXYHB5kpRvRNTMpSiUkIqKWYmwnoqYwNlBbxuSawk4UBIzP7wa9VkK11QWXW4FXVeFyK6i2uqDXShif343rHhIRxRHGdiJqCmMDtWVMrikiendPx5SxecjOMMHpVmCxuuB0K8jOMGHK2Dyud0hEFIcY24moKYwN1FZpol0Aajt6d09HXrc0HC6rhdXuhtkoo2tmEu9cEhHFMcZ2ImoKYwO1RUyuKaJEQUD3rORoF4OIiEKIsZ2ImsLYQG0Nu4UTERERERERtRKTayIiIiIiIqJWYnJNRERERERE1EpMromIiIiIiIhaick1ERERERERUSsxuSYiIiIiIiJqJSbXRERERERERK3E5JqIiIiIiIiolZhcExEREREREbUSk2siIiIiIiKiVmJyTURERERERNRKgqqqarQLEYtUVYXX27JTI0kiFMUbphJFH+sX31i/X4miAEEQwlyixOSLjYl+PUUTz2148LwGhvExeKqqQhAEXmdhwu9w+PDcnlmgsZHJNREREREREVErsVs4ERERERERUSsxuSYiIiIiIiJqJSbXRERERERERK3E5JqIiIiIiIiolZhcExEREREREbUSk2siIiIiIiKiVmJyTURERERERNRKTK6JiIiIiIiIWonJNREREREREVErMbkmIiIiIiIiaiUm10REREREREStxOSaiIiIiIiIqJWYXAfI6/Xiueeew4gRIzBgwADcdNNNOHLkSLPH79+/HzNmzMCwYcMwfPhw3HHHHTh69GgES9wyLa3fjz/+iClTpmDgwIHIz8/Hww8/jNra2giWuGVaWr+Tvffee8jLy0NJSUmYSxm8ltbPV6dT/8VqHVtaP7fbjYULF/qPnzRpEgoKCiJY4vh04sQJzJ49G/n5+Rg4cCBmzJiBAwcO+PcXFBRg0qRJGDBgAMaMGYPly5c3eH5rvmeJ7Ezn9cEHH2z0XRwzZox/P89rYA4dOoSBAwdi7dq1/m28ZikUGBvDh/Ex/BgbI0ylgDz//PPqsGHD1M8++0wtKChQp0+frl566aWq0+lsdGxlZaV6wQUXqDNnzlQLCwvVH374Qf3Tn/6kXn755arD4YhC6c+sJfU7fvy4OmTIEPW+++5TDx48qO7cuVMdN26ceuutt0ah5IFpSf1OVlJSop533nlqbm6ueuTIkQiVtuVaWr+nnnpKnTRpklpeXt7gn8fjiXDJA9PS+t1///3q+eefr27evFn96aef1JkzZ6oXXHCBarFYIlzy+HLttdeqv//979Vdu3b5z9uFF16o2u12tbKyUh02bJh63333qT/99JO6evVqtW/fvurq1av9zw/2e5boTndeVVVVr7nmGvXpp59u8F08ceKE//k8r2fmcrnUq6++Ws3NzVXXrFmjqqrKa5ZChrExfBgfw4uxMfKYXAfA6XSqAwcOVN944w3/tpqaGrVfv37q+++/3+j4t99+Wx04cKBaV1fn33b06FE1NzdX/eqrryJS5pZoaf2+++47ddasWarb7fZvW7p0qdq/f/9IFLfFWlo/H0VR1Ouuu0694YYbYjq5DqZ+N954o/r4449Hqoit0tL6HT58WM3Ly1M/++yzBsePHj06Jr9/saK6ulq988471cLCQv+2goICNTc3V921a5f60ksvqRdeeGGD7/3ChQvVSy+9VFXV4L9nie5M59Xr9aoDBgxQP/zwwyafz/MamIULF/pjte8HJK9ZCgXGxvBhfAw/xsbIY7fwAOzduxc2mw3Dhw/3b0tOTkafPn2wffv2RscPHz4cL7zwAvR6vX+bKNafaovFEv4Ct1BL69e/f388/fTT0Gg0AIADBw7gP//5Dy644IKIlbklWlo/n5deeglutxs333xzJIoZtGDqV1hYiJycnEgVsVVaWr8vv/wSSUlJGDlyZIPjP/300wavQQ2lpKRg4cKFyM3NBQBUVlZi6dKlyMrKQs+ePbFjxw4MHTrU/70HgPz8fBQVFaGioiLo71miO9N5PXz4MOx2O84666wmn8/zembbt2/HW2+9hb///e8NtvOapVBgbAwfxsfwYmyMDs2ZD6HS0lIAQMeOHRts79Chg3/fybKzs5Gdnd1g2+LFi6HX6zFkyJDwFTRILa3fycaOHYuioiJ07twZixYtClsZWyOY+n3//fd47bXXsHr1apSVlYW9jK3R0vrV1NSgrKwMO3bswKpVq1BVVYV+/fph9uzZ6NGjR0TK3BItrd+hQ4fQpUsXfPjhh1i8eDHKysrQp08f3HvvvXFzQyHaHnroIbz99tvQarV48cUXYTQaUVpa6v8B5NOhQwcAwLFjx1oVR9qKps7rvn37AAArVqzA5s2bIYoiRo4ciVmzZiEpKYnn9QwsFgvmzJmDBx98sNE54jVLocbYGD6Mj6HF2Bg9bLkOQF1dHQBAq9U22K7T6eB0Os/4/BUrVmDlypW4++67kZ6eHpYytkZr6rdgwQKsWLEC7dq1ww033ACbzRa2cgarpfWz2+24++67cffdd6N79+6RKGKrtLR++/fvBwCoqoonn3wSzz77LJxOJ66//npUVFSEv8At1NL6Wa1WFBcX44UXXsCdd96JF198ERqNBtdffz1OnDgRkTLHuylTpmDNmjW44oorcNttt+HHH3+Ew+Fo8jMAAKfT2eo42RY0dV737dsHURTRoUMHvPTSS7j33nvxxRdf4NZbb4XX6+V5PYNHH30UAwcOxG9/+9tG+3jNUqgxNoYP42NoMTZGD1uuA+Dr3u1yuRp09XY6nTAYDM0+T1VV/POf/8SLL76Iv/zlL5g8eXLYyxqMYOsHAH379gUALFq0CBdddBE++ugjXHXVVWErazBaWr8nnngCPXr0wB//+MeIlbE1Wlq/wYMHY8uWLUhLS4MgCADqP79Ro0Zh7dq1mDFjRmQKHqCW1k+j0cBqteKZZ57xt1Q/88wzuOiii/DOO+/gxhtvjEzB41jPnj0BAHPnzsWuXbuwcuVK6PV6uFyuBsf5/sgajcZWxZG2oqnzOnfuXFx//fVIS0sDAOTm5iIjIwN/+MMf8MMPP/C8nsa7776LHTt24P33329yP69ZCjXGxvBhfAwdxsboYst1AHzdIsrLyxtsLy8vR2ZmZpPPcbvdmD17Nl566SXcd999+L//+79wFzNoLa3fwYMHsWnTpgbbMjMzkZqaGpNdqFtavzVr1uCrr77CwIEDMXDgQNx0000AgCuuuAIvvfRS+AvcQsFcn+np6f7EGgAMBgOys7MT4vPLysqCRqNp0AVcr9ejS5cuMbvUWCyorKzEunXr4PF4/NtEUUTPnj1RXl6OrKysJj8DoP77H8x12Bac6byKouj/4ehz9tlnA6jvusfz2rw1a9bgxIkTGDVqlD9eA8AjjzyCG2+8kdcshQRjY/gwPoYHY2N0MbkOQK9evWA2m/H111/7t1ksFuzZs6fZMdRz5szBhg0bsHDhQkydOjVCJQ1OS+v31Vdf4Y477mgwOdvhw4dRVVUVk2NaW1q/Dz/8EB988AHeffddvPvuu3jiiScA1I+bj8XW7JbW76233sKwYcNgt9v926xWK4qKivx3jmNJS+s3ZMgQeDwe/PDDD/5tDocDR44cQbdu3SJS5nhUUVGBO++8E1u2bPFvc7vd2LNnD3JycjBkyBDs3LkTiqL492/duhU9evRAu3btgoqTbcGZzuucOXMa/Y3wXbs9e/bkeT2NBQsWYP369f5Y/e677wIA7rjjDsydO5fXLIUEY2P4MD6GB2NjlEV7uvJ48fTTT6tDhw5VP/744wbrvblcLtXj8ajl5eX+pbfWrFmj5ubmqkuWLGm0jvDJy3PFkpbUr6qqSh0xYoQ6Y8YMdd++fer27dvVK6+8Ur3mmmtidp3kltTvVFu3bo3ppbhUtWX1O3r0qDp48GD1tttuU/ft26d+//336tSpU9VLLrkkZtdhb+nnN3XqVPXyyy9Xt2/fru7fv1+dOXOmOnz48AZrY1JjN954o3rppZeq27ZtUwsLC9U777xTHTJkiPrzzz+rFRUV6pAhQ9R77rlH3b9/v7pmzRq1b9++6tq1a/3PP93n1Jad7rx+/PHHam5urvr888+rxcXF6qZNm9QxY8aod955p//5PK+BO3m5GV6zFCqMjeHD+BgZjI2Rw+Q6QB6PR33qqafU/Px8dcCAAepNN93kT7aOHDnS4KKdNm2ampub2+Q/3zGxpiX1U1VVPXjwoDpjxgz1vPPOU4cOHared999ak1NTbSKf0Ytrd/J4iG5bmn9du/erU6bNk0977zz1EGDBqkzZ85Ujx49Gq3in1FL61dbW6s+8sgj6rBhw9T+/fur06ZNU/fv3x+t4scNi8WiPvLII+oFF1yg9uvXT50+fbq6b98+//5du3apf/jDH9Rzzz1XHT16tLpixYoGzz/d59SWnem8rl+/Xr3qqqvUfv36qRdccIH697//vcGNLp7XwJ0aC3jNUigwNoYP42NkMDZGjqCqqhrt1nMiIiIiIiKieMYx10REREREREStxOSaiIiIiIiIqJWYXBMRERERERG1EpNrIiIiIiIiolZick1ERERERETUSkyuiYiIiIiIiFqJyTURERERERFRKzG5JiIiIiIiImolJtdERERtyNq1a5GXl4eSkpJoFyUknn/+eeTl5UW7GEQU5xgbKRSYXBMREbUho0aNwltvvYUOHTpEuyhERDGDsZFCQRPtAhAREVHkpKenIz09PdrFICKKKYyNFApsuaY2Y8yYMXjuuefwj3/8A+effz769euHP//5zygqKgIA3HvvvZg6dSrWrFmDsWPH4txzz8WVV16JzZs3R7fgRJRwxowZg0WLFmHevHkYNmwYBg4ciLvuugs2mw2LFy/GyJEjcd5552HmzJmoqqoCADgcDixcuBCXXnopzj33XAwaNAjTpk1DQUGB/3XvvfdeTJ48GatXr8bo0aMxcOBATJkyBXv37vUf01TXxx07dmDSpEno378/hg4dinvuuQeVlZUtqlNJSQny8vKwbt063HLLLejfvz9GjRqFf/3rX/B6vQ3qPm/ePEyZMgX9+vXDAw88AACorq7Gww8/jPPPPx99+/bFH/7wB2zZsqXBezidTjz55JO44IILMHDgQNx3331wOp0tKicRxS7GRsbGeMfkmtqU5cuX4+DBg3jyySfxxBNPYPfu3bjnnnv8+3fv3o1XX30Vd9xxB/71r39BkiTMnDkTNTU1USw1ESWi1157DceOHcMzzzyDv/zlL/jggw8wceJEfPHFF3j88cdx55134pNPPsFzzz0HAJgzZw7WrFmDGTNm4LXXXsN9992H/fv346677oKqqv7XLSgowDPPPIPbb78d8+fPR1VVFSZNmoTy8vImy7F9+3ZMnToVer0ezz77LO6//35s27YNN9xwAxwOR4vr9eijj8JsNuP555/HlVdeiUWLFmHhwoUNjnnjjTfQt29fvPDCC7jmmmvgdDoxZcoUfPLJJ5g1axYWLVqErKws3HjjjQ1+RM6ePRtvv/02br75Zjz77LOoqanB0qVLW1xGIopdjI2MjXFNJWojRo8erY4ePVr1eDz+bc8//7yam5urVlZWqvfcc4+am5urFhcX+/dv27ZNzc3NVTds2BCNIhNRgho9erQ6YsQI1e12+7dddtll6sCBA1WLxeLfdvPNN6u/+93vVKfTqU6fPl1dt25dg9d57bXX1NzcXLW8vFxVVdUfx7Zv3+4/pqysTO3bt686f/58VVVVdc2aNWpubq565MgRVVVV9dprr1WvuOKKBrHx4MGDau/evdWVK1cGXKcjR46oubm56pQpUxpsf+KJJ9RzzjlHra2t9df9kksuaXDMW2+9pebm5qrfffedf5vX61X/9Kc/qVdffbWqqqq6b98+NTc3V121apX/GEVR1HHjxqm5ubkBl5OIYhdjI2NjvGPLNbUpffv2hSRJ/sdZWVkAgLq6OgD14226du3a7H4iolDp168fNJpfpz5p3749evTogaSkJP+21NRU1NbWQqvV4tVXX8W4ceNQVlaGrVu34s0338Rnn30GAHC5XP7nZGdnY/Dgwf7HHTp0wMCBA7F9+/ZGZairq8OuXbtw0UUXQVVVeDweeDwedOnSBTk5Ofjyyy9bXK+rrrqqweOxY8fC7Xbj22+/9W/r3bt3g2O2bNmCjIwMnHPOOf4yKIqC0aNHY/fu3aipqcGOHTsA1Hed9BFFEWPHjm1xGYkodjE2/oqxMf5wQjNqUwwGQ4PHolh/f8k35uXU/YIgNNhPRBQqZrO50Taj0djs8f/73/8wb948HDx4ECaTCb169fIfr57U9TEzM7PRc9u1a4cff/yx0XaLxQKv14tXXnkFr7zySqP9Op0uoLqc7NT3900QdPLwmlPrWV1djePHj+Occ85p8jWPHz/uf35aWlqDfRkZGS0uIxHFLsbGXzE2xh8m10RERDHu8OHDuO2223DJJZfg5ZdfRpcuXSAIAt544w3873//a3Csb5Kfk1VUVKBdu3aNtptMJgiCgKlTp2L8+PGN9p96wzEQp77/iRMnAKDJ9/dJSkpC9+7dsWDBgib3Z2dn+384VlRUoFOnTv591dXVLS4jESUGxkbGxljDbuFEREQxbvfu3XA6nZgxYwa6du3q71Xj+/F4cutMUVERDhw44H9cVlaGb7/9FsOHD2/0umazGX369MHBgwfRt29f/7+zzz4bzz//PL7++usWl/Xjjz9u8Hjjxo0wGAzo379/s88ZOnQojh07hnbt2jUox5dffoklS5ZAkiTk5+cDADZs2NDgub7un0TU9jA2MjbGGrZcExERxbhzzjkHGo0G8+fPx/Tp0+FyubB27Vps2rQJAGC32/3HqqqKW265BbNmzYIkSVi0aBFSUlIwefLkJl/7zjvvxIwZM3DXXXfhd7/7HRRFwWuvvYZdu3bh1ltvbXFZ//vf/6Jdu3a46KKLsG3bNrzxxhuYNWvWabt1Xn311Vi5ciWmTZuGW265BR07dsRXX32FV155BZMmTYIsy+jWrRuuvfZaPPPMM/B4POjduzf+85//oLCwsMVlJKLEwNjI2BhrmFwTERHFuG7dumHhwoVYtGgR/vKXvyAlJQUDBgzAihUrMHnyZOzYsQN5eXkAgE6dOmH69OmYN28e6urqcP755+PFF19Eampqk6994YUX4tVXX8WiRYtwxx13QJZlnHPOOXj99dcxYMCAFpf1r3/9K7Zt24a33noLHTt2xMMPP4zrrrvutM8xGo144403sHDhQsyfPx+1tbXo3Lkz7rrrLkyfPt1/3COPPIL27dtj5cqVqKmpwYgRI3DLLbfg2WefbXE5iSj+MTbWY2yMHYJ6cn8JIiIiilv33nsvtm3bhk8//TTi711SUoKLL74YTz75JK6++uqIvz8RUXMYGylS2HJNREREzVIUBWe6D+8b50hE1FYwNlJTmFwTERFRs6ZOnYpt27ad9pjOnTtj+fLlESoREVH0MTZSU9gtnIiIiJp18OBB2Gy20x6j1Wr94xqJiNoCxkZqCpNrIiIiIiIiolbiOtdERERERERErcTkmoiIiIiIiKiVmFwTERERERERtRKTayIiIiIiIqJWYnJNRERERERE1EpMromIiIiIiIhaick1ERERERERUSsxuSYiIiIiIiJqpf8P0f9ynvxQ/WoAAAAASUVORK5CYII=", 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", 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    " ] @@ -3770,7 +3798,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -3803,7 +3831,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -3836,7 +3864,7 @@ "outputs": [ { "data": { - "image/png": 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4mY2IiEhrGJA6KF83GwBA8pViVFTVSFwNERFR58KA1EE52pjB3toUtSoRFzIKpS6HiIioU2FA6sD8/jqLxHFIREREmsWA1IH9Pat2AVQqzqpNRESkKa16HHxmZiZOnjyJa9eu4ebNm7CysoKTkxNGjBgBR0dHTddId+DW0xJmxvoorajGpesl6Nezm9QlERERdQotCkgHDx7EF198gfPnz0MURcjlcpiYmEChUKCiogKCIGDgwIGYM2cOwsLCtFUz/UVPJoNPXxucSsxFXGo+AxIREZGGNCsgZWVl4fXXX0dqairCw8OxYMEC+Pj4wNzcXL2NQqFATEwMjh49ildffRX9+vXDe++9h169emmteKqbVftUYi7i0vLx0Gg3qcshIiLqFJoVkKZNm4aZM2di06ZNMDQ0bHIbuVyOsLAwhIWF4bXXXsP333+P6dOn448//tBkvdSIT19r6MkEZBeUI6+oHHZWplKXRERE1OE1KyD9/PPP6NatW7MPamJiglmzZmHy5MmtrYuaydTYAO69uiEpswhnU/MRHtQLgiBIXRYREVGH1qy72I4ePYrCwpbPtWNlZdXifajl6h9eu+vPDLyz9QxEkXe0ERERtUWzziC9+uqrEAQB7u7uGDZsGEaOHInAwMA7Xm6j9uXnZoOIQ6moqKpF2rUSKKtVMDLUk7osIiKiDqtZASkyMhLR0dGIiYnBzp078fXXX8PIyAgBAQEYPnw4hg8fjv79+2u7VroDOytT2FubILewQupSiIiIOoVmBSRvb294e3tjxowZAIBLly7h9OnTiI2NxdatW7F69WpYW1sjODgYI0aM4NgjCQzsa4MDhdcAgJfYiIiI2qhVE0W6urrC1dUVjz76KAAgKioK27Ztw759+7Bnzx4GJAmMGOiIAzF1ASkhoxBBnnYSV0RERNRxtSogFRYW4tixYzh58iSioqKQk5MDU1NTjBw5EiNGjNB0jdQMdt3+vr1/++E0+Ll1h4E+nyRDRETUGs0KSLW1tTh79iyOHTuGY8eOITk5GUDdpbcHHngAI0aMgJ+fH/T1W5W3SAMMDWTo20OOzJybuFFSif3RVzAxuI/UZREREXVIzUo0Q4YMQVlZGRwdHREcHIynn34aw4YNg6Wlpbbro2YSBAFvTB+EE+ezsenXZPzvRCaGDXCElYWR1KURERF1OM26BlNaWgpLS0uMGjUKoaGhGDlyJMORDhIEAcEDHOHqJEdVdS22/5EmdUlEREQdUrMC0vbt2/Hkk08iJSUF8+fPx9ChQ/HYY4/h008/xblz57RdI7WATBDw2Bh3CABOXshF2rUSqUsiIiLqcASxhfeEl5aW4sSJEzh+/DiOHz+O69evo1u3bhg2bBhGjBiB4cOHw97eXlv1tovaWhUKC8vadAx9fRmsrMxQVFSGmhqVhiprvq/3JOHYuWz0drDAm08EQibrOo8fkbr3XRl7Lx32XjrsvXRu7b1cbgI9Pc3dnNTiUdXm5uYIDw9HeHg4gLo5kU6dOoWoqCgsXboUNTU1SExM1FiB1DpTR7ki5mIeMnNu4nhCNkJ8e0hdEhERUYfR6qhVXFyMw4cPY+fOndi7dy+OHz8OlUqFgQMHarI+aiW5mSEeGNEXABB55BLKK6slroiIiKjjaPYZpMuXL+PMmTPqr4yMDIiiiH79+iE4OBhPPfUUgoKCYGZmps16qQXCApxwJC4L2QXl+OX4ZTw6pp/UJREREXUIzQpIQ4cORUlJCURRRI8ePRAcHIznn38ewcHBsLGx0XaN1Er6ejI8NsYda36Iw6HYawjx6wGn7gywREREd9PseZCGDRuG4OBgODs7a7sm0iBvF2v49+uOs6n5+P5gCl5+xA+C0HUGbBMREbVGswLShx9+qLUCiouLsXbtWvzxxx8oLS2Fh4cHXn75ZQQGBgIAZs6ciRMnTjTYZ/Dgwdi8eTMAoKqqCu+++y727t2LyspKhIWF4Y033oC1tbXWau5oHrmnHxLSC5F4uQhnU/MR4G4rdUlEREQ6rVkB6Yknnmj2AQVBwLffftvs7RcsWIAbN25g7dq1sLGxwebNm/HUU0/h559/Rt++fXHx4kUsXboUY8aMUe9jYGCg/n7p0qWIiYnBRx99BENDQ7z11luYN28etmzZ0uwaOju7biYYN6QX/nciExGHUuHT1xoG+npSl0VERKSzmnUX2+nTpxEdHY3i4mKIoviPXypV8+eAyMzMxJ9//omlS5ciMDAQLi4uePPNN2FnZ4fdu3ejoKAABQUF8PX1ha2trfqrW7duAIDc3Fzs3LkTixcvRmBgIAYOHIi1a9ciOjoaZ8+ebVVDOquJQ/vAysII+SWV2Hv6qtTlEBER6bRmnUGaPXs2fvvtN6Snp2P48OGYOHEixowZA1NT07vv/A+srKywYcMG+Pj4qJcJggBBEKBQKHDx4kUIggAXF5cm94+NjQVQN4i8nouLC+zt7REdHQ1/f/821deZGBnq4aHRrtiwKxG/nryM4QMcYC03lrosIiIindSsgLRw4UIsXLgQ8fHx2LNnD9asWYMlS5YgNDQUkyZNQkhICAwNDVv84nK5HKNGjWqwbN++fcjMzMR//vMfpKSkwMLCAsuXL8eff/4JU1NTjBs3Ds8//zwMDQ2Rm5sLKysrGBk1fCCrnZ0dcnJyWlzPrfT12zYbZ/1snpqc1bOthvs44o+z15FytRjb/7iE56f43H2nDkgXe99VsPfSYe+lw95LR5u9b9FM2r6+vvD19cXrr7+O6Oho7NmzB0uWLIFSqcS9996LiRMnYtiwYZDJWlfomTNnsGjRIoSHhyM0NBT/+c9/UFVVhYEDB2LmzJlISkrCe++9h+vXr+O9995DRUVFk8HMyMgIVVVVraoBAGQyAVZWmrkdXi430chxNOX5f/li/rojOJWYiwdH94N33847TYOu9b4rYe+lw95Lh72XjjZ63+JnsTWmUqlw6tQp7NmzB7/88gvkcjn+/PPPFh/n4MGDWLhwIQICAvDZZ5/ByMgINTU1KCsrg6WlpXq7PXv2YP78+fjzzz+xa9cubNy48ba73P71r3/Bz88PixcvbtV7qq1VQaGoaNW+9fT0ZJDLTaBQVKC2VreezfP1niQcPpMFZ3tzLH9qSKd7Tpsu976zY++lw95Lh72Xzq29NzMzkvZZbI3Fx8fjyJEjOH78OKqrq1t1e/2WLVuwcuVKjBs3Dv/973/VZ4X09fUbhCMA6NevbjbonJwcODg4oLi4GEqlssGZpLy8vDY/MFdTDxysrVXp3MMLHxzhgqgLubiSW4pDsdcw2t9J6pK0Qhd731Ww99Jh76XD3ktHG8G0VQHp7Nmz+O2337B//37k5OTAxcUFU6dOxYQJE+Dq6tqiY23btg0rVqzA9OnT8cYbbzSYxHD69Ono2bMn3nnnHfWyhIQEGBgYoE+fPrC1tYVKpUJsbCyCg4MBABkZGcjNzUVQUFBr3lqXYGFqiAdHumDbwVT8fDQdQZ52MDcxuPuOREREXUSzA1LjUNSrVy888MADGD9+PDw9PVv14hkZGVi1ahXuvfdezJkzB/n5+ep1xsbGGDt2LFatWoWBAwdixIgRSEhIwHvvvYennnoK5ubmMDc3x8SJE7F48WKsWrUKJiYmeOuttzB48GD4+fm1qqauYnSAE47EXUdWfhl+OZaBx8PdpS6JiIhIZzQrIIWGhiI3NxeOjo6YOHEiJkyYAG9v7za/+L59+1BdXY0DBw7gwIEDDdZNnjwZ7777LgRBwObNm7Fq1SrY2tpixowZeOaZZ9TbrVixAqtWrcILL7wAAAgJCWn12KOuRE8mw2Nj+uH9iDj8fvYaRvn1QE87c6nLIiIi0gnNGqTt6ekJmUwGBweHuz7HSxAEHDx4UGMFSqG2VoXCwrI2HUNfXwYrKzMUFZXp9DXpT35OQOzFG/B07oZXHvXvFM9p6yi974zYe+mw99Jh76Vza+/lcpP2H6Q9efJkjb0g6ZZHRrvh3KUCJF8pRuzFGwj0tJO6JCIiIsk1KyDdOkiaOpfu3Uwwfogzdv15GT/8ngofVxsYGfA5bURE1LW16lyUKIr4+OOPcePGjQbLc3Nz8fHHH2ukMGo/44f2hrXcCAWKKuyNuiJ1OURERJJrVUBSqVT45JNPkJeX12B5Tk4OPvnkE40URu3HyEAPj4TVzS+151Qm8kvaNkkmERFRR9fq0UxtnICbdEyghy08enVDdY0KPx6+JHU5REREkmrRRJHR0dEAgNraWgBAYmIiysvL1etb88Ba0g2CIOCxe92x9OvTiEnOQ1JmEfr3tpK6LCIiIkm0KCBNnz4dgiCozx69+eab6nWCICAiIkKz1VG76mVnjtH+Tvj9TBa2HUzB0plB0Gvlg4eJiIg6shYFpEOHDgGoO4MUHh6Ozz//XP1sNAANZsKmjunBkX0RlZiLrBtl+OPsddwzqKfUJREREbW7Fp0ecHJyUn8BgK2t7W3LqGMzNzHAlJC+AICdx9Jxs1wpcUVERETtT+PXTzh4u+Mb5eeEXnbmKKuswc/HMqQuh4iIqN21KiDp6enhnXfeQc+eDS+/9OrVi5NKdgIymYDHxtRdOj0Sl4UruTclroiIiKh9NSsgpaen37Zs8uTJsLS0bLDM2tq6wWNJLl3i7eIdlYezFQb3t4MoAtsOpPDMIBERdSnNCkjPPPMM3n33XRQWFjbroNevX8fy5cvxzDPPtKk4ktbDo91gqC9DyrUSRCXmMiQREVGX0ayAtHPnThQXFyMkJASzZ8/GTz/9hOTkZJSWlkKlUqG4uBjJycnYunUrnnnmGYSHh6OkpAQ7duzQdv2kRdZyY0wI7g0A2PRrElZtjmVIIiKiLqFZt/mbm5vj3XffxfTp0/HFF19g2bJl6skib2VkZISQkBBERERgwIABGi+W2t+4wc44Fn8dBYoqXLqugLJaBSNDPsyWiIg6txbNg+Tt7Y3169ejvLwcMTExuHr1KkpLS2FlZYUePXogMDAQxsbG2qqVJGBooIepo1yxYXciACC7oAx9HOUSV0VERKRdLQpI9UxNTRESEqLpWkhH+bl1V3+/bns8XnssAI42ZhJWREREpF18jgTdlSAI6u8VZdV4b9tZZBeUSVgRERGRdjEg0V0ZGsjg1tMSLo5yOHU3RUmZkiGJiIg6tVZdYqOuRRAELHo8AABQWlGN97+Pw7UbpfjvtrN49VF/9OjOy21ERNS58AwSNYsgCBAEARamhnjlUT/0sjOHokyJ974/i6x8nkkiIqLOpcUB6YsvvkBubq42aqEOoi4k+atD0vvbzjAkERFRp9LigPTll18iLCwMs2fPxp49e6BU8mnvXZG5iQFeedQfznbmUJRX14WkG6VSl0VERKQRLQ5Ix48fxzvvvANRFLFw4UKMHDkSy5Ytw7lz57RRH+kwcxMDLHzUH872dSHpve/PMiQREVGn0OKAZGxsjPvvvx+bNm3C77//jlmzZiEuLg6PPPII7rvvPnz99dfNfmYbdXzmJgZY+O+6kHTzr5B0jSGJiIg6uDYN0nZwcMDMmTPx/PPPIzAwEKmpqXjvvfcQGhqKpUuXorSUvyi7gvqQ1NveAjfLq/E+QxIREXVwrQ5Ip0+fxhtvvIFhw4bh//7v/2BgYIC1a9ciOjoa7777Lvbu3YsFCxZoslbSYeYmBnj5337qkPTetrO4lseQREREHVOL50H64IMPsHv3bmRnZ8PR0REzZszAlClT0KNHD/U2EyZMwMWLF/Hdd99ptFjSbXVjkvywOiIOmTk38d73Z9V3uxEREXUkLT6D9PXXX8PPzw8bN27EoUOH8MILLzQIR/V8fHzw0ksvaaJG6kDMjA2w8N9+6ONg8dekkmdxlWeSiIiogxFEURRbskNiYiLc3NxgaGh427qqqipcuHABAQEBGitQCrW1KhQWtm1eH319GayszFBUVIaaGpWGKus4yiursToiDpdzbv41RskPzvYW7fLaXb33UmLvpcPeS4e9l86tvZfLTaCnp7n5r1t8pKlTpyI5ObnJdefOncPMmTPbXBR1fKZ/nUlycaw7k7Q6Ig5Xcm9KXRYREVGzNGsM0n//+18UFxcDAERRxKeffgorK6vbtktKSoKFRfucJSDdZ2psgJcf8cOaH+KRka3A+3+NSWqvM0lERESt1awzSH379kVUVBSioqIgCALOnz+v/rn+KyYmBjKZDIsWLWpRAcXFxViyZAlCQkIQEBCARx99FDExMer1J0+exJQpU+Dr64tx48bh119/bbB/VVUVli1bhuDgYPj7++Pll1/mPEw6pD4kuTjKUVZZg/e/P4vMHJ5JIiIi3dbiMUhhYWH49NNP4enpqZECZs2ahRs3buCtt96CjY0NNm/ejMjISPz8888QRRGTJ0/GzJkzcf/99+OPP/7A2rVrsXHjRgQHBwMAFi1ahJiYGLzzzjswNDTEW2+9BTMzM2zZsqXVNXEMkuaVV9Zg7Y9xSL+ugJmxft28SQ7aOZPE3kuHvZcOey8d9l462hyD1OKApEmZmZkIDw/Htm3bMGjQIAB1l/DCw8MxadIkFBQUICkpCT/99JN6n5dffhnFxcXYtGkTcnNzERoais8//xyjRo0CAGRkZGDcuHGIiIiAv79/q+piQNKO8soafPBjHC5pOSSx99Jh76XD3kuHvZeONgNSi+dBEkURP/30Ew4fPoyKigqoVA0/DIIg4Ntvv23WsaysrLBhwwb4+Pg02F8QBCgUCsTExGDMmDEN9hk6dChWrlwJURQRGxurXlbPxcUF9vb2iI6ObnVAIu0wNdbHgkf8sPbHOFzKUmB1xFmtnkkiIiJqrRZHrTVr1mDJkiVITU1FTU0NRFFs8NU4MP0TuVyOUaNGNZgyYN++fcjMzMTIkSORk5MDBweHBvvY2dmhoqICRUVFyM3NhZWVFYyMjG7bJicnp6VvjdqBiZE+FjzsBzcnS/WYpMs5CqnLIiIiaqDFZ5B27tyJmTNn4rXXXtN4MWfOnMGiRYsQHh6O0NBQVFZW3jbfUv3PSqUSFRUVTc7HZGRkhKqqqjbVoq/fttN09af5NHm6r7Ow0DfEK4/5Y/X3Z5F6rQSrI+LwyqP+6NtDDkEQ2nx89l467L102HvpsPfS0WbvWxyQSktLERoaqvFCDh48iIULFyIgIACrV68GUBd0lEplg+3qfzYxMYGxsfFt64G6O9tMTExaXYtMJsDKyqzV+99KLm99HZ2ZFYC3nxuOpV+eQtLlQqz4Nga9HSzw4YJQjYQkgL2XEnsvHfZeOuy9dLTR+xYHpEGDBuHMmTMYMmSIxorYsmULVq5ciXHjxuG///2v+qyQo6Mj8vLyGmybl5cHU1NTWFhYwMHBAcXFxVAqlQ3OJOXl5cHe3r7V9ahUIhSK8lbvD9SlWbncBApFBWprOWjvTl56aCDe33YWaVklyLiuwIGTGQjq3/p/dwB7LyX2XjrsvXTYe+nc2nszMyNpB2nPnj0br7zyCmpqauDr69vkmZqgoKBmH2/btm1YsWIFpk+fjjfeeKPB2YPAwECcPn26wfanTp1CQEAAZDIZBg0aBJVKhdjYWPVt/xkZGcjNzW1RDU3R1J0ItbUq3tXwDwz0ZHh+8gAs+PhPAMBHkQmYOqoME4b2bvOZJPZeOuy9dNh76bD30tFGMG1xQKp/lMgnn3wCAA1+iYmiCEEQkJSU1KxjZWRkYNWqVbj33nsxZ84c5Ofnq9cZGxtj+vTpmDx5MlavXo3JkyfjyJEj2Lt3LzZu3AgAsLe3x8SJE7F48WKsWrUKJiYmeOuttzB48GD4+fm19K2RRCzNDOHqJMeN4kooypSIPJKOK7mlmDWhP4wM9aQuj4iIuqAWz4PU+IxOUwYPHtysY33++ef44IMPmlw3efJkvPvuuzh69Cjef/99XL58GT179sSLL76ICRMmqLcrLy/HqlWrsG/fPgBASEgIFi9e3OSjUJqL8yC1v/qP4ZG469h6IAW1KhG97Mzx4hQfdO/WsmvL7L102HvpsPfSYe+l02knitRVDEjSSrlajE9/ToCivBrmJgZ47sEB6N+7+YGXvZcOey8d9l467L10JJ8o8uOPP8ZDDz0Ee3t7fPzxx/+4rSAImDt3rkaKo67JvVc3LJkRhI92JCAz5ybWRMThkXvcMGZQT43d4UZERPRPmnUGydPTEz/++CMGDhx412ewtWQMkq7iGSTdoKyuxbd7k3HyQi4AYLiPA54Y6wED/X8el8TeS4e9lw57Lx32XjqSn0FKTk5u8nsibTI00MPsSV5wtrfAj4fT8GdCDrILyjF3sg+sLIzufgAiIqJW0vjUk6WlpZo+JHVhgiBg7GBnLHjYD2bG+ki/rsDyb6KRllUidWlERNSJtfg2f6VSiW+//RanT5+GUqlU330kiiLKy8uRlpaG+Ph4jRdKXZu3izXefDIQH+1IQNaNMry37QymhXsgxLeH1KUREVEn1OIzSO+99x7WrFmD3NxcXLp0CVlZWaioqMC5c+eQlJSEOXPmaKNOIthZmeKN6YMwyN0WNbUivvktGVv3p6CGM9cSEZGGtTgg7d+/HzNnzsSuXbswbdo0DBgwAD/99BP2798PJycnqFT8ZUXaY2yoj+cmD8CDI10AAIfOXMOaiDgoym9/Jh8REVFrtTggFRYWIiQkBADg7u6OhIQEAHWzWj/zzDPYs2ePZiskakQmCLh/uAtenOoDY0M9XLxajBXfRONK7k2pSyMiok6ixQHJwsICSmXdX+u9e/dGdna2emB2nz59kJ2drdkKie7Av58t3ngiEPZWJihQVGHV5lhEJeZKXRYREXUCLQ5IgYGB2Lx5MyoqKtC7d2+YmJjg4MGDAICzZ8/C3Nxc40US3YlTdzO8+WQgBvS1hrJGhS92XcAPv6eiVsUJ4omIqPVaHJDmzp2LuLg4PPPMM9DX18djjz2GN998E1OmTMGHH36IsWPHaqNOojsyNTbAS//yxfghzgCAX09kYsWmUyirrJa4MiIi6qhafJu/p6cnfvvtN6SkpAAAXn75ZZibm+PMmTMICwvjXWwkCZlMwEOj3dDL3hzf7ElGbHIeruWV4sUpPujR3Uzq8oiIqINp8cNqo6Oj4eXlBTOz23/pKBQKHDt2DBMnTtRYgVLgo0Y6tmv5pfgoMgE3iipgbKiHp+/zgp9bdwDgs9y0jJ976bD30mHvpaPNR420+EhPPPEELl261OS6xMRELFq0qM1FEbVFHwc51v7fKHg4d0OlshYfRSbglc9O4J0tZ9DCvweIiKiLatYlttdee019d5ooili6dGmTg7EvX76M7t27a7ZColboZmGE1x4PwJZ9F/H7mSwUKqpQqKhCgaIS3S1NpC6PiIh0XLPOII0dOxaiKDb467v+5/ovmUwGPz8/vPPOO1orlqgl9PVkmBbugcfvdVcve/u7GFzIKJSwKiIi6giadQYpLCwMYWFhAIAHHngA77//PlxdXbVaGJGmhAU44Wj8deQUlkNRVo01P8QhPKgXpo5yhYG+xp/XTEREnUCLfzsUFBTg4sWL2qiFSCsEQcDSmUH4cN4IjPZ3AgDsj76Kt7+LQVZ+2wbjExFR59TigFRdXQ0rKytt1EKkNYIgwNhQH9PHemDe1IEwNzHA1bxSLP8mGr+fucbB20RE1ECr7mJbt24dzp49i4qKCm3URKRVfv26Y8VTgzHAxRrVNSps2Z+CD7efg6KMD7wlIqI6LZ4HKTw8HNevX0dtbW3TBxQEJCYmaqQ4qXAepI6tub1XiSIOxV7DT4cvoaZWBbmpAWZN9MJAV5t2rLZz4edeOuy9dNh76WhzHqQWz6R9//33a+zFiaQkEwTcG9gL/Z2t8MWuC8jKL8O6n+Jxz6CeeHi0Kwz09aQukYiIJNLiM0hdAc8gdWyt6b2yuhbb/7iEg7HXAABOtmaYc583etrx4cstwc+9dNh76bD30tGpmbTrHTlyBO+88w4WLFiAq1evYv/+/cjKytJYYUTtydBAD4/d646XHvKF3MwQWTfKsPzbGByIvgoV/4YgIupyWhyQKioqMGvWLMyZMweRkZH47bffoFAo8P3332PKlClITU3VRp1E7WKgqw2WzxoMX1cb1NSq8P2hVKz7MR4lpVVSl0ZERO2oxQFp7dq1uHDhAr755hucOnVKfXv0f//7X9jb2+PDDz/UeJFE7UluZoh5/xqIaeHuMNCX4XxGId7cdBpxqflSl0ZERO2kxQHpt99+w4IFCzB06NAGT0a3s7PDc889h9jYWI0WSCQFQRAQFtATS2YEoZedOUorqrE+8hw277uIquqm7+AkIqLOo8UBSaFQwMnJqcl1lpaWKC8vb3NRRLrCqbsZFj8RiLGDewEADp/NwvJvopGZc1PiyoiISJtaHJD69euH3bt3N7nu999/R79+/dpcFJEuMdCX4ZGwfnj5ET9Ymhsiu6Acb38Xg71RVziAm4iok2rxPEjPPfccXnjhBRQXF2P06NEQBAHR0dHYsWMHIiIisGbNGm3USSQ5bxdrLJ81GN/8loyzqfn48XAaEtILMHuSF6wsjKQuj4iINKhV8yDt3r0ba9asQU5OjnqZjY0NXnrpJTz00EMaLVAKnAepY9N270VRxNH46/j+UCqU1SqYGetjxvj+GORhq/HX6mj4uZcOey8d9l462pwHqU0TRaanp6O4uBhyuRx9+/aFTKa5wqTEgNSxtVfvswvKsGF3ono80nAfBzx6jztMjVt8YrbT4OdeOuy9dNh76ejcRJF79uzBkiVL0LdvXwQEBEChUODhhx/G77//rrHCiHSdo40Z3pg+COOHOkMA8GdCDpZ8FYULGYVSl0ZERG3U4oC0c+dOLFiwAMXFxepl3bp1g62tLV544QUcPHiw1cV88cUXmD59eoNlixcvhoeHR4OvsLAw9XqVSoX169dj5MiR8PPzw9NPP42rV6+2ugailtDXk+GhUDe89ngA7LqZoFBRhTU/xGHzvouoVNZIXR4REbVSiwPSpk2bMHPmTKxfv169rG/fvvjss8/w5JNP4tNPP21VIVu3bsW6detuW37x4kU8++yzOH78uPpr+/bt6vWffvoptm3bhhUrViAiIgIqlQqzZ8+GUqlsVR1EreHeqxuWzRqMewJ6AqibDuCtr07j4pUiiSsjIqLWaHFAunLlCkaNGtXkupCQEKSnp7foeLm5uXj22WexevVq9OnTp8E6URSRlpaGAQMGwNbWVv1lbW0NAFAqlfjqq68wb948hIaGwtPTEx988AFycnKwf//+lr41ojYxMtTD4+HuWPhvP9jIjXCjuBLvbTuL7w+mQsnJJYmIOpQWByRbW1ucO3euyXXJycmwsrJq0fEuXLgAAwMD7Nq1C76+vg3WXblyBeXl5ejbt+8dX6+srAzBwcHqZXK5HF5eXoiOjm5RHUSa4tXHGsufGoIQX0eIAA7EXMVbX0fjUlaJ1KUREVEztfh2m0mTJuGzzz6Dqakp7r33XlhbW6OwsBCHDx/GRx99dNsYorsJCwtrMKboVikpKQCAzZs34+jRo5DJZAgJCcH8+fNhYWGhnmbA0dGxwX52dnYNpiBoDX39to2Erx9Jr8kR9dQ8utB7C31DzL7PG0H97fHVr0nILSzHqi2xmBjcB5ND+sKgjZ8vXaULve+q2HvpsPfS0WbvWxyQ5s6di/T0dLz99ttYuXKlerkoihg3bhxefPFFjRWXkpICmUwGOzs7fP7557hy5Qree+89pKam4ttvv0VFRQUAwNDQsMF+RkZGKClp/V/rMpkAKyuzNtVeTy430chxqOV0ofehQWYI9HbEFzsT8EfsNfzvxGUkpBfgpUcD4Nazm9TlaY0u9L6rYu+lw95LRxu9b3FAMjAwwPr165GSkoLY2FiUlJTAwsICgwYNgqenp0aLe+655/DYY4+pL9u5u7vD1tYWDz/8MBISEmBsbAygbixS/fcAUFVVBROT1jdLpRKhULTtmXJ6ejLI5SZQKCpQW8t5MdqTLvZ+1nhP+Pa1xtd7kpGZcxMLPzyK+0e44L7hfaDfif7q1MXedxXsvXTYe+nc2nszMyONnklq9Yx27u7ucHd311ghTZHJZLeNaap/1ltOTo760lpeXh6cnZ3V2+Tl5cHDw6NNr62pyb5qa1WcOEwiutZ7X9fuWP7UYGzZdxExF2/g56PpOHPxBp6a1B89bc2lLk+jdK33XQl7Lx32XjraCKYtDkiiKOKnn37C4cOHUVFRAZWqYVGCIODbb7/VSHGvvvoq8vLy8M0336iXJSQkAADc3NzQq1cvmJubIyoqSh2QFAoFEhMTMW3aNI3UQKRJclNDPPfgAJxOysOW/ReRmXsTy7+JxgMjXDBuiDP0Osls9EREHV2L/2+8Zs0aLFmyBKmpqaipqYEoig2+Ggemthg7dixOnjyJjz/+GFeuXMGRI0fwn//8B5MmTYKrqysMDQ0xbdo0rF69GocOHUJycjLmz58PBwcHhIeHa6wOIk0SBAFDvOzx9uwh8HPrjppaEZFH0vHOljPILmjbI26IiEgzWnwGaefOnZg5cyZee+01bdTTwD333IN169Zhw4YN+PLLL2FhYYH77rsPL730knqbefPmoaamBosXL0ZlZSWCgoKwadMmGBgYaL0+orawNDfCi1N9cOJ8DrYdTEH6dQWWfh2NqaNcMSawJ2SCIHWJRERdVosfVuvn54cvvvgCQ4YM0VZNkuPDaju2jtj7QkUlvv4tWf0cN/de3TBrYn/YdetYd8V0xN53Fuy9dNh76ejUw2oHDRqEM2fOaKwAIgKs5cZY8LAvnhjrASMDPaRcLcZbm07j8NksqFQqtPDvGCIiaqMWX2KbPXs2XnnlFdTU1MDX17fJ2+mDgoI0UhxRVyIIAkL9neDlYo2vf03CxavF2LzvIrb/kQZ7K1O8+WQgBF52IyJqFy2+xNZ4rqNb/4ctiiIEQUBSUpJmqpMIL7F1bJ2h9ypRxKGYa9j+Rxqqa+v+E30kzA33BvaCTKa7Iakz9L6jYu+lw95LR5uX2Fp8Bum7777T2IsTUdNkgoB7g3rBw7kbln5d91zBH35Pw6nEXDwx1gMujnKJKyQi6txaHJAGDx6sjTqIqAm97Mzh5mSJkrIq3CyvRmbOTbz9bQxCA5wwNaQvTI15tyYRkTa06jb/u3nwwQdbUQoRNSYIAhZNCwAAKMqr8ePvqTh5IReHz2Qh9uINPBLmhqFe9hybRESkYW0eg6Q+kCBAT08Penp6iI+P10hxUuEYpI6ts/c+6XIhNu9PQU5h3fMC+/e2wrRwdzjaaOYBy23R2Xuvy9h76bD30tGpMUiHDh26bVl5eTliYmLw5Zdf4pNPPtFIYUTUtP59rLFs1mDsO30Fu09cRlJmEZZsOo3xQ50xKbgPDA30pC6RiKjDa3FAcnJyanJ5v379UF1djRUrVmDbtm1tLoyI7sxAX4ZJw/pgiJc9th5IwblLBfjfiUycupCLaeHuGOjaXeoSiYg6NI0+GdPDwwMXLlzQ5CGJ6B/YdjPB//1rIOZO9oGVhRHySyqx7qdz+GRHAgoVlVKXR0TUYbX4DNKdKJVKbN++HTY2Npo6JBE1gyAIGORhC28XK+w6fhn7o68iNuUGzmcU4oERLhgT2BP6GrwuT0TUFbQ4IIWFhd12x4xKpUJRURGqqqra5SG2RHQ7Y0N9PBzmhmEDHPDd/otIu1aCHw+n4cT5bDwx1hNuPS2lLpGIqMNo1TxITd1SbG5ujtGjR2PYsGEaKYyIWqennTlefzwAf57Lxk9/XMK1G2VYtSUWIwc64qHRbjA34dxJRER30+Lb/LsC3ubfsbH3f7tZrsT2Py7h2LlsAIC5iQEeCnXF8IGOkGlh7iT2XjrsvXTYe+lo8zZ/Dkwg6sQsTA0xc0J/LJoWgJ62ZiitqMbXvyXj3a1ncC2vFEDdMxT5dxIRUUMaG6RNRLqrX89uWDIjCAdjruGX4xlIu1aCpV9H497AnkjNKoFMJmDR4wGckZuI6C8MSERdhL6eDOOGOGNwfzt8fzAVsSk3sC/6qnp9lbIWxkb8XwIREcBLbERdjrXcGHOn+OClhwbCRm6kXv7+92dx8UqRhJUREekOBiSiLmqga3e8OSNI/XNGzk38d9tZrP0xDldyb0pYGRGR9JoVkNauXYvc3NwGy4qLi6FSNRytn5KSgsmTJ2uuOiLSKgsTA7j1tISLowVC/XtATybgfHohln4djQ27LiCvuELqEomIJNGsAQdffvklxowZA3t7ewBAbW0tgoODsX37dnh7e6u3q6ioQHJysnYqJSKNE4S6wdn1348d7Iyfj6bjdFIeTiXmIjo5D6P8euC+4S6wNDOUuFoiovbTrDNITd0CzNuCiToHQRDUd6/ZW5ni2QcG4K0ZQRjgYo1alYjfz2Th9c9P4uej6aioqpG4WiKi9sExSER0m94OFljwiB9eedQfLo5yVFXXYveJy3jt85PYf/oKqmtqpS6RiEirGJCI6I7697bC4icGYe7kAXCwNkVpRTUifk/DfzacwvFz2VCpeCaZiDonTnpCRP9IEAQM8rCDX7/u+DMhB78cz0CBogpf7UnCvtNXMGVUX/i5deckk0TUqTAgEVGz6MlkCPHtgaFe9jh05hr2nMxEVn4ZPopMgJuTJf4V6govF2upyyQi0ohmB6Tt27fj6NGjAOoGaAuCgB9++AF2dnbqbRpPBUBEnY+hgR7GD+mNEN8e+O3UFRyMuYq0rBK8u/UMfN2646kHBqCbCf/2IqKOTRCbcTuap6dn8w8oCEhKSmpTUVKrrVWhsLCsTcfg052lw963r6KbVdj9ZwaOxmdDJYoQBCDY2wEPjHCBbTcT9R2vvASnXfzcS4e9l86tvZfLTaCnp7mh1c0KSF0NA1LHxt5LI6ewHDuP1c2hBAB6MgGhfj2Qnq2Anp6MD8PVMn7upcPeS0ebAYnnwYlIIxysTfHC1IHIL1Vi487zSLxciENnstTrcwrK4djdTMIKiYiar1kB6fr16y06aI8ePVpVDBF1fP16WeH1aQGIT8vHj7+n4WpeKQDgjY1RGORui3sG9YSHczeeTSIindasgBQWFtai/5l19DFIRNR23n2s8frjAZj7wVH1stiUG4hNuYGetmYYE9gLQ7zsYWSgJ2GVRERNa1ZAWrVqFf/aI6IWMzbUg1tPSwDA9HB3HD6ThRPnc3DtRhm++S0ZPx1OQ4hvD4wOcEJ3SxOJqyUi+ptODdL+4osvcPz4cWzevFm9LCkpCStXrsT58+dhbW2NGTNm4IknnlCvV6lU+Pjjj/HTTz/h5s2bCAoKwpIlS9CrV69W18FB2h0bey+dpnrf+C62sspqHIvPxu9nriG/pPKvdYB/P1uM4eW3VuPnXjrsvXS0OUi7RUc6d+4cfvvtN1y4cEFjBdTbunUr1q1b12BZUVERZs6cCWdnZ0RGRmLu3LlYvXo1IiMj1dt8+umn2LZtG1asWIGIiAioVCrMnj0bSqVS4zUSUcvd+jBcADAzNsC4Ic54d04wXpzqg/69rSCKwJmUG3jv+7NY8tVpHInLQlU1n/dGRNJp1iU2hUKBOXPmIC4uTj1JpL+/P9asWQNHR8c2FZCbm4u33noLUVFR6NOnT4N1P/74IwwMDLB8+XLo6+vD1dUVmZmZ2LBhA6ZOnQqlUomvvvoKCxcuRGhoKADggw8+wMiRI7F//35MmjSpTbURkfbIZAL8+9nCv58tsm6U4tCZLJw4n42sG2X4du9FbP/jEkb69kCYvxO6d+PlNyJqX806g7Ru3TokJibixRdfxIYNG/Daa68hPT0dS5YsaXMBFy5cgIGBAXbt2gVfX98G62JiYjB48GDo6/+d44YOHYrLly8jPz8fycnJKCsrQ3BwsHq9XC6Hl5cXoqOj21wbEbUPJ1tzPDHWA2vmDscjYW7obmmMssoa7I26gte+OImPIs8hKbMIOjQigIg6uWadQTp8+DAWLFiAJ598EgAQEhICe3t7LFy4EOXl5TA1NW11AWFhYQgLC2tyXU5ODtzd3Rssq3+0SXZ2NnJycgDgtrNYdnZ26nWtpa/ftuuY9ddBNXk9lJqHvZdOW3tvaW6EicP6YPzQ3ohLy8eB6Ku4kFGIs6n5OJuaj562Zrg3qBeG+TjCyECPs3Tfgp976bD30tFm75sVkG7cuAFvb+8Gy4YMGYLa2lpkZ2fD1dVV44UBQGVlJQwNDRssMzIyAgBUVVWhoqICAJrcpqSkpNWvK5MJsLLSzIR2cjkvDUiFvZeOJnp/j4057hnSB1dyFPjfnxn4PeYqrt0ow9d7kvHT4UsYM9gZCZfyYaivh/++MIIh6S/83EuHvZeONnrfrIBUU1NzWwixtKy7dbeqqkrjRdUzNja+bbB1/euZmprC2NgYAKBUKtXf129jYtL6ZqlUIhSK8lbvD9SlWbncBApFBWpreVdDe2LvpaON3lsY6eHRMDfcP6w3jsZdx6GYa8grrsDOI5fU2xyMykBAPzvIZF03JPFzLx32Xjq39t7MzEi3HjWizTEBDg4OyMvLa7Cs/md7e3vU1NSolzk7OzfYxsPDo02vralbNWtrVbztUyLsvXS00XsjfT3cG9gL9wT0xLlLBTgQfQVJV4oBAOt/SoC13AgjfBwxYqBjl55TiZ976bD30tFGMG1z1NLmae2goCDExsaitvbv231PnToFFxcX2NjYwNPTE+bm5oiKilKvVygUSExMRFBQkNbqIiLpyGQC/Pp1x7x//X1Th6mRHgoVVdj152W89tlJrPkhDtHJeajmLysiaqVmn0FaunQpzM3N1T/Xnzl68803YWb293gdQRDw7bffaqS4qVOnYuPGjXjjjTcwe/ZsnDt3Dt988w2WLVsGoG7s0bRp07B69WpYW1vDyckJ77//PhwcHBAeHq6RGohINxkayNSzdC98xBdnUvNxLD4bSZlFuJBRiAsZhTA3McCwAQ4YOdARTrbmdzkiEdHfmhWQ6s/GNL6c1tRyTV5ys7GxwcaNG7Fy5UpMnjwZtra2ePXVVzF58mT1NvPmzUNNTQ0WL16MyspKBAUFYdOmTTAwMNBYHUSkewRBwKLHA9TfD/VywFAvB+QVV+D4ues4fi4bxaVK7I++iv3RV+HqJEfIwB4I6m8HY8M2jy4gok5Opx41oiv4qJGOjb2Xji71vlalQkJ6IY7FX0d8WgFUf/2vzshQD0P622Gkbw/0dZR3mrvfdKn3XQ17Lx1tPmpEY39GKZVK7NmzBxEREYiIiNDUYYmIWkVPJoOfW3f4uXVHcWkVTpzPwdH468grqsDR+Gwcjc+Gk60ZQgb2QPAAB5ib8KwzEf2tzQEpPT0dERER+OWXX1BSUtJgPBIRkS7oZm6ECUN7Y/wQZ6RcLcbR+GzEXMxD1o0yfH8oFT/9kYYAd1uM9O2B/r2tIPvrrBInoiTquloVkGpqarBv3z5EREQgJiam7vr/0KF44IEHODiaiHSWIAjwcLaCh7MVHr+3H04l5uJo3HVcySvF6aQ8nE7KQ3dLY4wc6IhhAxzwxa5EQAAWPR7AkETUxbQoIF29ehU//PADfv75ZxQWFqJHjx4AgM8++wyjRo3SSoFERNpgamyAsICeCAvoicycmzgafx2nEnORX1KJn49l4OdjGeptyypqYG7KS3BEXUmzAtKBAwcQERGBEydOwNTUFOPHj8eUKVPg5uaGwYMHt+lZbEREUuvtYIHpDh54OMwNsRfzcDQ+GylXi9XrX/38BII87TDEyx6ezlZdesZuoq6iWQHpxRdfhIeHB9asWYN77rlH/Ty0mzdvarU4IqL2ZGSgh2EDHDFsgCMycxRY9k0MAKBSWYtj57Jx7Fw2LM0NMdjTHkO97dHHwYKX3og6qWYFJD8/P8TFxWHt2rU4e/YsJk+eDC8vL23XRkQkGWd7i7qJKEVg8kgXRCXlIfZiHkpKlTgQcxUHYq7CzsoEQ73sMcTLHo42vEGFqDNpVkCKiIhARkYGIiMj8csvv2DLli3o168fxo0bx7+eiKhTajwRZf8+1pgW7o7z6YU4lZiDuNR85BVVYNefl7Hrz8twtjfHUC8HDO5vB2u58V2OTkS6rsUTRapUKhw5cgQ7duzA4cOHUVNTg4CAAEyZMgXh4eGQy+XaqrXdcKLIjo29l05X6n2lsgZnU/MRlZiL8+mF6okoBQDuvbphiLc9Aj3s2m1+pa7Ue13D3ktHmxNFtmkm7aKiIvzyyy/YsWMHUlJSYGBggBEjRuCzzz7TWIFSYEDq2Nh76XTV3t8sVyLm4g1EXchByrUS9XI9mQCfvjYY4mUPP7fuMDLU01oNXbX3uoC9l47OBqRbJSQkIDIyEnv27MHp06c1cUjJMCB1bOy9dNh7oKCkEqeTcnEqMRdX80rVy40M9ODfrzuGeNnD28Ua+noyjU5Eyd5Lh72XTocISPWUSiUMDQ01ech2x4DUsbH30mHvG8q6UYqopFxEJebiRnGlerm5iQEGedgi7VoJTIz0sWha2yeiZO+lw95LR/JnsS1atKjZBxQEAatWrWp1QUREnYWTrTmm2Jpj8si+SM9WIOpCLk4n5UJRXo0jcdfV223dn4Kh3g7o6yRXP+aEiKTVrID0888/QxAE2NvbQyb753TGu9qIiBoSBAGuPSzh2sMSj9zjhuTMYvx5/jpOXcgDAPx+Ngu/n82ClYURAj3sENTfDn17MCwRSalZAWn8+PH4448/oFQqMW7cOEycOBGDBg3Sdm1ERJ2OnkwGbxdrePWxwo3iSpRX1sDZ3hxxaQUoulmlnmPJysIIQZ52CPRkWCKSQrPHIFVUVODw4cPYs2cPjh49iu7du2PChAmYOHEi+vfvr+062xXHIHVs7L102PuWuXWQdnVNLc6nFyI6OQ9n0/JRpaxVb2ct/+vM0l9hqakz9ey9dNh76ejcIO3S0lIcOHAAe/bswcmTJ9GzZ09MmjQJEydOhIuLi8aKkwoDUsfG3kuHvdcMZXUtzmcUIqYFYYm9lw57Lx2dC0i3Ki4uxoEDB/Dbb7/h9OnTcHd3x44dOzRVnyQYkDo29l467L3m/VNYspEbYdBfY5bce3WDtbU5ey8Bfu6lI/ldbP+kqqoKFRUVqKysRG1tLbKysjRRFxERATA00EOAuy0C3G2hrK5FQnohYi7mIS4tHwWKKuyPvor90VdhIzfGSH8n+Pa1hrOdOQRB0Og8S0RdTavOIOXm5mLv3r3Yu3cv4uPjYWpqijFjxmD8+PEYPnw49PXbnLskxTNIHRt7Lx32vv00CEup+aiqvvXMkjEGeXRH4uUiGBtqZp4lujN+7qWjE2eQbg1FcXFxMDExwejRozF79myMHDmyw08OSUTUkRga6GGQhy0GedSdWUrMLEJcWgGiLuSgQFGJ/dHX1NtuO/jXPEuOTQ/wJqLbNSsgPfroo4iPj4eRkRFGjRqFDz/8EKNGjYKRkZG26yMiorswNNBDoKcd7g12QW6eAmdTbuBUYi5iL94AAByKzcKh2CzYyI0QWD91AMMS0T9q1iU2T09P6OnpwcvLCyYmJv98QEHAt99+q7ECpcBLbB0bey8d9l46jXsviiJWbY5FeWUNetqZ4dylwkaX4f4e4M2w1Db83EtH8ktsQUFB6u/vlqc0/Gg3IiJqBUEQ8J/pg9Tf149Zik7ORXxaQaMB3n+FpX+YZ4moq9H4w2o7A55B6tjYe+mw99JpSe//aYB3cyalpIb4uZeO5GeQiIio82g8wPvWqQMKbzmzxLBEXRkDEhFRF9Y4LJ3PqHvcyZ3CUv2z4eqjEkMTdVYMSEREBOD2SSlvncH71rDUzbxuWhdzEwMsnRkEmUxzlzWIdAUDEhER3eafwlJxqRIAUFyqxGufn8RQbwcM8bJHT1tziasm0hwGJCIi+ke3hqXqmlqcTcnH57suAAAKFFX49WQmfj2ZCSdbMwz1sseQ/vbo3u2fp4Qh0nUMSERE1GwG+noI6m+Hg2euQVSJGBPYE6eT8pCQXoCsG2WIPJKOyCPpcHOyxBAvewR52kFuxictUMfDgERERC0iCAIWPR6g/n6IlwPKKqsRe/EGohJzkZxZhLSsEqRlleD7g6nw6mOFIV72CHC3hYkRf+1Qx8BPKhERtVjju9fMjA0Q4tsDIb49UHSzCtFJuYhKykVG9k2czyjE+YxCfLfvInzdumOolz18+trAQJ+Du0l3dYiAlJubi5CQkNuWv/POO5gyZQqSkpKwcuVKnD9/HtbW1pgxYwaeeOIJCSolIiIrCyOED3ZG+GBn5BaWIyoxF6cSc5FTWI6Y5DzEJOfBxEgfgzxsMdTLHp7OVpDJOF0A6ZYOEZCSk5NhZGSEgwcPNvirxcLCAkVFRZg5cybCwsKwbNkyxMXFYdmyZTAzM8PUqVMlrJqIiOytTXH/CBfcN7wPruSWIiqx7sxS0c0qHD+XjePnsmFpZojB/e0xxMseLo4WEARB/dgqzrNEUukQASklJQV9+vSBnZ3dbeu+/fZbGBgYYPny5dDX14erqysyMzOxYcMGBiQiIh0hCAJ6O1igt4MF/jXaFalXi3EqMRcxyXkoKVPiQMxVHIi5CrtuJhjc3w7nLhXA0FAPix4PYEgiSXSIgHTx4kW4uro2uS4mJgaDBw+Gvv7fb2Xo0KH44osvkJ+fj+7du7dXmURE1AwyQYCHsxU8nK3w+L3uOJ9RiKjEXJxNvYG84gr872Smetv/nbiMYG8HThtA7a5DBKSUlBRYWVnh8ccfR0ZGBnr37o3nnnsOISEhyMnJgbu7e4Pt6880ZWdntzog6bdx8GD9A/M0+eA8ah72XjrsvXQ6au/19WUI9Kx7hEmlsgZnUvJx/Fw2zqcXAAB+PpaBn49lwNXJEkO97TG4vz2sLIwkrrqhjtr7zkCbvdf5gFRTU4P09HS4ubnh9ddfh7m5OX799Vc888wz+Prrr1FZWQlDw4ZzbBgZ1f3HU1VV1arXlMkEWFmZtbl2AJDL+VePVNh76bD30unovZ9ob4kJI/pi4fqjuFmmRPduJjifXoBLWSW4lFWCbQdSMKBvd4z0d8IwH0dYmutOWOrove/ItNF7nQ9I+vr6iIqKgp6eHoyNjQEAAwYMQGpqKjZt2gRjY2MolcoG+9QHI1NT01a9pkolQqEob1PdenoyyOUmUCgqUFuratOxqGXYe+mw99LpbL1/Y/ogAHVjl4pvVuF0Ut2dcGnXSpBwKR8Jl/LxeeQ5DOhrjSFe9hjkYQdTY2l+pXW23nckt/bezMxIo2eSdD4gAYCZ2e1nc/r164fjx4/DwcEBeXl5DdbV/2xvb9/q16yp0cyHvLZWpbFjUcuw99Jh76XT+XovwtzEAGEBPREW0BP5JRWITs7D6cQ8ZObexLlLBTh3qQBf70mCT18bDPGyh69rdxgZ6rV7pZ2v9x2HNoKpzgek1NRUPPLII/jss88wZMgQ9fLz58/Dzc0N/fv3R0REBGpra6GnV/cfxKlTp+Di4gIbGxupyiYiIi3obmmC8UN6Y/yQ3sgpLMfppFxEJeYiu6AcZ1PzcTY1H4YGMvi5dcfg/vbw6WsNA/32D0vU8el8QHJ1dUXfvn2xfPlyLFu2DFZWVvjxxx8RFxeHyMhI2NjYYOPGjXjjjTcwe/ZsnDt3Dt988w2WLVsmdelERKRFDtamuH+4C+4b1gdZN8oQlZSL00m5uFFcidNJeTidlAcTIz0E9LPFYC979O9tBf2/LsFwniW6G0Gs/5TosPz8fKxZswbHjh2DQqGAl5cXFi5ciMDAQADAuXPnsHLlSiQmJsLW1hazZs3CtGnTWv16tbUqFBaWtalmfX0ZrKzMUFRUxlOu7Yy9lw57Lx32vo4oiriccxNRibmITs5D0c2/b9YxNzFAoIctgjztsONYuvqZcm0NSey9dG7tvVxuotExSB0iILU3BqSOjb2XDnsvHfb+dipRRNq1EkQl5SI2OQ+K8urbtnnzyUC4OMrb9DrsvXS0GZB0/hIbERFRa8gEAe69usG9Vzc8NqYfkq8U4/Rfs3dXKGsBACu+jYGbkyVG+joiyNMOxob8tUh1+EkgIqJOT08mg3cfa3j3sca0cHcs/ToaijIlyitrkJZVgrSsEnx/MBWD+9sjxLeH+plw1HUxIBERUZdioK+Ht2fX3RVdUqbEnwnZOBafjbziChyNv46j8dfR09YMI317INjbAeYmBhJXTFLgGKQmcAxSx8beS4e9lw573zaiKOLilWIcO3cdMRdvoPqvHurryTDIwxYhAx3h0dsKsibOKrH30uEYJCIiIi0SBAGeva3g2dsKj91bjVMXcnE0/jqu5pUiKrFuriXbbsYYObAHhvs46tzz4EjzGJCIiIhuYWZsgHsG9URYgBMyc2/iaHw2ohJzcKO4EjuOpuPnY+nwde2OkQMd4eNq0+aHm5NuYkAiIiJqgiAI6OMgRx8HOR4Z7YaYi3k4Gn8dqddKEJeWj7i0fFiaGWKkbw/cN8oVJnp/X37jRJQdHwMSERHRXRgZ6mG4jyOG+zgiu6AMx85l48+EbJSUKfG/E5fxvxOX0b+3FUYMdERAv+5Y80M8IEAjE1GSNBiQiIiIWsDRxgwPj3bDlJC+iE/Lx7Fz2UhIL0BSZhGSMotgYqinnmdJWa2S5MG51HYMSERERK1Qd4ebHYZ4O6AaAn49eglH4rJQoPj78SZvb47ByIE9MNTbHnJTQwmrpZbibf5N4G3+HRt7Lx32XjrsvXRu7b1SWYv4SzfwUeT5BtvoyQT4unXHCB9H+LhaQ0/Ggd2awNv8iYiIOgCZTICfmy3celpCVStiqLc9/jyfg8ycmziTcgNnUm5AbmaIYd4OGD7QEU7dzaQume6AAYmIiEiDBEHAoscD1N+PCeyFa3mlOJ6QjZMXcqAoU2Lv6SvYe/oKXBzlGDHQEUP628HUmDN26xIGJCIiIg1rfOdaTztz/PuefvhXqCvOXSrA8XPZOHepABnZCmRkKxBxKBUB7rYY4eOI/n2anrGb2hcDEhERUTvR15MhwN0WAe62KClT4uT5HPyZkI2s/DL1jN3WciMMG+CIET4OsLMylbrkLosBiYiISAKWZoYYN8QZYwf3wuWcmzh+LhtRibkoVFSp51Zy79UNI3wcEehpC2PDul/ZnISyfTAgERERSUgQBLg4yuHiKMe/73HDmZR8HE/IRmJGIVKuFiPlajG2HkxBkIcdhvs4IPLIJeCvcU4MSdrDgERERKQjDPT1MMTLHkO87FGoqMSff12CyyuqwPGEbBxPyFZvW3izCjZyYwmr7dwYkIiIiHSQtdwY9w3rg0nBvZF6rQTHz2XjdFIulH/Nc/XGhlMI9XfC2MHOsLIwkrjazocBiYiISIcJggD3Xt3g3qsbpob2xfyP/gQAKGtU2B99FYdir2G4jwPGD+kNe2sO6tYUTuVJRETUQRgb/H1e44UpA+DeqxtqVSKOxmfjP1+ewue/nMeV3JsSVth58AwSERFRB2FoIINbT0sAgH8/WwS42yH1WjF+PZmJc5cKcDopD6eT8uDT1wYTg3vDvVc3aQvuwBiQiIiIOojGs3QDQL+e3fDSQ91wNa8Ue05l4nRSLhLSC5CQXoB+PS0xMbg3fPra8I63FmJAIiIi6kDuFHR62Zljzv3eeHCkC/ZGXcGfCdlIvVaCdT+dQy87c0wY2htBnnaQyRiUmoNjkIiIiDoReytTPDnOE/99dhjGDXaGkYEeruaV4otdF/CfL0/hSFwWqv+6E47ujAGJiIioE7KyMMLDYW54//lheHCEC8yM9ZFXVIFv917Ea5+fwL7TV1CprLltP1EU1bN1d2W8xEZERNSJmZsY4P4RLggf3AtH465jX/RVFN2swg+/p+F/Jy5jTGAv3DOoJ8xNDCCKIt7ZcgYQ0OVn6mZAIiIi6gKMDfURPtgZowN64uSFHPx2KhO5RRX45XgG9kZdwSi/Hgj1d0JaVgkAQFmtgpGhnsRVS4cBiYiIqAsx0JchxLcHRvg4IuZiHvaczMSVvNK/Jp28KnV5OoNjkIiIiLogmUzA4P72eGtmEOY/7PvXpJN/rz8QcxWqLjwWiWeQiIiIujBBEODT1wY+fW1w4XIB1kTEAwB2HE1H4uVCPDXRCzaWXe+huDyDRERERAAAtx7d1N8b6suQfKUYS746jZMXcrrcnW2dIiCpVCqsX78eI0eOhJ+fH55++mlcvcrrqERERC1R/ygTt56WWDorCK495KioqsGXuxPxxa4LKK2olrrEdtMpAtKnn36Kbdu2YcWKFYiIiIBKpcLs2bOhVCqlLo2IiKjDqH+UyaLHA+BgbYbXpwXgwZEukAkCTifl4a2vTuPC5UKpy2wXHT4gKZVKfPXVV5g3bx5CQ0Ph6emJDz74ADk5Odi/f7/U5REREXUogiCo5z/Sk8lw/3AXvPHEINhbm6LoZhXWRMRh28EUKKtrJa5Uuzp8QEpOTkZZWRmCg4PVy+RyOby8vBAdHS1hZURERJ2Di6McS2cGYXSAEwDgYMw1LP82Bpk5NyWuTHs6fEDKyckBADg6OjZYbmdnp15HREREbWNkoIfp4R546SFfWJoZ4np+Gd7+Lga/nrwMlarzDeDu8Lf5V1RUAAAMDQ0bLDcyMkJJSUmrj6uv37bsqKcna/BPaj/svXTYe+mw99Lpar0P8LBFv16W+HpPEmKSbyDySDoS0gsx535v2FqZtGst2ux9hw9IxsZ1czMolUr19wBQVVUFE5PW/YuSyQRYWZlppD65vH0/LPQ39l467L102HvpdKXeW1mZYcnsYByKvooNOxOQcrUYizdG4ZkHfXBPUK92f4abNnrf4QNS/aW1vLw8ODs7q5fn5eXBw8OjVcdUqUQoFOVtqktPTwa53AQKRQVqb52alLSOvZcOey8d9l46Xbn3g/rZ4O3ZQ/DFrgtIuVqMD384iz/jr2HmhP6wMDW8+wHa6Nbem5kZafRMUocPSJ6enjA3N0dUVJQ6ICkUCiQmJmLatGmtPm5NjWY+5LW1Ko0di1qGvZcOey8d9l46XbX3VhZGePVRf/wWlYmdxzIQk3wDqVdLMHNCfwx0tblt+/oJJzV5lkkbwbTDByRDQ0NMmzYNq1evhrW1NZycnPD+++/DwcEB4eHhUpdHRETU6clkAiYG98EAFxts2H0B2QXlWPdTPEYHOOHh0W4wMtADUBeO3tlyBhCARY8HtPuluJbo8AEJAObNm4eamhosXrwYlZWVCAoKwqZNm2BgYCB1aURERF1GbwcLvDUjCNuPXMLBmGs4fCYLiZeL8Mx9XnBxlENZrUJaVt0NVMpqFYwM9SSu+M46RUDS09PDK6+8gldeeUXqUoiIiLo0QwM9PDbGHb6u3bHp10TkFpZj1eZY3De8D8YM6il1ec3WNe5JJCIionbl7WKN5U8NQZCnHWpVInYey8CaiDipy2o2BiQiIiLSCnMTAzz7gDeevs8LJkb6yOhAM28zIBEREZHWCIKAYG8HLJ81GO49LaUup9kYkIiIiEjrbCyN8X8P+UpdRrMxIBEREVG7kOnwbf2NMSARERERNdIpbvMnIiIi3WdoIIPbX+OQDA10+xwNAxIRERG1C0EQsOjxAPX3uowBiYiIiNqNrgejerp9fouIiIhIAgxIRERERI0wIBERERE1woBERERE1AgDEhEREVEjDEhEREREjTAgERERETXCgERERETUCAMSERERUSMMSERERESNMCARERERNcKARERERNQIAxIRERFRI4IoiqLURegaURShUrW9LXp6MtTWqjRQEbUUey8d9l467L102Hvp1PdeJhMgCILGjsuARERERNQIL7ERERERNcKARERERNQIAxIRERFRIwxIRERERI0wIBERERE1woBERERE1AgDEhEREVEjDEhEREREjTAgERERETXCgERERETUCAMSERERUSMMSERERESNMCARERERNcKApAUqlQrr16/HyJEj4efnh6effhpXr16VuqxOp7i4GEuWLEFISAgCAgLw6KOPIiYmRr3+5MmTmDJlCnx9fTFu3Dj8+uuvElbbeWVkZMDf3x87duxQL0tKSsK0adPg5+eHsLAwfPfddxJW2Pns3LkTEyZMgI+PDyZOnIjffvtNve7atWuYM2cOAgICMGLECKxbtw61tbUSVtt51NTU4MMPP8To0aPh7++Pxx9/HHFxcer1/NxrxxdffIHp06c3WHa3Xmvk97BIGvfRRx+JQ4YMEQ8fPiwmJSWJs2bNEsPDw8WqqiqpS+tUZs6cKU6aNEmMjo4W09PTxWXLlokDBw4UL126JKalpYk+Pj7i2rVrxbS0NHHjxo2il5eXeOLECanL7lSUSqU4ZcoU0d3dXYyMjBRFURQLCwvFIUOGiIsWLRLT0tLE7du3iz4+PuL27dslrrZz2Llzp+jl5SVu2bJFzMzMFD/99FPR09NTPHPmjKhUKsXw8HDxmWeeES9evCgeOHBAHDx4sPjhhx9KXXansH79enH48OHisWPHxMuXL4tvvPGGOGjQIDE3N5efey3ZsmWL6OnpKU6bNk29rDm91sTvYQYkDauqqhL9/f3FrVu3qpeVlJSIAwcOFHfv3i1hZZ3L5cuXRXd3dzEmJka9TKVSiWPGjBHXrVsnvvnmm+K//vWvBvssWLBAnDVrVnuX2qmtWbNGfOKJJxoEpM8//1wcMWKEWF1d3WC78PBwqcrsNFQqlTh69Gjx3XffbbB81qxZ4ueffy7u3r1bHDBggFhcXKxeFxERIQYEBPAPNA24//77xXfeeUf9882bN0V3d3dx3759/NxrWE5OjjhnzhzRz89PHDduXIOAdLdea+r3MC+xaVhycjLKysoQHBysXiaXy+Hl5YXo6GgJK+tcrKyssGHDBvj4+KiXCYIAQRCgUCgQExPT4N8BAAwdOhSxsbEQRbG9y+2UoqOj8cMPP+Ddd99tsDwmJgaDBw+Gvr6+etnQoUNx+fJl5Ofnt3eZnUpGRgaysrJw3333NVi+adMmzJkzBzExMfD29oalpaV63dChQ1FaWoqkpKT2LrfTsbGxweHDh3Ht2jXU1tbihx9+gKGhITw9Pfm517ALFy7AwMAAu3btgq+vb4N1d+u1pn4PMyBpWE5ODgDA0dGxwXI7Ozv1Omo7uVyOUaNGwdDQUL1s3759yMzMxMiRI5GTkwMHB4cG+9jZ2aGiogJFRUXtXW6no1Ao8Oqrr2Lx4sW3fdbv1HsAyM7ObrcaO6OMjAwAQHl5OZ566ikEBwfjoYcewu+//w6Avde2N954AwYGBrjnnnvg4+ODDz74AOvXr4ezszN7r2FhYWH46KOP0KtXr9vW3a3Xmvo9zICkYRUVFQDQ4Bc3ABgZGaGqqkqKkrqEM2fOYNGiRQgPD0doaCgqKytv+3dQ/7NSqZSixE5l6dKl8Pf3v+1MBoAme29kZAQA/G+gjUpLSwEAr732GiZNmoSvvvoKw4cPx/PPP4+TJ0+y91qWlpYGCwsLfPLJJ/jhhx8wZcoULFy4EElJSex9O7pbrzX1e1j/7ptQSxgbGwOo+yVc/z1Q9y/NxMREqrI6tYMHD2LhwoUICAjA6tWrAdT9h9A4CNX/zH8PbbNz507ExMRg9+7dTa43Nja+rff1/1MyNTXVen2dmYGBAQDgqaeewuTJkwEA/fv3R2JiIr7++mv2Xouys7Px8ssv45tvvkFgYCAAwMfHB2lpafjoo4/Y+3Z0t15r6vcwzyBpWP0pvby8vAbL8/LyYG9vL0VJndqWLVvw4osvYvTo0fj888/Vf0U4Ojo2+e/A1NQUFhYWUpTaaURGRqKgoAChoaHw9/eHv78/AOCtt97C7Nmz4eDg0GTvAfC/gTaq75+7u3uD5W5ubrh27Rp7r0Xx8fGorq5uMO4RAHx9fZGZmcnet6O79VpTv4cZkDTM09MT5ubmiIqKUi9TKBRITExEUFCQhJV1Ptu2bcOKFSvw+OOPY+3atQ1OpwYGBuL06dMNtj916hQCAgIgk/Fj3xarV6/Gnj17sHPnTvUXAMybNw8rV65EUFAQYmNjG8y9c+rUKbi4uMDGxkaiqjsHb29vmJmZIT4+vsHylJQUODs7IygoCImJiepLcUBd783MzODp6dne5XYq9WNeLl682GB5SkoK+vTpw899O7pbrzX2e7jtN+NRY2vXrhUHDx4sHjx4sMH8C0qlUurSOo309HTR29tbnDt3rpiXl9fgS6FQiCkpKaK3t7f4/vvvi2lpaeKmTZs4D5IW3Xqbf35+vhgUFCS+9tprYmpqqhgZGSn6+PiIO3bskLjKzuGTTz4R/f39xd27dzeYB+nUqVNiZWWlOGbMGPGpp54Sk5KS1PMgffTRR1KX3eHV1taKjz76qDhu3Djx5MmTYkZGhvjBBx+I/fv3F+Pi4vi516LXXnutwW3+zem1Jn4PMyBpQU1Njfjee++JQ4cOFf38/MSnn35avHr1qtRldSqfffaZ6O7u3uTXa6+9JoqiKB45ckScNGmSOGDAAHHcuHHir7/+KnHVndetAUkURTE+Pl58+OGHxQEDBoijR48WN2/eLGF1nc9XX30lhoWFid7e3uL9998vHjhwQL3u8uXL4syZM0UfHx9xxIgR4rp168Ta2loJq+08iouLxaVLl4qhoaGiv7+/+Mgjj4hRUVHq9fzca0fjgCSKd++1Jn4PC6LISWGIiIiIbsXBGERERESNMCARERERNcKARERERNQIAxIRERFRIwxIRERERI0wIBERERE1woBERERE1AgDEhG1yPTp0+Hh4YF///vfd9xm/vz58PDwwOuvv97k+n//+9/w8PDAvn37/vE1bv0aMGAAQkNDsWzZMpSUlKi33bFjBzw8PHDt2jX1ssb7Nv6qf6jxP7l8+TI8PDwwZMiQ2x6MeafXvZvW7ENE0tCXugAi6nhkMhni4uKQk5OjfkZVvfLychw+fPiO+6anp+Ps2bNwd3dHREQExo4d2+R2Xl5eeOutt9Q/V1dX48KFC1i7di2SkpLw/fffQxCEO77Ov/71Lzz00ENNrmvOAysjIyPh6uqKzMxM7N27F/fff/9d9yGizoMBiYhazMvLC2lpadi7dy9mzJjRYN3hw4dhYmICuVze5L47duyAk5MT5syZg4ULFyIzMxO9e/e+bTtzc3P4+fk1WBYUFISysjKsX78e8fHxt62/lYODwz+u/ye1tbXYuXMnHnnkEZw9exYREREMSERdDC+xEVGLmZqaYtSoUdi7d+9t6/bs2YOxY8dCX//2v7/qg8fo0aMxZswYmJqa4ocffmjRaw8YMAAAcP369dYV3wzHjx9HXl4eQkNDcf/99yM2NhZpaWn/uM/rr7+O6dOnY/v27Rg9ejT8/f3x5JNPIjk5+bZt4+Pj8e9//xs+Pj4IDQ3Fxo0bG6y/du0aXn31VYwYMQLe3t4IDg7Gq6++iqKiIo2+TyK6MwYkImqVCRMmqC+z1SstLcXRo0cxadKkJvc5evQobty4gQcffBDGxsYYP348fv755ybH+NxJRkYGAKBXr17/uJ1KpUJNTU2TX3cTGRmJfv36YcCAAQgPD4eZmRkiIiLuul9SUhI++OADvPDCC3j//fdRVFSEadOmIS8vr8F2S5cuxcSJE7Fhwwb4+/vj/fffV1+WrKiowBNPPIFLly7hrbfewqZNm/DEE0/g119/xQcffHDXGohIMxiQiKhVQkNDYWJi0uAs0oEDB2BjY4NBgwY1uc+OHTvg7u4OHx8fAMCUKVNQWFjY5GBtURQbhJqCggL89ttv+Oyzz+Dv768+k3Qnn376Kby9vZv8KiwsvON+RUVF+P333zFlyhQAgImJCSZMmIBffvkFFRUV//iaN2/exIcffoipU6dizJgx2LhxI5RKJb777rsG2y1YsADTp09HcHAwVq1aBQMDA5w6dQpA3eBwBwcHrFu3Dvfeey+GDh2K5557DiNHjsTp06f/8fWJSHM4BomIWsXY2BhhYWENxiH9+uuvGD9+fJODpwsLC3H48GE8++yzUCgUAIB+/frByckJP/zwA+67774G20dHR8Pb27vBMplMhmHDhmH58uX/OEAbAB5++GE8/PDDTa670/goANi1axdqa2sRGhqqrvPee+/FTz/9hD179mDq1Kl33Ldnz54IDAxU/2xnZwd/f39ER0c32O7WbUxMTNC9e3f1a/Xv3x/btm2DSqXC5cuXkZmZibS0NKSnpzfr7BcRaQYDEhG12vjx4/HCCy8gJycHRkZGOHnyJF566aUmt921axeqq6vx0Ucf4aOPPmqwLisrC5cuXYKrq6t6mbe3N5YtWwYAEAQBRkZGcHR0hLm5ebNqs7OzU5+paokdO3ZApVJh/Pjxt62LiIj4x4DU1N1xNjY2uHDhQoNlJiYmDX6WyWQQRVH989dff43PP/8cxcXF6N69OwYMGAATExPcvHmzpW+HiFqJAYmIWi0kJARmZmbYu3cvTE1N0bNnzzte+oqMjIS/vz/mz5/fYHl5eTmef/55fP/991i8eLF6uZmZWasCTltcuHABycnJmDdvXoOzPEDd5cPNmzcjKSkJ/fv3b3L/pgZR5+fnw8bGptk17N69G++++y5eeeUVTJkyBdbW1gCA//u//0NCQkIL3g0RtQUDEhG1mqGhIcaMGYN9+/bB2NgYEydObHK7hIQEpKSkYMWKFRgyZMht64cOHYpffvkFCxcuhLGxsbbLvqPIyEgYGRnhySefvO1MlbOzM7Zu3Yrvv/8ey5cvb3L/y5cvNzgTlpubi7Nnz+KZZ55pdg2xsbGQy+WYPXu2ellZWRliY2ObvDOQiLSDg7SJqE0mTJiAs2fPIioq6o4BKTIyEgYGBggPD29y/QMPPACFQoE9e/ZorK6cnBzExcU1+XXx4sXbtlcqlfjf//6H0NDQJi/jOTo6YvDgwdi9ezdKS0ubfE1RFPHss89iz5492LdvH2bPng1LS0tMnz692XUPHDgQCoUC7777LqKiorB79248/vjjyM/Pv+sgcSLSHP45QkRtMmzYMMjlcjg6OjYYQ1SvqqoKv/76K4YPH45u3bo1eYzw8HAsW7YMERER6rvH2mr79u3Yvn17k+s8PT3xyy+/NFh28OBBlJSUYMKECXc85oMPPohTp05h9+7dMDIyum19jx49MGvWLKxatQoVFRUYNmwYPvvsszu+76ZMnjwZ165dQ2RkJLZt2wZ7e3uMGjUKjz32GN58883bxmoRkXYI4q0jA4mIqFVef/11nD59Gr///rvUpRCRBvASGxEREVEjDEhEREREjfASGxEREVEjPINERERE1AgDEhEREVEjDEhEREREjTAgERERETXCgERERETUCAMSERERUSMMSERERESNMCARERERNcKARERERNTI/wMxsBc/JMraOgAAAABJRU5ErkJggg==", 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", 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    " ] @@ -3917,13 +3945,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:07:22,799] A new study created in memory with name: my_study\n", - "[I 2024-07-02 17:07:22,837] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", + "[I 2024-07-09 11:28:52,778] A new study created in memory with name: my_study\n", + "[I 2024-07-09 11:28:52,831] A new study created in memory with name: study_name_0\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:682)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", " warnings.warn(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", " warnings.warn(\n", - "[I 2024-07-02 17:07:23,954] Trial 0 finished with value: -0.33771030427395493 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0051470093492099, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.33771030427395493.\n" + "[I 2024-07-09 11:28:54,439] Trial 0 finished with value: -0.3385354804881733 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7165411263860415, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.3385354804881733.\n" ] } ], @@ -4007,35 +4049,35 @@ " \n", " \n", " 2227\n", - " 2.042239e+01\n", + " 2.042016e+01\n", " UnscaledPhyschemDescriptors\n", " 7.0\n", " MolWt\n", " \n", " \n", " 2229\n", - " 2.025405e+01\n", + " 2.025192e+01\n", " UnscaledPhyschemDescriptors\n", " 9.0\n", " ExactMolWt\n", " \n", " \n", " 2228\n", - " 1.802308e+01\n", + " 1.802156e+01\n", " UnscaledPhyschemDescriptors\n", " 8.0\n", " HeavyAtomMolWt\n", " \n", " \n", " 2267\n", - " 2.386346e+00\n", + " 2.387309e+00\n", " UnscaledPhyschemDescriptors\n", " 47.0\n", " LabuteASA\n", " \n", " \n", " 2230\n", - " 2.106611e+00\n", + " 2.106656e+00\n", " UnscaledPhyschemDescriptors\n", " 10.0\n", " NumValenceElectrons\n", @@ -4048,39 +4090,39 @@ " ...\n", " \n", " \n", - " 1189\n", - " 3.564139e-07\n", + " 1784\n", + " 4.610784e-07\n", + " ECFP\n", + " 1785.0\n", + " c1(OC)c(OC)ccc(C)c1\n", + " \n", + " \n", + " 583\n", + " 4.610784e-07\n", " ECFP\n", - " 1190.0\n", - " C1=C(C(=O)N)N(C)SN=C1c(c)c\n", + " 584.0\n", + " C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1\n", " \n", " \n", " 995\n", - " 3.564139e-07\n", + " 4.610784e-07\n", " ECFP\n", " 996.0\n", " C(C(N)=C)(=O)N(C)C\n", " \n", " \n", " 845\n", - " 3.564139e-07\n", + " 4.610784e-07\n", " ECFP\n", " 846.0\n", " c(c(c)C)c(O)c\n", " \n", " \n", - " 583\n", - " 3.564139e-07\n", - " ECFP\n", - " 584.0\n", - " C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1\n", - " \n", - " \n", - " 334\n", - " 3.564139e-07\n", + " 1375\n", + " 4.610784e-07\n", " ECFP\n", - " 335.0\n", - " N(C)(C)C\n", + " 1376.0\n", + " S1(=O)(=O)N=C(c)C=C(C)N1C\n", " \n", " \n", "\n", @@ -4089,17 +4131,17 @@ ], "text/plain": [ " shap_value descriptor bit \\\n", - "2227 2.042239e+01 UnscaledPhyschemDescriptors 7.0 \n", - "2229 2.025405e+01 UnscaledPhyschemDescriptors 9.0 \n", - "2228 1.802308e+01 UnscaledPhyschemDescriptors 8.0 \n", - "2267 2.386346e+00 UnscaledPhyschemDescriptors 47.0 \n", - "2230 2.106611e+00 UnscaledPhyschemDescriptors 10.0 \n", + "2227 2.042016e+01 UnscaledPhyschemDescriptors 7.0 \n", + "2229 2.025192e+01 UnscaledPhyschemDescriptors 9.0 \n", + "2228 1.802156e+01 UnscaledPhyschemDescriptors 8.0 \n", + "2267 2.387309e+00 UnscaledPhyschemDescriptors 47.0 \n", + "2230 2.106656e+00 UnscaledPhyschemDescriptors 10.0 \n", "... ... ... ... \n", - "1189 3.564139e-07 ECFP 1190.0 \n", - "995 3.564139e-07 ECFP 996.0 \n", - "845 3.564139e-07 ECFP 846.0 \n", - "583 3.564139e-07 ECFP 584.0 \n", - "334 3.564139e-07 ECFP 335.0 \n", + "1784 4.610784e-07 ECFP 1785.0 \n", + "583 4.610784e-07 ECFP 584.0 \n", + "995 4.610784e-07 ECFP 996.0 \n", + "845 4.610784e-07 ECFP 846.0 \n", + "1375 4.610784e-07 ECFP 1376.0 \n", "\n", " info \n", "2227 MolWt \n", @@ -4108,11 +4150,11 @@ "2267 LabuteASA \n", "2230 NumValenceElectrons \n", "... ... \n", - "1189 C1=C(C(=O)N)N(C)SN=C1c(c)c \n", + "1784 c1(OC)c(OC)ccc(C)c1 \n", + "583 C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1 \n", "995 C(C(N)=C)(=O)N(C)C \n", "845 c(c(c)C)c(O)c \n", - "583 C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1 \n", - "334 N(C)(C)C \n", + "1375 S1(=O)(=O)N=C(c)C=C(C)N1C \n", "\n", "[1570 rows x 4 columns]" ] @@ -4164,17 +4206,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:07:28,219] A new study created in memory with name: my_study\n", - "[I 2024-07-02 17:07:28,415] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:29:02,748] A new study created in memory with name: my_study\n", + "[I 2024-07-09 11:29:02,829] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:829)\n", " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:829)\n", " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "[I 2024-07-02 17:08:13,378] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", - " \r" + "[I 2024-07-09 11:29:52,067] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", + " \r" ] } ], @@ -4203,25 +4243,6 @@ " chemprop = pickle.load(f)" ] }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "build_best(buildconfig_best(study), \"../target/best.pkl\")\n", - "with open(\"../target/best.pkl\", \"rb\") as f:\n", - " chemprop = pickle.load(f)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -4231,185 +4252,187 @@ }, { "cell_type": "code", - "execution_count": 73, - "metadata": {}, + "execution_count": 72, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[17:10:22] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n", - "[17:10:22] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[17:10:22] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[17:10:22] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[17:10:22] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 5 6 7\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 6 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 9 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 9 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 7 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 10 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 8 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 9 10 11 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n" + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 5 6 7\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 6 7 8\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:02] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 9 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 9 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 7 8 9\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 8 9 10 12 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 4 5 8 9 10\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 10 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 9 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 10 11 12 15 16\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 9 10 11 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[17:10:24] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[17:10:24] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", - "[17:10:24] Can't kekulize mol. Unkekulized atoms: 2 3 4 15 16\n", - "[17:10:24] Can't kekulize mol. Unkekulized atoms: 2 3 4 14 15\n", - "[17:10:24] Can't kekulize mol. Unkekulized atoms: 11 12 13 15 16\n" + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 8 9 10 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 9 12 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 9 10 11 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 10 11 12 15 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 9 10 11 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 15 16\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 2 3 4 14 15\n", + "[11:31:03] Can't kekulize mol. Unkekulized atoms: 11 12 13 15 16\n" ] }, { @@ -4495,7 +4518,7 @@ "0 n1c([CH2:1]N[CH3:1])[cH:1][cH:1][cH:1][n:1]1 387.854 " ] }, - "execution_count": 73, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -4533,7 +4556,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 73, "metadata": { "scrolled": true }, @@ -4542,41 +4565,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:19,102] A new study created in memory with name: transform_example\n", - "[I 2024-07-03 14:48:19,106] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "[I 2024-07-03 14:48:19,801] Trial 0 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-03 14:48:20,050] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-03 14:48:20,174] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-03 14:48:20,264] Trial 3 finished with value: -1.2487063317112765 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-03 14:48:20,353] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-03 14:48:20,430] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,516] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,632] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,770] Trial 8 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,800] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,830] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,857] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,886] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:20,914] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-03 14:48:21,018] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,154] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,220] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,251] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n" + "[I 2024-07-09 11:31:07,022] A new study created in memory with name: transform_example\n", + "[I 2024-07-09 11:31:07,066] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:31:07,377] Trial 0 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-09 11:31:07,441] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-09 11:31:07,486] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-09 11:31:07,541] Trial 3 finished with value: -1.2487063317112765 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-09 11:31:07,558] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-09 11:31:07,692] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:07,821] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:07,839] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:07,987] Trial 8 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:08,004] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:08,019] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:08,036] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:08,054] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:08,069] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-09 11:31:08,086] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,151] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,169] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,186] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,241] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:21,314] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,343] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,371] Trial 20 finished with value: -1.218395226466336 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,403] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,419] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:21,448] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,589] Trial 24 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n" + "[I 2024-07-09 11:31:08,259] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,277] Trial 20 finished with value: -1.218395226466336 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,296] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,300] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:08,318] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,384] Trial 24 finished with value: -0.7803723847416694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,404] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-09 11:31:08,423] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n", + "[I 2024-07-09 11:31:08,441] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n" ] }, { @@ -4590,21 +4614,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:21,621] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-03 14:48:21,650] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n", - "[I 2024-07-03 14:48:21,679] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:21,815] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:21,847] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:21,915] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:21,947] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:21,979] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:22,011] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-03 14:48:22,027] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:22,057] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,088] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,107] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:22,137] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,203] Trial 39 finished with value: -0.40359637945134746 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n" + "[I 2024-07-09 11:31:08,604] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-09 11:31:08,622] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-09 11:31:08,692] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-09 11:31:08,711] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-09 11:31:08,730] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-09 11:31:08,749] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-09 11:31:08,755] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:08,775] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:08,794] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:08,799] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:08,818] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:08,886] Trial 39 finished with value: -0.40359637945134735 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n" ] }, { @@ -4619,10 +4640,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:22,341] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,360] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:22,447] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,479] Trial 43 finished with value: -0.9246005133276612 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n" + "[I 2024-07-09 11:31:08,962] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:08,968] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:09,026] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,045] Trial 43 finished with value: -0.9246005133276612 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,116] Trial 44 finished with value: -0.8908739215746116 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,136] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,156] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n" ] }, { @@ -4636,74 +4660,71 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:22,615] Trial 44 finished with value: -0.8908739215746116 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,648] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,680] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,713] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,743] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,774] Trial 49 finished with value: -0.5696273642213351 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,811] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,848] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,884] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,919] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,953] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:22,989] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-03 14:48:23,025] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-03 14:48:23,060] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-03 14:48:23,096] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-03 14:48:23,133] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-03 14:48:23,171] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-03 14:48:23,209] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-03 14:48:23,245] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-03 14:48:23,283] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-03 14:48:23,321] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n" + "[I 2024-07-09 11:31:09,176] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,195] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,214] Trial 49 finished with value: -0.5696273642213351 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,239] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,263] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,288] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,312] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,337] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,360] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-09 11:31:09,383] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-09 11:31:09,406] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-09 11:31:09,430] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-09 11:31:09,453] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-09 11:31:09,477] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,501] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,524] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,548] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,575] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,601] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,626] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-09 11:31:09,651] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:23,357] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-03 14:48:23,392] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-03 14:48:23,427] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-03 14:48:23,466] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-03 14:48:23,502] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-03 14:48:23,538] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-03 14:48:23,575] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,612] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,650] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,685] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,722] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,760] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,797] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,834] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,873] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,908] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,946] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:23,984] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,021] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,059] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,098] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n" + "[I 2024-07-09 11:31:09,676] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-09 11:31:09,701] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-09 11:31:09,726] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-09 11:31:09,751] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,777] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,802] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,828] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,853] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,878] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,903] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,926] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:09,951] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,001] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,042] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,083] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,135] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,209] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,280] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,334] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,382] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,430] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:24,136] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,174] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,214] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,254] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,291] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,329] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,582] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,620] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,662] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,701] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-03 14:48:24,741] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-03 14:48:24,779] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-03 14:48:24,821] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-03 14:48:24,861] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n" + "[I 2024-07-09 11:31:10,485] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,526] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,583] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,610] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,639] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,668] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,698] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-09 11:31:10,732] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-09 11:31:10,766] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-09 11:31:10,802] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-09 11:31:10,832] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n" ] } ], @@ -4762,52 +4783,67 @@ }, { "cell_type": "code", - "execution_count": 159, - "metadata": { - "collapsed": true - }, + "execution_count": 74, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:27,098] A new study created in memory with name: non-transform_example\n", - "[I 2024-07-03 14:48:27,100] A new study created in memory with name: study_name_0\n", - "[I 2024-07-03 14:48:27,189] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", - "[I 2024-07-03 14:48:27,278] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", - "[I 2024-07-03 14:48:27,320] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,361] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,392] Trial 4 finished with value: -3543.953608539901 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,447] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,488] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,528] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,604] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+02, tolerance: 2.760e+01\n", + "[I 2024-07-09 11:31:14,315] A new study created in memory with name: non-transform_example\n", + "[I 2024-07-09 11:31:14,317] A new study created in memory with name: study_name_0\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "[I 2024-07-09 11:31:14,410] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", + "[I 2024-07-09 11:31:14,469] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", + "[I 2024-07-09 11:31:14,512] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,552] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,569] Trial 4 finished with value: -3543.953608539901 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,589] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,607] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,634] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,689] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+02, tolerance: 2.760e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-03 14:48:27,656] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,685] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-03 14:48:27,716] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:27,745] Trial 12 finished with value: -3630.72768093756 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:27,775] Trial 13 finished with value: -3431.942816967268 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:27,807] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:27,885] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:27,915] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:27,945] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n" + "[I 2024-07-09 11:31:14,731] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:28,021] Trial 18 finished with value: -8873.66926266963 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:28,063] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:28,094] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-03 14:48:28,126] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-03 14:48:28,142] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:28,186] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-03 14:48:28,262] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-03 14:48:28,291] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,319] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-09 11:31:14,748] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-09 11:31:14,765] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,781] Trial 12 finished with value: -3630.72768093756 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,799] Trial 13 finished with value: -3431.942816967268 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,816] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,883] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,900] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,917] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:14,972] Trial 18 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:15,001] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:15,017] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-09 11:31:15,035] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-09 11:31:15,039] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:15,066] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-09 11:31:15,131] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-09 11:31:15,150] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,168] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,186] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { @@ -4821,19 +4857,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:28,350] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,426] Trial 28 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,456] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,536] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,566] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,610] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,639] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,656] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:28,686] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,718] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,737] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:28,770] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,850] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-09 11:31:15,247] Trial 28 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,263] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,330] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,349] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,378] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,397] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,401] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:15,417] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,435] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,440] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:15,459] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,523] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,589] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,594] Trial 41 pruned. Duplicate parameter set\n" ] }, { @@ -4841,98 +4878,90 @@ "output_type": "stream", "text": [ "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-3630.72768093756]\n", - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-9958.573006910125]\n" + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-9958.573006910125]\n", + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-9964.541364058234]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:28,931] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:28,949] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:29,027] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,060] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,138] Trial 44 finished with value: -2270.5407991891466 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-9964.541364058234]\n" + "[I 2024-07-09 11:31:15,649] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,668] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,733] Trial 44 finished with value: -2270.5407991891466 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,753] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,772] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,791] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,810] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,830] Trial 49 finished with value: -3668.255064135424 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,856] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,883] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,910] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,935] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,960] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:15,984] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,010] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,035] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,059] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,085] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,109] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:29,171] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,204] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,238] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,272] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,304] Trial 49 finished with value: -3668.255064135424 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,342] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,382] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,432] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,471] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,512] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,551] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,588] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,627] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,664] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,703] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,744] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,784] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,824] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,863] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-09 11:31:16,136] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,162] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,188] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,215] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,242] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,268] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,293] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-09 11:31:16,319] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n", + "[I 2024-07-09 11:31:16,347] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n", + "[I 2024-07-09 11:31:16,374] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n", + "[I 2024-07-09 11:31:16,399] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n", + "[I 2024-07-09 11:31:16,425] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-09 11:31:16,452] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-09 11:31:16,480] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-09 11:31:16,507] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-09 11:31:16,533] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", + "[I 2024-07-09 11:31:16,561] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", + "[I 2024-07-09 11:31:16,589] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", + "[I 2024-07-09 11:31:16,614] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:29,902] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,941] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:29,981] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:30,019] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-03 14:48:30,058] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n", - "[I 2024-07-03 14:48:30,099] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n", - "[I 2024-07-03 14:48:30,152] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n", - "[I 2024-07-03 14:48:30,193] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n", - "[I 2024-07-03 14:48:30,236] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-03 14:48:30,276] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-03 14:48:30,318] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-03 14:48:30,357] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-03 14:48:30,399] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", - "[I 2024-07-03 14:48:30,442] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", - "[I 2024-07-03 14:48:30,481] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", - "[I 2024-07-03 14:48:30,524] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", - "[I 2024-07-03 14:48:30,565] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,606] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,647] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n" + "[I 2024-07-09 11:31:16,641] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,669] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,695] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,722] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,750] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,779] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,811] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,841] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,871] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-09 11:31:16,901] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-09 11:31:16,931] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-09 11:31:16,960] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-09 11:31:16,989] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-09 11:31:17,018] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-09 11:31:17,046] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-09 11:31:17,076] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-09 11:31:17,108] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-09 11:31:17,136] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-09 11:31:17,166] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:30,686] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,726] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,767] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,809] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,851] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,895] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-03 14:48:30,939] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-03 14:48:30,984] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-03 14:48:31,029] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-03 14:48:31,075] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-03 14:48:31,121] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-03 14:48:31,163] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-03 14:48:31,209] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-03 14:48:31,252] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-03 14:48:31,291] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-03 14:48:31,334] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-03 14:48:31,379] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" + "[I 2024-07-09 11:31:17,195] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" ] } ], @@ -4985,22 +5014,22 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 160, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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    " ] @@ -5034,7 +5063,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -5043,7 +5072,7 @@ "array([1126.56968721, 120.20237903])" ] }, - "execution_count": 162, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -5071,7 +5100,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -5080,7 +5109,7 @@ "array([2.94824194, 3.92008694])" ] }, - "execution_count": 163, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -5100,7 +5129,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 78, "metadata": { "scrolled": true }, @@ -5109,42 +5138,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:56,090] A new study created in memory with name: ptr_and_transform_example\n", - "[I 2024-07-03 14:48:56,129] A new study created in memory with name: study_name_0\n", - "[I 2024-07-03 14:48:56,233] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-03 14:48:56,312] Trial 1 finished with value: -0.002490897902963267 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-03 14:48:56,353] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-03 14:48:56,395] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-03 14:48:56,424] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-03 14:48:56,466] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,509] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,540] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,616] Trial 8 finished with value: -0.0029994624596888677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,646] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,674] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,704] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,733] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,760] Trial 13 finished with value: -0.002869169486971014 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-03 14:48:56,789] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:56,864] Trial 15 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:56,895] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:56,923] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:56,999] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n" + "[I 2024-07-09 11:31:21,722] A new study created in memory with name: ptr_and_transform_example\n", + "[I 2024-07-09 11:31:21,762] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:31:21,874] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-09 11:31:21,944] Trial 1 finished with value: -0.0024908979029632677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-09 11:31:21,986] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-09 11:31:22,029] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-09 11:31:22,045] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-09 11:31:22,064] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,082] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,099] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,152] Trial 8 finished with value: -0.0029994624596888677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,169] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,186] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,203] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,219] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,235] Trial 13 finished with value: -0.002869169486971014 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-09 11:31:22,252] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,316] Trial 15 finished with value: -0.005505338042993083 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,333] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,349] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,412] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:57,030] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:57,061] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:57,088] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:57,103] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:57,131] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:57,211] Trial 24 finished with value: -0.0029994624596888664 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:57,240] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-03 14:48:57,271] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n", - "[I 2024-07-03 14:48:57,299] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n" + "[I 2024-07-09 11:31:22,429] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,446] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,464] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,468] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:22,485] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,551] Trial 24 finished with value: -0.0029994624596888677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,568] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-09 11:31:22,586] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n", + "[I 2024-07-09 11:31:22,604] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n" ] }, { @@ -5158,18 +5187,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:57,367] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-03 14:48:57,398] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-03 14:48:57,476] Trial 30 finished with value: -0.005505338042993084 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-03 14:48:57,507] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-03 14:48:57,537] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-03 14:48:57,569] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-03 14:48:57,585] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:57,617] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:57,648] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:57,668] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:57,700] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:57,780] Trial 39 finished with value: -0.0015694919308122952 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" + "[I 2024-07-09 11:31:22,673] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-09 11:31:22,694] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-09 11:31:22,765] Trial 30 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-09 11:31:22,784] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-09 11:31:22,806] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-09 11:31:22,827] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-09 11:31:22,834] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:22,854] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:22,874] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:22,880] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:31:22,901] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:22,959] Trial 39 finished with value: -0.0015694919308122948 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,025] Trial 40 finished with value: -0.0019757694194001384 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,031] Trial 41 pruned. Duplicate parameter set\n" ] }, { @@ -5177,24 +5208,7 @@ "output_type": "stream", "text": [ "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-0.0021653695362066753]\n", - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.004846526544994462]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2024-07-03 14:48:57,859] Trial 40 finished with value: -0.0019757694194001397 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:57,876] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-03 14:48:57,954] Trial 42 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:57,986] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,066] Trial 44 finished with value: -0.003412828259848677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.004846526544994462]\n", "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.00496231674623194]\n" ] }, @@ -5202,73 +5216,76 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:58,098] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,131] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,160] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,193] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,224] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,262] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,307] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,345] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,381] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,418] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,455] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-03 14:48:58,492] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-03 14:48:58,530] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-03 14:48:58,564] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-03 14:48:58,602] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-03 14:48:58,639] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-03 14:48:58,675] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-03 14:48:58,710] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-03 14:48:58,747] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-03 14:48:58,782] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-03 14:48:58,818] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n" + "[I 2024-07-09 11:31:23,096] Trial 42 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,116] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,186] Trial 44 finished with value: -0.003412828259848677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,206] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,225] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,245] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,263] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,282] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,305] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,327] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,351] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,375] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,399] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,423] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-09 11:31:23,447] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-09 11:31:23,471] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-09 11:31:23,493] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-09 11:31:23,517] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-09 11:31:23,541] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-09 11:31:23,564] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:58,854] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-03 14:48:58,892] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-03 14:48:58,928] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-03 14:48:58,964] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-03 14:48:59,000] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-03 14:48:59,035] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,072] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,108] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,147] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,184] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,221] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,262] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,300] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,335] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,373] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,412] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,449] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,486] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,525] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,567] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,609] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n" + "[I 2024-07-09 11:31:23,590] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-09 11:31:23,616] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-09 11:31:23,641] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-09 11:31:23,665] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-09 11:31:23,691] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-09 11:31:23,714] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-09 11:31:23,739] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-09 11:31:23,764] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-09 11:31:23,790] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-09 11:31:23,815] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,840] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,865] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,890] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,915] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,940] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,965] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:23,991] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,016] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,041] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,196] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,224] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-03 14:48:59,647] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,685] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,724] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,766] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,804] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,842] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,883] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,923] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:48:59,963] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-03 14:49:00,002] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-03 14:49:00,042] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-03 14:49:00,085] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-03 14:49:00,126] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n" + "[I 2024-07-09 11:31:24,253] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,280] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,306] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,335] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,361] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,388] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,416] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,446] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,474] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,500] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,526] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,554] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,578] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-09 11:31:24,604] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-09 11:31:24,630] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-09 11:31:24,659] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-09 11:31:24,685] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n" ] } ], @@ -5325,7 +5342,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5352,21 +5369,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similar to log scaled models trained without the PRF transform, log-transformed models trained with PTR functions will reverse the probabilistic class membership likelihoods from the PTR function, and then further log reverse transform the log scaling applied to the original data: " + "Similar to log scaled models trained without the PRF transform, log-transformed models trained with PTR functions will reverse both the probabilistic class membership likelihoods from the PTR function and reverse the subsequent log transform from any log scaling, scaling predictions back inline with original data: " ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1219.10660868])" + "array([0.3506154])" ] }, - "execution_count": 175, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -5380,14 +5397,7 @@ "import pickle\n", "with open(\"../target/best.pkl\", \"rb\") as f:\n", " model = pickle.load(f)\n", - "model.predict_from_smiles([\"CCC\"]) == model.predict_from_smiles([\"CCC\"], transform=None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows " + "model.predict_from_smiles([\"CCC\"], transform=None)" ] }, { @@ -5415,7 +5425,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 81, "metadata": { "scrolled": true }, @@ -5424,18 +5434,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:10:49,185] A new study created in memory with name: covariate_example\n", - "[I 2024-07-02 17:10:49,226] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 17:10:49,349] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n", - "[I 2024-07-02 17:10:49,448] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n", - "[I 2024-07-02 17:10:49,504] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n", - "[I 2024-07-02 17:10:49,556] Trial 3 finished with value: -3803.9324375833753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833753.\n", - "[I 2024-07-02 17:10:49,586] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n", - "[I 2024-07-02 17:10:49,640] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n", - "[I 2024-07-02 17:10:49,689] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n", - "[I 2024-07-02 17:10:49,732] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n", - "[I 2024-07-02 17:10:49,822] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n", - "[I 2024-07-02 17:10:49,890] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n" + "[I 2024-07-09 11:31:27,376] A new study created in memory with name: covariate_example\n", + "[I 2024-07-09 11:31:27,417] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:31:27,540] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n", + "[I 2024-07-09 11:31:27,607] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n", + "[I 2024-07-09 11:31:27,662] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n", + "[I 2024-07-09 11:31:27,714] Trial 3 finished with value: -3803.9324375833753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833753.\n", + "[I 2024-07-09 11:31:27,730] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n", + "[I 2024-07-09 11:31:27,751] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n", + "[I 2024-07-09 11:31:27,771] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n", + "[I 2024-07-09 11:31:27,800] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n", + "[I 2024-07-09 11:31:27,863] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n", + "[I 2024-07-09 11:31:27,903] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n" ] } ], @@ -5484,7 +5494,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -5493,7 +5503,7 @@ "array([52.45281013, 52.45281013])" ] }, - "execution_count": 83, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -5539,7 +5549,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 83, "metadata": { "scrolled": true }, @@ -5548,22 +5558,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:10:50,166] A new study created in memory with name: vector_aux_example\n", - "[I 2024-07-02 17:10:50,207] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 17:10:50,282] Trial 0 finished with value: -2200.6817959410578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-02 17:10:50,337] Trial 1 finished with value: -2200.95660880078 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-02 17:10:50,397] Trial 2 finished with value: -5798.564494725643 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-02 17:10:50,441] Trial 3 finished with value: -972.2899178898048 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8916194399474267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -972.2899178898048.\n", - "[I 2024-07-02 17:10:50,500] Trial 4 finished with value: -647.3336440433073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 17:10:50,552] Trial 5 finished with value: -653.3036472748931 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 17:10:50,584] Trial 6 finished with value: -3807.8035919667395 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.986e+01, tolerance: 1.914e+01\n", + "[I 2024-07-09 11:31:28,198] A new study created in memory with name: vector_aux_example\n", + "[I 2024-07-09 11:31:28,241] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:31:28,308] Trial 0 finished with value: -2200.6817959410578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-09 11:31:28,330] Trial 1 finished with value: -2200.95660880078 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-09 11:31:28,380] Trial 2 finished with value: -5798.564494725643 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-09 11:31:28,437] Trial 3 finished with value: -972.2899178898048 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8916194399474267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -972.2899178898048.\n", + "[I 2024-07-09 11:31:28,473] Trial 4 finished with value: -647.3336440433073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-09 11:31:28,505] Trial 5 finished with value: -653.3036472748931 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-09 11:31:28,524] Trial 6 finished with value: -3807.8035919667395 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.986e+01, tolerance: 1.914e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.901e+01, tolerance: 1.892e+01\n", + "/Users/kljk345/Library/Caches/pypoetry/virtualenvs/qsartuna-9ZyW8GtC-py3.10/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.901e+01, tolerance: 1.892e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 17:10:50,664] Trial 7 finished with value: -5019.459500770764 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1376436589359351, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 17:10:50,748] Trial 8 finished with value: -2756.4017711284796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 17:10:50,794] Trial 9 finished with value: -771.797115414836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.74340620175102, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n" + "[I 2024-07-09 11:31:28,603] Trial 7 finished with value: -5019.459500770764 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1376436589359351, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-09 11:31:28,674] Trial 8 finished with value: -2756.4017711284796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-09 11:31:28,704] Trial 9 finished with value: -771.797115414836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.74340620175102, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n" ] } ], @@ -5618,7 +5628,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -5634,7 +5644,7 @@ " (40, 512))" ] }, - "execution_count": 85, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -5654,7 +5664,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5665,7 +5675,7 @@ " 237.80585907, 346.48565041])" ] }, - "execution_count": 86, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -5694,18 +5704,18 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Peptide,Class,Smiles\r\n", - "MDLITITWASVMVAFTFSLSLVVWGRSGL,0,N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)O\r\n", - "ARRGGVLNFGQFGLQALECGFVTNR,0,N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O\r\n", - "GWCGDPGATCGKLRLYCCSGACDCYTKTCKDKSSA,1,NCC(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N1[C@@H](CCC1)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CS)C(=O)N[C@@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](C)C(=O)O\r\n", - "NGNLLGGLLRPVLGVVKGLTGGLGKK,1,N[C@@H](CC(=O)N)C(=O)NCC(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)O\r\n" + "Peptide,Class,Smiles\n", + "MDLITITWASVMVAFTFSLSLVVWGRSGL,0,N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)O\n", + "ARRGGVLNFGQFGLQALECGFVTNR,0,N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O\n", + "GWCGDPGATCGKLRLYCCSGACDCYTKTCKDKSSA,1,NCC(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N1[C@@H](CCC1)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CS)C(=O)N[C@@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](C)C(=O)O\n", + "NGNLLGGLLRPVLGVVKGLTGGLGKK,1,N[C@@H](CC(=O)N)C(=O)NCC(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)O\n" ] } ], @@ -5722,16 +5732,16 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:10:55,776] A new study created in memory with name: zscale_aux_example\n", - "[I 2024-07-02 17:10:55,823] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 17:11:21,299] Trial 0 finished with value: 0.8886986575836505 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': , 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8886986575836505.\n" + "[I 2024-07-09 11:31:33,161] A new study created in memory with name: zscale_aux_example\n", + "[I 2024-07-09 11:31:33,213] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:31:58,237] Trial 0 finished with value: 0.8735224395254063 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': , 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8735224395254063.\n" ] } ], @@ -5783,23 +5793,23 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([[ 0.21269231, -0.91153846, 0.29038462, -0.69846154, -0.22230769],\n", + "(array([[ 1.31176471, 0.08058824, -0.27176471, 0.56470588, -0.62529412],\n", " [-0.99521739, -0.59826087, -0.34695652, -0.03086957, 0.13391304],\n", " [ 0.08083333, -0.6125 , 0.82916667, -0.05083333, -0.56083333],\n", " ...,\n", - " [-0.02178571, -0.91785714, 0.45392857, -0.37642857, -0.03107143],\n", - " [ 0.93357143, -0.78964286, 0.62928571, -0.50857143, -0.50107143],\n", + " [ 0.93357143, -0.02785714, -0.04214286, -0.36 , -0.02785714],\n", + " [ 0.30461538, -0.55307692, 0.31307692, -0.11076923, 0.00846154],\n", " [-0.1232 , -0.3364 , 0.2328 , -0.1368 , 0.2304 ]]),\n", - " (7062, 5))" + " (7060, 5))" ] }, - "execution_count": 89, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -5819,16 +5829,16 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.2, 0. , 1. , ..., 0.2, 0.8, 0.2])" + "array([0.2, 0. , 0.8, ..., 0.2, 0.2, 0. ])" ] }, - "execution_count": 90, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -5846,12 +5856,12 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { - "image/png": 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NqgORF17MIbJmmms26GOwlIXiFayxY8di7Nix7y0zZswYjBkzRuvvTZs2xebNm2l3rURofTS0IWUMFi0zv9Dyug44LBQdWvXIFcTN2g0qFFrtKk2BFtOuNuRQvFi4smmV1waLMAj1Or+6xeW4yp7CZXKvdFctY3sOZ+X62rY+lf7wRe8iVBaKV7BKG1Ka1Vl/TIS2xdr1JWSQYR17JjQAWckxdbTalcvtqA2lubKUCgvlkPXkUhssQhbU60zN5hZG/fuYSyG5KsWOyIFxfxC5S3M1hUf+fwk9EqNXsCijhHgGuT5urGO2hNRDy+rDArlWiolpl3XflKwMSWm9U/J9KAkpr4nW/5DQ8sefci6/3rvbEnnjLM5qVSZCLbbXWIINCNXQryJUFnoFiyFyDZh8oOWOZAENpZDWB1hMvJlQSxyfAYq1K4oWYlx7peUa5UaMO49Fu7QsmEr71qjTbcceIqumczkU19ZcReQMPy6299fhg4n8A2fkYobeRags9AoWZVhYTsQM4Eqw5CjBXaQrLO6fEixGQhGzaEFprkwl1CkmRpElrFcRK8GCzAdtzyTZ4xsi2zy4QuRyw1sTOSeai8dqUjmTfuf0lBpKhYKVkJAAJyenIsf9/PwwYMAAnDx5EoGBgbh//z5q1qyJMWPGoFevXqRcVlYWli5dij///BOZmZno1KkT5syZUyQBKQ1oWTPEfKyEfgDlymvDp7yusFA+WR/XBot7RsuVzSLAXB0x6RvEwEI5E1M/jTQNfGDpttelfhaTSz5oO3fzkDNEznmZQeRgpz5E7u1ch8grhxqr9U3n7vBGv4pQWZQKBev27dswMTFBVFSURpJRc3NzXLx4EaNHj8aQIUOwfPly3L59G/PmzUNOTg769esHAFi4cCGio6OxZs0aGBsbY8GCBZg0aRK2b99Ova+03Egs2uJzLp+2hLq4xLSrKywsTCzuJYvySqhTCS4iFlZj1udqg/akg9aqPRaTC1qTSz7n8kG9fHhsVyIPrcLFXfV03Ulky3pZ3Lk7JwhqSyx6F6GyKBUKVmxsLOrUqUMytauzadMm2NvbY968eQCA+vXr4+HDh/jpp5/Qr18/JCQkIDIyEhs2bICDgwMAICAgAD169MDly5fRokULZv2m9bESo7SxGKz49EFoPbStbrQ+3kKRa3CQSxHhU4/Q42IsVSwUR1oWWzEKaHEWLNYuUCUcV4fFOyK0vG8ZLjXD0rlDiHxbxWV1r/GjIZFnGLwhskWJLen50CgVClZMTAzq1y8+n0h8fHwR92GTJk3w5MkTPH36FFeuXAEAtG3LrfioW7cuqlatigsXLjBVsGjN6Fi48MQM1EI/UGLcUbr2U+gAJkahlUppfN9x1ojpG61YHhYWGD6wsDLTsPaxsNJ+SM+ZlktZvfzXE7nA9n3NuOSiO/cHcu1OaELkZzmfEtlCgrzWeguWsigVClZsbCwsLCwwZMgQxMXFoXbt2nB3d4eTkxOsra3x7NkzjfKPHxf8YyUlJSEhIQEWFhZFsrZbW1vj+fPnkl2DmAGTRVyElIOPlC6WwvJKGGjFWD6khLUlhHUfWMTjsI7xkRsWsZ98ytCqn9b7IlTZ8r+6hciq5dxxl+ecBSv3t7+JfL5+OSL3ryuoKZ3Qp2lQFopXsHJycnD//n3Y2dlh5syZKF++PH7//Xe4ublhy5Yt6Nu3L2bPno39+/fjm2++wZ07d0jW9rdv3yIjIwPGxsZF6jUxMUFWVlaR42KRclWNmEGbhdtDG7QUDdoBz7SC0OVSUJQQq6QEWAS5s56YqCPVPad1n1j3l7WbXQzJu/sR+VUFbvywSUonchnX0UTumP2UaX/eRR/kriwUr2CVKVMG586dg6GhIUxNC/Z++vTTT3Hnzh1s2rQJoaGhePLkCebNmwcvLy9Uq1YNo0ePxsKFC2Fubg5TU1NkZ2cXqTcrKwtly5aV+nKKwPqDwDoGS2i7clk5hNQnpcVNKLQGSdZ9llL5o+Fuo4lUijIty7ZQlODKlmsi8PnZk9wfxkZETP/zLnc4sDGRzyTkEfmbWmz7pkd5KF7BAoBy5coVOdagQQOcOnUKADB+/HiMHTsWL1++hJWVFU6ePAlDQ0NUr14dNjY2SElJQXZ2toYlKzExEVWrVqXeV6UNVlJaQvi0K1d/WKJkhUxMu6yD/aWshw9C4wmVFicnBG3KltAAc9arDpWAen867n9QbBmXO92InBu2jcjRn/UishQKlj4GS1koXsG6c+cOBg0ahPXr16NNmzbk+I0bN2BnZ4ft27cjLi4O8+bNIwrToUOH0KJFC5QrVw6tWrVCXl4eLl68iHbt2gEA4uLikJCQAEdHR+r9lVJZKe2B0+rQtjwoWXGRCykthrQC/6WEVlB0aUDo89GGmBgsKRf+iEG9rYycSUR+0IWLx3KZVp3Im4fcIrJX84pM+/YuegVLWShewapfvz7q1asHHx8feHt7w8LCArt378aVK1ewd+9eJCUlwc/PD82aNYODgwMOHjyI/fv3kzisqlWromfPnpg7dy6WLFmCsmXLYsGCBWjdujU+++wzeS/uHVjE/rC2GLFORSGUkoLcpfwwK0FpUEfKAY1WXB/tuDu+KE35Kw4W1iN1WP/fsPhesO5n2GZug+ezJ14TeXUmN5Qa3KpN5H1xKUSWYqscPcpC8QqWgYEBNmzYgJUrV2LKlClIS0tDkyZNsGXLFjRs2BAA4O3tjXXr1iEhIQF2dnZYv349Wrfmti5YtGgRlixZggkTJgAAnJycMHfuXEmvQwlpGlgMRKz7LKZMcUipbMmlHGhDylgl1vdWrrgruRSvktpinf6A9UIOFhM1Fs9q0QNuyBzUqTyRjedzbkFM/JqIfatyZaQgX7+KUFEoXsECgCpVqsDPz0/r799++y2+/fZbrb+bmZlh8eLFWLx4MYvu8ULK2RrrYGA+HzFaAbhyB8Krw3rpuVCXjBJipGghxnIqtLwYCxyLgH0h74+Y/zExLj9aCL2vcq0W1MbEppwFK/LYGyIPas4lF0U2t//gzPP/EXn152z7BuhdhEqjVChYpQkWsVC0gpOFKl5iFAExeXbEKBHFlWfhKqAV20bj+viWF+oWooUYi6qY/rC2ugiFtltLzP+5tnpYW5K09YeFxZuFMowJW4stnxTHrSisWYZbTPWNbY7OfdBT+tErWAyhtRRaG0IHK6FuAaGI6YMYSuq/0IFIqEJAyyXL2pVGC1rPUKgCz9pqJbQPQmPI1BF67SX1X0xftCFGCaf17aM1oaTXH2ci97XYTuS5eT8QuWPtMCLXfzJM53Z1Qm/BUhR6BUsihM6yaLnGWMdjsQg2FROPRcOCJeV9UofPQMFkVs4DFs9QaFtyWb/EtEVLkSrORSi0L7TKi4n3oqUIsvg+aiuvXiZojymRy/1lR+QbVTgXYcWT3LB68jln2XKqJqDDOmJgkF9yIT2SUSoUrJycHKxduxaRkZFISUlBkyZNMGPGDLIK8N9//8XSpUtx48YNVKxYEb169cKkSZNI3quEhIQi+xUCgJ+fHwYMGMCs36wDLoX2gc9xPvWIsa7RGiCkuocsFEihbWlrV+i5rO8ZrQBmoUhppWPtbmaJtjaFPjcW8ZVyfR+1oa0/S4x3EvnJS055mreHS2adPZvL25htLq3CY2CoV7CURKlQsNavX489e/Zg6dKlsLW1RUhICEaNGoWDBw/CyMgII0aMQI8ePbB48WI8fPgQXl5eyMvLg6enJwDg9u3bMDExQVRUFFQqzoRqbm4u2TVIGdvCpwwtMzwfxMR4KeFjWxwsBgfWcSVikC3mRaZ6lPreqcP6+qRUhqW0kPJBW53VhnL5ru55cnvg7rjPxV35tG9I5EFmydT7pqf0UCoUrKioKPTq1Qvt27cHAMycORN79uzBlStXYGBggJSUFMyYMQPly5dH7dq10bt3b5w8eZIoWLGxsahTpw6sra3lvAzqiPlIKsFtpo6US+2lgoWrRmmKCB+kHMxLg2KkBFg/f9YhAlI+Z/X+ZCx8TuRXSVwAe/Q2zmqVsKw+kSuHHiKy8aQJrLpIUIKLMC8vD0FBQdizZw9ev34NR0dHzJ8/H7a2tkXKrlmzBkFBQcXWM2DAAJI9wNXVFf/884/G761bt0Z4eDj9C6BIqVCwLC0t8ddff2Ho0KGoVq0adu3aBWNjYzRu3BgvX74EAOzcuRMjRozAs2fPcPz4cTg4OJDzY2JiUL9+fW3VSwLrDw5raAWwSzXYymVhEnourUBi1nE3fNql5TpmEZyurQ9C61GqUkvLDc9iFSGf8ixWEYpB2/MvM59TkpJ9VhA5sjqX1d3oTwsi54wfQb1v70MJCta6desQERGBpUuXwsbGBitWrMCoUaNw4MABje3qAGDEiBH44YcfNI5t2bIFO3fuhIuLCzkWExODhQsXokuXLuSYkZERlE6pULDmzJmDyZMno3PnzjA0NISBgQHWrFmDWrVqoVatWnB3d8fq1auxatUq5Obmom3btpg/fz45PzY2FhYWFhgyZAji4uJQu3ZtuLu7FxuXJRYlK0O0PqpiAnppxXXounJQStcoi5VUcsTuvNsurXPluhah0Ho3+Vyvrt8PMQqNUJTwjWONtns19EA6kX+O4xKKhtX9g8guL/oROSefi83SVC0+TLKzs7F582ZMnz4dHTt2BACsWrUKHTp0wOHDh9GrVy+N8uXKldPYa/jmzZvYtm0bFi1ahEaNGgEAkpKSkJSUhObNm8PKykqya6FBqVCw7t69C3Nzc6xduxZVq1bFnj17MH36dGzfvh22tra4f/8+hgwZgj59+uDRo0fw8/PDvHnzsGzZMuTk5OD+/fuws7PDzJkzUb58efz+++9wc3PDli1byP6ErCnt8SC0PuByB6fzgUV6BRauDqVZ49QRY9ni065QpLTwCX0fWCosUl63OkInUkoOeFdn8zecfKzxMSK/uMxZqlRqVhrT86e5Ez7vx7JrAOgFuXfu3Pm9vx89erTY47dv38Z///2nMa5WqFABTZo0wYULF4ooWO/i4+MDBwcH9O/fnxyLiYmBSqVC3bp1BVyBMlC8gvXs2TNMmzYNYWFhxO3XrFkz3L17F2vWrIGVlRVSU1Px008/AQCaNm2KihUrwsXFBS4uLvjkk09w7tw5GBoawtS0YIntp59+ijt37mDTpk3UFSwluP+k/ChJGSNB+17RsqbRQq64KBYWTBYuHxZKm1woyWLHQgkrjQH4fNrauo1Tnr6cX5nI5atxlqr8l0+InObQksiVGPVPHbldhM+fF8SoVaummZPC2tqa/KaNv/76C5cvX0ZkZKTG8djYWJibm8PHxwenT5+GmZkZevTogXHjxhVxOSoNxStYV69exdu3b9GsWTON482bN8eJEyfw8OFDYopU/w0AHjx4gE8++UTDBFlIgwYNcOrUKer9FRNjoIQYFj6IcUGKKa8rSovBEoNcMVt86pGyfjH1iEHM/7Hcrl4prc1yxVrR+s5qY/gwNZefR08if24TSuR1fVy4trarKQDK1gU00GahKomMjAwAKKL4mJiYIDU19b3nbtmyBV999RU++eQTjeOxsbHIysqCvb09XF1dcevWLSxfvhxPnz7F8uXLdeqnVChewbKxsQFQYCa0t7cnxwtXBmZmZiImJkbjnMK/69atizt37mDQoEFYv3492rRpQ8rcuHEDdnZ2TPsuZlbOOl6C9eBD61wacWMsFF1aFhdtZVispGL9/MVcr7YyQs/lU48YaFlXWCpVLFKh0FLCFKEkifi2dtwfRuTv94wn8pv/fiTyP+e5YTUwjtuXcIqmjYAJcluwCr1E2dnZRAaArKwslC1bVut5T58+xblz5xAcHFzkNx8fH3h5eaFixYoAgIYNG8LIyAgeHh7w9PRElSpVKF8FPRSvYNnb26NVq1bw8vLCggULYGNjg8jISJw5cwY7d+5EWloaRo8ejcDAQAwYMABPnjyBt7c3OnbsiMaNGyMvLw/16tWDj48PvL29YWFhgd27d+PKlSvYu3cv077T+vhIGUfBepYr1YealvtPLouXlC5CPohxIyrJHcYX1jGELO9PaY/3U5qLUJ3vssYR+ZRdCJHXLRtL5MhPLhE5/4V6HqwhTPsGyK9gFboGExMTUatWLXI8MTGRBK0XR1RUFCpXrowvvviiyG9lypQhylUhDRo0AFDgktQrWCIwMDDA+vXrERgYiFmzZiE1NRUNGzZEWFgYcQVu3LgRa9euxdatW2FhYYGuXbti8uTJ5PwNGzZg5cqVmDJlCtLS0tCkSRNs2bIFDRs2fF/TiofWjJ7FB1Bb/ULLyx1HI1cQv9CVaHLfp3cR4+4WWiftFXlSQEOpYrEiVglWQj7IZZl/G8pZWBp8yg2fv/bJI3Lu7/FEnl2rG5GXUemNsmncuDHKly+Pc+fOEQUrLS0NN2/exNChQ7WeFx0djdatW6NMmaIqibOzM2rWrElyYgHA9evXYWRkhDp16lC/BpooXsECgIoVK2LBggVYsGBBsb9/+eWX+PLLL7WeX6VKFY2HwxIpg9xpfUzEWCRYuyB0rZNFYLXQ+sVA675qQ4x7Ti5Yu9O1tcUCGooMaxcxrW+KmAmW0P6wfm7fludyNkUG3ODaHfyGyIu2f07khf4R3MltBjPtGyD/VjnGxsYYOnQo/P39UblyZdSoUQMrVqyAjY0NunXrhtzcXLx69Qrm5uYaLsSbN29i4MCBxdbZvXt3LFmyBPb29mjfvj2uX7+O5cuXY+TIkShfvrxUl6YTpULBKk2wsAYpITBU6LlCrT1i4neEuAu11ccHpSnDSnNBsh4YWaTP4NOWlC73ksqzfm5CY55YTwSEwjq2sPxoLo5IVetTIodt5gK4p1zjBv3Vc7g4LSmQ20UIAJMmTUJOTg7mzp2LzMxMODo6YtOmTTAyMsLjx4/RuXPnIvsAv3jxApUqVSq2vqFDh0KlUiE8PBxLliyBlZUVXFxc4ObmJtEV6Y4qPz9f/idSChner/gU/Up2S4hBCUqKEtt5X1tSKros6hFTp5TltaG0eyimreIULLkUfhYWbLkQ2k/1IPchKysROT+PG0aNWnB1vvn/7d0AoILx9zr2kj/fhB+gUs9B595U6vnY0VuwKKMEhYP1bFPKVUdCKa5d1m41oecqbcCRckVhaYkhU6e09FMqWIQUaEOuSYo2bp8ZSeToUZz7r91mboV7WJtzRG6fzC0drFCK0jTooYNewaKM0qwQUs4wabv2aMEiOF1MDI1c7whrlyKttAtyTTS0QcsqKaYPuiKXtUmu75eULtTPr00jcob3SiJ/1ZtzI5pKvF2eElyEejgUqWBt3LgRp06dKrJTdnx8PPr27YvffvsNNWtyL/1///0Hf39/HDlyBJmZmWjRogVmzpypscHzmTNnsGLFCty7dw/VqlXDxIkT0bNnT9CG1geBlgVAynQF2vpGK8hVSH9YB+7SqpNFPUpAypgwFopDaXimfL4FcsVs0XomUl4XH7b+XInI19sHEnn5vOFE3jK/ApENTh3iTqa/9W0RDGUOctejieIUrB07diAwMJBsi1PIvXv34ObmRjLFqrNo0SJcu3YNP/30EypWrEh27/7zzz9hYmKCe/fuYcyYMXB1dcWKFSvw999/w9PTE5UrV6a+VY7QDwLr1TwsENpnWrNo2nE3crl+WCgESltSzyJwWgnQshTSWu1YUjt8ymubDIn5HrF+tnL974bFpxHZeYoFkUO6civiTqltCZNepwWR1bYx1PORoBgFKyEhAQsWLMC5c+eK5LbYuHEjNmzYgLp16+Lx46L/TFFRUZg8eTJatizY92nKlCno27cv7t69i6ZNm2Lr1q1o1KgRPDw8AAD169fHzZs3ERoaKvtehEq2qNBCypm2kNVWSr5P2pBj8OaLXFYiFvWzHsCFuphpI+X1sahHrv/dH3b9TGSDkV2JbPL0HpEdbGyJ/OiNeqJR9uhdhMpCMQrWv//+CyMjI+zfvx9r167FkyfchplRUVHw8/ODhYUFhg0bVuRcS0tLHDx4EN988w3Mzc3xv//9D5UqVSKJzqKjo9GlSxeNc9q2bQtfX1/k5+dDpVKxvbj3IGamJ8YypLRVgbRnvKxjT1jM0JVs0WFtYVTqe/RunbQUdxpKlZSpJMRYpNWhZf2Sa+HEzoXpRO517yCRLYdw4Shm8Y+IHJDLeWJCtKdqpIZewVIWilGwOnXqhE6dOhX72549ewAA586dK/Z3X19feHp64vPPP4ehoSHMzMywefNmmJubAyhIp1+4p2Eh1tbWyMjIQHJyMipXrlxctToh9B+fRRwCC7ejEmb0tNtlYT1kXX9psTawUEq0wfp9UaoFSy4rkdDnLOV7IeVkJPctNzHPOvmAyCbtOAvWT93MJOuPHuWhGAVLDDExMbC1tcXixYthZmaGkJAQTJgwAbt27UK1atWQmZlZZHfvwr+zs7OLq1JnlDYASrmahxZCB5/i+sZiVktr5k7LTSpl7ihaFlXWsLZm0ArSpv1/I9c7zuJ+SxlSILQ/wx5xHpQj/3HD5385XPmkLE7xGjN6HZHLbunLqIcccmdy16NJqVewrly5gkWLFuHYsWOoXr06ACAwMBBff/01Nm/ejDlz5sDExKSIIlX49/t2+KaJEpQkFrAIxhZTP6s6+NYjpSVOybN7deSK92E9yAuth0/9uiLXO87nuJSrOVm/a51yk4icvIxbIeiRNJTIYT+9IHL+EGn3uzUwkLQ5PSVQ6hWsixcvwtLSkihXAGBkZIQmTZogPr5g081q1aohMTFR47zExESYmZkRNyItWCdtFNoW6/gj1jEYciuUUvZFypV3UsZ7SfkMWVwXi/7LMdHQBot8anLFN7J+10L+q0rkESFTiTz2xTMif/9PHSLvslTWhFiPtJR6BcvGxgbJyclITEyEtbU1ACAvLw93795F+//fpsDBwQHnz5/XOO/s2bNo2bIlDGRW+Vksr5dSYaIVHyYmV5auH1Vawb1KSEnA5/6JufcsBmEpYeHao+WW5UNxq2Pl6osSoKXkiXkXToObtHfcz+VsNOvjQuRnOxoRuUaJLYlHH+SuLEq9gvXVV1/B1tYWkyZNwqxZs1C+fHls3rwZz549IysOnZ2d0b9/f/j7+6N///44fvw4/vzzT4SGhsrce03kcvkoYWZIa9Bm6VIU06aUgeRKsOIoIQZLCfWzVHzE9EWMsqWEe0yrjOAg/b3cYqlEFy57e/nsiUR2SU0hcsjD8kR2+6TE7ohGr2Api1KvYJmZmWHbtm1Yvnw5xo8fj6ysLDRr1gw7d+4k2d4bNGiAdevWYcWKFdi6dStq1qyJFStWUM+B9T74WHrkWnrMGlozZyEfTBauC6GUlmdVWvrJB1rK5Yd0T4pD2/dFykUxpeUeq/d59HEuieiyUC7uapfxGiJfP83tVxjQXJoY30L0Qe7KQpEK1tKlS4s93qZNG8TExBQ5XrVqVaxcubKYMzicnJzg5MR+rwLWq2rUkSt4VCis3VG69kVo+0LduaxTdghFLgWetbuNhduZD6XRVVqImNWuYtpibYFl7Yr//CvO61FpP5f7aoP3TCL/WJ5zHeZG7iOy4eD+VPqgp/SgSAWrNMM6fofWR4b1kn0+/RF6rpD7xjpQloXSyKfPLOLrxJwr5r0WWiefe05r0iHl5EhXWEywWMeqsVbaWE9S1BWp4dkRRD72YhmRdzfnrEgv//uRyFx4PDv0LkJloVewKMMifodWH8SUpxVISiuoX8r7SaNN1rE7LPrGQtHUBgtLj1KtRwCd+0ZLQRFTJ4v/Qyld90K/R8N8uUVRt9y5bXCaHp9D5F6pnLKVk5+rcz91Qa9gKQu9gvUBorQ4IyWtEKSFlGkOtLUrpdIgxlIh1BUk9BpZW56UqqixtmyzdqcLrVMd1jFe2voffOspkS2qc7kV88EpNsd/zSNyOy7PqJ6PEEUpWBs3bsSpU6cQHh5e7O9z587FP//8g2PHjgEo2DqnuL0JAaBmzZo4evQoAGD9+vUIDAwsUqa4eC4lIsYqpgTLAGvXTnH1KEGxFINccVHaEGOdZG1tZPFOyaWgC4GWC5R1Cg4prbe0npW2e+UcwbkFjSImEzm262oiXz/qQmTDRE7Z6l+XStfei96CpSwUo2Dt2LEDgYGBcHBwKPb3qKgo7NmzBzVqcNlEWrRogVOnTmmUu3LlCiZOnIhx48aRYzExMejbty9mzJjBpvMiUOrs+H2wdmtKFYyt5HvPWimR0urDIs6QT7tKe6bq0LL20IB1SAFrBY416n1IqjaKyOMec8HsB70HEXnFAU4JuzNJ2sB2/SpCZSG7gpWQkIAFCxbg3LlzqFOnTrFlEhMTMW/ePLRu3RpPnjwhx42NjWFlZUX+Tk9Ph5+fH/r374+BAweS47Gxsfj+++81yn4s0LJysXaJ0Y7NktJ1IeWAoDQXIR+UMEgKRYwiKNUzUpoioo4S+sbCXdjan7NIVeq5icgLLnDjjYlhUyK/yJB9iNUjI7I//X///RdGRkbYv38/1q5dq6FAAUB+fj5mzpyJvn37oly5cti3b5+WmoANGzYgIyMDXl5e5Fh2djYePHiAevXqMbuG0gittAu0kEpRE6NsSWlVKo3QigmiBYtBnpZLjMa7p+QYM9ZB7nL9r3xyiovBat4li8izL5kQeVW7rkT+6vQf3MkSpHLXuwiVhewKVqdOndCpUyetv4eFheHFixfYsGEDNm7cqLXcq1evEBYWhmnTpqFSpUrk+N27d5Gbm4tDhw7B19cXWVlZcHR0xIwZM8jWOqwQs8KOlimdRayCEgK8tbUrpA+s77GYOvkgt2uJL7RWOwq1ctJaXEErL5OSFGgxqUXEWIeljA9lwcqfOQtWHrcVIVKWc6sFc/Zywe9lUl4z7c+7GKokbU5PCciuYL2P27dvIygoCDt27ICxsfF7y0ZERMDc3ByDBg3SOB4bGwsAKFu2LFavXo2kpCQEBARg2LBhiIyMhKmpKbP+s3alyDXTk2ugENIuLXce65ghoXVKZR0RW4bFQCdmMBdav9AyQicyQq+luMUbLFYRsjgu5YSMdZ1hs24RefsmzloUFpdK5NRx3CKtt6u5PFgfX4CKHsUqWFlZWZg+fTrc3d3RuHHjEstHRkaiX79+RRSmfv36wcnJCZUrVybHGjRoACcnJxw7dgzffPMN9b4XB4sVTVLGBIlpV4wlgU9/hJxLy+pA634rzfpFy3IjpfKvtJWyUiH0HRTzfrFI38BCIWOtzK0r34zIu6Z9SeQhtzlX4Oh0bqucfhahXJkMVyp9eB8GeguWolCsgnX16lXcuXMHQUFBWLt2LQDg7du3yMnJQYsWLRASEkJWHN6+fRuPHj1C7969i61LXbkCAGtra1SqVAnPnz8vtrwYhM5kaX0YhZanpbRJ2X8hipqYvrOwWrHIQSQUFi4wbfULrYeF24lPeTFl+CCV9UbMO8vif1gbtCyhrNM08Glr5G/c9myuN4cTedeEU0RWTWhFvT/vQ+8iVBaKVbDs7e1x+PBhjWPh4eE4fPgwwsPDUbUqt/FAdHQ0LC0ti7V0rVq1Cn/++Sf+/PNPqFQFb9/jx4+RnJwMOzs76v1mrXywQIzFRls9fM4VOsjL4SLU1hcxbjilxU6xtpzR+j/Qdg9pKVW0nqmYAV9IPSysRHzqZ43S/j/U6bg/jMjmFtzwuWXUSSKnR94n8g/tJhH5tzZs+wboFSyloVgFy9TUFLVr19Y4VrFiRZQpU6bI8Zs3b6JRo0bF1tO1a1ds2rQJCxcuhIuLC16+fIklS5agZcuW6NChA7P+A/QsQHLFWvCB1sAldPAsqV1arj0WM2W5ZuisLRh86hf6LrCw3vI5V4yLkOX/NJ97I8b1xsLipbRVinzQdq8GZo4h8rnETCIbVOeWCJqVP0DkeU2SGPROT2lBsQqWEF68eKGxclCdTz/9FCEhIVi9ejUGDBgAY2NjdO7cGV5eXsSiRRMxAyOtc4VCa8arDTksP6wtLrRcP0Kvj1acG2ukDGwujdC4D1IqLiziKPn0QegEhBba6vQz3UnkrmHu3A/Z6US8Yc/FaZ17yi3OasN20ToAvQVLaShKwVq6dOl7f584cSImTpxY5HhISMh7z2vXrh3atWsnqm98kcsSoq0tKREz+MvhThUz+2YRwyQGPhZAKeO9tPWND1IuGpAL2u8MLTc/rXZpWe6knBzxqTN3F7cWMN+kHJGvfc5tldP8r1FErtMwQ1BbYtEHuSsLRSlYHzJSBl+yCPrlU56Fe0lXxNTHIiheaN9oKZm06mGtRMoVG8cCMS5F2u3QorS49ljf++F7uhHZZ9sqIi+Zya0Q/DnuKpHLJyVzJ7f/XlBbeko/egVLZmgNLHL1gdbybVr9YdmOlNZJPtCy1rC4D3zqpxVbyMKNVBqVF11hbeUUWr8SLMLaCN1Vgch7zDjrlKnRf1yhZZwZSWVkJEm/CtG7CJWFXsGiDGsTuJi2aLXLGhp9YxHQzdqCxfoZsl7ppg1agdBiBmdaEwEWC06U6tZkbbWSKhTgfQhta8zAFCLP7sC5CJMXG3CFcris7qhQXteu6YRewVIWegWrFMA6zoEPcikFtGEd8ybmPrGwBgrtAx+kDCou7XWyfN9pKXsskCsIXShiFPtLbbhM7hMecmka8lK4gPewlt2JzKUf1fOxoCgFa+PGjTh16hTCwwu2GnB2dsb58+eLLbts2TI4ODigc+fOxf6uUqlw+/ZtAEBycjIWL16MEydOQKVSoWfPnvD09ETZsmWpX4PQmSlri4pcZngWaSloB8LT6otQaFmSlOAiVkfb/ZHKLcwKFv8rNJQdWrFqLCyYQr9BLL6btFBvKy+fs061/JtTmfpOzCHyqmdhRLb6XNrNlw0MSi6jRzoUo2Dt2LEDgYGBJDs7AKxZswZv374lf+fn58PDwwOpqano2rUrTE1NcerUKY16Hj58CFdXV4waNYocmzRpEjIyMhAWFoa0tDTMmTMH6enpWLZsGfXrUMIKKBYfHzHuLlozZ11n6WIGFimvj3V6CNaDEuv3Ti7Fi/W7LFQZ1aXsu+XF1EPrXRNTj1yK11ZDPyJ3+9aEyP/rzMVmGTaoS+TLedL67JTgIszLy0NQUBD27NmD169fw9HREfPnz4etrW2x5ffv348ZM2YUOX706FHUrFnwPP/44w+sWbMGjx8/Rr169eDl5SVZZgAxyK5gJSQkYMGCBTh37hzq1Kmj8du7ua22b9+Oa9eu4ddff0W5cgX+bysrbtlsXl4e3N3d0aJFC5LO4fLlyzh//jwOHjyI+vXrAwB8fHwwatQoTJ06VSMjvBwo2XyurU6hAwKLAUobhW1JaaETCgsrkZh2WStbYvqghImGUlNa0LJgKQ0WihctXPPmEjncxJfIQ8ZySa529+E2hO6Q0JRpf5TIunXrEBERgaVLl8LGxgYrVqzAqFGjcODAARgbGxcpHxMTg9atWyMgIEDjeOEWd2fPnsWMGTPg6emJL774Av/73//g5uaGyMhIMqYrFdkVrH///RdGRkbYv38/1q5diydPnhRb7tWrVwgMDIS7uzvq1atXbJk9e/YgNjYW+/fvJ0lEo6OjYWVlpfEgWrduDZVKhYsXL1Lf7JnFzIrFLI5WP5Uc+yEVpWXwFoMYixqLVX4s6iltsAjcLy1I6RZU57+3XNqFypZqw6cZF24y6F8u7ioykQuE788ZtpghtwUrOzsbmzdvxvTp09GxY0cABdvVdejQAYcPH0avXr2KnBMbG4tGjRppGEvUCQkJQZcuXTBs2DAAgJeXFy5fvoytW7fCx8eH2bXQQHYFq1OnTujUqVOJ5UJCQmBqaoqRI4sPFczOzsaaNWvwww8/aFjCEhISUK1aNY2yxsbGqFSpEp49eyaq78UhV2wAa6VNndIwiLF2IbAIGC+NigJry6bQ95GF5VKpz4JFDJgS3MisY7D4KJTa6lyygwtg/+Yvbqu1rF1niZxym4vHip81VFDfxEIr0ai22OZCjh49Wuzx27dv47///tNw31WoUAFNmjTBhQsXilWwYmJitOoAeXl5uHTpEmbOnKlxvE2bNkX2KlYisitYfHjz5g12796NCRMmwMTEpNgyBw8eRGpqqkbsFQBkZGQUa5Y0MTFBVlYW9b6yGDBZBD8rWfljCa2ZPguLC+sgd9bX9SHFHLJW0OW2tEq5yk/J1ns+z0G9zECXF0SOaPQHkcdc4pKIhq18SuSvhu7mKjozpMS2xGKokjao/l2eP38OAEWMGtbW1uQ3dVJTU5GQkIDo6GhEREQgOTkZ9vb2mDFjBurWrYu0tDSkp6fDxsaGV31Ko1QoWFFRUcjOzsbAgQO1ltm3bx86d+4Ma2vNDZ9MTU2RnZ1dpHxWVhbMzMyo91UdJcRryLWKUK4gVBp1so5VUtq9oRW0TGshBIvYItbPTkw9H5OyRetcuWKwWk7mrFO7274h8pO1lmqlOAXLfm9Xpv1hhTYLVUlkZBQkX33XqGFiYoLU1NQi5e/cuQOgYAGbn58fMjMzsX79egwePBgHDhxATk6O1vpYGEhoU2oUrC+//BIVKlQo9veUlBRcuHABa9asKfKbjY0NoqKiNI5lZ2cjJSWliDLGEhazOFrpDIR+4MUMtnzqpIFcVigx9UjZLi343Gfa6TXeBwvFhfXqTl2htYpQ6P+Hkley0kLbNc62zSRyyjAuJ8KQxpxCkrzkBpGz13xLZE0bDBvkjsEyNTUFUDDGFspAgUGjuLRIDg4OOHPmDCwsLEjcdFBQEDp27IhffvkF3333HalPHW31KY1SoWBFR0cXu8lzIZcvX0Z+fj7atm1b5DdHR0f4+/sjPj4etWvXBgCSW6tVq1ZsOiwRtD5otALVaa2sExMfUlIflBw7JaWLjZZCUNpjyPjA5xrluF5az0quNA3ayvOBtTKnrfzkZ5FE/mzFp0TO+5zbozCjRUMiV7t2lzu56PBEHbkVrELXYGJiImrVqkWOJyYmolGjRsWeU7hasJCyZcuiZs2aSEhIQKVKlWBmZobExESNMomJibJnAOCD4hWsZ8+eITk5GY0bN9Za5ubNm7C1tSWpG9Rp3rw5WrZsCQ8PDyxcuBDp6emYP38++vXrx+QBsbBUiSnP2p1Dy5rF0uLEYrWVXPFG6rBW5oQqunIpW1Jaj5RkpWGh2LP4BonpAwvrvRj8GnJxVN90DCayS0ITIlf7l0vTkP8ihTtZAgVLbho3bozy5cvj3LlzRMFKS0vDzZs3MXRo0YD/Xbt2ISAgAH/99RcJ2Xnz5g0ePHiAb7/9FiqVCi1btsT58+eJNQsAzp07p5EzU6koXsF68aIgqPDdnFjvltH2u0qlQlBQELy9vTF8+HCYmJigR48emDVrFoPesnGBCK2T1ixUHaGxXLRiv2jEKynBmsLCDcPagiWmz3Ld89LyrOWIuxKzek5bPWJCE8TAun5t+DzaSeTIS9yK9rwqXIqBiOqc62rXE3Mi/8a0ZwXQWkWoK8bGxhg6dCj8/f1RuXJl1KhRAytWrICNjQ26deuG3NxcvHr1Cubm5jA1NYWTkxP8/f3h6emJyZMnIzMzEwEBAahcuTIGDBgAAHB1dYWbmxuaNGkCJycn7N27F7du3YKvr28JvZEfVX5+vrzLDkopw/uFF3tcaWkaaM30pFQKaCgOShhoWd9jpSk6UsbmsF68odT/Y1p9YW31Y33/5IqlDOn0K5Gf/8XFY9ku4cxT6XacNevCi9dE7lh9uM795Mvaf3dQqWd8U91XPObm5iIgIAC//PILMjMzSSb3mjVr4vHjx+jcuTP8/PyIAvXvv/9i5cqVuHbtGvLz8/HFF19g1qxZGisRIyMjsW7dOjx//hx2dnaYMWOGPpP7x44YhUOohYFPu6yDfpUwEAkpK6UFiM+5fGChJIt5B2m5f/mUFwoLlxWt+mnAIs5JyomXOrQsYSzeWXW2GCQXe7xjDpcHy9ruOJHPDHflylQvsdkPAkNDQ8yYMaPY7W9q1qyJmJgYjWNNmzbF5s2b31tnv3790K9fP5rdlAS9gsUQMa46ueJHxEDLBUH7nrC2aghVaFkoXnxg7Wqm5f4VCmtrlpRWHdqWH1r/k9rqZBGOIEaxY/3OrkwfRuRyi3YR+We/DCK7nHEk8ohGXGJSKZA7yF2PJnoFizJSuiKEBhuL6YPS3FEloaRgZCnqF4PSAvC1wTouTVtbUt4T2vdNSouhtnbFoLTvy7QfuNQM6//nRmSXKS+JnLFyL5GnJ3Mr1cN7MO4c5I/B0qOJ7ApWSkoKAgIC8Pfff+PNmzdo1KgRpk2bRlYInDlzBitWrMC9e/dQrVo1TJw4ET179tSoY9OmTYiIiMCLFy9gZ2cHT09PjZQNc+fOxZ49ezTOqVGjBo4dO8b+AikgdLbGwhWgDdaxX9pQ0odXyhgjoe1KiRjLgNDr1bbS7GNCacqQNsT8r8oVLK+Ne824YHbTKC4eS9Wdy+Ru2oNLR2CQrQ9x/piRXcGaOnUqXrx4gYCAAFhaWiI8PBwjR47Evn37kJ+fjzFjxsDV1RUrVqzA33//DU9PT1SuXJkEuK1btw4hISHw9fVFkyZNsG3bNri7u2P//v2wtbUFULDX0dixYzWWiRoaGjK5HqGKhRIGSdYBxkIHQKncJFJaWWiVZwFr66SSA8zlgvZzlzKoXGh5oRMsKV2pQtvaYrCYyKqr3F6EWwy54845nkRenvVI577pgt5FqCxkVbDi4+Nx+vRpREREkKSf8+bNw8mTJ3HgwAEkJSWhUaNG8PDwAADUr18fN2/eRGhoKNq1a4f09HSEhIRg+vTp+OabbwAAc+bMQXR0NC5evAhbW1vk5+fj7t27cHNz07pbN03kWrXFIr5GKLRii2i4Uli7PYS6Z1m4h5S60u3dMqytmeqUFmsp7YkDrdgjPucKLc/Coi7X+97q2ggi5yeeI7LLC2ci52zkArYruXExW1KgV7CUhawKloWFBYKDg9GsWTNyTKVSQaVSIS0tDdHR0ejSpYvGOW3btoWvry/y8/Nx8eJFZGRkaLgMDQ0NsX//fvL3w4cPkZ6ejnr16rG/IEhrqaI1aPCpk5aZX6pAbikDxlnfG7lchEKfm9B7omSlSlv9LPjQLXZKUJiEoq0/5vac8vTajYvHMr3MlTfu14bIl5MTiNyyCs0eFo9ewVIWsipYFSpUwJdffqlx7NChQ4iPj8fs2bOxb9++YnfRzsjIQHJyMuLi4lCxYkXExMQgMDAQDx48gJ2dHTw8PNCyZUsAQGxsLAAgPDwcJ06cgIGBAZycnODh4QFzc3MoCVrKGYvZrJQWCRqB/yzcD2KeD60Zulz3WGkDoDpKcLPKgRiLp5KRY7UlX9Yv5hJUO46NJ/KFKhFEDinTksiHxqVI0i89ykT2GCx1Ll26hFmzZqFbt27o2LEjMjMzi+yiXfh3dnY23rx5g8zMTMyfPx/Tpk1D9erVsWvXLgwfPhyRkZGoX78+YmNjYWBgAGtra2zYsAEPHz7E8uXLcefOHWzduhUGBgbFdUVnaLl5pIypYKHAsei/kDrFuPCEWlzEBGizuPdS1qOOXIOelIOwkqxNtFyEH/p9Eov6tSQMXc79MIkTXd/OJLLLFS4PVnRKfSI7so9Q0a8iVBiKUbCioqIwffp0tGzZEv7+/gAAExOTIrtoF/5dtmxZlClTBpmZmZg9ezaxhDVt2hSXL1/G9u3bsWDBAri7u2Pw4MGwsLAAADRs2BBWVlb4/vvvcf36dTRv3lyS6xMzmIsJQtdWJ4t4H9YxWEIGEVr5f5Ts6lJaDJaY8rSgZaUp7UoBS8R8X2hNKOV6PgcjuPHI2ISbnA+ovYrIr+5w5atu5jY8lgK9i1BZKELB2r59O3x9fdGjRw8sW7aMWKmqVatW7C7aZmZmMDc3J+5D9V26VSoV6tevj8ePC/4BDQwMiHJVSIMGDQAAz58/l0zBEvNBkMvKoa1O1sG1NFYgKTm+50N1ESpBKWFtgVFSLKVc1kwxCrbQCaXQdlnTPvkHIjeoaEdk9R3n9tx4RuRPknOJXKs8487pURyyK1gRERFYtGgRnJ2dMWfOHKhUnAru4OCA8+fPa5Q/e/YsWrZsCQMDAzg4OEClUuHKlSvo0aMgi1vhqsHCNA6enp5ITExEWFgYqeP69esAADs7O7BECW4+JQf9KrVvSnhurFGCO1oJ90Ebclnd5FrQwBIpVzuy/qaEbTYh8p3Uu0RePNyUyKt3c8OqkVcYV9Eq9isK9RYsZSHrZs9xcXHo3bs3OnbsiAULFmj8ZmpqiufPn6N///5wcXFB//79cfz4caxcuZKkaQAK0jIcP34cvr6+qFWrFsLDw7F3717s27cP9erVw9GjRzFu3DhMnDgRffr0QVxcHHx8fPDZZ59h5cqVOvdd22bP2pDLnaMOa7cji3OFUlxbrAd1GkH57yLGSsBCoRGqzKvDui0prR+s3x91irsu1m1qa59PPVK6juXKg/XjgVdEXhK8hcgTf+Cyuu99HkbkMtU4s5Xh4G0695Mve+/T2ex5YD3dN3vWwyGrBevQoUN4+/Ytjhw5giNHjmj81r9/fyxduhTr1q3DihUrsHXrVtSsWRMrVqzQ2EV74cKFCAoKwty5c5GamoomTZpg8+bNJC1D586dERgYiODgYISEhMDc3By9e/fGlClTmFyTlK4FWjNfKWeYciDXR11orArrwOPS4qampaRqK8PCxcUH2pYqFhZsWt8L1t9BKS166m2NuLqTyLF5nG3C/eI67oSJnDVrZdPuRObSj+r5WJBVwRo7dizGjh373jJOTk5wcnLS+ruRkRE8PDxIMtLi+Prrr/H111/r3E8hKCFQWckWMjEKma73SgkBtywUFKGDtBICz8VYm6RU5mlZxVjec7ni4sRMwmhZM+Vb4PEjkTvuDyPy331ciJw4hzs+w11aB5GBSr81j5KQPQbrQ4bPB4HFTJ+Fm4TWzF1oeRr3ilY6BjGwUDiFtsXC+iJmABRqvWPtBlVHygUNukJLQaUVOiD0ObNQEMW4svn0IW0wlzvx2+rcKsJBFbg8WCZDbIm88SkXFDW2kqBmdUIfg6Us9AqWREg5aLP+cPGpk4UVgrYFS0poKXMslGcxygQt5UZK5U9KWCpqtO6xlM+NhWtdaP1iJjjLu78gcpmrRkTO9+ZisFT/nCDyN7X+E1S/ng8LvYJFGRZxOixmlWIGaqEfKLkHOiVYO2g9ZzH3Xls9Qs9l4fpmYQljbR2W0kVXXJA7i/9z1u+ylJZcWqj3LSs3ncgRfllEHt7zNJETN9wi8j8+rYhcy45VDzn0LkJloVewZIZ1/A7twNp36+RTRsxgro5UypncCuH7kHJBghKsQUL7r4Q+q0Mr/qg4pIzrE9Mun3NLi7LlNpBbRdhR7bjrcnsih4VweRl/qFRZgl5x6DO5KwvZFayUlBQEBATg77//xps3b9CoUSNMmzYNDg4OAAr2EAwPD8ezZ89Qq1YtjBgxAgMHDtSoY9OmTYiIiMCLFy9gZ2cHT09PtG3blvx+69Yt+Pr64saNG6hcuTJcXFwwbJi0u5yLgYWyQqstWjESLD+wQhVLFrFZLOJBaPWBlvLBepDkUz9rq6SUrk8a91NKKxufOml9X6RUntXrb+rNxV398FlZIruMeU3k9EW/ENlw1WQic2sL2WGot2ApCtkVrKlTp+LFixcICAiApaUlwsPDMXLkSOzbtw8XLlyAv78/Fi9ejM8++wxnzpzBvHnzULFiRXTp0gUAsG7dOoSEhMDX1xdNmjTBtm3b4O7ujv3798PW1hbJyclwdXVFp06d4O3tjStXrsDb2xvlypUroqjRQAlxDtpgMSjRUphou0FpWTJYuFJpDfx8+sPCZSbUzUPL8iRmsYS2tviUpxX3piTLDOu+iHkvpHSDakNbmQsvHhD5Z58MIv99hQt+X2VpSOQX3UOJ3Ciqd4nt6vmwkFXBio+Px+nTpxEREYFWrQp81fPmzcPJkydx4MABlCtXDtOmTUPv3gUvpq2tLSIiInD69Gl06dIF6enpCAkJwfTp0/HNN98AKEg8Gh0djYsXL8LW1ha7d++GkZERfHx8UKZMGdSvXx/x8fEIDg5momCxjpcSM5vmg5QuKG31aENI/bSUOhYDvFwDrZRuWFqxYnyULTEKGYt4Mm3nsoTF/y2t2Dmh7Qrtg9D3Qkw/W/17lcg3tPTHYul3RP61Kpc3q1FxhSmjdxEqC1kVLAsLCwQHB6NZs2bkmEqlgkqlQlpaGiZP5syrhQlJ7927hwkTJgAALl68iIyMDPTs2ZOUMzQ0xP79+8nf0dHRaN26NcqU4S61bdu22LhxI16+fIkqVapQvSa53Et8PiBSunCEwtI1yUJpEFo/i2ciR5D1++qkNcCyjjkU05ZQWLv0hbTD+lwW7y9rBZgP6nU6LXUksgGuF1s+/x6327Nr3lzq/Xkf+iB3ZSGrglWhQgV8+eWXGscOHTqE+Ph4zJ49mxyLjo6Gs7Mz8vLyMHDgQHTu3BlAwVY7FStWRExMDAIDA/HgwQPY2dnBw8MDLVu2BFCwoXPDhg012rC2tgYAPHv2jLqCpQ6fAUTMR0aMCZzWICYUKa1xJcF6hs4HoautxCgTYlbq8YH1veIDa9cwa1evVIj5RohRtoW2K3RSI8alyKfMFoPFXP25s4jcN/s5kVVPLYmck5dN5DJc+JaejwTZY7DUuXTpEmbNmoVu3bqhY8eO5HjdunWxb98+XL9+HUuWLIGFhQVmzJiBN2/eIDMzE/Pnz8e0adNQvXp17Nq1C8OHD0dkZCTq16+PzMxMGBsba7RjYlKwYWdWVhakQsoZJgtLlRJWZLFQCoTAok1aVjHWsOiPlJYHMefKrQwBbJ81rZhQueISafWBTz2L0gYRWXXjFJF/DG1J5AN19hE5J+EvIpdZym2bwwp9olFloRgFKyoqCtOnT0fLli3h7++v8ZulpSUsLS3RuHFjvHr1CkFBQZg8eTLKlCmDzMxMzJ49m1jCmjZtisuXL2P79u1YsGABTE1NkZ2drVFfoWJlZmbG9JpYzKZpuRyEflikcnW8W4+u/WHhTqBVJ616aLXLwo0otF1acUDaUJryJ5TC/kgZJybUAiT0OYt5/mKssWJiKdUtWN9njSfyHz0PEdmgzbdENsqWNtGoPgZLWVBXsJ4/fw4bGxtB52zfvh2+vr7o0aMHli1bRixOJ06cQPXq1WFnZ0fKNmrUCNnZ2UhJSSHtNGrEhQ+qVCrUr18fjx8X/HPZ2NggMTFRo73Cv6tWrSr8AkuA9SDJAqEfQNYDJi3XREnnibkmWm5eJcD6nWUdhM7a2iSXC1UIUlq5+ZzLesGLmHOF9l+jfPoUIm8xCeTK/NmYyG8jIon8zLkzkWuV2DM9cpOamoro6GgkJiaie/fuSElJQd26daFS6aa5ClawPvnkE+zatQv29vZFfouOjsbo0aNx+fJl3vVFRERg0aJFcHZ2xpw5czQuJDAwEHXq1EFAQAA5dvXqVVSqVAlVqlSBg4MDVCoVrly5gh49egAA8vPzcffuXbRr1w4A4OjoiJ9//hm5ubkwNCxYPnv27FnUrVsXlpaWoA0tq4iYOoWey2KAZTGQ8qGkmb7SEDObVto1ilFe1aF1jVK6naSshwasF7/Qsn4JhZYFS+s7+0MKkTuqHfco/wWRV4+yILKV71qu0EI3sEYf5K4769evx8aNG5GZmQmVSgV7e3sEBgYiOTkZmzdvRoUKFQTXyUvB2rx5M9LTC7YIyM/Px549e3DixIki5S5fvlwk3ul9xMXFYcmSJejatSvGjBmDly9fkt9MTU0xatQoTJ06FS1btkSHDh1w7tw5bNq0CZ6enjAwMED16tUxcOBALF68GGXLlkWtWrUQHh6Ox48fY/DgwQCAgQMHIjQ0FHPmzMGoUaNw7do1hIWFwdvbm3c/acB6MNHWFg132/vqpNUfloqmkga2dxF6X5VwLUqLx5JS6ZRLwZWqLRYTRDHlhcLiW6ZeZv+DJ0Su/4rLfXVTLYA97+LfRDbp9kmJ9dNEH4OlG9u3b8eaNWswZswYfPXVV/j+++8BAEOHDoWnpydWr16NefPmCa6Xl4KVlZWFoKAgAAUuuD179hQpY2BgAHNzc7i7u/Nu/NChQyT9wpEjRzR+69+/P5YuXYq3b98iJCQEy5YtQ/Xq1TFv3jx89x2XZ2ThwoUICgrC3LlzkZqaiiZNmmDz5s2oV68egIL4rdDQUPj6+qJ///6wsrKCp6cn+vfvz7ufQlDyMnElBOtKBesVc3za0tauXMoELRenOqU9Lk3buUqjNFtmS0uf+dA7K57IYae47O3LbTnjQNamm0RWzRlLZBPGfdOjO+Hh4XBzc8PkyZORm5tLjn/55ZeYMmUKgoOD2SlY7u7uRHFq3Lgxdu3ahebNmwtu7F3Gjh2LsWPHvrdM37590bdvX62/GxkZwcPDAx4eHlrL2NvbY9euXTr3UwisgzLFDDK0lC3WSgEN5UhMTJWYOtVhoWyzcJnRsnKKgZZlk9Z7LWVqCamQ0lUv5l1jrZyJeUdcvLhIqiWvvydy5ooDRC5Tk7Ns7Y9PIfLAejo3yxu9i1A3nj59itatWxf7W7169TS8a0IQHIN1+/ZtnRr6WBA6UIhxnwlVpGh99NQRMwCydC8owYLFom+0VtsJVWL4INcKTSmfNWuE9FnK4HspraWsEaOQz/mP896Un8ZtlZNfjvPNGX7KKWFly+Tp0kWd0a8i1I1q1arh8uXL+Pzzz4v8duPGDVSrVk2negUrWIWuwvdRmGn9Y4fPYChm2bKYIE5tdYopow0xM0balgRa9561O0mMsq0NKZUY1sHS2sqwVqC1tauEOnWFtZJfWmIL1dHWZ/fmXKD6kX5cjqs3rdtw55puJHLWjaKLwVii3+xZN7799lusWbMGpqamJAdneno6Dh06hI0bN8LV1VWneqkqWOXLl4e1tfVHrWDR+lCwCCrXBmsrgZSB3DTuPwvFlYW7kLVyo60/YpQt1m44KesXipjnRfu9FhMTyFrpFXMuC7e/ep017yYTOfc6V6ZCmy7F1vPXnbJEHtukxO7okYnRo0fj8ePH8Pf3J3k4hw0bBgDo3bs3xowZo1O9VFyE6enpiI6OxsKFCwUHgqWkpCAgIAB///033rx5g0aNGmHatGlwcHAAULDS0M/PDxcuXICZmRm6d++OGTNmoGzZghc3ISEBTk5ORer18/PDgAEDAABz584tEphfo0YNHDt2TFBf+SClWV2u2BnWLgV1aFiWWMSM8ekXn+NC3SdiBmkWlkR1WCveQvsmt6L+vjp1fV603G0s/idoPQcWcXRM4r3GcFaNn24mEdnw+ggiB3iHcuX7DhBWvw7oXYS6oVKp4OPjA1dXV5w9exapqakwNzeHo6Njka32BNWbn59Pzaa4d+9ebN++Hfv27Su58P8zYsQIvHjxAgsWLIClpSXCw8Oxd+9e7Nu3DxYWFujVqxeaN2+OqVOnIjExEV5eXujcuTMWLlwIADh+/DgmTpyIqKgojRxa5ubmMDU1BQB89913+PzzzzF06FDyu6GhISpXrqzztQ7vF67zuUJRWiyJmI+tOiw/8izcRqxX0tHqg5QLJ/iUUYJCzqJ+WujaT1rxeNpQ2jsrJdr6nP/iHpH3NeYm7T1Gc3kayg5yIPIaIy4B6aRPh9DsYrFce7WJSj32lUfqfG5eXh6CgoKwZ88evH79Go6Ojpg/fz5sbW2LLX/nzh2sWLECV69ehYGBARwdHTFz5kxUr14dAJCbm4sWLVoU2dpuwoQJmDhxos79lALBFqz3Ub16ddy7d6/kgv9PfHw8Tp8+jYiICLRq1QoAMG/ePJw8eRIHDhyAgYEBypQpg1WrVsHExAR2dnaYNGkSdu7cifz8fKhUKsTGxqJOnTpkA+d3KUw86ubmBisrKyrXKTUs3BJi6uEDiz4LMfkrLcidVrssXNAsEGPBEto3odZE1kqSOrQVPtaWataWOz5lpPx/4vOt0Xi2BtyQ+dUAzjZhNoezWsUN3krk06NaEHnSpzp3s1Sxbt06REREYOnSpbCxscGKFSswatQoHDhwoEiezOTkZLi6uqJly5YIDw9HdnY2li5dilGjRmHfvn0wMTHBgwcPkJWVhV9//VUjOTjNre5mzZpVYhk/Pz/B9VJRsPLz8/H8+XOEhoaiRo0avM+zsLBAcHAwmjVrRo6pVCqoVCqkpaXhxo0b6Nq1K9mcGSiwRqnnwYqJiUH9+vW1tvHw4UOkp6eTvFh6dIOFO0foYCHko8piIGIdU6WOEgYZdcRco5TKrlz1sHgHpK6PL3LFRQktQwv1ttbV3UHkSp6diPzalCtv/QunbPllcK5DKTAouQhTsrOzsXnzZkyfPp0Ei69atQodOnTA4cOH0atXL43yUVFRSE9Px/Lly4nHacWKFejYsSMuXbqEdu3aISYmBuXLl0fjxo3fbY4a586dK3IsPT0dKSkpqFSpkoaOIgTBClbjxo217suTn5+P5cuX866rQoUKZJPmQg4dOoT4+HjMnj0bBw4cQOfOneHn54dDhw7ByMgIXbt2xeTJk4nSFRsbCwsLCwwZMgRxcXGoXbs23N3dSVxWbGwsgIJEYidOnICBgQGcnJzg4eEBc3NzSAWL2CzW8ThiLANClTA+FgAarg+5rHis7xOf8mKsBKyRMn5HCdYSGrBwWdN6v7T1h8UCCRbvtVYX4fOBRA6rzrkIh22/RWRV5XJEXlSmI5G3dCmxWdHIvYrw9u3b+O+//8hWdUDBON+kSRNcuHChiILVrl07rFu3jihXQEHScgBIS0sDULIRhQba4rHv3buHCRMmoF+/fjrVK1jBGj9+fLEKVvny5dGxY0fUqVNHp44AwKVLlzBr1ix069YNHTt2xJs3bxASEoKePXsiKCgIT58+xaJFi/DixQusWLECOTk5uH//Puzs7DBz5kyUL18ev//+O9zc3LBlyxa0a9cOsbGxMDAwgLW1NTZs2ICHDx9i+fLluHPnDrZu3UoeJmukdAspYQbIIhCWhgWLFrQ+9iyeLZ/yfPrJwuqmDTHKM4v7L6V1Tdd4KFr3WEq3Oev/AzHuSD5lXMZyylNHtfIjdncl8hbPa0R2HbGNK3R7UIl9UwqdO3d+7+9Hjx4t9vjz588BoEjeKGtra/KbOjVr1kTNmprPIDg4GKampnB0dARQYCTJycnByJEjcfv2bVStWhXDhw9/bwJyWtSvXx8TJ07EmjVr0LNnT8HnC1awWAWVRUVFYfr06WjZsiVZJlmmTBnUrVuXBLR/+umnyM3NxZQpUzBz5kxYWlri3LlzMDQ0JBrwp59+ijt37mDTpk1o164d3N3dMXjwYFhYFGzA2bBhQ1hZWeH777/H9evXqWSk54PSVhfSglZALe3ZuBJWNAmtX4zFS66YKlorvoSWV1pQtFB0tRSJuQdC/w/FfF/EuIv51KMNKd+Lv/u4ELnj/jAiu4I7vuVWd6Z9eBe5VxFmZBQkX3031srExASpqaklnh8eHo7t27dj7ty5ZBHanTt3kJeXh0mTJsHGxgbHjx/HrFmz8PbtW3z77bf0L+IdypcvjydPnpRcsBh0isF6+fIltm3bhvPnzyM1NRWWlpZo164dnJ2dddpxevv27fD19UWPHj2wbNky8nBsbGzQoEEDjbKFfz958gSWlpYoV65ckfoaNGiAU6dOASgwNxYqV+/W8fz5c+oKlhhrAC1LAh+EftBoDap8oB0/xSLuik/9Qq1EUg4OLILuWbwXLFw+SotdksoVK+ZdZr1Ig/WkUwzqdXrnc1uu/XWAy+RexojTbDburUTklGzOalNZgs0IaW2Vo81CVRKFho7s7GwNt19WVhZJrVQc+fn5WL16NdavXw93d3c4OzuT33777Tfk5uaSsb5x48Z4+vQpNm3aRE3Bevr0aZFjubm5SEhIwE8//aSzi1KwgnX79m0MGzYMWVlZaNGiBWrUqIGXL19i48aN2L17N3bu3EmWV/IhIiICixYtgrOzM+bMmaPhfnR0dMS1a9fIikGgwFxoaGiImjVr4s6dOxg0aBDWr1+PNm24TLo3btyAnZ0dAMDT0xOJiYkICwsjv1+/fh0ASBmaSOkKlHJlGq3AVjksMHINrixWtLF2V9Gqk9b7Isblw+J/Qq5nR+N5iVnpSOt+KG01LR/U+7zp9o9ENjiwmchD47mBfq/lGiI/PjmcyFN0i5MuVRS6BhMTE1GrFrdlUGJiIho1alTsOW/fvsWsWbPw22+/YdasWXBxcdH4XV1RK6Rhw4bYv38/tX536tSp2NCn/Px8mJqa8trBpjgEK1hLly5FtWrVEBoaqpH2ICEhAaNGjcKyZcuwevVqXnXFxcVhyZIl6Nq1K8aMGaOxoaKpqSlGjhyJAQMGYMGCBXB1dcXjx4+xbNky9O3bF5UrV0alSpVQr149+Pj4wNvbGxYWFti9ezeuXLmCvXv3AgC6d++OcePGISgoCH369EFcXBx8fHzQq1cv5oFzciEmBouWpUXMTJXGR1jMB5h1/I1UcWg0z1Vaedb1sK6fhouQxT0W812g1Qcls3s9tzBqdAVDIqvHZoXd4TaBzr9zRe1s9nmwDGV2ETZu3Bjly5fHuXPniIKVlpaGmzdvauShVMfT0xNHjhzBypUri8Q5paWloUuXLpg5cyZJHA4UGEne9W6JYcmSJUUULJVKhfLly6NNmzY6L4gTrGBdvXoV/v7+RXJKVa1aFRMmTMDcuXN513Xo0CG8ffsWR44cwZEjRzR+69+/P5YuXYpt27Zh+fLl6Nu3L8zNzdGnTx94eHgAKHD/bdiwAStXrsSUKVOQlpaGJk2aYMuWLST7aufOnREYGIjg4GCEhITA3NwcvXv3xpQpU4ReOi9orW6hNatUR0oXgRglgsbMloUyQctSQiu2hcXyd2394VMPrfLakNJCwtrSSsNFyFrZZlFGHaVNgrTVr84PazjjwWu146G9DhI5e0smkYN6cJatafS6pxVaLkJdMTY2xtChQ+Hv74/KlSujRo0aWLFiBWxsbNCtWzfk5ubi1atXJBH4L7/8goMHD8LT0xOtW7fGixcvSF3m5uaoUKEC2rZti1WrVsHS0hK1a9fG4cOHsX//fmzcuPE9PRGGuvJGE8EKloWFBV6/fl3sb7m5ucWa87QxduxYjB079r1l7O3tsX37dq2/V6lSpcQEYF9//TW+/vpr3v1igZQfQBbxHWLq5KMUiHFfqFPcTF8bUro3lBDEK3S1mlzxOLRQQj9Z9oGFNU2Me5ZPnUL7w6ctFmj77ljFuRD5Rd0wIo/67Rsij96QS+QO3/zMVXqJvQVL7iB3AJg0aRJycnIwd+5cZGZmwtHREZs2bYKRkREeP35MUi8NGDAAv/32GwBg+fLlRVI8FZZZsmQJ1qxZgwULFiApKQn169fHTz/9hA4dOojqpxC3n0qlwvjx4wW3IXirnL1792LVqlX46aef0LJlS3L8/v37cHd3x4gRIzBoUOlZjqorUm6VIwYWM3ol1yNEwRLajjpKcOeJsZYKhZY1gxZKCFqnha4TB7mUmI/13gNA4HUuGHrCpT+I/PIQl1C08uecO+lkf87l1bkGF4/FiodvgqnUU6u8G5V6lIyQxKUqlQq3bt0queA7CLZgRUZGIisrC0OGDEHNmjVRtWpVJCcn48GDB8jLy0NwcDCCg4NJp6KiogR3So84WKxKEmOFEgrt+pU2IPAZ3FhbBmjF1PEpI6UiyBrWLkghsHBZi/nfo7WwRcqJCZ961JnSgFslnw87Invt49IxTBucTeQqPX/hTj7DXsGSO9FoaeL27dvM2xCsYBWXGMzW1hb29vbUOlWaUcIsns/HjVZMlTqslTAhg7xcH2laioi28ixcXaxjWJRg7ROKlG4qXetk/T+s7Tgtdz4LhP7/Cb2W9iFcroWTg+sQOWwFVz7m27NEfr6XTWyPNpTgIvwQuX//vk7b7QlWsHTZ8LAkUlJSEBAQgL///htv3rxBo0aNMG3aNDg4OAAAfv31V4SGhuLRo0do0KABpkyZgi+++IKcv3//fsyYMaNIvUePHiXK4B9//IE1a9bg8ePHqFevHry8vDTS+dOCRcAorSB3Pv3kU55F/bSgbRkQU4ZFu0pQ4PmUkesesoD1s5bqmUo50dEGLWupmPrFxJCOVEsoqho2isjpIceJXPl3bhVh47JcqgI9yiUlJQWBgYE4f/48srOzURg5lZ+fj/T0dKSmpkrjIiwkNTUVGRkZyMvLK/KbkDxYADB16lS8ePECAQEBsLS0RHh4OEaOHIl9+/bh5s2b8PLywuTJk9GjRw/8888/cHd3R0hICMl9FRMTg9atWyMgIECj3sJMsGfPnsWMGTPg6emJL774Av/73//g5uaGyMhIyVI1iBlwWAxuUi7ZFwqN+pUQJyQU1oOPUFhb42hZiVgoK0q7zzTPE4uY95S1dZKWBUsb65fPJvKP5dOJbDZ/NJGNwnYTea1THyKPbyqoKZ1Qyb7dc+nEz88Pv//+Ozp06ID79++jbNmyqFOnDi5evIi0tDT4+PjoVK9gBSs+Ph5eXl64evWq1jJCNL34+HicPn0aERERaNWqFQBg3rx5OHnyJA4cOIBjx47h66+/hru7OwCgbt26uHXrFoKCgoiCFRsbi0aNGhVJHVFISEgIunTpgmHDhgEAvLy8cPnyZWzdulXnGycUodYpWnE62s7l0zehZbQhR4A8CzeGmFgiJayMotV/WteorQ9KWFBBK26MpTuNdTyYElzHSojrU6/zO+dEIudMDiFyZjUuD1bqLS4Gy64Xt6JQCopLlqmnZE6ePImJEydizJgx2Lx5M86fP4/AwED8999/GDp0KO7evatTvYIVrEWLFuHBgweYMGECbGxsRG+WbGFhgeDgYDRrxqW5ValUUKlUSEtLQ3x8PL7//nuNcz755BNERkYiJycHZcqUQUxMDDp16lRs/Xl5ebh06RJmzpypcbxNmzY4fPiwqL6XBK2ZHq04Kj7t8inPIhBWWxldYfHxFjMrl8uiJqVFR67BUMlWSVr9Ka4eFnWz+NYI/X+W0o0olJ1bKxF59Q1XIl+bz3lyFm3nttB5Xklts+ecYTq3q4ctaWlpaNGiBYCCDZ43by7I0l+uXDmMGDECQUFBmDVrluB6BStYFy5cgK+vL3r16iW4seKoUKECvvzyS41jhw4dQnx8PGbPno2TJ08W2SfoyZMnePv2LdLS0mBoaIiEhARER0cjIiICycnJsLe3x4wZM1C3bl2kpaUhPT0dNjY2GnVo291bLCwsVdpg7cLhA61gdtorH1lb5cTUI2YQE/PcaD0TWvF4tP4/tFm/+PRBHSUoaiXdBzHfDj7vHZ/jfBD6PIX+T9CKDxR6jatvvCRy5c+4rXI277IlsuoUt+ee86lWguoXi95FqBvq+T3r1KmDpKQkpKSkoFKlSqhatSoSEhJ0qlewglW+fHlUrFhRp8b4cOnSJcyaNQvdunVDx44dcePGDWzatAlt27bF559/jgsXLpBtcN6+fYv79+8DKAhG8/PzQ2ZmJtavX4/BgwfjwIEDyMnJAVD87t5ZWVnMroMvtJQSWhYpoYgZPJWKXNZAodDqJy3lltZ1Ca2ftSWnNMQ30rJOilGMWFhIWbxHQlHvm/pGzm/HcsrMzcBkItc41oXIBipuOx3uTHboXYS60a5dO2zYsAGNGzdGrVq1ULFiRezbtw+urq7466+/YGFhoVO9ghWsvn37YseOHWjfvj31hxkVFYXp06ejZcuW8Pf3BwC4ubkhOTkZ7u7uyM3NhZ2dHUaPHo0VK1bA3NwcDg4OOHPmDCwsLEh/goKC0LFjR/zyyy/47rvvABTs7q1OSbt76woLkzaLjzStD5cYix0tK52uKMEtJaVSIrSMGEuSOlIG77OIJ5LSGsoSMTFvYp4bLQuvUFi8U1sMFhff1i9ViJyzMZLIvl9waRq8pTVm6RHA5MmT4ezsDC8vL2zfvh1jxozBsmXLsGHDBqSlpemUxR3QQcEqW7YsLl68iK5du6JZs2ZFtsZRqVRYsmSJ4I5s374dvr6+6NGjB5YtW0YsTsbGxpg3bx68vLyQkpICa2tr7NixA1WqVIGZmRkAbrWgeh9r1qyJhIQEVKpUCWZmZkhMTNQok5iYiKpVqwruJ0uk/DCLiZ3SVg+f43z6QwPWA6FcFjq5Bm+hihqtBQRSTkzE1CPHc5fy+pRcv5i2hD5b+ysjiDy1HHc898wNIuf/l0PkeS0sBfVHLHoXoW7UqFEDBw8exIMHDwAArq6uqFKlCi5dugR7e3v0799fp3oFK1j79u2Dubk58vLyil1JqItVKyIiAosWLYKzszPmzJmjUceqVatgamoKd3d3WFtbAwAOHz5M8mDt2rULAQEB+Ouvv4jC9ebNGzx48ADffvstVCoVWrZsifPnzxNrFgCcO3eO5NmiidJmsqwVChYBzCxdLCwsGaUlvoc1Yt4FFvfkQ7rnxcVgiUHKCYKUz4GWYq+tzJG6K4gcFs+FmPy4qDyRTScN5crc4WK2XBqV2B3RqKB3EepCVFQUOnbsqLF9Tu/evdG7d29R9QpWsI4dOyaqwXeJi4vDkiVL0LVrV4wZMwYvX3IvpKmpKWxtbeHr64vGjRvDzs4O27Ztw7Vr10gclpOTE/z9/eHp6YnJkycjMzMTAQEBqFy5Mtkh29XVFW5ubmjSpAmcnJywd+9e3Lp1C76+vlSvBWBjORE6sLNYqUfLtcfaGlBSMLDSBhAWgcRKgEVAPa22hPIhrnZkvdBC6LdJSfcG0H6NbWO5lYNHq4cS2firhkTOfxlH5O/q2THonXZUKr0FSxcmTJiASpUq4ZtvvkHfvn3RvHlzKvUKVrBoc+jQIbx9+xZHjhzBkSNHNH7r378/li5diqSkJHh7eyM1NRWffvoptm7dStLWV6tWDWFhYVi5ciV+/PFH5Ofn44svvsC2bdtgYlKwrUH79u2xZMkSrFu3DqtWrYKdnR02bNggWZLR90FrFR6f+sVAK76K1vXS+AhLmcJAW7tCYaGos3DniVHI5QpsFhpnJrcbXB3WK1DFlBdTp1yKl7Zn/vhNKpE7DuD2/VM1URuQk7lV77/EcSkbnDkdTI/C2LdvH3777Tf88ccf2LlzJ2rVqoU+ffqgT58+sLW1LbkCLajyC3PCv4fOnTtj7dq1aNy4MTp16vReN+DHssHz8H7hgspL6TJjHegrtE4xAdK6fniltIiwsGrQSlvAItCXVn9Y/0+wsCzKFYxPwzJL672mNZGSy4Il5nuU989BIqvsGhD5/vADRP5vRz8i33zFxSj/YDdEcF+Fkpy1g0o9Fibs+6pULl68iN9//x2HDh3Cq1ev8Nlnn6Fv37744YcfBNfFy4LVunVrlCtXjsj6paDCkStYl8VslkV/WM6iWccDsYg94nNcSkuPmP4IPZfFREAMrC02Qlf0SdUvFu1K+S7w6YPQd3bzvm5Ezpq7isj1Frck8o3e+4n8dotmkmzW6F2E4mnVqhVatWqFqVOnIjAwEDt37sSVK1fYKVjqGzwvXbpUcCN6tMNiRqcEl4zcM1UWgeesLSK04tz49IGWa1dpsO6/lIM/7b5IqZzLNaFUh9ZkSuNeZaQR8eUtztBg9ed1Ijf4hitexopzEepRPtnZ2fjrr7/w+++/4/jx48jLy8NXX32Fvn376lSf7DFYHxpSBoPT6oPS6qeB0pb9K+E+sXjXaNXD2n1Na5KihOdImw/pmlhMmtSPd9wfRuSeQ4yIXKY2lyrIsFk9IlcpyyUalQL9KkLdOH78OH7//XccPXoU//33Hz777DPMnDkT33zzjajE6noFizKs40FYx4DwgbUbgfb10vroKi1Ng5SKrlDrh5QxTyzc4Ep7XrT/D/gcl/I+KTkAX/MaXYjcE1y8U5m+fYice/A3Ij+zrUVkK82UkUzQ58HSjTFjxqBWrVpwdXVFnz59UKtWrZJP4oEiFKykpCQsXboUJ0+eRFZWFhwdHeHl5UVW+R07dgxr167F/fv3YWFhge7du2Py5MlFkpwCQHR0NJydnREWFoY2bdqQ466urvjnn380yrZu3Rrh4cKC1XVFjOIl5iPDeqUci4GIRj1iBgcW16FrnA1NWLujaV2jkgdqKd3KJUHrWdGqUx1alnwlB8tfPsFZi1ZHcmPR707NiNxMAdux6SmZnTt3ks2eaaIIBWv8+PHIy8tDcHAwypUrh9WrV8PFxQWHDx/Gv//+iwkTJmDSpEno0aMH4uPjMX/+fKSkpGjEhgHA69ev4enpiby8vCJtxMTEYOHChejSpQs5ZmRkVKScWJRgqRKqtPGxSEi5Ko92rBPrD7DQFXa02lIycll9aCl8tKyVfOrkU0bXVYRiVszx6RcLWEw6xaBxr7abEXlLBW77td9bX+NOeMnFXSV8wrmXbBj1Tx39AjTdYKFcAQpQsFJTU1GjRg2MGTMGDRsWJAoZN24c+vbtizt37uDnn39GmzZtMHbsWAAFO117eHhg7ty58Pb21tjEeeHChbC1tcWTJ0802khKSkJSUhKaN28OKysrptejBJM2i5keLeuXVAqClAHIrO8frXpYWEi1HZcrFlEJ8YFSxaJJqXBK+V1TckxYfio3tri+nckdv32GK1SDu7cnn3EK2XcSpF3UuwiVBS8FKzIyUlCl/fr14122YsWKWLlyJfn71atXCAsLg42NDezs7DBixAgYGGi+NAYGBnj79i3evHlD9iH89ddfcfnyZaxfvx59+vTRKB8TEwOVSoW6desKug5dkNJ0LVdb6tByk7AcGFnHYInpg5TWPVpIuYhCHSUPvKUBPu8d6/8V1lZ69hZ17njH/dyK+uunRxI5wJYLbB949jB3Qv2PN7fUxwovBWvmzJkafxeaIdVzlKqbJoUoWOrMmzcPu3fvhrGxMdavXw8zMzM0adJEo8zbt28RFhaGTz/9lChXjx8/hq+vL9atW0fydakTGxsLc3Nz+Pj44PTp0zAzM0OPHj0wbtw4DQsYbeRSLFjHYAn9QAkNFJdKcWQdgyW0XRbB40LbZWHNkNJaKqZduepUEiwsVWKUNqFt8UHMt6zOPE55Gtq9CpFHLcslcv7mHCKr6uueBVwX9KsIlQUvBevo0aNEvnXrFmbMmIFx48bh66+/hrW1NZKTk3Hs2DGsWbOmSFyUEIYPH45BgwZhx44dGD9+PCIiItC0aVPye05ODjw9PXHnzh3s2FGwgiM3NxczZszAoEGD4ODggMePi/4TxsbGIisrC/b29nB1dcWtW7ewfPlyPH36FMuXL9e5v8VeQykxpUt5rhiFjAasXWO0zmVthZQrbkVKWLfL2iLMElptShkjJbR+1grwyEZviDxqUS8ib/4xisgTb3Un8pqG0j5nfaJR3YiMjMSXX34JCwuLIr+9ePECkZGRGD16tOB6eSlYNWrUIPLEiRMxbtw4jcaqVq2KH3/8EdnZ2VixYgW+/PJLwR0BADs7OwCAr68vrl69iu3btxOF7c2bN5gyZQrOnz+PoKAg2NvbAwA2bNiAjIwMTJw4UWu9Pj4+8PLyIvksGjZsCCMjI3h4eMDT0xNVqlTReq6SkSsYlEawri71C7kWpQXlCkVM0LI2WKeZUNqKLxarI5VgRaHdJou0JHLBOvYy5MtDRO64P4XI39SeTOQDDbYTOe9xWSIbDhpFvW/vordg6casWbOwa9euYhWsW7du4aeffmKnYKlz7969Im67QurVq1esBel9vHr1CmfOnEH37t1RpkxBdwwMDGBnZ4fExEQAQGJiIkaPHo0nT55g06ZNcHR0JOfv3bsXiYmJJCVDodty9OjR6NevH3x8fFCmTJkiycIaNCjYR+r58+eyKlhy58bRBdZByzRmpyxcF2La4gNrhVnMfRVjgRNanoUbVK56aMGyD6wnZ7RWgkqpnKuj3lb3ycOJvPnbDUT+LTWTyEbtuF2dc2/FM+2bHt1xc3PDvXv3ABToDePHjy82ZCgpKUnnvFiCFaw6dergwIED+OKLL4r8tmvXLrISkC8vX77E1KlTERoaig4dOgAoiLO6efMmOnXqhNTUVAwfPhxv3rzBjh070KhRI43zw8PDkZPD+bwTEhLg7OyMxYsXkz46OzujZs2aGu7L69evw8jICHXq1BHU35KQMt6EdjqD99XDOi6CtrJT2i1YSmhLTLu0JgtKU3T0yBdTpwSGTXhN5Orp3ETf9DHnmlM170Bkg4SX0nSssG29i5A3Y8eOxZ49ewAA+/btQ5MmTUhcdyEGBgaoUKECBgwYoFMbghWs8ePHY/LkyXjw4AG++uorWFhY4OXLlzh8+DDu3r2LkJAQQfU1bNgQTk5OWLx4MRYvXoyKFSti48aNSEtLg4uLC/z8/PDo0SOEhoaicuXKePHiBTm3cuXKGu5LADA0LAhCrFq1KiwtLQEA3bt3x5IlS2Bvb4/27dvj+vXrWL58OUaOHIny5csLvQXvhcVAQWtljzbEBKHz6QOfdml/qFm4Oli42OSOT+NbJ+t3lta5tJBSURZSXkoXNAvLVmlXzg7P4HIs/jiTs1rNX8UlrM5vwhkfEju2IrIkebD0aRp407JlS7RsyW3SPW7cONja0l2UoMpXXwrIk8LM6jdv3kR+fj4MDAzQokULeHh4wMHBQXAnXr9+jZUrVyIqKgqvX7+Gg4MDZs6ciXr16qFFixbI0pIN9+jRo6hZU/ND9fjxY3Tu3Bnbtm3TyOS+Y8cO7NixA48ePYKVlRW+//57uLm5FUkBwZfh/YrPAK+EgUWMdYqFJUwuC49UfaEVeyQ0zkkdMfUowQqpNAVUaROi4hQsWqvz1OFz71lbuVnE+4lpSx3/itz2OFYbxxM5Pz2ZyIf/48rHpHKrDid9yj5NQ1bu71TqMTHsSaWejx2dFKxCsrKykJqaikqVKjFNd6BEtClYHwNKUCL51M+yHW1tyqVY8umPmPJKsCqpU1pc1rTaoqFgCW2ztEPrvVYvkz6TW3Vu6jeNK3TyIBFVn3AxyjmW3LlGBj149Foc2bkHSy7EA2PDb6jUU1p49eoVfH198ffffyMjIwPvqkUqlQo3b94UXK9gF2EhqampyMjIQF5eHl6+1PQzV69eXddq9RSD0lZnlQaFRcpB40MdoNSRctGAUEqLUkgbWgseWCur6tCy0vKBjTt1MJEmJz0kcss2nYm89m4Kkd3P/cKd2ou9gqWPwdINHx8f/PXXX+jZsydsbGx09my9i2AFKz4+Hl5eXrh69arWMrdu3RLVKT2aCI2FYL06i08fSnseJzlQmoKijtDYHNauGtb1KI3iroWWi1Bp75qSlfmwfWoxv0kPiJif9ozIbqeiiPxrt15E7s+0Z3rEcOLECcyePRuDBg2iWq9gBWvRokV48OABJkyYQFXT00MPFnl1pJw9KvUDqwTLoFyDJOvnz0IJV7KyRSPIXcw95pMHq6S+6NIHWsjlcj/4kGv367JcwHuHn7ll/KcmjCFyv/vnGfdOE30eLN0wMjKiHuAO6KBgXbhwAb6+vujVq1fJhXmSlJSEpUuX4uTJk8jKyoKjoyO8vLxQv37B7phz584lyykLqVGjBo4dO1akrri4OAwYMADz5s3TWFp569Yt+Pr64saNG6hcuTJcXFwwbNgwatdQiJgBkNaHjsVALTRAljUltSVlf8WsvBSjECgtrovFPadlpaEVOK0OrQUhfPogpCzrYHNarj1aiz2kVKoT6oQROUzteHhPLqHo4+Nc/M6KqVxg+2opNnvWuwh1omvXrvjtt9/w+eefU61XsIJVvnz5Ikk7xTJ+/Hjk5eUhODgY5cqVw+rVq+Hi4oLDhw+jbNmyiImJwdixYzF06FByTmE6BnXevn2L6dOnIz09XeN4cnIyXF1d0alTJ3h7e+PKlSvw9vZGuXLlMHDgQKrXIuXHhJZ7TmgZWoOtUGgvIad1n8Q8cyWkaaB1X1m4CFlbQrWVp7WqkU9/dFUKaPwPvFtejILCwtUstC3mluU+LkTc0HQnkQO/42KzZuZzITKBFR5R74/SycvLQ1BQEPbs2YPXr1/D0dER8+fP12ohSk5OxuLFi3HixAmoVCr07NkTnp6eKFuWU1r/+OMPrFmzBo8fP0a9evXg5eWFdu3aUetzkyZNEBgYiEePHqF58+YwNTXV+F2lUmH8+PFaztaOYAWrb9++2LFjB9q3b6+xwbOupKamokaNGhgzZgxJUjpu3Dj07dsXd+7cQbNmzXD37l24ubnBysrqvXWtWbOm2LxWu3fvhpGREcnqXr9+fcTHxyM4OJi6gqUNFh8TMeXlcimJsTDQ6LMS3CFilFJ9bJu8sLB6Kgla/xMsFGahfaCFeltp2VweRtNbXKLRr2tyObFyLdoT2eDEH0RWSWHBUoCLcN26dYiIiMDSpUthY2ODFStWYNSoUThw4ECx2QYmTZqEjIwMhIWFIS0tDXPmzEF6ejqWLVsGADh79ixmzJgBT09PfPHFF/jf//4HNzc3REZGEi+XWHx8fAAUeOguXLhQ5HfJFKyyZcvi4sWL6Nq1K5o1a1asprdkyRLe9VWsWBErV64kf7969QphYWGwsbGBnZ0dHj58iPT0dNSrV++99Vy4cAG7du1CZGQkOnbsqPFbdHQ0WrduTbbiAYC2bdti48aNePnyZandi1AuWJjkaX/YlTa7F9qWmDJCUUKAudJisKScgMjtfleaa5qPq1GuScHE77mcjGGLOGVm1Ugj7vgazoKV/0bTm8IauRONZmdnY/PmzZg+fToZh1etWoUOHTrg8OHDRUKLLl++jPPnz+PgwYNEWfLx8cGoUaMwdepUVK1aFSEhIejSpQsJ6fHy8sLly5exdetWohiJ5fbt21TqeRfBCta+fftgbm6OvLy8YlcSirFqzZs3D7t374axsTHWr18PMzMzxMbGAijYEufEiRMwMDCAk5MTPDw8YG5uDgBIS0uDp6cn5s6di2rVqhWp9/nz50W28LG2tgYAPHv2TBIFi0XOmtISbKprQO+79et6jUq4T9pQQt8+VEuY0vojhpImDmKQcsKkBGVOjFv+WV9uK5Xn9Tnlyd6HU2xOG1UissMlbnWhKb2wZa3Q8CqJ4fbt2/jvv/803HcVKlRAkyZNcOHChSIKVnR0NKysrDQsUa1bt4ZKpcLFixfRo0cPXLp0CTNnztQ4r02bNjh8+DDbi6GAYAWruMByWgwfPhyDBg3Cjh07MH78eERERCA2NhYGBgawtrbGhg0b8PDhQyxfvhx37tzB1q1bYWBggIULF6JFixbo3bt3sfVmZmYWMU2amJgAgNYs8bpCK15KzKyWRUC1tvppxaewhFawOS0riBg3qRikVNSldMUqLY+THMgVF6kNpd4nXVC/lodv7hDZZRY3mT/kz1mwbrx+SmSTIZy7sDTRuXPn9/5+9OjRYo8/f/4cAIoYOqytrclv6iQkJBQpa2xsjEqVKuHZs2dIS0tDeno6bGw0NxrSVp8Qhg0bhgULFqB+/folLnhTqVTYunWr4DYEK1gssbOzAwD4+vri6tWr2L59O3x9fTF48GBYWFgAKNi7sHCrm+vXryMuLg7R0dE4cOCA1npNTU2RnZ2tcaxQsTIzM2N0NeKgFRjMIq6Hj/uNteKoqwtQqLKlDRaKGi1LnzZYK8NCFUdafeCzgEBpShvt+y/m+bNWhmm9p0InBbS+j+qYlTEh8oE63ID7MrcPkT+twNV55i23GIteSPZ70HlfFjpkZGQAQLEGjdTU1GLLFxeXZWJigqysLGRmZmqtT6xxRD1be0kb2ui64Y1gBYtPaoNt27bxru/Vq1c4c+YMunfvTmKkDAwMYGdnh8TERBgYGBDlqpAGDRoAKNCW9+7di6SkpCJxVwsWLMDBgwcRGhoKGxsbJCYmavxe+HfVqlV595UPLII+pVyqzgehA6aULkIhrhQxFkDWSpIS4r1o1U8rxkgJChwLpFq8wVqBV0fMJIn1SkMxk86w9dxGg9/c58bCg2+SiJxWgVMk7CtXKLEtquTnlVyGB9osVCVRGJOdnZ2tEZ+dlZWlsSpQvfy7xo/C8mZmZsTTVJyBpLj6hBAezm13t3DhQmoB8+oIVrCK0+TS09Nx7949mJmZoVu3boLqe/nyJaZOnYrQ0FB06NABQEG6hZs3b6JTp07w9PREYmIiwsLCyDnXr18HUGDx8vf3J1puId26dcOkSZPQp0/BrMLR0RE///wzcnNzSXqHs2fPom7durC0tBTU35JgnVJBHVofGVpKHi0Llpg+CCnLwp3Lx0qgjhilSi53njaEDoZCr11MeaHIpYDq2i6L90XMfVXyKkIxVuwpcZWIvLfseiKvS3AmsrslF6elun2WO/kTLi/jh0qhuy8xMRG1anHJVxMTE9GoUaMi5W1sbBAVFaVxLDs7GykpKbC2tkalSpVgZmZWrIGEpnFk8ODBmDVrFvr160etTkAHBUtd61MnNTUVo0ePLnG137s0bNgQTk5OWLx4MRYvXoyKFSti48aNSEtLg4uLC27duoVx48YhKCgIffr0QVxcHHx8fNCrV6/3apyWlpbkAQwcOBChoaGYM2cORo0ahWvXriEsLAze3t6C+ioUWpYnFoqXXMgR2CpXjBELhYCF20voubT6ycKiqg5rd57c/2dyfV+EInRiogQlTFs9OVs3cXJFzl3YrabaasFfufxYz7u1JnLR5VcMoGTB0pXGjRujfPnyOHfuHFGw0tLScPPmTY08loU4OjrC398f8fHxqF27NgDg/PmC7PetWrWCSqVCy5Ytcf78eXz33XfkvHPnzsHBwYFav42MjIp4ymhALQarYsWKcHNzw5IlSwRnSA8ICMDKlSvh4eGB169fw8HBATt27ED16tVRvXp1BAYGIjg4GCEhITA3N0fv3r0xZcoU3vVbWloiNDQUvr6+6N+/P6ysrODp6Yn+/envDsXCNaINFoOYGFcDLSsQnzpL6idrRY61S4t1bItQWLumWVskWFvptMFSqZHLMqsNWi5ZuZ4Vn3oaL/yGyKOOcaEwi4dz7rBN33LurIwcidMmyKxgGRsbY+jQofD390flypVRo0YNrFixAjY2NujWrRtyc3Px6tUrmJubw9TUFM2bN0fLli3h4eGBhQsXIj09HfPnz0e/fv2IgcTV1RVubm5o0qQJnJycsHfvXrIzCy0mT56M5cuX4/Xr12jcuHGxsdnVq1cXXC81BauQpKSkkgu9g7m5ORYuXIiFCxcW+/vXX3+Nr7/+mnd9MTExRY7Z29tj165dgvsmBjEfYylnx6zdf1K6PlmihI896zppQeu9EKPw07L2Ke2502iTxeSPlsWW9aRTTJ1bDBYT+U8jLiXClr+uEdmg3QgiP37+kMj1JA7HkotJkyYhJycHc+fORWZmJhwdHbFp0yYYGRnh8ePH6Ny5M/z8/DBgwACoVCoEBQXB29sbw4cPh4mJCXr06IFZs2aR+tq3b48lS5Zg3bp1WLVqFezs7LBhwwaqMVMLFy5Ebm4uZsyYobXMrVu3tP6mDcEKVnFZTnNzc/H8+XOsW7cOTZs2FdyJjwExHxwWq9FYuJek7E9JsA7oZ9GWUPeJXAq5UFgrKGLeO7mg0bfSMiHjcy4tayzr+zA0hxuAy1w9TeSwVv8Q2TXdichN6Hud3o/MFiygYBu7GTNmFKus1KxZs4gBxNLSEj/99NN76+zXrx/1+Ch1Fi9eXHIhHRCsYDk7OxdJZlYY+F6tWjXMnj2bTs8+MKScBbOYrbNQHFjCwj0gVNEVasWRK8idBazdnXzqYQFrZZpPeRrti4HFt6y0WPtjUp4Q2WzuFSK7nGtD5LxLJ4hs0Kq5JP3iGpdfwSqNsAgXAnRQsIpLwaBSqVC+fHk0atQIBgb63bxpwzqYlZZVhNbKQZYfT9arNlmcW1qgFXcnFCkH29L8HEuLxVNp/VTvQ9haLk1D/hhuFVv+kwQiv72XTOSb9TiPTnvNXJl6FMbLly+xbds2nD9/HqmpqbC0tES7du3g7OyMChV08+8KVrBat+ZWRWRkZODNmzeoVKkSjIyM3nPWx4MYEziLD4uYQHWh5Vn3WQi0Viixjumgda6UAcy03ilt/aFVD59zhSK3W07MtYpRbmlZY2m9j9pg/XzCbH8lsqU1N+Y5dOQMC9XW/UBkk9xc6n14LwpwEZZGbt++jWHDhiErKwstWrRAjRo18PLlS2zcuBG7d+/Gzp07pQtyj46OxvLly3Hjxg3iHrS3t4eHhwfatm0ruL6kpCQsXboUJ0+eRFZWFhwdHeHl5UWC2P79918sXboUN27cQMWKFdGrVy9MmjSJZHct6XwAmDt3Lvbs2aPRbo0aNahv/VNarBxK7qeSZrC0Bg1tdaoj1FUkJrZNCcqiOqUltkypgz+twHY+dbJWaOWyMmtDvT+dgqcTOcruDyK/CufiitJmc6mMshYOod6f96JXsHRi6dKlqFatGkJDQ2FlZUWOJyQkYNSoUVi2bBlWr14tuF7BCtalS5fg4uICW1tbjBs3DlWqVEFiYiJ+//13jBo1CuHh4WjRooWgOsePH4+8vDwEBwejXLlyWL16NVxcXHD48GFkZmZixIgR6NGjBxYvXoyHDx/Cy8sLeXl58PT0LPH8wmyvMTExGDt2rEYujsKko3pKRow1TkxbUp6ra91C46WkXG1JCymDipX6/AFlDf5i2lfaKkm57yXftpz3hxH5uB2XmsFpEef/MxzAWbC+SONch5KgV7B04urVq/D399dQroCCnV4mTJiAuXPn6lSvYAUrMDAQDg4O2LRpk4aCMmHCBIwcORJr1qzB5s2bedeXmpqKGjVqYMyYMWjYsCEAYNy4cejbty/u3LmDxMREpKSkYMaMGShfvjxq166N3r174+TJk/D09CzxfHt7e+Tn5+Pu3btwc3MrcgNZIsYtyDpuidbKPqGr3YT2R9cPqRirEgt3mDakbFdK9wyfvrGw8Ml1f1i7JktqU4xFVcr/FRYxpGLa4oN6nS8zuRyPB8y4eOT7zbkdTDr2WUfk8r/0JTLdTdn00MTCwgKvX78u9rfc3FyNbX+EIFjBun79OlauXFnE+mNgYIChQ4fCy8tLUH0VK1bEypUryd+vXr1CWFgYbGxsYGdnh5ycHADAzp07MWLECDx79gzHjx8nWVxLOh8AHj58iPT0dMFZ5sUi1LIh9Fw+5VnEIolpl3XKhpLqF/NMtJVh4XoRUw+f8qxX+fFpi1b/tZURihIsOTTapBWDRUthktI6xdqi2nE/p1R91ZvbC69SPS4Gq9LvU4icnlf8oM0M/SpCnRg/fjz8/f1Rq1YttGzZkhy/f/8+Vq9ejQkTJuhUr2AFq1y5ckTpeZecnBydd50GgHnz5mH37t0wNjbG+vXrYWZmhpYtW8Ld3R2rV6/GqlWrkJubi7Zt22L+/Pm8zgeA2NhYAAXb/Jw4cQIGBgZwcnKCh4cHzM3Nde5vcUgZPyJlALMYhYIPSou1EUJpDKCW0rVHa6DmQ2lJ96AOCyWFdvtCyys5jk4MQ1ZWIvKOaSlETo3hFJtJr/8kctmyapaPFv0Y9uz/0bsIdSIyMhJZWVkYMmQIatasiapVqyI5ORkPHjwg4UfBwcEACrImvLt/ojYEK1gtW7ZEcHAwOnTooLGbdXp6OoKDg0XtDzR8+HAMGjQIO3bswPjx4xEREYHatWvj/v37GDJkCPr06YNHjx7Bz88P8+bNw7Jly0o8v2nTpoiNjYWBgQGsra2xYcMGPHz4EMuXL8edO3ewdetWqqklWAdZirEA8KmTT3k+yBW0XFL9Ug7qrJUYpQ1icqUH4dMfJcf4CHHFsXbtirFss373+bTLmn8G9SGy5TJuz0GXCs+IfHc6F/Be6Vcuv5J0wSl6hFKzZk3UrKn5f2hrawt7e3tR9QpWsKZNm4YBAwagc+fO6NixI6ysrPDixQv8/fffyMzMFLU/UKFLz9fXF1evXsX27dthbGyM1NRUkum1adOmqFixIlxcXODi4oJPPvnkvef7+fnB3d0dgwcPJps5NmzYEFZWVvj+++9x/fp1NG9OLxmcXMuThfZHDLRiZMTUI+RcuSwofPqgBJSsqLFQ/ln0QUz9fCiuD7S+I7TizbSVERMfprT3EWoOmrz/cTmuco9eInKNTzOJbFZG+NJ+UegtWDrRp08ftGjRQudYK20IVrBq166NXbt2ISgoCMePH0dqaioqVqyI1q1bY8KECUTJ4curV69w5swZdO/eHWXKFHTHwMAAdnZ2SExMREJCAjp27KhxTqFC9ODBA1StWvW95xf+/e5O2Q0aNAAAPH/+nKqCJSYuQmidYgLMWXyExQyGtD+wtOKctMF6JaWYdoXC2kUl1AUt5tzSeM+FvEss3mspLX1KcCOLYYvhYiI3dyxHZPsz44lslplO5BMvnhPZqRrjzgF6BUtHJk6ciPnz56NPnz4lFxaAYAULKLAUBQYGUunAy5cvMXXqVISGhqJDhw4AgLdv3+LmzZvo1KkTgKKbNxf+XbduXV7ne3p6IjExEWFhYaSO69evk2thBeuPgxLcGLTgM0gKuZ+03B7qCFUmxSjGYurUBq1VcmIGSVp1aoO1AkQLWgsjaMDiHoux3rN4L2hx5+xIIrs2r0jk4QMSibxl6mUiZw2/zZ0cN5xt5/ToTIUKFahbrwAdFSyaKeUbNmwIJycnLF68GIsXL0bFihWxceNGpKWlwcXFBffu3cPo0aMRGBiIAQMG4MmTJ/D29kbHjh3RuHFjAHjv+QDQvXt3jBs3DkFBQejTpw/i4uLg4+ODXr16Ud2R+12UMOPShlwzQzFKhJBzxQyKrF2XtMpoa1dMLJQ2WAc2Ky0GS0pLmBDlWAkxWLRWEYp5H+WK69wazg3CW8utIrLn5+WJrKrdnsj3fm9F5K6sOqhGfj6dzPGqkot8UIwZMwaLFy9GXFwcGjduTBbIqePo6Ci4XsEKFouU8gEBAVi5ciU8PDzw+vVrODg4YMeOHahevTqqV6+OjRs3Yu3atdi6dSssLCzQtWtXTJ48mdf5ANC5c2cEBgYiODgYISEhMDc3R+/evTFlyhShly8IFjliaJ2rrR51pIwJo62ASDlQCK1fytgvoeeK6Y9QpUFXJYNvGSmvRY46WV+fUEurmAmINsR8F1j8n214lE3kBrWNiby88kAih73kPC5jmzej0i5v9GkadGLBggUAgFWrCpRmlYpTMfPz86FSqXDr1i3B9aryBeZVcHFxQXJystaU8vXq1dMppXxpY3i/8GKP0wrQLS0WAylnknIrWEJhbRmQa+EEn/6oI5eizroPtKD9XgttUx0lWA+lrF9MPb5lfiby0cgsIndJ/Y7IadlcvshPLEbp0kVB5L/aVnIhHqgqDyu50AfE+fPnSyyjvg8zXwRbsFillP9QUFosAa1l1KVl8BdSp9KUUjGxMEqwYKlDK9ZGHRqxebqghIkMbVhY6Fg8H1pWNFr3/mt/LqXPnW+448d6cfFVww24MJkqm7dyhaaxV7D0Qe66oYvyxAfBCharlPIfCqytFkJh8SGSa0AT0jcWSiAL5YNPf5SAmNV8Ugaey7XKUui5rBeNlISU90notYpxBTNXHHNziJi/rw2Rnxpzilf+tTNEvuXSi8ifUelNCegVLJ159eoVNm3ahH/++QcvXrxAaGgooqKi0LhxY3Tp0kWnOgUrWKxSyn8oiLFC0IJFbJGY+Bqh0LgnpV1ZEQqLmBrWKFlxYW2BlfL/SW7ksswymbAO5NIuTHwcTeTDNesSuX/DK0RuPlwtWNqSeneKolewdOLRo0f48ccfkZWVhVatWuH27dvIzc1FXFwc1q1bh3Xr1hVJF8UHwTFYzs7OuH37Nt68eVNsSnn1AHchKeUBIC4uDgMGDMC8efMwYMAAjd+Sk5PRu3dvrFy5Em3acDOHpKQkLF26FCdPnkRWVhYcHR3h5eWlsTrw1q1b8PX1xY0bN1C5cmW4uLhg2DBxPmZtMVhiUIL1Swl9oAELV4HSYp60Idd7obR4vNKCrjFYSnbbKyEOkNb3S71M3i9cjNMfE7js7S8TOcvWsEfc2HUsj8uV1bkG+zQN+S9CqdSjspLAnakgJkyYgEePHiE8PBxmZmb49NNPsXfvXjRt2hQTJkzAy5cv8fPPP5dc0TsItmCxSin/9u1bTJ8+Henp6UV+S0hIgLu7O168eFHkt/Hjx5O9gsqVK4fVq1fDxcUFhw8fRtmyZZGcnAxXV1d06tQJ3t7euHLlCry9vVGuXDkMHDiwSH2sEBOPJSYOgYVFTcpAfqHtFtcW6w+8mNVNfOqhBev3orQrOuqUBmWalmWItYXuQyKrTz8im80MJvLwJ15Ezo8+QuSqDNMAFYvegqUTZ86cwZIlS1ChQgXk5mqmuhg0aJDOGQcEK1h+fn46NVQSa9asQfny5Ysc/9///ocVK1agRo0aRX5LTU1FjRo1MGbMGDRs2BAAMG7cOPTt2xd37tyBvb09du/eDSMjI/j4+KBMmTKoX78+4uPjERwcLKmCpQ25YkbEDPJSfnh1dRGwiD0Tc1+11aNenrX1UIwiyPqdYqGEi6lHG6XRnVoIH9cxn3OltJYpTdEd+20akbcstSZy7s4tRM6+lUTkegvYBE9rRZ+mQWcKd4J5l+zsbI20DYLq1LUzb968QVpaWrG/Cc2DdeHCBezatQuRkZFF/JxHjhyBh4cHvvjiiyKBZhUrVsTKlSvJ369evUJYWBhsbGxIhvbo6Gi0bt1a4+a1bdsWGzduxMuXL1GlShVBfdUVJSglUrrHaMXC6Npnpd0DKYNyaSHlO0vruXyo0HARqiO0HlrPk7WiyyTuSkudv43jXITte3KRNrHRXH4s25kJRDbTebTVwxoHBwds3LgR7dq1g4mJCYCCEKe8vDzs3LlTI95cCIIf+e3btzFjxgzcvXtXaxkhCbnS0tLg6emJuXPnolq1ops1bdy4EQDw+PH7/3HmzZuH3bt3w9jYGOvXryeZWJ8/f06sW4VYWxfMPJ49eyaZgqUN1rM4FhYpMf1RWiyPru2LsVQJrUdo34TC2jIntIzQvrE+V+73kU+7pcXCROt/i1bqBzH1V77yA5EtXnPKVg2vU0R++MaEO86FY7FD7yLUiWnTpuHHH39Et27d0KZNG6hUKmzatAn37t1DfHw8IiIidKpXcJD7999/j6dPn2LkyJGoVKlSsWX69+/Pu76pU6cCKMjGDgCNGjWCn59fkSD3x48fo3Pnzti2bZtGkHshd+/eRWZmJnbs2IGDBw8iIiICTZs2RdeuXdGrVy+NzO+PHj1Cly5dsGPHDjg4OPDuqzosgtxLC0pWkuSIwZLLjaE094lcQe7q0LoPSgvYL86CRatNdT7U2CltCH3OIV9GEjnxdCaRa/h+TuT8mDgiq9q15WQJAsfznwZRqUdV/ePLBhAXF4egoCCcO3cOKSkpMDc3h6OjI8aPH49GjRrpVKdgC1ZsbCxWrVqFr776SqcG1YmMjER0dDQOHDgguq5Cl6Cvry+uXr2K7du3w8/PD6ampsjOztYom5VVkHW3uP2GWMHiYy9XELVQq5gcsTlSDhRKtWrwpbQHNkvpLvoQkVL5LO2rlDuVGUPkdcGPiFwjidvsOf8NN968Mjcisry+Ej0lUbduXY2QIxoIVrBsbW2RkZFBpfG9e/ciKSmpSNzVggULcPDgQYSGvn/J6atXr3DmzBl0796dxFgZGBjAzs4OiYkFL7yNjQ2RCyn8u2rVqlSuQx0xuYZo5SmSMiCZlrLF51whSGk9UodFjJfQMkKh9czFuPxYu5SUFlBNI25MLgsp62+ZmP8D1s9qi8FiIl/35JKLNrvJbeWssqlE5BuvuHo6CgtN1g29i1AnLly4oPU3lUqFcuXKwdbWttiFeO9DsII1depULF26FFWqVIG9vb2ozO3+/v7IzMzUONatWzdMmjQJffr0KfH8ly9fYurUqQgNDUWHDh0AFKR7uHnzJjp16gSgYAfsn3/+Gbm5uTA0LNgX6uzZs6hbty4sLelnfistgcFKjk+hUaeUwey0+qCEGbo2xFwvn/gzKWMI+dTDWnmhEYvG+n1RwjdCqKLG+lk9VVuuPzgwkMhuD22JHNKB2+D5y7sXuYqkULD0qwh1wtnZmawUVI+aUl89aGBggH79+sHHx4foEiUhWMGqW7cu8vPzMXx48UnTVCoVbt68yasubRYkS0tLXtalhg0bwsnJCYsXL8bixYtRsWJFbNy4EWlpaXBxcQEADBw4EKGhoZgzZw5GjRqFa9euISwsDN7e3rz6WBqRcsYtpaVFKneB0I+3lLNpuWKtxFgVWA+GLO6/UmMLaQWk8znOIvhdKLQC2GlNuGaf54LZEciJ4z/lvDr5N28TWdXgM53b1SMd69evx5QpU9CvXz/06tULlpaWePXqFQ4dOoSff/4ZM2bMgKGhIVavXo2aNWvC3d2dV72CFaxZs2YhJSUFgwYNkn0FHlAQHL9y5Up4eHjg9evXcHBwwI4dO0iqCEtLS4SGhsLX1xf9+/eHlZUVPD09BQXiC4HFx15pyopcbkEh7SohAJz1c1aaxUsJA7KUlqcPBTHxkiyes5ThFHxQr7Pvye1EzinPWTHqzOayfGfX55YLmnzannp/3oveRagTISEhGDx4MLy8uISx9erVg4ODA8zMzHDkyBGEhxcsbNu2bRtvBUvwKsLmzZvDz88P33zzTcmFP2D0qwgLoKVE0J55yrWoQC5lTp3SouSLQWn3lnWdLFcRKuF5yoXQ+xC4i4u7Kr95P5G7xnMenV99uPyQWSPDiGwd/qeu3eRNfrw/lXpUtadTqae00Lx5c6xfvx6ff/55kd/++ecfjB07FteuXUN0dDRGjBiBa9eu8apXsAXL2toaZcuWFXraR09pcefQmmFqQyp3kRJip8QEa7Ow+rCOqREaSyTXQgEx9XwoyohcLlAlWCr5vKfayvwWz7X1YvxLIv/1gHML5p/g0jRcX8HlyuLyvjNEH4OlE1ZWVjh37lyxCta5c+eIty45ORkVKlTgXa9gBWv06NEIDAxE3bp1UadOHaGnf/AI/TArbcWM0EBlMefSKl9SHbTuq5hnKOa+sob16kWhCqgSBmEpJ0F8oN0fWu8drW+T0O8Ly2/Hu+XV69zDpVPE5p1qbeXmEDHvIbdVTntHLtGoHuXy448/YuXKlcjIyED37t1haWmJly9fIioqCtu3b8fEiRPx/PlzrF+/vtg8nNoQrGAdPnwYjx8/xtdff40KFSoUWbaoUqkQFRUltNoPBlr/yFJaJFiU13YuLSuHkDqElpEyIFoJ1kwprXq0yrOuR2mrCGm4CGmlsxBTnk89cgXI82HLJG45f34St8Ls4neniJz4y3dErpCRReT2Fal3pyh5giJ+9Pw/I0eOREZGBkJDQ0msVX5+PszNzTFx4kSMGTMGkZGRyM7OJsnR+SBYwbKyskK3bt2EnvZRQkthEjqAs/hY0XILSRnwrCusY63kWuUpV6yY0trlAy1XJmtXlhBoTFzEtqWE2D8x98H1J0cir6u7jchrag4j8trlW4lsoL4B4XIXQW3phN5FqDMTJkzAyJEjcfnyZSQnJ6Nq1ar45JNPUK5cwaKF3r17o1+/foLqFBzkzpK4uDgMGDAA8+bNK7JVDgDMnTsX//zzD44dO0aOpaSkICAgAH///TfevHmDRo0aYdq0aRpb4Li6uuKff/7RqKt169ZEU9UFbUHuSljxx2IlkBLcNkps531tfUgr6ZSgRGqrXxtKu4di2qIRWyi0zdII6/e04/4wIg9db0Xk1LPJRLacwClhr5py++BamnJKGCvy7/hRqUfVYBaVeoojKysLS5cuxZ9//onMzEx06tQJc+bMQeXKlbWec+nSJaxatQo3b96EmZkZnJycMGPGDLJdX0JCApycnIqcV9y2e1Ii2IJVyIkTJ3D+/HmkpaXBwsICDg4OJNmnLrx9+xbTp09Henp6sb9HRUVhz549qFGjhsbxqVOn4sWLFwgICIClpSXCw8MxcuRI7Nu3D/Xq1QMAxMTEYOHChejSpQs5z8jICCxg7U5QQnl1PtQPdXGwDnxmPUiyjkmSS6mSMmBbSYpmaXGHSgl7S6gLkV2dufAY1fDXRM5cxKVsqGxbi6tI95zc/CkFFqyFCxciOjoaa9asgbGxMRYsWIBJkyZh+/btxZaPi4vDyJEjMXDgQCxcuBDJycnw9vbG5MmTsXVrgbXw9u3bMDExQVRUlEZyUHNzc159evXqFXx9ffH3338jIyMD79qdhOT3VEewgpWdnY1x48bh1KlTMDQ0hIWFBZKTkxEcHIy2bdti48aNMDY2FtyRNWvWaE1Dn5iYiHnz5qF169Z48uQJOR4fH4/Tp08jIiICrVq1AgDMmzcPJ0+exIEDBzB58mQkJSUhKSkJzZs3h5WVVbH1S4GUs00pzeq0Ali1lZfbraKOEtxYYq5PLkWaloVPSheXOkoOlqeNEtzF6ijt/oUFcwlF/1ed82KsmjyNyCfbcCnb86MvEVklRWYjhcdgJSQkIDIyEhs2bCBepoCAAPTo0QOXL19GixYtipwTGRkJa2trzJkzhyhPCxYswJAhQ/Do0SPY2toiNjYWderUgbW1bms1fXx88Ndff6Fnz56wsbGBgYFBySfxQLCCtWbNGly8eBHLly9Hz549YWhoiJycHPz222/w9vbG+vXrMXny5JIrUuPChQvYtWsXIiMji+xLmJ+fj5kzZ6Jv374oV64c9u3bR36zsLBAcHAwmjXjtiZQqVRQqVRISyvIRRITEwOVSoW6desKvVSdYGHlEBODpQ0lrExkCet4M1qKX2mxNopRgPjEFirNPSqlW1AO5HJ70oozlcvq5uLGpSjqmJZL5HrXXxBZ9b2a1crKRpJ+lRYuXizYOqht27bkWN26dVG1alVcuHChWAWrT58++OqrrzQsU4VyamoqbG1tERMTg/r16+vcrxMnTmD27NkYNGiQznUUh2AF67fffsOECRM09gosU6YM+vXrh6SkJOzcuVOQgpWWlgZPT0/MnTsX1apVK/J7WFgYXrx4gQ0bNmDjxo0av1WoUAFffvmlxrFDhw4hPj4es2fPBgDExsbC3NwcPj4+OH36NMzMzNCjRw+MGzdOJ0tbSShhxsVi9ZqYAUGO2anSrA5KcOdIqbgoYTWaGJS2ivBjhdb3i1YfVv7MueAuNuLGDxu1jZ9hpuYLfM3FZkmSCIuSi7Bz587v/f3o0aM61ZuQkAALCwuYmGimr7C2tsbz58+LPac4xSkkJARWVlZo1KgRgIJx3sLCAkOGDEFcXBxq164Nd3f3YuOyisPIyAi2trYlFxSIYAXr1atXaNKkSbG/NWnSBAkJCYLqW7hwIVq0aIHevXsX+e327dsICgrCjh07eClDly5dwqxZs9CtWzdiCYuNjUVWVhbs7e3h6uqKW7duYfny5Xj69CmWL18uqK9CkWuWJXSmp4RYFXWkulcsXFRKsx7SalfJbm25VsrKPZlSwj3TBp/3S8y3Sa7/obtt/kfkhvbcVjktmmcS+eVYbhHW/lnORB5JvTfFIHMM1uPHj9+rnE2ePLnYsdzExARZWVnFnFGUZcuW4e+//0ZQUBCMjIyQk5OD+/fvw87ODjNnzkT58uXx+++/w83NDVu2bEG7du1KrLNr16747bffik00KgbBClatWrVw8eLFYjt94cKFYq1Q2oiMjER0dDQOHDhQ5LesrCxMnz4d7u7uaNy4cYl1RUVFYfr06WjZsiX8/f3JcR8fH3h5eaFixYIkJA0bNoSRkRE8PDzg6elJfT9F1u4lPuZ2oR8uPohRkuRQNFlYWWidKwYW95WW4i2XJYaWMqQEa4kQlDAZElqnEiyh4mJCvydy/9NcULaTAac0VOrPLcQaWV3wEKsIdLVQVa1aFQcPHtT6+/Hjx5GdnV3keFZWVok7xLx9+xbz589HZGQkFi1aRBatlSlTBufOnYOhoSFMTQush59++inu3LmDTZs28VKwmjRpgsDAQDx69AjNmzcn9RSiUqkwfvz4Eut5F8FP/4cffsDSpUthamqKnj17okqVKnj58iV+++03hISEYMKECbzr2rt3L5KSkorEXS1YsAD79u3DnTt3EBQUhLVr1wIouME5OTlo0aIFQkJCSJDc9u3b4evrix49emDZsmUaGnKZMmWIclVIgwYNAADPnz+nrmDRijHgcy4tSkv9tAcCJVgY+RynVQ+LWD5t9avXKdQyRAslxHKVBuSyYMmFmHdwYCAXB9Tr+WdENihTlchZ9zi34AkHzirTlf8OK7ojc5C7kZHRe2OhYmJikJKSguzsbI1xOjExEVWrVtV63ps3bzBhwgRER0cjICAAX3/9tcbvhbmq1GnQoAFOnTrFq98+Pj4ACoxEFy5cKPK7ZArWjz/+iJs3b8Lf3x8rV64kx/Pz89G/f3+4ubnxrsvf3x+ZmZkax7p164ZJkyahT58+RX4LDw/H4cOHER4eTh5GREQEFi1aBGdnZ41VBoU4OzujZs2a8PPj8oNcv34dRkZGTLb6YZETSR3WHy4W7hZ1aFnUlAqf6xCTw0noe8GnDGsLltAgdyVYzkpbkDvrUABaKTLkClkQg3ofGi/k3IJJn0cT2TWlJZFNJo8hcqPMeMa9eweFp2lo1aoV8vLyNLxgcXFxSEhIgKOjY7HnZGdnY8yYMbh16xY2bdpUZKuaO3fuYNCgQUW2sblx4wbs7Ox49ev27dslF9IBwQqWgYEBfH19MWLECJw/fx6pqamoWLEiWrduLTiKX5vGamlpWexvFStWRJkyZVC7dm0ABQ9myZIl6Nq1K8aMGYOXL1+SsqampjA3N0f37t2xZMkS2Nvbo3379rh+/TqWL1+OkSNHak0LIQbWVgihZWh90MQMnnyQI+CdRdxKabHiiHm/lDwAarvn2sprQy63ma7QiiHUVl7oe620ODoxqNf58M0dIv9lwE3mszftJnKZptzKQZMORVfFMUXhaRqqVq2Knj17Yu7cuViyZAnKli2LBQsWoHXr1vjss88AFChUhXqFsbExNm7ciIsXL2LlypWoV68eXrzgVmxWrFgR9evXR7169eDj4wNvb29YWFhg9+7duHLlCvbu3SvTlRYgWMHKzMyEqakp6tevTxSqW7duiVoiqSuHDh3C27dvceTIERw5ckTjt/79+2Pp0qUYOnQoVCoVwsPDsWTJElhZWcHFxUWQpU1ulBAwrrQgWm0UtqWEdBOsBz0pEaNc8oGF0sniXinhf5FGX9RRmpKsNNT71nH/rmLL5KXncPKzVCLfSeWG2KrvDzH6aFi0aBGWLFlCwomcnJwwd+5c8vvly5cxbNgwbNu2DW3atMFvv/2G/Pz8YvcALCyzYcMGrFy5ElOmTEFaWhqaNGmCLVu2oGHDhkXOKWTYsGFYsGAB6tevj2HD3p9lX6VSkaSmQuC9VU5MTAxmz56NLl26wN3dnRxPS0tD27Zt0aBBAwQGBkqWb0putG2Vow0pV/wozcJAaxWWrtcl1+xYrudQWvopBrlWXLJAiAVZaUHotNqVCzH9CVuTROR0Gy73VR64/FhlI34lcpnhXFA8K/IvzKZSj8pxCZV6lIyzszMWLlyI+vXrw9nZucTyumytx8uC9fjxYwwbNgympqZFFCgjIyN4enpiy5YtGDx4MCIjI98brPaxIuUMk4XiooSZuxxKFZ/25YpbowULJUwuJe9DsuqUhFxWWiV8C+RSENX3IjxwltturdO3nAXLuBa3Pctfg/sTuSv13hSDwmOwlIS6wiRmX+L3wUvBCg4ORqVKlbBz584iGzKWLVsWLi4u6NmzJ7777jts3LgR8+fPZ9LZ0kBpsR6wsKjJFVyt7VwhZVkHU7Neni4GuVayKnlhg9Lit2ggpeuV9f2T6z39XyUuyD3nLef8MetSm8iqatyWbFZlOcVLj7I4fPgw2rZtiwoV2C3v5KVgnTlzBm5ubu/d7drKygojRozAjh07qHWuNMJiRid0tibXqiqhAfIskXKRgLbyQtsS2q7SrDW03kdaFhK5LC2slXUh7fCBxaRQSYqlLmh7PgPPqe0cYsplI8/46TCRDatzaRrOV+C2cfvMkmIHtcAz4qdEVCUXKfVMnjwZu3btgr29PTkWEhKCAQMGwNKSzsPipWAlJibySmnQsGFDrenu+RAXF4cBAwZg3rx5GDBgAJydnXH+/Pliyy5btgz9+vVDZmYm1q5di99//x3JycmoW7cuxo8fr5FN9tatW/D19cWNGzdQuXJluLi4lBjURhu5g77fRUprDJ/+aCuj6+pFMVY2PsfVoTVbF3ouC8ujGGWOlnIj17uphPpZInRyxuI506qf9XPQVueTXtyqwJ9PryayiykXl7zyjzAi/3iPk7FuCLX+aUXvIuTNu8pobm4uAgIC8Pnnn0urYFWuXBmJiYkllktOTi6S1JMvb9++xfTp05Genk6OrVmzBm/fviV/5+fnw8PDA6mpqejatcCjvXjxYpw6dQre3t6oU6cOfv/9d0yYMAFhYWFo06YNkpOT4erqik6dOsHb2xtXrlyBt7c3ypUrh4EDB+rU1/dBa9kya2gNerQ+sLSXe4tRqkqq+30IVVCktPSwtjYIfUeE3hOhkwW5F1fw7Y+ubdGyrrKwMIv5f1aaEqven3ATXyIfUHMRrum5icjVnDj7z+9DXYhMf7TRQxtaFsBCeClYjo6O+OWXX9CzZ8/3louMjNS6T2FJrFmzpkheqkqVKmn8vX37dly7dg2//vorypUrh4yMDERGRmLJkiVk0+dx48bh3Llz2Lt3L9q0aYPdu3fDyMgIPj4+KFOmDOrXr4/4+HgEBwczUbBYzLJYxCFI+RETqkTQ6JsS4ntoKaVKOJcFLAKzaVm/+JShZaEUo7jTblOMNYtPf6SMx2SBetyVOtU/54bSxye4VYQBuVWIPNCbXb8IeguWouClYDk7O+PHH3/E0qVL4eHhUWQn7OzsbAQGBuLEiRMIDg4W3IkLFy5g165diIyMLLJtTiGvXr1CYGAg3N3dUa9ePQAFuSk2bNiATz/9VKOsgYEB0tLSAADR0dFo3bo1ypThLrVt27bYuHEjXr58SX2rHHVYKzq0zOdSugXEIKRdPh9sWhYg2gMkX8T0n4WrlMU7SOv9YuFy5QPLGDLWFknWcWhCvzV8YK2QtU/+gcg1V3EpGO6P7UVki8mcEnY6N416H96LwhONfmzwUrCaNWuGWbNmYcmSJfj111/Rrl071KxZE7m5uXj69CnOnTuH5ORkTJ48GR06dBDUgbS0NHh6emLu3Lnv3Sg6JCQEpqamGDmS25Pc1NQU7du31yh37do1nD17liQue/78eZFkY9bW1gCAZ8+eMVWw+MBCCWM9mMg1EOk6i2cxEMllIaNlrWFh/RKzGIPW+8hCmdfWFgtKule07h8ti5FcIQJSurvV6wybx219k/ac22ew0V+nuXO/fUjkJs+HErkNl71Bj4J5d7s9MfBSsABgyJAhaNy4MTZt2oSjR48iK6vg5SpXrhzat2+PESNGoHnz5oI7sHDhQrRo0QK9e/fWWubNmzfYvXs3JkyYUMR6ps79+/cxfvx42Nvb4/vvC3Y9z8zM1NhUEgCpo/AaaCL0w8ynvJRWJdYKHwu3WUlt8jkuZVtKCwBnrYyyeH/VkVKpkiu4WmxZgJ6lT8pVh0pbHBT17SUiP47n3HE/rh1AZJdXXCb3sdcNiNzGmmYPtaB3EQpi/PjxRfSDsWPHwsjISOOYSqVCVFSU4Pp5K1hAwUaNrVq1AlDgsitTpoyoHBKRkZGIjo7GgQMH3lsuKioK2dnZ742ZunTpEsaNGwcbGxts2LCB3CBTU1NkZ2drlC1UrMzMzHTuOy1oWVHEDAIs+qCtDIs+sKinEBbBukqOkWOBXEqJlBa+0gALS582lBY7RYsOrlwerEt7uL1vTFK4rO6qipwnpo45d1wS9AoWb/r3719yIZEIUrDUeV9OLL7s3bsXSUlJReKuFixYgIMHDyI0NBRAgYL15ZdfalXmDh8+jOnTp6N58+ZYt24dzM05W6yNjU2RFZCFf7PIOK+0wGNagaRyBOu+rw8l9ZOFu0JpSg9rd4jQ8lJaDbX1R0y7SlYEWPZNrjAFPm3ROpfW/Rtz6Xsih074ncidl3HJRY/5cBN6m5bc6kLkDabSh/eij8HijZ+fH/M2dFawaODv74/MzEyNY926dcOkSZPQp08fciw6OhoTJ04sto5jx47Bw8MDnTt3hr+/fxFzn6OjI37++Wfk5ubC0LBg9nH27FnUrVuXWq4LbbAOHtcGLbcdnzqlVFJoxGCJcVfwuWdKs07xgVafxbxHYpR/bc9FSrcji1QOLGOw+LSpDq37x8ItLNQ6LBT1trYYLCby9v1cmd88uJxYr8ZzMTxPL3Ixw3o+PmRVsLRZkCwtLclvz549Q3JyMho3blykXGpqKry8vNC0aVPMmTMHqamc79vIyAiVKlXCwIEDERoaijlz5mDUqFG4du0awsLC4O3Nfs0sLSuKtjpZW5u0wUJxpL1km7VCqwTFSIzSI6VFQkp3KgtoKdZC2xJSllZcJOtJnphvB5/6WfN3Hxcibxn7D5GzLz4l8l/jOStXQ8p5lUpE7yJUFLIqWHx48eIFgKI5sQDgxIkTSEtLw9WrV+Hk5KTxW+vWrREeHg5LS0uEhobC19cX/fv3h5WVFTw9PSXxv4qB9YBGqzyt2S+fOml/VKV057IOJNfG/7V35nE17P8ff7Uq5ZK02JciShsJV0L29cp6iURE9n1PspRvRNlSpKwX1yVLdu61k0LWNrJFZakI7fP7o1+fc45zTp3pzJwzMc/Ho8fj3ZzPvOczc+bM5z3v9/vz/ijLo6bIkKsiw7hcMKwlwYaHiSm45rGVhwnbBDWuXrgLzmXz0hFEXnf/PJH3teymmI6VwhtYnEKFYrp06S/CmIHsrL5NB3kGLnlmO3Itz0zZx2TKgFBkyIcLOuXRz4V8P6ao6LG4/JusLPrp6gkfd1nwT1VBkntxWpbE9p+H9iRyzSqjJLZhkuIznuU3kgHVXsGM6PnV4bwH61dCkW/0bCShyuO1kCesKelcFPlQV2T+CNt5RfJ415QVcmXjpUAaTBnNTIQ12T4nNoxeutD9rbDhmRPWWXxFMNEqxS+JyCuqCMKCwl6uVot3CxQFsG9g8Unu3II3sBiGjaRveR4yTA0+bOeYMDUgMzGLkO4x6cJUfgobx5UHeXJnmBoYuRYilCfPSJjy7ms2jFs2nkey6GejD3SR1uej9VoTuVfzh0Re/foAkavVHE3k01PGEFkhaxHyIUJOwRtYDEP3YcXGQ0YWmDLyFDnbStk5WFxDkbNOZUFZ/WEqkZ/usbjkJVVkmEzZv1u2dErTL3x9js8WGDDN3gvqKI7vNZXIG1oLZhQOirBhqYc8lQFOGFjp6eliSepASZ2KQYMG4enTp1i9ejUePXqEmjVrws3NDa6urhJ1hYSE4Nq1a9izRzRHaunSpfj7779FttWtWxeXLl1i7kRQeRI62fBayHIsRT0YuZADoshBgG3kOV82w2Rl6awsRpUwdDyzTIXwuJwjRVc/28f1oQ4S+d+b34l8c+05Ir+NE4Tp9lZrQWSBX4tFeA8Wp+CEgRUfH48qVargwoULIusAVatWDZmZmRg7diycnJzg4+OD+/fvw8fHBzo6OmKV3fft24fAwEDY2dmJHSMhIQGTJk3CqFGCOHhpXSwmUWSSONshK7r94QKSBhG2E2LZyCuTpz/K8hjQHcClXQc2vi9Z+iONn8VQluV6S2svz7GEYcowYipXkC7uTScR+XRghECng2AJnTFXbIj8RyPRJVdYh8/B4hScMLASExPRqFEjsgizMLt27YKGhgZWrFgBdXV1mJiY4OXLlwgNDSUGVnp6Ory9vXH79m00atRITAdFUUhOToaHhwcMDAzEPuc6bAwITIUU6cK1HBkmdDN1vZkaHLgwG5Gp/EO6KNKbwRQV7SeXfz/Kym+k62mlG16++I8xkRN6FgjaRDYmskqDukSuoqYjS7d5flI4YWAlJCTAxMRE4mcxMTGwt7eHurqgq+3atUNISAg+fPiAWrVq4fHjx9DQ0MDx48exZcsWpKamiuh49eoVvn37hiZNmrB6HoBiZ4ixMaOMbqIqG6Eyps+L7cRXaceSZbBn4y2eKeh644She63YmMlWGUOEdGBjMgvbMOUJVeSLmvCxhCu5C1O/VR6RC069JPLsYXWIHObEZA+lwIcIOQUnDKzExETo6enBxcUFKSkpaNiwITw9PeHo6Ii0tDQ0a9ZMpH2pp+vdu3eoVasWnJyc4OQk/e5NTEwEAOzZswdXrlyBqqoqHB0dMWvWLJF1C5mAC8nGwnDtASsLXMrrYjvMRxcueGgUafwpK0TI9m9RUVTG/EMuP6ee3xYsfePz8SqRcy+lEPlrliAsaG0oKNmgCKgiPkTIJZRuYBUWFuL58+cwNTXFwoULoauri6ioKHh4eCA8PBy5ubli6wtWqVIFAJCXlydJpRiJiYlQVVWFoaEhtm3bhlevXsHf3x9JSUnYtWsXVFVVGT8vRcBGrgLXvCjKPi5ThtTP7k0BuDeoKis8Kg15vH109HF5X7Zhu2+3H1Un8slFAqOqyh3BQs49Pj0j8rSCeFb7Iwafg8UplG5gqaur4/bt21BTU4OWlhYAoGXLlkhKSkJYWBi0tLSQn58vsk+pYVW1alUxfZLw9PTEyJEjoaenBwBo1qwZDAwMMGzYMDx8+BDW1taMnQ8XQhRsv7nL42FSlIGgyAFbkeEZLniwpKHIWWpsw7U+S+qPIlMEZNGjrBAe2wgfa8TxCCJ/FGozfMtfRC5sKChGuq79H0RexErveLiM0g0sANDREU8EbNq0Ka5duwZjY2NkZGSIfFb6v7TFon9EVVWVGFfC+gEgLS2NUQNLGoo0vNg2whRZoqCi102RD36mkv7posjcNra9mZVlxh+XjFouhGGVhbK+h1GF84h8rfkmImvP6CtolPaOiHP+/Uew3Ubg5WINPkTIKZRuYCUlJWH48OEIDg5G27ZtyfZHjx7B1NQULVq0wIEDB1BUVETKKty6dQuNGzeGvr6+TMeYP38+MjIyEBERQbY9fFhShdfU1JS5k/kBukmobBxXGKbCArIkvNM9LtOJrYrM42JqYoAspQrYmNknrQ1d6Ca2y9NGluOyESJUlgEnqQ4WXR0VPWZFtjPVH2HYOHe63+de9bVEdkvoRuTMmgKv1dFsQRjxaGIrIp+kdaSKQfEhQk6hdAPLxMQETZo0wYoVK+Dj4wM9PT0cOnQI9+/fxz///AN9fX3s2LEDS5Yswfjx4/HgwQNERETAx8dH5mP07NkTkydPxubNmzFgwACkpKRgxYoV6Nevn9TZixVFkQM1U94jpgZqtvM6KurBkqaDrnFLd8CR5zrJ8t2y7ZESRpHHogsbgzzXksPp9IdtzxbbXmtF/j7o3hf/DXAT/GMWQUT952OJbD1KUIx0XLjAwAKcy9XP83OhdANLVVUV27ZtQ0BAAGbOnInPnz/D3Nwc4eHhZPbgjh07sHr1ajg7O8PAwADz58+Hs7PsN2vXrl0RGBiI0NBQbN++HdWqVUP//v0xc+ZMls5KHK69EXM1cffHfel6XUplebwpTHkV5TkPpt766fZHWWGnypKjxoZ3iM5xuXCPCEPXcGFKvzTYjgi8+COAyPNHTCDygRe3iGw/bCaRrx5YR2RtS8a7Jg4fIuQUKhRF8d9IBRgzcI/E7VxI9JXnoS7PA1OaHkVek/IGInmOz4bRxkYYS1mGhSIHVbZz1+h+12xcH0n7MvU9c/k5pUjo9mF7txNE3jddkOb+5zJtIh9YIVhCp85LwWLPPetLXt6NSQp3MpPnpT5uPyN6fnWU7sH62WDKFc3UvtJgY2CX5VhMhXBkORYT+pjSQzfX6mdCWSElNsKabM/QpbNvZbxflD27WF6ub/xK5O6DtIis6uVJZDchwytLh/nl2HgqD7yBpSDYzk+S1l6RDy6mBh+m38yVlU/D1CxPNr5PRRrtyjoXZYUF2YDNECEX8vEqi7H1IjmXyLu3ehB55DuBUVVQLFhP9+MHwb6jRetlswKf5M4teAOLRdh+oHHtbZCpt3iuPVQlIcv1lseQkmdfptrThWv3HRdysNiEqRcvuuFQpoxhWY7FBvJMbvmYKwj51Wuxi8jbOrkReY/hTiJrNBbMKEQzF1rHrRB8Dhan4A0shuFaOE8abHsPlO21YCNRXRhZBhy6s8aUNXhzzVCTdix52tP1OLIBl2disnkcJnLPmOybPHmpGw8JVhV581KwkohulkDWXuBA5K9b/iOyYAEdFuE9WJyCEwZWeno6HB0dxbb7+flh0KBBOHXqFIKDg/Hy5UsYGhpi+PDhGD9+PFRUVMT2CQkJwbVr17Bnj2gS+tOnT7F69Wo8evQINWvWhJubG1xd2U86VAbKyq+Stp2NgasyDDhs95GN8Iwi9chzLDY8IfIk4MtTgkEWJIUI5YELhnFl9GZXLxYvig0AvdcJllsrjk0ictWRtqz3iYe7cMLAio+PR5UqVXDhwgURo6latWq4evUq5s6di0WLFqFz5854+vQpFixYAE1NTYwZM0ZEz759+xAYGAg7OzuR7ZmZmRg7diycnJzg4+OD+/fvw8fHBzo6Ohg8eDCj58K10Igs7WVpwwVDo6KzreTRJw9sePEU6W1kCkUaZEzBRl4dl1DWd84UyvKiCh+rs1Cb03OLiTzARFDJXeWRIAlL+3dWuidCZVjsOS8vD2vWrMGZM2eQm5sLJycnLFmyBDVr1pS6T3BwMAIDA8W2JyQkEHnfvn3YuXMn3r9/j5YtW2Lp0qUwNzdn4xRkhhMGVmJiIho1agRDQ0Oxz96/fw8PDw+MHj0aAFC/fn0cO3YM169fJwZWeno6vL29cfv2bTRq1EhMx6FDh6ChoYEVK1ZAXV0dJiYmePnyJUJDQxk3sBSJPA97RSa2MqWHTnu2PQpswLX+VEbYNub570ixcOF7ED7W2K0CQ6p922pE/u+2oL32n4IXfOq1wNhSCMXF5bdRMsuXL0dMTAw2bdoETU1NeHt7Y/r06di7d6/UfRISEvDHH39g3rx5Ej8/evQo/P39sXLlSpibmyM0NBRjx47F6dOnyzTc2IYTBlZCQoLUiuqDBg0icnFxMW7duoU7d+5gypQpZPvjx4+hoaGB48ePY8uWLUhNTRXRERMTA3t7e6irC063Xbt2CAkJwYcPH1CrVi2Gz4hZlDVbSNq+bBcLFKaiOrk8ECor70pZXsifaUaksiivP1y+llyouSWMLOFLaddh5XrB8mznBwrGrGa9BOUY8q06ELlwfyCRdQRD2S9Leno6IiMjsW3bNhJpWr9+PXr16oV79+7B1lZySDUxMRHDhg2DgYGBxM+3bduGUaNGYcCAAQAAX19fdOvWDX///TcmTpzIzsnIACcMrMTEROjp6cHFxQUpKSlo2LAhPD09RfKy3r59i+7du6OwsBAODg4YMWIE+czJyQlOTk5S9aelpZGq8KWUesvevXunVANLWa56Rc5wVLYRwdQAz1RhTHnyeLiQG0TXwJZHpzx6hOHypAEmYOOasVHIlCnYrJtXFrVfZAv64HifyPMKugoabQwj4r8zBXm+/eh3jz4MhQi7du1a5ucXL16skN7Y2FgAJQ6OUho3bgwjIyPcuXNHooGVn5+PFy9eoEmTJhJ1fvz4ES9evED79u3JNnV1ddjZ2eHOnTu/toFVWFiI58+fw9TUFAsXLoSuri6ioqLg4eGB8PBwctF+++03/P3333j58iVWrVqF+fPnS4zJSiI3Nxeampoi26pUqQKgJB6sTLiQ28TGcZkaeCv6YJclnKDI/CemJgPI054p2EiKZ+oe4YInh818Pi57OWXRQ/d7ZiOVQZb7RVqboaMzBG3ONCcydeQQkVUa1yBy0+qKHV+4XgcrPT0denp6ZPwtxdDQEGlpaRL3SU5ORlFREc6ePYvVq1cjLy8Pbdq0wbx580T2q127tpjO+Ph4dk5ERpRuYKmrq+P27dtQU1ODllZJZdyWLVsiKSkJYWFhxMDS1dWFubk5zM3NUVRUhDlz5mDevHmoW7duucfQ0tJCfn6+yLZSw6pq1aqMng8XCjjSbSOP90YWmBoA6ezLxhRwRfW9InrY9h7R9QzRDSnLcw+y8V0rctKDLCjjZYqN+51tPWy/UHT3E8wW3GUhGLyFF4GOCBUsldPs5WuBohqMd401KuqhevPmTZnerxkzZog5O4ASh4c0Z0diYiIAQFtbG0FBQfj48SPWr18PV1dXREZG4vv3kustyYmibAeK0g0sANDREZ/62rRpU1y7dg0xMTHQ1NSElZUV+czMzAwAkJGRIZOBZWxsjIyMDJFtpf8bGRnJ03WZUdbgrKwHERvQGRhlKaTIdihKFgOIDcOIbS+BLDBVv0oYtq+nNLgwY62ibdl+iaBrAHHBw0hXvzDhqqskbp//6R8iJ44UeJEehAwj8lCG+lYmSp5FaGRkhFOnTkn9/PLly2LODqDE4aGtrS1hD2DgwIFwdHQUSVZv2rQpHB0dcenSJTRo0AAAJDpRpOlUFEo3sJKSkjB8+HAEBwejbdu2ZPujR49gamqK3bt3IyMjAwcOHCCfxcXFQV1dXeKMQUm0adMGBw4cQFFREdTUSpIRb926hcaNG0NfX7+cvenBNcOF7QRjtvOPKvrA58L3oIxcHLaojOFoNgxoeZDHc0Yn9C3PMekmgMuCPEnlXPjdCPdhR6FgFtte9bVENj/ch8hFJ/8jsmqtb+x27keUbGBpaGhInbAGlExoy8rKQn5+vojHKSMjo0xnx48zAQ0NDVGjRg2kpaURuyEjI0Pk2OXpVARKN7BMTEzQpEkTrFixAj4+PtDT08OhQ4dw//59/PPPP8jJycGoUaOwYcMGDBo0CE+ePMHatWvh6uoKPT09mY4xePBg7NixA0uWLMH48ePx4MEDREREwMfHh+WzE8CGYcF20rIsOtnIeZDluBWF7UR1eY6ryJwqRV4Hee4LWWA7oZrLnhYmYCN8ypROZV0/ace6/1FQdqHHEEEeUW5NwUCu1UYwoWrKkTpEPjeWyR5WTlq3bo3i4mLExsaS9J+UlBSkp6ejTZs2EvfZsGEDzpw5gzNnzpA6mW/evEFmZiZMTU2hr6+Pxo0b4/bt20RnYWEhYmJiMHLkSMWcmBSUbmCpqqpi27ZtCAgIwMyZM/H582eYm5sjPDyczPwLCQlBYGAgIiIiULNmTYwbNw4TJkyQ+Rj6+vrYsWMHVq9eDWdnZxgYGGD+/PlwdnZm/HyYCkex8VYuy7HY3lcZgxUbeUuyoMhj0YWNvknTyfb1Z1unMFzI5WIaLoU9f4QL109a/zsfF+T3/Jf/WdD+SEcin80VzmXqy04HheB6kruRkRH69u2LpUuXwtfXF9ra2vD29oa9vT1sbGwAlIT6srOzUb16dWhqaqJ79+4ICwvD8uXL4ebmhg8fPsDX1xetWrVCx44l13rcuHFYvXo1GjZsCEtLS4SGhiI3NxdDhgxR4tlywMACgFq1asHPz0/q5x07diQXsjzWrFkjcbuVlRUOHjxYof5VFLbfxOkOXEwlMMuyrzQ9bCQ8c+EhLAku5wMpcmYn295JRRqvXA6VsqlPnmdEZfcACh93g/5eIl/QFdS+2mYjNLZ8Faww8lAoZCXIImaRIu4XGl25ciV8fX0xdepUAICjoyOWLl1KPr937x5cXV2xe/dutG3bFi1btsT27dsRFBSEQYMGQVNTE127dsWCBQuIR2vYsGH48uULAgMDkZWVhZYtWyI8PFypRUYBjhhYvzJcSgwH6CcPM2UMMf2gZiPsqUhPH9uwnU+mrHAOU8YWFwZ2YRRVpkEaXDuusvpTvEUQcvoavpvIWwcJ6jLOunOVyC2FiltDfLldxuG6Bwsombm/atUqrFolecJA27ZtRZbAAYD27duL1LmShLu7O9zd3RnrJxPwBhbDcC2XgA3kyRVjY+YTE8eUtq88/WJ7UoEi7ym6Xki6emRpTxemwvWVZeYbEyhrFqGySqbQbfMqR7CQs5mFYIZa+2qC1UPyzwv6s85JEKJaQquXPD8DKhRFcd/k5SBjBu5RdhcqJcqYXq2swYyNc1WUgVoR2J6xyka5DbrHZfueVdT3RXfShSINS66WxQAAnz2CGle/zRSUZpj1cRSRI/xeCnbQ1CCiismCinZTZr57MZPnpb0yihE9vzpK92Clp6eLLIlTip+fHwYNGoSMjAysWbMGV65cgZqaGhwcHMRW3i5vFW1ZVuJmCjYGGbo6FfnwlAVl5MgoMlSkSAOCy8Yi2x4JZU0aUNSLgCztueAVpWvoyuPxloayZp1W1xTUbNTbPJnISwYIlsehXtcn8hO7VkRuWeGj0qAShAh/JZRuYMXHx6NKlSq4cOECSVgDgGrVqiE/Px/jxo2Drq4udu/ejYKCAixevBgLFizA9u3bAci2inZ5K3ErgsoyU48NmBoYmRhQ5BkcpMHG9ebad8g2ivTwKVKPNJj4TSjrvqssuYiyhC/pXvuZwwVJ5Dt67iey5pF+RFbJzCGy+bWbgp0HcCs/iId9lG5gJSYmolGjRmTxZWGOHDmC1NRUnD9/nizIvHDhQvj4+CAnJwe6uroyraJd3krcioANDxZTHhVlhV6kIU+SeXnJwD+T4aKsHCxpKNt45ikbpmYdy3MstvcVhg1vcvr3Z0S+10FQRNSu4V0iqzh2J/JDh+pEVsQsQkrJhUZ5RFG6gZWQkCC18uu1a9fQrl07YlwBJSUbLly4AEC2VbTLW4mbaSrL4EDX/S+tDRsDLxPXsLLkzVSWftKFbiibqb5xIYdIUbAxs5ONCR6yoKyJKHTRVRekpjy+L2Rg/SYIHVJf0olcR098GThW4UOEnELpBlZiYiL09PTg4uKClJQUNGzYEJ6ennB0dERKSgrs7OywZcsWREZGorCwEA4ODpg3bx5+++03mVbRLm8lbkXB5RChPHkXTOVUMIWkHCy2c6TkydNSpMGhSBR5X7CRj8VGvheXDDV5XqS4cJ+yUUpFWn+Ej7XxcS6RjYXaq1SvRuSIuoLk98ZvXIncuQ54fjGUamAVFhbi+fPnMDU1xcKFC6Grq4uoqCh4eHggPDwcOTk5iIyMRPv27REQEIDs7Gz4+flh8uTJ2LNnj0yraJe3EreWlpZiT5qDKDLHqzLMIpQnJCvPcZWVa8d2SIbtgZoL4VFF6mFaH10DWFkhP3n6wNSxIjYIlsrBW8HyzVs/CgwszytfiPzdQLBdIVSCQqO/Eko1sNTV1XH79m2oqakRQ6dly5ZISkpCWFgY1NXVUbVqVQQEBEBDo2S6a/Xq1TF06FA8fPiQ7FPWKtrlrcTdp08fMIk8tX3YqBEj7biKrOnExsyk8uCCx1Ce2Z9shEDYfuuXdixpsO0VYYPK0k9JMJUjJ4yyjGpl6Y8wOUvkDl0F4b8JZyYR+dgrWyI7FwqqvUNQsYE1KkOh0V8JpYcIdXTEY9RNmzbFtWvXYGxsDIqiiHFV+hlQstijrKtol7USN9OwMbAzFXaSRSddmJq+z1RIRlGDnjwPcmV5a9i+Nlz43pi63xVpjCoKeX5jdPeVx/slz3fIdojw6yxVImckC7xF6qcjiTzgq2CNQq8mPYm8UvJaxjw/MUo1sJKSkjB8+HAEBwcTYwkAHj16BFNTUzRt2hS7d+9Gbm4u8VaVhvwaNmwo0yra5a3EzTRceMOtaI0dQD5jQZ46Tky/eVaWsBFduJy4zQXjgwt9YAMmzoupXDKmvNby6FcW2oMFcwH/c7xP5D9bvyWy1pSBRO5ZJBpdYR1+FiGnUKqBZWJigiZNmmDFihXw8fGBnp4eDh06hPv37+Off/5BrVq1sG/fPsyZMwczZ87E58+fsXz5crRt2xYWFhYAyl9FW5aVuJmEC2EPed426U7lZiNcKMu+wlQGr5UsethIwJeGIu9TRSZLMwXX+snEi4OyPJiVJawtDeFjzdspKGA9OlwwW9D9qMBTtUu7KpE7JNwTKBLOimcJik/B4hRKNbBUVVWxbds2BAQEEAPK3Nwc4eHhaNasGYCSKu1+fn4YOnQoNDU10a1bNyxcuJDoKG8VbVlW4maLyuhFYXvGF93jMtW+onDN0yOPMcw2dAdDrhlVwihjMoYikedlhe0ZsVwwaIUR7s+bIeuIHOIjWPh5+yg9Ivs/yiTyvAaN2O3cD1DF7I5pPPRQeg5WrVq14OfnJ/XzRo0aISQkpEwd5a2iLctK3GzDxgOHC54BrhW7lNSGjRAoFx78XDD45OFnrQEmC0zlNDENU+U12E4FUCTC/VSfPprIm//eS+QBjQVJ7qebPxTsnM/XZviVUbqB9augyOR3Ydh4oHF5YJQUSqnsb9CVBa55IbkcTuMSygq3ydNGWZjpFxFZa1I/IgdpCSZMqVTrKtihULE5WMV8iJBT8AZWJUAeTxXbYUQ2Sgso+wHL5ZAWU9Pl2UCR+Xg8zKHI3Eaufeey/G6E+ZD7isgRVf8msttNwRRBSus1kY9Ua0bkwQpYTISi+BAhl+ANLCXDhpHE9lRotg0mRYU+lTUjj+1Zm7K0Z9tIZqp8B124UJaEDSR5ZpmqjycM29eJqWcT29+DtGPN+VNQpqGzUPtzdQTJ73D4i4iq/zVlvG88lQfewGIYNowPNmbS0B2o6bZRVkhUUl+k6VBk6JCp68fG9Hem7lNlheQUGQbngtelolQWI1OWZ5Miz0V4e/jkW0Q+fElQRLSn0JqDRf6CJdguqCs2ZsfPIuQWnDCw0tPT4ejoKLbdz88PgwYNwtWrVxEYGIjnz5+jXr16mDhxIvr1E8S/8/LysGbNGpw5cwa5ublwcnLCkiVLRAqM3rx5E2vXrsWzZ89Qu3ZtTJs2DX379mX8XCpLvg9ThmBlDgspa6BgwzDmQvhPkV5RafqZ8n5yLTwmqb2yPH2KvH5cM7zHbm1H5M458US+mJ1D5Ma7PxG5R8sXjBxXVvhZhNyCEwZWfHw8qlSpggsXLoiUTqhWrRpiY2MxYcIEuLi4wN/fH/Hx8fDy8kJhYSEGDhwIAFi+fDliYmKwadMmaGpqwtvbG9OnT8fevSWzPJ49e4aJEydi7NixWLt2Lf777z/Mnz8fNWvWZHx2IRdqvrCRp8PU4CkLTDwYufC2zrZnRZ592cjlk6ZTnnIS8tybXAs70j13SX1Q9ksJIN/3yfYLAhvlL4R1Ui9jiXz0qmD43D1FEDrctV9QvqH45hUiq1gw3jUejsMJAysxMRGNGjWCoaGh2GdhYWGwsrKCl5cXgJLipK9evcLGjRsxcOBApKenIzIyEtu2bYOdnR0AYP369ejVqxfu3bsHW1tb7Nq1C2ZmZpg1axbR8eTJE+zYsYNxA0uRM6kqSxhDlocw0x4eto0GLhhbytLD9lR+NtrQhe0aY2zorChs5NQxdVxF9kcW3GYIlmDblTqVyF1UPhO5QENQE6vw6ksia/divDti8LMIuQUnDKyEhASRtQSFefnypVj40NzcHKmpqXj79i3u378PAGjXTuC6bdy4MYyMjHDnzh3Y2toiJiYG3bp1E9HRrl07rF69GhRFsV5wFJAvv4btHCy2kcWokrZdnmRsOvvRbU/3nNjIqaJ7XzDlwWQqR4aNJG159AtTGTwtTF0/NvKc5PFmydIHee4veXJOv+sI1sX9NGUzkavvmEvkuI+CWYStFkmvz8gGfIiQW3DCwEpMTISenh5cXFyQkpKChg0bwtPTE46OjjA0NMS7d+9E2r95U/Jj+fjxI9LT06Gnp4cqVaqItDE0NCSLOaelpcHY2Fjs8+/fvyMzM1NsMWhlwdTAyLanhanBUFFvvGwngzPl7ZBn5h1dPdJ00oVuWFCe48pzLHlg+zekqNl9dF8W6O4ri5HPlDEnz0snXcNOuM2hPQZEDlcvJHKPLxuIbGEnGIsSPAR5vmbl9oznZ0PpBlZhYSGeP38OU1NTLFy4ELq6uoiKioKHhwfCw8Pxxx9/YPHixTh+/Dj69OmDpKQk7Ny5EwBQUFCA79+/Q1NTU0xvlSpVkJdXsqp5bm6uWJvS//PzmS0Ex/bbINeSb6Uhz8O8MsB2LhTdfWVpLwwbHiZFvghIg+28K2EUGZpiMweL7WvPRloA2x54aX3rfDyCyJ16C9YcrD1O0D7vlsCDZaitzVIPJcPPIuQWSjew1NXVcfv2baipqUFLSwtAyfqBSUlJCAsLw44dO5CamgovLy8sWLAAtWvXxoQJE7B8+XJUq1YNWlpaEo2kvLw8aP//zV2lShWxNqX/azP8A+DaA1CR+TKKHEgrM4rMCeNyGJlruWjK0q8MlJVPWFmupbTfWbgglx2121BELrgniLJoL/IkcnbRW5Z6KBm+0Ci3ULqBBQA6Ojpi25o2bYpr164BAKZMmYJJkybhw4cPMDAwwNWrV6GmpoY6derA2NgYWVlZyM/PF/FSZWRkwMioJCGxdu3ayMjIENGfkZGBqlWrolq1aiyemYCf6YEmT+4PXSOMjdwMJmDbKFVke66F2LhAZeizsrzZisxnU2T+pCxcOedB5KGR24l8ePpYIo8WciPp778k2Hn8hAofV1Z4Dxa3ULqBlZSUhOHDhyM4OBht27Yl2x89egRTU1Ps3bsXKSkp8PLyIgbT2bNnYWtrCx0dHbRu3RrFxcWIjY0lMwJTUlKQnp6ONm1Kli+ws7NDdHS0yHFv3bqFVq1aQVVVFZUVZRkZbCQkM3Uuko6rrHACF2ZesZ2DJQuV0YPJRqhfHrj04kAXZU1OkKUN3VDwmv3fiFz4t2DsOHJf4CQYfm4PkalvBeXq5/l5UbqBZWJigiZNmmDFihXw8fGBnp4eDh06hPv37+Off/7Bx48f4efnB0tLS9jZ2eHUqVM4fvw4ycMyMjJC3759sXTpUvj6+kJbWxve3t6wt7eHjY0NAGD06NFwdnbGunXr4OzsjMuXL+PMmTPYsWMH4+ejyFlhTHl92H6rVCTK6A8TRqCsOpnKqaJ7LK7le7HRB2lwIdzNRA4W2yVSZNHJVA6mstIRnlgeI/KrFMHCz/v0Qoj8bMFgItcWis6ITsNih2J+FiGnUKEoiiq/Gbt8+PABAQEBuHr1Kj5//gxzc3PMnTuX1LU6fPgwQkNDkZ6eDlNTU8ycORMdO3Yk+3/79g2+vr44e/YsAMDR0RFLly6Fnp6gHsmVK1ewdu1avHjxAvXq1cO0adPQp0+fCvd5zMA95Tf6SZHHsFNUwj7XDD9FwmUDmCm45mGSBzphLa5NBqjs0A0pzrghyLVa1kowi7C6pqA+lso/grUICwYJjC0ttf7ydVYGUodVfEwTpu6hU4zo+dXhhIFVGVGkgcUFDxPbAxrTHgw2jDo2PEBseInYPi5TRjVT3l5px5UHRRpwFb0O8tyPsqCsWZhcQNp3Er7wkaDN7zFE7jdaUB9LPXQ0kS8IneJQExemuykGb2BxC6WHCH9m2E64lAbbyePSahDJokdRSddsHEee3A1ZjstUWQRpsF2OQ5b7QpZ9mUqKlnYsuvtKa8O2Z5ZOPqEsfWHb28yUUaVIo53utQpXjZCoR9/dnMjU8fNEPqMvKN8+VHItbUbhC41yC97AUjJMvSWynSzNxoNLnv6UB1MGijyGGlMGnCwPfmXdR9KQZTDnwiQAWfZlwxsjjwHKdF+Y0sNGzilTL4JM3RfhRxoRuSj9K5E/2VsTWW3aPiIXDlXsJCp+FiG34A0shpGnDAFTDzQ2jAJFDnTSYNoQkGeAZ3sgZ8ODxUafpbVR1swxLhudwjCR+M3298kU8ngV5fmtsBHifPfNnshaM/4hctWUA0RW3TaNyJM+fKxwH3gqP5wxsCIjIxEaGorXr1+jQYMGmDp1Knr37i3SJi8vD0OHDoWbmxsGDRpEtufn52Pz5s04efIksrKyYG9vj0WLFqFhw4akzdKlS/H333+L6Ktbty4uXboERcBGeIMpL4c8oUy2ww7SKE8PG6FXeYxkpr5bNvKW2EBZHkR5kCdEKE+biqLIRHVFpgLIciymfmfSkNaf1w4niZx9fBSRG449TGTjou9EblpdsSE7vtAot+CEgXXs2DEsWbIEixcvRseOHREVFYXZs2fD2NgYtra2AIAvX75g5syZSEhIENt/1apVOH36NHx8fGBmZobdu3dj5MiROHHiBFlnMCEhAZMmTcKoUYIfhZqaGuPnoixPD1OeJ6aOywZ0Bm1F5srQhQ3vFFPHlQZTuU1MDaTS2stjuMiST8bUd8fEc0KRHjq611iea6CsEKcserod+JPIJr0FswXVr/9B5Kp/HSey6khnRvogK8WVIESYl5eHNWvW4MyZM8jNzYWTkxOWLFkidU3ghQsX4ujRoxI/mzZtGqZOnQoA6NGjB16+fCnyubOzM9asWcPsCdBA6QYWRVEICgqCq6srXFxKZll4enoiJiYG0dHRsLW1xaVLl7By5UqRsgulZGdn49ChQ/D29iZlF7y9vXHr1i3s378fU6dOBUVRSE5OhoeHBwwMDMR0cAVFhgvZ0M/UgMmEl0aehzcb+WNshGHpwkaoRpp+Nl406OZysZGDw3YyuzKMfjYMSEUmqtPtG12E+1P09y4i734s8FSFuGoReevfgujKt3GhRNbdIyjZ8CuzfPlyxMTEYNOmTdDU1IS3tzemT5+OvXv3Smy/ZMkSzJkzR2Sbn58foqOjMXToUAAlpZpev36NkJAQWFhYkHaly+8pC6UbWCkpKUhNTUX//qI1QsLCwoh84cIF/Pnnnxg7diwsLS1F2r18+RIURZGaWQCgqqqK5s2bk+rtr169wrdv39CkSRMWz6Rs5MkZYGM2DxvI4gGQ1l6W7XRge9BiKhTBVB8q4zlyTY80ncr6PZUHGzMs2fjOZdHPRphXnvCsND3bbD5Ibr9fl8jfvARGleGOaZKaswbXk9zT09MRGRmJbdu2kTF7/fr16NWrF+7du0ciVsJUq1ZNZEm7S5cu4dSpU9i1axdZ3SU5ORnFxcWwtbVF9erVFXMyMsAJAwsosUDd3d3x5MkT1KtXD56ennBycgIA+Pr6St2/9AK/ffsWTZs2JdtTU1ORm5sLAEhMTAQA7NmzB1euXIGqqiocHR0xa9Ysha1FqKxwlDTYeLuXpY2ipsKz4amSpp/u4KDIxF15cmHkCQsxNetUFv2VxRAUpqKTAJjyQssT5pPHU80Frx9dg1L0WMOJHFZoSOQ9yUKG1wbB8jjdlgn01KbVy4rBVJmGrl27lvn5xYsXK6Q3NjYWANCuXTuyrXHjxjAyMsKdO3ckGljC5OXlYfXq1Rg8eLDI0noJCQmoVasWp4wrgAMGVk5ODgBgwYIFmDp1KubOnYuzZ89i8uTJCA8PJ+sLSsPIyAjt2rXD2rVrUb9+fdSvXx9//fUXnj59inr1Sn4YiYmJUFVVhaGhIbZt24ZXr17B398fSUlJ2LVrF+fXI6T7sGIjB0RZBpm07UyE2Zi6BsrSIwvKCr0oK7TD9gDOdthR2SgrP1AabITr6SLyXR0U5AkV3zhHZN26AsOgf6gxkfOFKrwrgsrgwdLT00OVKqILBxkaGiItLa3c/f/++298+PABM2fOFNmekJCAqlWrYvr06bh79y709PQwePBguLq6KnV8V7qBpaFRUgHX3d0dzs4lCYEtWrTAkydPZDKwAMDf3x8LFy5Enz59oKamBkdHRwwePBiPHz8GUJLTNXLkSJLD1axZMxgYGGDYsGF4+PAhrK2ty1JPCzbc9kwdS57j0h1kmAp3SqM8/fIcX5FT9Okel6k8KrYHdWVNfmD7/lLWiwkT/EzfubKgHt0U/KMuGD5f5gg8RytbCqqpvxjyhci7IlntGqNU1EP15s2bMr1fM2bMgKamptj2KlWqIC8vr0zdxcXF2LVrF4YOHSqWS52UlITPnz+jZ8+emDJlCmJjY7F27VpkZ2djxowZFToXJlC6gVUa4mvWrJnIdlNTU/z3338y6wgPD0dOTg6KiopQvXp1zJgxAw0aNABQkpP1Y4J8aTgxLS2NUQOLLlx4ELFtzCmS0r6x4a1RlkEmSx/Y1sO2Ycq15HFFvQjIc1ymktO5PBmAC7854WMVX7knkJMFHpcu2+OJ3MJF4J056C+YXagIlL3Ys5GREU6dkr7MzuXLl5Gfny+2PS8vD9ra2mXqvnv3Ll69eoURI0aIfbZ9+3bk5eWRlB8zMzPk5OQgODgY06ZNU5oXS+kGloWFBXR0dBAXFyeSqJ6YmEgMpLKgKAoTJ06Ei4sLOnXqBKAk7Hjjxg0sWLAAADB//nxkZGQgIiKC7Pfw4UMAJYacImDjgaCshw8b+TVMt5HlYc/GedDdt6K5OBXpgzw66YZn2SgDoKywIxswYYzIc37K+k1w4UVNvpxJQShwWLSgYnvWZcH6g00b6xB5yKodgl2Xs78WobJDhBoaGjAxkb4mUEJCArKyspCfny/iycrIyCDOFmmcP38e5ubmEvVramqKecaaNWuGb9++ITs7W2IFAkWgdANLS0sL48ePx5YtW2BkZAQrKytERUXh+vXrIgaRNFRUVFCjRg2sW7cO+vr60NTUxKpVq2BkZIQBAwYAAHr27InJkydj8+bNGDBgAFJSUrBixQr069evzJuhInDBoKG7rzwDrCzHleVYbOZ+MBXuYTv3jC6K9NAwNTuLqf4oSycbsOH5oQMboUO270dFvlwK6/xe+JnIBzQFyexjTAXGgcqDq4KdR3dkpA8/C61bt0ZxcTFiY2NJ+k9KSgrS09PRpk2bMve9c+eOxJQhiqLQvXt3DBw4kNTEAkqcKAYGBkozrgAOGFgAMHnyZGhra2PDhg1IT0+HiYkJNm3aJDJLoCy8vLzg6+sLd3d3UBSFjh07Yv369cSi7dq1KwIDAxEaGort27ejWrVq6N+/v1iiHBOwkcDMtYGd7nGVPWgw1Rc29q0syeBsJz8rMteNbRTl1VHkxBO6+7KdnK6sGcBJnwWzBdU1BOG43AXriPwxWVDAutqBUUSuQetIFYPrldyNjIzQt29fLF26FL6+vtDW1oa3tzfs7e1hY2MDoGRlluzsbFSvXp2M4UVFRUhMTISbm5uYThUVFXTv3h1hYWFo0qQJWrZsiZs3b2LHjh1YsmSJAs9OHE4YWAAwduxYjB07ttx2kiq5V6tWDX5+fmXu17t3b7Gld9hAWW9WXBvc6EI3hFZen9kIEcpyLLrQPT8u3Edcy8fi0iw8QHG/J655RYVh6r5g+36XBdHjCsJQE0wFRSxn9R9J5JD2+kTO9d0s2HWZeO4Q0yg7RCgLK1euhK+vL/E2OTo6YunSpeTze/fuwdXVFbt37yZOlqysLBQUFKBGjRoSdc6ZMwe6urpYv3490tLSUK9ePSxZsgTDhg1j/XzKgjMG1s8OGw8Ted4G6T4A5RnEmMrfodMHRc50lOdYXDBuZUGRsy+Z8q4psixFZYPuiwZTvwm2f090+0A33cHet4jIDVUpIucGCLYf3fCeyF1SBTPjlFtTnDtUrVoVq1atwqpVqyR+3rZtWzFHir6+vkTnSinq6uqYMmUKpkyZwmhf5YU3sBhGHm8JGw83afrZzoViqpwA3X2ZgG4emiIHY2WF0pgKvdAd3Ni+1xSpk02YKvHBxgQGuv1k2wiTJ4f01OxUIusdEXiqIqxTiJw67zaRv+8WLI9To9yjyg9ThUZ5mIE3sBSEstztbDwA5UmKZ9swKdXJRtiIC5MNlAVT3lVpbZjyNnEhYVsZsPGdyNOGjX3Z6ANdQ3r9AYEBc2JGOpGfnu9F5AUegu148FwgC4qXs0ZlWOz5V4I3sBhGkXkRbDzo6BpMdI9F9+2xooaMsjw9dPflslElDNt948LAy4YepmC6P8q677j2okHbQypkwfRwFYQID74W1L5SaSNYLzcvNIrIWgowsHi4BWcMrMjISISGhuL169do0KABpk6dKjEpPTg4GIGBgSLx2Pz8fGzevBknT55EVlYW7O3tsWjRIjRs2JC0efr0KVavXo1Hjx6hZs2acHNzg6urK+PnQTdEyPbDhKn+0C27wFR9J7pvmJI8WGw/yJkygJkK89CFqWvPVN/k0VNZDFa6SHpxYNvrp8jrraznJl1v/Ja/qxL50QWBUbVvaLRghy81iJg8dSCRW1a4l7JTXESV34hHYahQFKX0b+TYsWNYvHgxFi9ejI4dOyIqKgobN27E/v37RRZ/fPDgAUaMGIHCwkIRA2vZsmU4ffo0fHx8YGZmht27d+PChQs4ceIEatasiczMTPTu3RtOTk5wd3fH/fv34ePjA29vbwwePFhSl8plzMA9cp+3IuDam6qiZqBxYXDl2rVnqj1dPcqa+aiskhx0UcZ9wuUcM0X2je599ClPkIP1vu/fRPbVHULkiC2Zgp3fvCWiSts1cvVVFuLaM1M53vrmMUb0/Ooo3YNFURSCgoLg6uoKF5eSSreenp6IiYlBdHQ0MbC+ffuGefPmwc7ODrdu3SL7Z2dn49ChQ/D29kafPiVrQHl7e+PWrVvYv38/pk6dikOHDkFDQwMrVqyAuro6TExM8PLlS4SGhlbYwJKFn+ltmqmHHttvmxVpK+++dD1YXDC85NlXkfl+8sBUfyrb75iNfDl5+iDtWMry3svDPfP9RHac/5vgg9MCcXlaLSI3ai8wVMYqID+qqFjp/hIeIZRuYKWkpCA1NRX9+/cX2R4WFiby/+rVq9GsWTN06dJFxMB6+fIlKIoSWWZHVVUVzZs3R3R0ids2JiYG9vb2UBdanLNdu3YICQnBhw8fUKuW4AchL2wk2bKBsh7CiippoMhEXKYMKXlCcmwbuvL0n22YMoyYMhxloaIhdHnOg6nrxPZ1ZaoPbOTj/ZElKMeg3t+ByDsmCBYffpol8HI1yp3ISB94KiecMLCAEg+Vu7s7njx5gnr16sHT0xNOTk4AgHPnzuHy5cs4ceIE/v33X5H9S9cvevv2LVnAGQBSU1ORm5sLoGRB5x8XkzY0NAQAvHv3jlEDiwtvYmx7G2TRo8i8G0ntFZmrRBemBjd5jksXtmcRygMbA6wivWvKMEzZNs7Z9t4qzfNLLSTi2Ub+RO6aMpPIlklJRB70UpDZflQBNS+Li8pvw6M4lG5g5eTkAAAWLFiAqVOnYu7cuTh79iwmT56M8PBwNGnSBMuWLYO/v7/ENYWMjIzQrl07rF27FvXr10f9+vXx119/4enTp6hXr+SHkZubK7YQZJUqJQmKeXl5YjoViSyGAN2HlTxJ67IMVmwPCPI8nOm86ctjNHDB2OKCV1EWnbLoUWTSMtd0Kgpp9xrbHiw2wvXKCtteaxlA5MyPhUQupPKJrNawEZF32nxVSL9KKeZDhJxC6QaWhoYGAMDd3R3Ozs4AgBYtWuDJkycIDw9HQUEBevfuDUdHR6k6/P39sXDhQvTp0wdqampwdHTE4MGD8fjxYwAlC0rn5+eL7FNqWFWtWlVMHxsostQC1/JlZBlMmDYoFBlCUORbubJylZgyRuXRKQtMDcJsv1AwoV9ZRoY89wvd7YoM2woj7TtJevpd4vYEhx1EbtBS0CbZV7A8ThsD8PxiKN3AKg3x/RjCMzU1xZkzZ5Camoq7d+8iMjISAFBYWPLWYGtrCx8fHwwYMABGRkYIDw9HTk4OioqKUL16dcyYMQMNGjQAABgbGyMjI0NEf+n/pcdnGzbeyuWZSaXIWX50B5PKkFTM1ADM5dlcdKF7H7HhkeCaHjbhcoiNKYOWbY+nLP0RRv3CBCLv0HtMZMqmE5G/z/4fkVvVUqynkg8RcgulG1gWFhbQ0dFBXFycSKJ6YmIiTExMEB4eLtL+3LlzWLduHSIjI6Gvrw+KojBx4kS4uLigU6eSmzwnJwc3btzAggULAABt2rTBgQMHUFRUBDW1kpXOb926hcaNG0NfXx+KgGsPbLYTubl0vmwMusIoa6DjwjXmwuxSaXp+JmNL0nHZCNUp65pxYeajMNLu63ffEokcoXuDyK4HXxNZvV41It9MFyS8Oxgz2UPJ8CFCbqF0A0tLSwvjx4/Hli1bYGRkBCsrK0RFReH69euIiIgQKRYKgBhEwttr1KiBdevWQV9fH5qamli1ahWMjIwwYMAAAMDgwYOxY8cOLFmyBOPHj8eDBw8QEREBHx8fxZ0oCyjyzU2egZSuh4cJg4Xt8JMwyqq3pGzDlSt9kAbb37sioZNbKA15QnJsG0CVJVwfsVGQU/Vn/nQiXzDZRuSWTwYSucN3fu2aXxmlG1gAMHnyZGhra2PDhg1IT0+HiYkJNm3ahLZt28q0v5eXF3x9feHu7g6KotCxY0esX7+eJLbr6+tjx44dWL16NZydnWFgYID58+eTnC9lwvbgzJTBxFS+F90QoTTonld5bRVpGAkjj8eFC54VeYxntmd6yqOTa95YScdlKheSjYk2suiXhjyeNqa+N2n7qtSoS+QDmhuJPCrUkMjf5hwmctxqQQ6WDa0eVAx+LUJuwYlK7pURLlRyZyMHS5ZjSYMLAyMTb/pM5a2xMZNOFp3KMgjYzieje+5s90Ge301Fc46UdS2VdR9xIZQprU2QkWAMmJE+msg7h50ncv4wQSFrbXVmqqyXxZXmfRnR4xgfVX4jnnLhhAfrZ4KNnCR53PnSUNYbPRuzKekcRxboevGUlTPChdCVMEwl/gsjy++JbU8Y3ftUUYYJ2+EwNs6Vqdm9irz3hY/V+XgEkdM66xJ5c/PdRFapYULkvOJvRNZmqX883IU3sFiEqYePPG5yWY4lDF2jTVkhgvJgO9lZluOy4VmpqOdD3jayIM89wjZMeXuFUVZYmenjsJEjyUbOpjzeZHkQ1hmuKth+878cIo/sW53I7yJeEflWc0HMbnATRrpTJkV8iJBT8AYWiygyvCSMPPlVykpmpZubVd5xmdb3I7J8V0zlm0lrT/f7YcrzyMax5LnfmfrdMHWt5PHMMAHbhjRdFBl2ZPu8Dk0RJLZHZAoS29X0tIhcZ46g5JBtLYEHSxEUF/EZP1yCEwZWZGQkQkND8fr1azRo0ABTp05F7969sXDhQhw9elTiPtOmTcPUqVMBAFFRUQgODsbr169Rt25deHh4YODAgaTt8ePHMW/ePDEdFy9eJNXemYLuw4RtI4YpQ0OehzZTgySd4zIVrmI794xuP5WVIyOPkcT2NZQGG0YSU15pJrxGbDwX2AjzccGDyRTfdTSIrFVQSGT3472JHF5HUL6hCYQWhFYAfJI7t1C6gXXs2DEsWbIEixcvRseOHREVFYXZs2fD2NgYS5YswZw5c0Ta+/n5ITo6GkOHDgVQUs9q/vz58PLyQocOHXDlyhUsWrQIenp6pC5WQkIC7O3tsX79ehFdNWvWVMxJgh3PA1MhOaY8akyFGpjwADA1qLOR96OsUIcic21k6QPbeVTCsJE3KEt7WbbT6YM8RhLdfDY2jCc2wpFsIK2fcz0/EvlxvypEjtj5VCC3FdTK6vfNnsh8IfdfD6UaWBRFISgoCK6urnBxcQEAeHp6IiYmBtHR0bC1tUW1aoKibZcuXcKpU6ewa9cuUoH94sWLMDMzw59//gkAcHFxweHDh3H16lViYCUmJsLMzAwGBuzf4op8W5MnB4sN44apByzTOVhM6WajvSLf9BV57rLsq6wwlTz7MuWxFUYWA4cJD5a0Y8qjUx5jjqkXRDaQ1jcf6iCRG0bYSGxT5+UYIhto1We0X+XBFxrlFko1sFJSUpCamor+/fuLbA8LCxNrm5eXh9WrV2Pw4MEi9bH09fWRlJSEW7duoW3btoiOjsazZ88wduxY0iYhIQFOTk7snQjLsPHGrcgcmcry1lpRuJDbwkZ7YdgwLOjqp0tlzPEp7/eqSEORDbj226b7/ErKFpRaSPc8QuQq4aOI3D32PpGpt0+IrNK2S0W7KTP8UjncQukGFgB8+/YN7u7uePLkCerVqwdPT08xg+jvv//Ghw8fMHPmTJHto0ePxoMHDzBmzBioqamhqKgIkyZNIlXcs7OzkZ6ejpiYGOzfvx+ZmZmwsrLCvHnz0LhxY8bPiQsPELZDIPLoZPvttLz+K/L7+ZlyT7hwTymSyt5/ZcOGx5MLuAYKVhC5sWk4kVVUBekm4Q53idzvm8Dw4kOEvx5KNbBycnIAAAsWLMDUqVMxd+5cnD17FpMnT0Z4eDjat28PACguLsauXbswdOhQsTDfu3fvkJmZiWXLlqFVq1a4desWNmzYgPr162PIkCFISkoCUBKO9PPzQ25uLoKDgzFy5EicOHECtWrVYvSc5JktyDVvkzyDqizb6eqRJ1esPH10+0U3vMFUvgxd2J7yznZuFhseUrZRVBiMqTxApvrAxj0lDBvPL7rt+w34QGTqXiyRXwSlSGxf66uK4B8tiU0YhQ8RcgulVnI/deoUZs2aBW9vb4wcOZJs9/DwAACEhoYCAGJiYuDi4oJTp07BxMREREfv3r3Rr18/TJkyhWwLDAzEX3/9hZs3b0JVVRWfPn2Cnp4eVFRKbvbv37+jc+fOcHd3J8eiCxcquSsLeQZe3jPAHFy7xlzIP1RWf+SBzrmwYRgJw+XrxAZ075GIrdlELr4eQ+RvAwUV1HUzM4mco6dH5GoaQyvcT1k5btyLET0D0s4woudXR6kerNJE9WbNmolsNzU1xX///Uf+P3/+PMzNzcWMq0+fPuH58+ewtLQU2W5jY4Pg4GBkZWWhZs2aYrMFtbW1Ua9ePaSnpzN4NiWwMbNHFj10+8D2A5Zuno6i+szUbEimPCtMedGYKkXBhhdKljZcy5dS5KxDpmHjmSKLfmlt2DiWsq7xPedzRNY5O4DIvkNzibzjSAMi994iqEx6bRrLnePhHEo1sCwsLKCjo4O4uDjY2dmR7YmJiWjQQHCT3rlzh4QLhalevTq0tbWRkJAAR0dHsj0hIQG//fYbatasiYMHD2L9+vX4999/UbVqVQAlockXL15gyJAhLJ4dO7P56B6XjfIKssDGjDU6Ro08OtgYcJgKV8iyXVmTCtg4riLD49La0+2Dsr1AbNwjXJuBKg1FGl43av5F5PAzzYmsWtCHyKcnfWG1Dz/CFxrlFko1sLS0tDB+/Hhs2bIFRkZGsLKyQlRUFK5fv46IiAgAQFFRERITE+Hm5ia2v5qaGlxdXREcHAwDAwO0bt0asbGxCAkJISFDR0dHrFu3DvPnz8eMGTOQm5uL9evXo2bNmhg0aBDj58S1kExl8Qwoe1CShcrcd0VAdwBnwzClu68ssPH9/ixGG1Pt6aLI550wttusiVw/8A6RVewFDoC8zUKz4KePYLU/P8IXGuUWSjWwAGDy5MnQ1tbGhg0bkJ6eDhMTE2zatImUYsjKykJBQQFq1Kghcf8ZM2ZAT08PISEhePfuHerVq4d58+aRuli1a9dGREQEAgICMGLECFAUhQ4dOmD37t2oUqWKRJ2VBba9IrJAd3BT9kDBZRSZt8TGcZVlNCgyB0tZSf1M52BVFpQdbv2xD18LdIh8co+gYnvxIkEy+1jrukTWuH5doKgz+zlYPNxC6QYWAIwdO1akbpUw+vr6SEhIkLqvmppamfsDJaHInTt3yt1PriGP8cTGbBs2Qj504VLOCxtt5EGROUlst6erh6n+KCuvi837mgtGjDS4NoHB/4Eg18ryNzUid2siyLX6vlVQ1T1lucCD1ZLlvgF8iJBrcMLA+hVQZCFFrg2GijRAJL3ps50oy4YHUJb2ihxYZLluTOWBKfK3okiYuE/YNry58CxgG6ZyBRt9FlT1HPW3kGdrpTuRm+08LNhhlmA7W/AhQm7BG1gKQpF5HFyYbSMM3XARE94Drnn35GnPhePKM3OMroGryNAbXT3S2kijMpRDkOcFhO5vSJEvPtKQJ1dw6OgMiW30/xaUb3h7QpD8XnOfZ0W6WGH4OljcgjewGEaRU/PpDnRM9YcuiszTKt1XnpmRbHu5hHVWhgGYSRRZQkAemNLD1G+US8jyXTFlhCsLaf05cNSYyJeuCXJ4v40ReLA0IgVupCrJDwRKzUSXhOP5+eENLIZhwzPARr6GPG+qXDPghKHTN67NwmT7urKdzyKPYcq1nDO271l5fk+S9mPDm8bmC5CsKGvmriz3csR1QTmG34UWGHm7TZPIz/XqEFmwgi57FPFrEXIK3sBSMlx4W2OqD1zyxihyUFekHmXlkHE5v4aNUBPXPCqS+sDG75ZrOrlw7YXx3i1Ick+fFEVko6DBRDY4IUhs1y38rJiO/T+VLUS4bNky5OfnY82aNWW2e/PmDVauXIk7d+6gatWqGDJkCKZNmwY1NcFEg3379mHnzp14//49WrZsiaVLl8Lc3JztUygT3sCqBNAdQNh+YEqDq2EwLs9QE4YNT6W043LB08oUXPC0VAYUaSSz7S1VJML9fNtEECI8O1UwL1DFQLDKSJUla4mctXAgu52rpBQXFyMwMBAHDx6Es7NzmW0LCgrg7u6ORo0a4cCBA3j16hWWLFkCVVVVTJ8+HQBw9OhR+Pv7Y+XKlTA3N0doaCjGjh2L06dPi63kokh4A0tBKGtmFN2cF2n6ZdmXqWTgyvDgZSrBXJohSjeMSHdAkyeEx9Q9xVS+oiLzHpVFebMImTKe2YZrHjK6+s+vq0rkcPW7RB6xPJ7IWn/aEzn2vSA3q1E1xrspRnElCBE+e/YMS5YswcuXL1GnTp1y2589exZv377FoUOHUL16dTRr1gwfP36Ev78/Jk2aBE1NTWzbtg2jRo3CgAElyxf5+vqiW7du+PvvvzFx4kS2T0kqvIFViVGkG55tLxDTHjhFeq3kyRNhQ6c0/bJsF0YWY46usVVZBnMueITp6FCk55Sp+6IyernC1X2J7PZSkLROJT0ncvZmQQHSflvbKKZj/09lCBHeunULJiYm2LJlC2bOnFlu+5iYGFhYWKB69epkW7t27ZCTk4OnT5+iXr16ePHihchyeurq6rCzs8OdO3d4A+tXgA0DSJ7cGXmS3GU5FtsDKZ2HMBfKIgijrKRvusjyfXItXMgUXB7kKwobv082EuQV+RuVpQ/COqksQUHrc9b7iNzN35DIv/3RhMir4gTlG5a1qnB3FE7Xrl3L/PzixYsV1u3i4kKrfVpaGoyNjUW2GRqWXO93795BXb3EjKldu7ZYm/j4eCgTFYqiuG/y8vDw8PDw8CiEihpYb968KXPfmzdviuREjR49GnXr1i0zyd3NzQ2Ghobw9/cn24qLi9GiRQv4+/ujbt26cHFxwYULF1C/fn3SJigoCCdPnsT58+fLPBc24T1YPDw8PDw8PISKeqiMjIxw6tQpqZ8Lh/lkRUtLC/n5+SLb8vLyAABVq1aFlpYWAEhso62tTft4TMIbWDw8PDw8PDxyo6GhARMTk/Ib0sDY2BiJiYki2zIySirqGxkZkdBgRkaGyLEzMjJgZGTEaF/oolp+Ex4eHh4eHh4exdOmTRs8efIEOTk5ZNutW7ego6OD5s2bQ19fH40bN8bt27fJ54WFhYiJiUGbNoqdZPAjvIHFw8PDw8PDwwny8/Px/v17EvLr1q0bDAwMMHPmTMTHx+PChQtYv349xo0bB03Nkqr548aNQ3h4OI4ePYrk5GQsXrwYubm5GDJkiDJPhTeweHh4eHh4eLjBvXv34ODggHv37gEAqlSpgh07dqC4uBjDhg2Dj48PRo4cicmTJ5N9hg0bhunTpyMwMBCDBw9GamoqwsPDlVpkFOBnEfLw8PDw8PDwMA7vweLh4eHh4eHhYRjewOLh4eHh4eHhYRjewOLh4eHh4eHhYRjewOLh4eHh4eHhYRjewOLh4eHh4eHhYRjewOLh4eHh4eHhYRjewOLh4eHh4eHhYRjewOLh4eHh4eHhYRh+sWcZKCwsxLlz53Dnzh28e/cO+fn50NbWhpGREdq0aYMePXpATU2tXD1xcXG4ffs2PDw8AJSspxQREYE3b96gQYMGGDduHOzs7Ng+HR4eHh4eHh6W4T1Y5fDmzRv07dsXixcvRkJCArS0tGBgYAANDQ3Ex8dj0aJF6N+/P96+fVumnjNnzmDEiBGIjo4GAPz7778YO3YsKIpCp06dUFBQgDFjxuDff/9VxGkphLS0NBQXF9Pe79OnT3j8+DHu3r2L+Ph4fPr0ibaOb9++ifz/6NEjnDlzBk+fPpVZR0FBAbKysiR+VlxcXO53Lo34+HicPXsWCQkJFdpfmNjYWLJml6wUFBTgwYMHOHfuHE6cOIELFy7g4cOHKCwspKXnxYsXRM7Pz8fZs2cRFhaGCxcuoKCgQGY9b968wZMnT1BUVCSxr3fu3KHVLwCgKAoXL17Ezp078e+//0rUTYeKXOdSsrKykJ6eji9fvsjVh1I+ffqEhw8fIjMzkxF9PDw87MAvlVMOHh4eKCoqQmBgIKpVqyb2+efPnzFr1ixoaGhg27ZtUvX069cP/fr1w6RJkwCUrJ3UoUMHzJgxg7QJDg7GuXPncPToUZn6VlBQgK9fv6JGjRpinxUXFyMtLQ116tSRSZcw8fHxePnyJRo1agQzMzPa+5fSqlUrHDt2DPXr15ep/dGjRxEaGkoG7tJbU0VFBY0bN8bEiRPxxx9/lKkjMzMTCxYswIsXL3Du3Dl8+fIFkydPJoO0iooK2rVrh02bNkFXV1eijry8PKxYsQLHjx9HYWEhLC0t4e3tDQsLC9Lmw4cP6NixY5kGW0FBAYKCgnD16lVYW1vDy8sLXl5eiIyMJH3p3LkzNmzYAC0tLZmu0Y/QucYURWHLli3YtWuXxMFeV1cX48aNE1njSxJv3ryBp6cnvn79ikuXLiE9PR2urq54/fo1atSogczMTJiYmGDnzp0wNDSUqic7OxszZ87ErVu3AAAGBgbw8fFBly5dSBtZrnNOTg68vLzIdV6/fj1mzJiBW7duQU9PD1lZWWjRogXCwsKgp6dX3mWSCN17+datW9ixYwdiY2ORm5tLtletWhWtWrXChAkTYG9vX6aOvLw8/O9//0NcXBz++ecf5Ofnw8vLC8ePHwdFUVBXV8fAgQPh7e0NDQ2Ncvv04sULnDhxAtnZ2XB0dISjo6PI5zk5OVi9ejX8/Pyk6vjw4QPu378PMzMz1K9fH/Hx8di8eTN5Xnh4eMDS0rLcvkhj+fLlmD59Oq015OLi4nDnzh2kpaUhLy9PJLpgZWUlk47U1FTExsZiwIABAIDnz59j3759JLrg4uKCRo0alauHiWvM8/PAG1jlYGtriwMHDpRpaMTHx8PFxQWxsbFS21hZWSEqKoo8oH///Xfs3LkTzZs3J21ev36Nvn374sGDB2X2iUsGgKurq1T9MTExsLS0RJUqVQAAu3fvltp2z549WLduHdzc3NCuXTsYGhpCU1MT+fn5yMjIwM2bN7F7927Mnz8fI0eOlKpn7ty5iI+Px8KFC+Hg4IBly5YhNjYWa9asQdOmTZGQkIClS5fCxsYGK1eulKjD398fp06dwqxZs6CiooKIiAg8e/YMmzdvRseOHQGUXF8HBwfEx8dL7UtAQACOHDmC/v374/LlyzA2NkZKSgr8/PxgY2OD+Ph4LF68GJ07d8aCBQuk6nFycoKKiorEz96+fQsjIyMSor548aJUPUFBQdi/fz/mzJlDrnGVKlWQl5dHrvH69esxevRoTJ06VaqeSZMmIScnBz4+PjAxMcGsWbOQlpaGzZs3Q19fHxkZGZgzZw5q1qyJoKAgqXqWLl2KuLg4eHt7Q0VFBdu3b8eVK1ewcuVKDB48GIBs13nZsmWIjo6Gi4sLzpw5g5ycHBQXF2PLli1o0KAB0tPTMWPGDJiYmGD16tVS9TB1nU+dOoX58+ejZ8+eEq/zrVu3cPbsWQQEBKBnz55S9axcuRLnz5/H9OnTMWTIEKxduxYnTpzA4sWLyb3s7++Pfv36Ye7cuVL1ACUeOHd3dxgaGkJFRQWvXr1Cjx49sHbtWmhqagIo/5kRFxeH8ePH48uXL6hSpQo2btyIOXPmwMzMDNbW1khISMDt27cRERFRZqpDWR7JCRMmYPXq1cQwb9OmjdS2mZmZmD59Ou7cuQMjIyOx65yeno527dohKCgI1atXl6rn9u3bmDRpEho2bIjIyEjcu3cPY8aMQcOGDWFiYoLk5GSyeLCNjY1UPUxcY56fDIqnTDp27Ehdvny5zDYXL16kfv/99zLb9OnTh/rrr7/I/+PGjaOOHj0q0ubkyZNUt27dyu3T//73P6pTp05UZGQkdezYMcrZ2ZmysrKirly5Qtq8f/+eMjMzK1PPunXrqN9//53y8/OjevXqRbm5uVGdOnWibty4QX379o26e/cu1atXL2rNmjVSdYwbN44yMzOjhg8fTi1cuFDkz8LCgpo2bRr5vyycnJyoI0eOlNnmn3/+obp27VpmG3t7eyouLo7836VLF+ratWsibWJiYih7e3upOrp06SJyLQsKCqiZM2dS1tbWVExMDEVRJde3efPmZfZF+NivX7+mzMzMqLNnz4q0uXHjBuXk5FSmniVLllBmZmaUq6srtWnTJvK3ceNGysLCglq5ciXZVhYdOnSgiCfbCwAAK3pJREFULly4UGab8+fPU46OjmW2ad26NZWQkED+79ixI7kupTx+/Jhq3bp1mXok7bdmzRrK3NycXCdZrnOHDh2o2NhYiqIo6uPHj5SZmZnI90dRFHXv3r1yz4up69y7d28qLCyszDZhYWFUnz59yj2vGzdukP+7d+9OnT9/XqTNlStXqA4dOpSph6IoasSIEdSKFSvI/2fOnKFatWpFubu7UwUFBRRFlX+tXVxcqKVLl1JfvnyhduzYQVlaWlLLli0TabNhwwZqxIgRZfalRYsWVPPmzanmzZtTZmZmUv/K+95nz55NOTs7UykpKRI/f/78OeXs7EzNnTu3TD2DBg2iVqxYQRUVFVEURVGjRo2ivLy8RNr4+PhQf/75Z5l6mLjGPD8XvIFVDkFBQVT79u2pAwcOUC9evKDy8vIoiqKovLw86tWrV9Thw4epdu3aUevWrStTz9GjRykLCwtq7dq11NOnT6mYmBiqW7du1IEDB6jY2Fhq586dVOvWranw8PBy+8Q1AyA8PJxq06YNtXfvXpHtNjY21KtXr8o9n9K2z549K7NNcnIyZWVlVWYbOzs76unTp+T/7t27Uw8ePBBpk5SURNnY2JTZl5cvX4psKywspNzd3ak2bdpQSUlJMl1fW1tbcv5FRUWUubk59eTJE5E2r169olq1alWmHooqMXzat29PrVmzhsrPzxfpq6zXuFWrVlRycnKZbeLj4ylra+sy27Rr1466d+8e+b9v375ihtKjR48oOzu7MvW0bt2aev78udj2hQsXUpaWltSdO3dkus52dnZET15eHtWiRQvq4cOHIm2ePXtGtWnTpkw9FMXMdba2tpZ4Xj/2p7x7+cffROfOnanHjx+LtElJSSn3+6Koku/+xYsXIttiY2MpGxsbavbs2RRFlf/MkHQ/P3r0SKTNixcvyjWs79y5Q3Xp0oVyc3OjEhMTqTdv3lBv3ryhXr9+TdnY2FDR0dFkW1nY2tqK/bZ/JC4ursyXKYqiKCsrK5Hfe/v27St0nZm4xjw/F3ySezlMmzYNI0eOhL+/P3r16gVra2u0aNEC1tbW6NGjB/z8/DBs2DDMmjWrTD0DBw7E6tWrcfr0aQwcOBCjRo3C69ev4e3tjZEjR2Lz5s1wd3eHm5tbuX3KzMxEw4YNyf/q6upYt24d7Ozs4OnpieTkZJnOLSsrCw0aNAAA1KlTB2pqamI5JvXq1ZOa6F2Km5sb9uzZgwMHDmDChAn48OGDTMcXpmXLlggPD5eaFE9RFHbs2IEWLVqUqadjx45YsWIFSYwfOHAgduzYQZKc8/PzERQUhLZt20rVYWJigjNnzohsU1NTQ1BQEGrXro3x48fj+fPn5Z6ThYUFwsPD8e3bN4SHh0NVVRXHjx8XaXP06FE0bdq0XF3dunVDZGQkEhMTMXjwYCQlJZW7z4/Y2dlh3bp1yMnJkfj5169fERAQgNatW5epp0ePHli8eDG5z1xcXBAYGEj0fvjwAb6+vujUqVOZelq0aIG//vpLbPvKlSvRqlUrTJo0iUwKKYvWrVtjw4YNSEpKgr+/P7S0tLBv3z6SwwcAERERMDc3L1cXE9fZxMQEJ06cKLPN0aNHy83padOmDTZs2EAmDPTs2ROHDx8WaRMRESFTzpOuri4+fvwosq1Vq1ZYu3YtTp8+LVNOUI0aNfDmzRsAwLt371BUVISMjAyRNmlpafjtt9/K1GNnZ4fjx4+jZs2a8PT0RHp6OurWrYt69eoBAIyNjVG3bl3UrVu3TD1aWlrlTqYQzn+ThoGBAZ48eUL+b9SokdgElpcvX5YZZgSYucY8Pxd8DpaMFBQU4OnTp0hPT8f379+hpaUFY2NjNG/enMTXZSUlJQUpKSnIycmBuro6jI2NYWFhQXKVymPIkCHo0aMHKfdQytevXzFy5EhkZ2fD398fY8aMKTPWP3r0aDRt2hRz587FX3/9hcDAQIwaNUokH2jjxo24ceMGDhw4UG6/8vPz4e/vj6ioKCxfvhyLFi2SOTH46dOnGDduHDQ1NdGmTRvUrl1bJAcrJiYGOTk5CAsLQ8uWLaXqef/+PcaOHYu3b9+iffv2qFevHo4ePQodHR00btwY8fHxUFVVxf79+4lx+SOXLl3CtGnT0K5dO8yfP18k/y4jIwNjxoxBamoquSek8fjxY7i7uyM7OxtASe7Ss2fP8OXLF1haWiIhIQFXr17Ftm3bxJJhyyI8PBxbt26Fp6cnNm3ahOPHj8t0jVNTUzFu3DikpaXB3Nxc7Bo/fvwYBgYGCAsLK1Pf169fMXXqVNy+fRvNmjVDgwYNcOvWLeTn58PIyAipqalo1KgRdu3aBX19fal67t69C3d3dxgbG+N///ufSELy169f4eHhgbt37wJAmdf51atXmDBhAl6+fAk1NTUsWbKE5ANZWFggMTERr169wp49e2ROegYqfp1v3ryJSZMmoXnz5mjXrp3YdY6OjkZcXByCg4Ph4OAgVc+LFy8watQoaGhooHv37jA0NERoaCiaNGmCpk2b4sGDB+S8yvpNACV5anFxcfDx8YGFhYVIUvzevXuxatUq9OjRA+fPn5d6rYOCgnD48GH069cP//33H9TV1fHbb79hzpw5aNmyJRISErBw4UK0a9cOXl5e5V4nADh27Bh8fX3x559/Ytq0aWjTpo3M19nHxwc3btyAl5cX7O3tRZ7DhYWFiImJgZeXF9q3b48VK1ZI1RMWFoYtW7Zg3rx56Nu3L+Lj47Fs2TIsXboUTZs2RVxcHFatWoVBgwZh5syZUvUwcY15fi54A6sSUpYB8P79e7i6uirVALhy5QoWL16Mjx8/4ty5czLPvMrKysLBgwcRGxuLtLQ05ObmQktLC7Vr10br1q0xZMgQmWYX5efn4+TJk7h+/bqIIVu7dm3Y2dnhzz//LHc2WUxMDA4ePAh3d3eRiQhAycxRX19fnD59GnFxceWeU0xMDPT19WFra4ucnBysXbsWd+/ehZGREdzc3MocZKURHx+PefPmITk5mdY1LiwsxNmzZxETE0OucZUqVVCnTh3Y2dmhW7duMr8wREdH49q1axKvca9evWTS8+LFC0RGRuKPP/5A48aNRT4rKCjAtm3bcPLkSZw9e7bc80pOToaenh6MjIxQUFCA8PBwcp1dXFzQrFkzmc5LmIpe52fPnmHXrl2IjY1Feno6uc6l9/Lo0aNhamparp6srCzs3btX7DobGxujTZs2cHd3F7tuksjOzsasWbNw8+ZNhISEiP2e9+/fD19fXxQVFUl9ZhQVFWHr1q24ePEi9PX1iRdz7ty5pMRHq1atsG3bNokzrqXx5s0bzJ8/H7m5uUhOThaZDFQW+fn58Pb2xrFjx6CiogI9PT1iyGZlZaG4uBh9+vTBqlWryp2lu3XrVmzfvh15eXn47bffkJubi7y8PPL54MGD4ePjU2a9QyauMc/PBW9gVVK4ZABImpr86dMnXLlyBT169EBxcbFMU5Pz8vKQlJQEU1NTaGlp4cmTJ9i7dy8yMjJgamqKsWPHwsjIqNxrw5QeSXh4eGDVqlUwNDREcXExVFUrFmX/cbZUWURGRqJPnz4iBsutW7ewdetWREdHo2/fvpg8eTJMTEzK1fVjsdubN28iIiICqamptIrdMqGne/fumDJlCgYOHFju8RShB5BchmDdunW4du0aunfvLnMZgoKCAkRFRSE6OhqfPn1CQUEBdHV10bBhQ3To0KHMEDUbekp59eoV9PT0JBpAKSkpOHfuHCZOnEhLZ1paGuLi4mBsbAwrKyupMzHLori4GMHBwThy5Aj27t2L2rVry7xveno6YmNjkZGRIRJdaN26tUy/r1K+f/+OmJgYvHjxQsSQtbOzo9UfNq4xT+WEN7B+YuQxAGSFqanJz58/h5ubG96/f4/atWtj1apVmDJlCurUqQNTU1M8efIEnz9/xv79+8s0JCTpmTx5MurWrSuzntIyFZLw9vbGjBkziCetrEGdKT0tWrTAtWvXSLjt6tWr8PDwgIODA5o2bYqHDx/iwYMHCA8PR6tWraTqOXPmDGbPno3ff/8dO3bswL///ospU6agY8eOMDU1RWJiIm7cuIHNmzeL1KJiS0/z5s2hqqoKZ2dnLFiwoNzcHbb1MFWG4NOnT3B1dUVGRgYaNmyItLQ0ZGZmwsnJCe/evcOTJ0/Qvn17bNq0Cdra2qzrkaT33bt3yMvLQ9WqVWFoaEir7hRTOpiirHqAFEXh3bt3MtUDZErPj7x79w4ZGRlo1KhRuXlcPD8Zysmt5/lZYGpqsoeHBzVt2jTq6dOn1IIFCygrKytq0aJFVHFxMUVRJTMlZ8+eTbm7u7Oux8bGhpFp5EzpMTMzoz58+ED+HzFihFjpDF9f33Knx/ft25cKDg4m/w8dOpQKDAwUabN161Zq4MCBCtFjZmZGnT9/nurSpQv1+++/U3v27CGzdOnAlB6myhDMnTuXmjJlCvX161eKokpm3P3vf/+jFi9eTFFUyYzd/v37i/xu2NRTypEjR6hevXqJ3ZPNmzenevfuTUVGRipEB1Pk5uZSixcvplq2bEk1b96cGjp0qNisRlmePUzpoSiK2r17NzVx4kRqz549ZHZ36XWysLCg/P396Z8oT6WFN7AqIaNGjaJGjx4t0x/bepiamiw8Jb20ltGPU6UTEhLKLK/AlJ7nz59TgwYNotzc3Ki0tDQx/bJO12dKz48G1u+//y52Ts+ePSt3GrmlpaXIMdu3by9S0oKiSspGWFpaKkRP6Xl9+/aNCggIoKysrKgOHTpQAQEBInW2yoMpPUyVIbC3txcrh/H9+3fK0tKSGEsPHjwot34VU3ooqmTgt7KyotavX0/duHGDSk5Opl69ekUlJydTN27coAICAihra2tq3759rOqgKIpKTU2V+a8smKoHyJSe7du3U7a2ttSUKVMoe3t7avz48VSHDh2okydPUklJSdTff/9NtW3blgoNDS1TD8/PA7/YcyXEwcEBQUFBaNy4Ma1ZUWzoKZ2aLFw2onRq8vTp01GrVi1MmDChXD1aWlr4/v07AKBmzZoYNmyY2KzKz58/l5s8y4Sexo0b4+DBg9i4cSP++OMPLFu2DH369Cn3HNjS82NOS+PGjcVKLXz69Knca1O/fn1cv34df/75J4CS0GN8fLxIDt+DBw/KzU9jSk8p2tramD17Ntzc3LB//34cO3YM27dvh76+PszMzFCjRg0EBASwrqe0DEH9+vVFyhAIr5AgSxkCTU1NvHv3TiQEnZ2djfz8fJIMrq2tXe7ahkzpAUrKOSxfvhzOzs5in5mYmKB9+/Zo1KgRtm7dKnWlBCZ0AEDfvn3LLZ9AURRUVFTKTCs4c+YMVq5cSVZX6NOnD+bNm4dp06YhLCyMlBspLyeMKT2HDh2Cv78/unXrhhs3bsDd3R2BgYGkWr+pqSl0dHSwdu1amZ6JPD8ByrbweCrG3r17KVtbW+r169dK1ePl5UUNGDCAunfvnkhhRoqiqD179lBmZmbUtGnTyvVgzZ07lxo+fDiVlJQk9llRURF17do1qmfPnpSPj49C9JQSHR1NdenShZozZw71+fNnWp4npvSYmZlR1tbW1B9//EHNnj2bGjNmDOXs7EzCYNHR0VS/fv1I6EgaTBW7ZUpP8+bNRTxzwjx9+pTau3cvtXjxYmrChAkK0RMYGEg5ODhQa9asoXr16kX169ePGjlyJBUbG0vl5eVRDx48oPr06VNuSG7ZsmWkiO/379+pZ8+eUaNGjaKGDBlC+uTq6kpNmzZNIXooiplCvkwVA37x4gXl5OREDR48mLp9+3aZf2XBVEFgpvRYWVmR4qjFxcWUhYWFmGf35cuX5XrheX4eeAOrEjNx4kSZHq5s6snKyqLGjh1LNW/eXOKSQvv27aMsLCzKfTh9/PiRGj58ODVnzhyxz6KioigzMzPKw8OD+vLli0L0CJOdnU3Nnj2bcnR0pCwsLCpkYMmjJy0tjbp8+TK1fft2au7cudSAAQOoli1bUt++faMoqiRMO3ToUOrjx4/l6oqMjKScnJxI3oxwLlirVq2orVu3ytQnJvT8GPqsKEzpKSwspDZu3Ej98ccf1Lhx46jk5GTqzJkzJDenefPm1MiRI6nPnz+XqefLly9kCanS/Xr37k2qu48aNYpycXGh0tPTFaKntO3SpUvJcjA/UlxcTC1cuJAaPnw4qzpKSU5OpmxsbKhz586V21YagwcPpkJCQsS25+TkUAMGDKA6depE3b59u9xnD1N6+vfvL7Kaxdu3b8WeM4GBgcRA5vn54WcRVmJKi0OWNVNLUXqYmpr8+fNnsRBMZmYmPnz4IFPFc6b1CBMZGYkjR45g3bp1tKZ/s6GnqKiI1ORJTk6GiYkJrenx8ha7ZUJPdHQ0WrVqBXV1+TIVmNIjjYqWIYiPj8eLFy9gaGgIS0tLUnjy69ev0NHRkfn4TOhhopAvU8WASwkJCcGlS5dw8OBBma+FMEzVA2RKz6lTpzBv3jxMnDgR06dPF/nswYMH8PX1xaNHjxASEoIOHTpU6Jx5Khe8gcXDw8PzCyCpkG9p8VM7OzuZCvkyoYNJmKoHyJSeq1ev4sOHD2J5arGxsdi1axfGjh0LW1tbGmfIU5nhDSweHh4enp8WpuoBKqKuIM/PBW9g8fDw8PAojNGjR8scZt29ezfLveHhYQ++TAMPDw/PTw4TRg1ThhFTZWaY6g/X9PD8PPAGFg8PD89PDhNGDVOG0cSJE6Grq4uAgACEhISgXr16Su0P1/Tw/DzwIUIeHh6eX4B9+/YhICAAx48fr7BRw4SOUiZNmgRNTU1s3LixwjqY6g/X9PD8HPAGFg8PD88vAhNGDRM6AObKzDDVH67p4an88AYWDw8Pzy8CE0YNU4YRU3CpHiCTengqP7yBxcPDw8PDw8PDMHxRDx4eHh4eHh4ehuENLB4eHh4eHh4ehuENLB4eHh4eHh4ehuENLB6F8yun/f3K514e/LXh4eH5meANrF+MTZs2iawWr2iSkpIwYsSICu3r5OSEhQsXMtyjirFw4UI4OTnR2keec6fDmzdv0KVLF3z69AkAkJ+fj23btqFXr16wsbFBz549sXnzZuTn50vV4evrK/E+SUhIwPjx42Fvbw8HBwcsWLAAHz58IJ/fvn0bZmZmuH37dpl9NDMzw6ZNm0j/fH19ceLEiTL3GT16NMzMzKT+DRs2TGyfhw8fYt68eejcuTOsrKzQrVs3eHl54fXr1yLtFi5cWKbuM2fOSG1nYWEBBwcHzJs3D+/evSvzHAAgPT0d06dPh52dHTp16oTQ0NBy9wEEv92y/sojMTERs2bNQocOHdCyZUs4ODhg5syZiI+Pl6kPdDhy5AjMzMzw5s0bmfe5efMm/vjjDxQUFIhsHzVqFE6dOsV0F3l4WIWv5M6jUM6cOYN79+4puxtyM3nyZLi6utLaRxHnTlEUFi1ahDFjxqBmzZoAgFWrVuH48eOYPHkyLC0t8fDhQ2zZsgVv376Fr6+vmI47d+5IXMrjw4cPGDNmDGrXrg0/Pz/k5eVh3bp1mDBhAg4dOgQNDQ2Z+3nw4EEYGxsDKJnWvmvXLvj5+ZW7n7m5Oby9vSV+pqOjI/L/vn374Ovri7Zt22LOnDkwNDTEy5cvERYWhnPnzmHXrl1o3rw5aW9gYIDNmzdL1N2oUSOp7QoLC5GSkoJ169bh3r17OHnyJLS0tKSew/Tp0/HlyxcEBAQgLi4OAQEBqFu3Lvr27VvmuQ8dOhQdO3YU256cnAwvLy/Y2tqWuX9SUhKGDx8OGxsbLF26FPr6+khLS8PevXsxbNgw7N69GzY2NmXqYJv27dujbt262Lp1K2bMmEG2L168GO7u7mjbti309fWV2EMeHtnhDSwengrQoEEDZXdBIufPn0diYiLCwsIAAJmZmTh06BDmzp2L8ePHAygZxAAgICAAc+fOJYYYAHz9+hWLFi2CkZER0tLSRHRfvHiR6Cs9/2rVqmH8+PG4d+8e7O3tZe5nRQdyXV1dmfaNjY3F6tWr4eLigiVLlpDtbdu2Rbdu3TBw4EAsXrwYR44cIZ9pamrKpFtSOzs7O2hoaGDBggW4ePGiVGMpMzMT9+/fx9KlS9GpUyd06tQJ+/fvx927d8s1sIyNjYlRKqxv7ty50NPTw4YNG8rcPzw8HHp6eti+fTvU1QWP/m7duqFXr17YunWrzN40NvH09MTIkSMxYsQIGBoaAigxrK2srBAcHIylS5cquYc8PLLBhwh/cY4cOQJzc3PExcVh+PDhsLS0RJcuXcgADZSEnMzMzBAVFYVJkybB2toanTt3xpYtW1BcXEzaCYd9ShEOSW7atIm8+UtqK0x8fDzGjh0LW1tbdOnSBcePHxdrU1xcjNDQUHTv3h0tW7ZEz549sWfPHpE2r169wqRJk9C2bVtYW1tj+PDhuHz5skib+/fvY9y4cWjVqhXatWuH2bNnIz09HYAg5HXgwAF06dIFrVq1wvXr18VChE5OTtiwYQN8fX3Rpk0btG3bFvPnz0dWVlaZ556Xl4ctW7agV69esLS0RI8ePRAaGipyXUePHo25c+di+vTpsLGxwdixY6Vet5CQEPTs2ROampoAgJycHPz5559i4cwmTZoAgFiozN/fH7Vq1cKgQYPEdOfl5QEoMXJKqVGjBgCQ8ywlOTkZI0eOhKWlJbp37y72vZRegzdv3qBr164AgEWLFtEOu0ojLCwM1apVw+zZs8U+q1mzJhYuXIiuXbvi27dvjBwPACwtLQEAqampUttUq1YNtWrVwvnz50FRFBISEpCdnY1WrVrRPh5FUZg3bx5SU1MREBAAIyOjMtt/+PABFEWJ3FsAULVqVSxevBi9e/cW2R4ZGQlnZ2fyew8ICBAJK1+4cAEjR46Era0tWrZsiV69emHfvn1l9iEmJgajRo2CtbU17O3tsWDBAhLKLsXS0hJ16tRBeHi4yPb+/fvj8OHDYu15eLgKb2DxoLi4GDNnzkSfPn0QGhqKVq1awd/fH1evXhVpt3z5cujq6mLTpk34448/sHnzZgQEBMh8nKFDh2LIkCEASkJEQ4cOldguPT0do0aNwpcvX7B27VrMmDED69atI0aPcH82btyIAQMGkBwjX19fbNmyhZzXxIkT8f37d/j7+2Pr1q2oUaMGPD098fLlSwDAkydPMGrUKOTl5cHf3x8+Pj549OgR3N3dUVhYSI61efNmLFiwAMuWLZMaiin1RPj5+WHOnDm4fPkyJk6cCIqiJJ47RVGYNGkSduzYgaFDh5JzCAwMFAuDnT59Gjo6OggODiaeqB95/vw5Hj16hB49epBt9evXx/Lly4lBVcrFixehoaEhEvq6fv06jh07Bj8/P6iqij8aevfuDQMDA6xYsQIZGRl4/fo1/P39YWBggN9//12krZ+fH2xsbBAcHIyOHTti1apV2LVrl5hOQ0NDYnh6enpKDdGVQlEUCgsLJf6VJslTFIVr166hffv20NbWlqinT58+mDJlCqpWrSqyvSy95ZGSkgKgbO+muro6lixZgtu3b2PatGlwdXWFq6sr+vTpI9MxhNmyZQuuXr2KadOmEa9kWXTu3Blv377Fn3/+iX379uHZs2fk3Hr16gVnZ2fSdt++fViwYAEsLCywefNmeHh4YM+ePVi1ahUA4L///sOUKVNgYWGBrVu3YtOmTahfvz5WrFiBuLg4ice/c+cO3NzcoKWlhcDAQCxevBjR0dFwdXVFbm6uSNtevXrh5MmTItucnJxQVFSE8+fP07pOPDzKgg8R8oCiKEyePJkYPK1bt8b58+fx33//ieR8WFhYYN26dQAAR0dHfPv2Dbt27YKnp6eIV0MawiGOskIxERERKCoqQmhoKAlfNW7cWCSJOSUlBYcOHcLs2bPh4eEBoGQ1exUVFYSEhGDkyJEoLCzE8+fPMXnyZHTq1AkAYGVlJZLgvW3bNtSoUQM7d+5ElSpVAJQM+nPmzEFSUhI53siRI9GrV68yz09VVRXh4eGoVq0agBJPyZQpU3D16lU4OjqKnfvly5dx48YNrF+/noSHOnToAC0tLQQFBcHV1RVNmzYFAGhoaMDHx4d4piRx69Ytco5lcf78eRw9ehSjRo1C9erVAQBfvnzBkiVLMH36dDRu3FjifgYGBvDx8cHs2bNx+vRpAED16tWxe/duse9/2LBhmD9/PoCS7yU9PR0hISEYPXq0iPGmqamJFi1aACgxTMzNzcvs+507d2BhYSHxs6CgIPTq1QuZmZnIy8ujvdhuamqqRN1z5swh91gpwsZ3Tk4OHj58CD8/P9SrVw+dO3eWeozi4mJkZ2dDS0sL58+fh7OzMxYtWkSrnwBw48YNbNmyBY6OjvD09JRpn5EjR+L9+/cICwvDihUrAAB6enpwcHCAq6sruW+Ki4uxZcsWdOvWjRhUAPD9+3dERUWhoKAAycnJcHZ2Fgm/2traom3btrh9+zasra3Fjh8QEIDGjRsjJCQEampqAABra2v07dsX//zzD1xcXEhbS0tLbNu2Dc+ePYOJiQmAEk+biYkJbt68ieHDh9O8Yjw8ioc3sHgAQMQro6mpiZo1a4qFTwYOHCjyf8+ePbF7927cu3dPYvJtRYmNjYWNjY1IbpC1tTXq1KlD/r916xYoioKTk5PIYOfk5ITg4GDExsaia9euMDU1hZeXF65duwYHBwc4OjqKDGixsbHo1KkTMa6Akmtx6dIlACCz4UqNgLJwcnIixlXp/+rq6rhz5w4cHR3F2kdHR0NdXV3McBswYACCgoIQHR1NDKwmTZqUaVwBJeG+3377Db/99pvUNufOncOcOXPQunVrzJs3j2z39fWFsbEx3NzcpO574sQJzJ8/H7169cLgwYORl5eHnTt3Yty4cdizZw8ZCAGIeWS6d++OCxcu4Pnz5zA1NS3zPMrCwsICPj4+Ej8r9RyVDt5FRUW0dBsYGCA4OFhs+495T9IMMWtra6xYsUJqgjtFUZgzZw7+/fdfLFiwAFeuXEFkZCR69OgBBwcHbN26Fd27d5dqQJaSnp6OOXPmwNjYGGvXroWKiorI58K/B6DE8C81amfMmAE3NzdcvXoVN2/exO3bt3HixAmcPHkSixcvhqurK1JSUvDx40d0795dRI+7uzvc3d0BgHhRv379ipSUFLx69QoPHz4EAImzU79//464uDi4u7sTLyRQ4mE1MTHB9evXRQysUuP4zZs3IvdV3bp1ac1K5OFRJryBxQMAYoOCqqqqWGjkxxyPUgMoOzub0b5kZ2dL9D4YGBgQuTTnR1picHp6OlRUVLBz504EBwfj/PnziIyMhIaGBrp16wYfHx9Ur14dWVlZMs1K+jGUJIkfr4+qqir09PSkXp/s7Gzo6ekRg6CU0vP88uUL2fbjDDlJ5OTkSA2JASWewf/973+wt7fHli1biFH577//IioqCv/88w+Ki4vJH1AyWJcO0Js3b4atra1IMnWHDh3Qp08fBAUFYePGjWR7rVq1RI5deo3lvVd0dHRIrpM0qlevDh0dHbx9+1Zqm2/fvqGgoIB48ICSF4vydAPihpimpiaMjY1FdEni1KlTOHXqFMLCwuDg4IABAwZgxIgRmDt3LiZOnIjg4GA0b968TAOrsLAQM2fOxJcvXxASEkJy4IT5cf+pU6di2rRp5P/q1aujX79+6NevH4CSMPm8efOwdu1a9O/fn/y2yvpdfPr0Cd7e3rhw4QJUVFTQsGFD2NnZAZBcz+zz588oLi7G9u3bsX37drHPhV9wAJD7WPg3ULr9x208PFyFN7B4ZCYzM1Pk/48fPwIQfRD/6DWoSBKxnp6eSG2lUoQTqUu9NLt27ZJofJR6u4yMjLB8+XJ4e3sjPj4eZ86cwfbt26Gnpwdvb29Uq1ZNYtLs5cuXZfJaCfPj9SkqKkJmZqaIJ06Y6tWrIzMzE0VFRSJGVkZGBoCS60AHPT09iYMPRVFYvXo19uzZg379+sHPz0/EG3b27Fnk5eWRAVcYCwsLODs7Y82aNUhNTUW3bt1EPtfS0kLLli1FwqmAuCFV+n0qaoq9g4MDbt++jby8PLHBGwAOHTqE//3vfzh8+HC5HqMfkdUQ+5G7d++iRo0acHBwAFAyWSA4OBhDhgzB+vXrUbt27XKT/NeuXYu7d+9i2bJlUkPBhw8fFvnf0NAQ6enpGDx4MGbMmCGW+2hubo5Zs2ZhypQpxAsKQOx3kZmZiSdPnsDW1hZz587F8+fPERERAVtbW2hqauL79+84dOiQxD7p6OhARUUFbm5uEl+KfnwxKL1/fvwNfP78mfbvgodHWfBJ7jwyc+HCBZH/z549C21tbZJvoaurK5aIfvfuXZH/JSVP/0i7du1w7949EV3JyckiM95K35YzMzNhaWlJ/j59+oSgoCBkZWXh3r17+P333/HgwQOoqKigRYsWmDVrFpo1a0a8G3Z2drh+/bpIWOPJkyfw8PDA48ePZbkshCtXrojouXjxIgoLC0kC8o/nbm9vj8LCQlLEspTSGZOtW7emdfw6derg27dvYsbN+vXrsWfPHowdOxbr1q0TCzVOnToVhw8fFvkrzXc7fPgwpk6dCqAkTHn37l0RD0VeXh4eP36M+vXri+j877//RP6PiopC7dq10bBhQ7F+/+jBY4Jx48YhKysLgYGBYp+9f/8eO3fuhKmpKW3jSh7q1q2LrKwsEWO0Xr16JJxas2ZNsRl+wpw7dw4RERHo27evSDjtR4R/D5aWljAyMkKtWrWgrq6O/fv3k9mgwjx//hxVqlRBw4YN0aRJE+jp6eHff/8VaXPs2DF4eHigoKAAsbGx6NGjB9q2bUvupytXrgCAxHPQ1dWFubk5nj9/LtK3pk2bYtOmTWKFaUt/+8JpAQCQlpaGunXrSj13Hh4uwXuweGTm9OnT0NfXR6dOnRAdHY19+/Zh1qxZJHzWuXNnREVFwdraGg0bNsSRI0fIbL1SSt+OT548CWtra7GBGQDGjBmDw4cPw93dHdOmTUNRURE2bNggUsjSzMwMAwYMgJeXF1JTU9GyZUukpKRgw4YNqFevHho1aoTCwkJoaWlh/vz5mDZtGmrVqoUbN27g6dOnpEjo5MmTMXz4cEycOJHMZgoMDISVlRU6dOhAqzDou3fv4OnpCVdXV7x79w7r169Hx44d0bZtW4nn7ujoiLZt22Lp0qVIT09H8+bNER0dje3bt8PZ2Zl2rlKHDh0AlOSVlXpCnj59iu3bt8PS0hK9evUSm+FlamqKevXqiYVkSw0kYU/NjBkzMGXKFMyYMQNDhgxBfn4+du3ahfT0dLHZpHv27IGOjg7Mzc0RFRWFq1evwt/fXyxfCADJW7t58yZMTEwkJkiXkpOTg/v370v93NLSEmpqarCxscGMGTMQGBiIZ8+eYeDAgdDT00NSUhLCwsKQl5cn0fhik9JinhMnTsTMmTNRs2ZNREZG4uTJkxg8eDCOHTuGCRMmICgoSMzrmZaWhkWLFqFmzZoYOnSo1Gtgbm4uMVdPTU0Ny5cvx5QpUzB48GC4uLjAxMQE379/x/Xr17Fv3z7MmDGDhDmnTZuGFStWQF9fH05OTkhJScHGjRvh4uKC6tWrw8rKCidOnICFhQWMjY1x9+5dhIaGQkVFBd+/f5fYt9IJKXPmzMGAAQNQVFSEnTt3Ii4uDpMnTxZpGxsbi3r16olMuPjy5QuSkpIwbtw4Opedh0dp8AYWj8zMmDED0dHROHjwIGrXro1ly5aJLP2yaNEiFBYW4n//+x/U1dXRp08fzJkzR6QwYI8ePXDs2DEsXLgQQ4YMwfLly8WOo6enh7/++gurV6/GwoULoaOjg/Hjx4stleHn54eQkBAcOHAAaWlp0NfXR58+fTBz5kyoqalBTU0NO3fuREBAAFavXo3Pnz+jUaNGWLFiBanzZG5ujj179iAgIAAzZ86Erq4uOnXqhLlz55abVP4jffv2xW+//YaZM2eiatWqcHZ2xqxZs8o895CQEGzcuBERERH49OkT6tWrh9mzZ5dZ60oa9evXh4WFBS5fvkwMrHPnzoGiKDx8+FDizKvdu3cTA7A8unbtitDQUGzduhVTp06Fjo4OrKyscPjwYZGK6EBJ9fgdO3YgMDAQ9evXF5kp+SO6uroYO3YsDh48iMuXL+P69etSq8I/efKkzBlkd+7cIYasp6cnzM3NSUX37Oxs1K5dG507d8akSZNQu3Ztmc6bKXR1dbFv3z74+/tj5cqVyM/Ph4WFBUJDQ+Ho6IhOnTpJTLIHgJcvXyInJwcAypyIcPHiRamzJzt37oxDhw4hLCwM27Ztw6dPn6CpqQlzc3Ns2LBBpLyHi4sLqlatirCwMFJ1f8KECZgwYQIAYM2aNVi5ciVWrlwJoKTSvY+PD44fP46YmBiJx3dwcEBYWBg2b96M6dOnQ0NDAxYWFggPDxebVXz16lWxyR9Xr16FhoZGmbM0eXi4hArFr7DKUw6lxSD9/PwkFqDkKZkxaG9vjzVr1ii1H2fPnsXixYtx5coVmRLjebgFRVESvXy/EjExMRg3bhwuXLhAKrkDJZ7tZs2aiZSG4OHhMnwOFg/PT0SPHj3QtGlT/PXXX8ruCk8F+NWNKwDYsWMHxowZI2JcPXz4EPHx8WL1yHh4uAxvYPHw/ESoqKjA398fu3fv5pcU4al03Lx5E2/fvhUpKwGUpAN4eXmJlGrh4eE6fIiQh4eHh4eHh4dheA8WDw8PDw8PDw/D8AYWDw8PDw8PDw/D8AYWDw8PDw8PDw/D8AYWDw8PDw8PDw/D8AYWDw8PDw8PDw/D8AYWDw8PDw8PDw/D8AYWDw8PDw8PDw/D8AYWDw8PDw8PDw/D/B8+SIr3s4niSAAAAABJRU5ErkJggg==", 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", "text/plain": [ "
    " ] @@ -5898,7 +5908,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 91, "metadata": { "scrolled": true }, @@ -5907,40 +5917,40 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:12:03,348] A new study created in memory with name: example_multi-parameter_analysis\n", - "[I 2024-07-02 17:12:03,385] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 17:12:03,502] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:03,604] Trial 1 finished with values: [-1.3561484909673425, 0.9875061220991905] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:03,658] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:03,713] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 17:12:03,741] Trial 4 finished with values: [-0.5238765412750027, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:03,793] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:03,836] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 17:12:03,877] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:03,957] Trial 8 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:03,987] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:04,026] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:04,055] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:04,084] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:04,114] Trial 13 finished with values: [-0.5361950698070447, 0.23560786523195643] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:04,142] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:04,210] Trial 15 finished with values: [-0.543430366921729, 0.514747412346662] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 17:12:04,249] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:04,291] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:04,356] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 17:12:04,386] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n" + "[I 2024-07-09 11:32:43,089] A new study created in memory with name: example_multi-parameter_analysis\n", + "[I 2024-07-09 11:32:43,133] A new study created in memory with name: study_name_0\n", + "[I 2024-07-09 11:32:43,247] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,315] Trial 1 finished with values: [-1.3561484909673425, 0.9875061220991905] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,368] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,422] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-09 11:32:43,438] Trial 4 finished with values: [-0.5238765412750027, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,458] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,477] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-09 11:32:43,493] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,557] Trial 8 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,573] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,590] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,608] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,625] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,641] Trial 13 finished with values: [-0.5361950698070447, 0.23560786523195643] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,657] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,724] Trial 15 finished with values: [-0.5434303669217287, 0.5147474123466617] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-09 11:32:43,742] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,760] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,814] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-09 11:32:43,832] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:12:04,414] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668667] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 17:12:04,444] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:04,461] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-02 17:12:04,491] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 17:12:04,572] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 17:12:04,625] A new study created in memory with name: study_name_1\n", + "[I 2024-07-09 11:32:43,849] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668667] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-09 11:32:43,867] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:43,871] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-09 11:32:43,892] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-09 11:32:43,959] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-09 11:32:44,015] A new study created in memory with name: study_name_1\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n" ] }, @@ -5955,8 +5965,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 17:13:12,240] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 17:14:17,087] Trial 1 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 45.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n" + "[I 2024-07-09 11:33:50,633] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n", + "[I 2024-07-09 11:34:59,968] Trial 1 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 45.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n" ] } ], @@ -6009,7 +6019,7 @@ }, { "cell_type": "code", - 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