fix(JAQPOT-407): remove extra config (#107)

* fix(JAQPOT-407): remove extra config * fix: update api files * chore: reformat files * fix: build * chore: do not reformat openapi files
ntua-unit-of-control-and-informatics · Nov 5, 2024 · 531f7aa · 531f7aa
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diff --git a/examples/example_classification_StringLabels.py b/examples/example_classification_StringLabels.py
@@ -14,7 +14,6 @@
 )
 from jaqpotpy.datasets import JaqpotpyDataset
 from jaqpotpy.models import SklearnModel
-from jaqpotpy.doa import Leverage
 from jaqpotpy import Jaqpot
 
 path = "jaqpotpy/test_data/test_data_smiles_CATEGORICAL_classification_LABELS_new.csv"
@@ -73,7 +72,6 @@
 print(cross_val_scores["output_0"]["fold_1"]["confusionMatrix"])
 print(cross_val_scores["output_0"]["fold_3"]["confusionMatrix"])
 
-
 # skl_predictions = molecularModel_t1.predict(prediction_dataset)
 # # skl_probabilities = molecularModel_t1.predict_proba(prediction_dataset)
 # onnx_predictions = molecularModel_t1.predict_onnx(prediction_dataset)

diff --git a/examples/one_hot_encoding_example.py b/examples/one_hot_encoding_example.py
@@ -9,7 +9,6 @@
 )
 from jaqpotpy.datasets import JaqpotpyDataset
 from jaqpotpy.models import SklearnModel
-from jaqpotpy.doa import Leverage
 from jaqpotpy import Jaqpot
 
 path = "jaqpotpy/test_data/test_data_smiles_CATEGORICAL_classification.csv"
@@ -40,7 +39,6 @@
     remainder="passthrough",
 )
 
-
 model = RandomForestClassifier(random_state=42)
 molecularModel_t1 = SklearnModel(
     dataset=dataset,

diff --git a/examples/onnx_example_classification.py b/examples/onnx_example_classification.py
@@ -9,7 +9,9 @@
 from jaqpotpy.doa import Leverage, BoundingBox, MeanVar
 from jaqpotpy import Jaqpot
 
-path = "jaqpotpy/test_data/test_data_smiles_CATEGORICAL_classification_LABELS_new.csv"
+path = (
+    "../jaqpotpy/test_data/test_data_smiles_CATEGORICAL_classification_LABELS_new.csv"
+)
 
 df = pd.read_csv(path).iloc[0:100, :]
 smiles_cols = ["SMILES"]
@@ -42,7 +44,7 @@
 )
 
 molecularModel_t1.fit()
-pred_path = "./jaqpotpy/test_data/test_data_smiles_categorical_prediction_dataset.csv"
+pred_path = "../jaqpotpy/test_data/test_data_smiles_categorical_prediction_dataset.csv"
 test_dataset = pd.read_csv(pred_path)
 
 prediction_dataset = JaqpotpyDataset(
@@ -69,20 +71,20 @@
 # print("ONNX Probabilities:", onnx_probs)
 
 # Upload locally
-# jaqpot = Jaqpot(
-#     base_url="http://localhost.jaqpot.org",
-#     app_url="http://localhost.jaqpot.org:3000",
-#     login_url="http://localhost.jaqpot.org:8070",
-#     api_url="http://localhost.jaqpot.org:8080",
-#     keycloak_realm="jaqpot-local",
-#     keycloak_client_id="jaqpot-local-test",
-# )
+jaqpot = Jaqpot(
+    base_url="http://localhost.jaqpot.org",
+    app_url="http://localhost.jaqpot.org:3000",
+    login_url="http://localhost.jaqpot.org:8070",
+    api_url="http://localhost.jaqpot.org:8080",
+    keycloak_realm="jaqpot-local",
+    keycloak_client_id="jaqpot-local-test",
+)
 
 # jaqpot = Jaqpot()
-# jaqpot.login()
-# molecularModel_t1.deploy_on_jaqpot(
-#     jaqpot=jaqpot,
-#     name="Demo: Regression topological and minmax scaler on y",
-#     description="Test",
-#     visibility="PRIVATE",
-# )
+jaqpot.login()
+molecularModel_t1.deploy_on_jaqpot(
+    jaqpot=jaqpot,
+    name="Demo: Regression topological and minmax scaler on y",
+    description="Test",
+    visibility="PRIVATE",
+)
diff --git a/examples/sdk_testing.py b/examples/sdk_testing.py
@@ -6,48 +6,48 @@
 jaqpot = JaqpotApiClient()
 
 # Get a model by id
-model = jaqpot.get_model_by_id(model_id=1691)  # 1812)
+model = jaqpot.get_model_by_id(model_id=1860)  # 1812)
 print(model)
-# 
+#
 # # Get model summary
 # model_summary = jaqpot.get_model_summary(model_id=1691)
 # print(model_summary)
-# 
+#
 # # Get shared models with organization
 # shared_models = jaqpot.get_shared_models()
 # print(shared_models)
-# 
+#
 # # # Take a synchronous prediction with a model
-# input_data = [{"SMILES": "CC", "X1": 1, "X2": 2}]
-# prediction = jaqpot.predict_sync(model_id=1852, dataset=input_data)
-# print(prediction)
-# 
+input_data = [{"SMILES": "CC", "X1": 1, "X2": 2, "Cat_col": "CAT_1"}]
+prediction = jaqpot.predict_sync(model_id=1853, dataset=input_data)
+print(prediction)
+#
 # # Take an asynchronous prediction with a model
 # input_data = [{"SMILES": "CC", "X1": 1, "X2": 2}]
 # prediction = jaqpot.predict_async(model_id=1812, dataset=input_data)
-# 
+#
 # # Take prediction with a model and a csv file
 # csv_path = "/Users/vassilis/Desktop/test_csv.csv"
 # prediction = jaqpot.predict_with_csv_sync(model_id=1812, csv_path=csv_path)
 # print(prediction)
-# 
-# 
+#
+#
 # # Testing QsarToolBox
 # get_model = jaqpot.get_model_by_id(model_id=6)
 # print(get_model)
-# 
+#
 # # Test QsarToolBox calculator
 # prediction = jaqpot.qsartoolbox_calculator_predict_sync(
 #     smiles="CC", calculator_guid="1804a854-9041-4495-9931-7414c22a5e49"
 # )
 # print(prediction)
-# 
+#
 # # Test QsarToolBox Model
 # prediction = jaqpot.qsartoolbox_qsar_model_predict_sync(
 #     smiles="CC", qsar_guid="c377150b-77ae-4f99-be14-357b85dd8d1f"
 # )
 # print(prediction)
-# 
+#
 # # Test QsarToolBox Profiler
 # prediction = jaqpot.qsartoolbox_profiler_predict_sync(
 #     smiles="CC", profiler_guid="723eb011-3e5b-4565-9358-4c3d8620ba5d"

diff --git a/examples/upload_on_jaqpot_example.py b/examples/upload_on_jaqpot_example.py
@@ -1,15 +1,14 @@
 import pandas as pd
+from jaqpotpy.doa import Leverage, MeanVar, BoundingBox
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 
 from jaqpotpy.descriptors.molecular import TopologicalFingerprint, MordredDescriptors
 from jaqpotpy.datasets import JaqpotpyDataset
 from jaqpotpy.models import SklearnModel
-from jaqpotpy.doa import Leverage
-from jaqpotpy.models.preprocessing import Preprocess
 from jaqpotpy import Jaqpot
 
-path = "./jaqpotpy/test_data/test_data_smiles_regression.csv"
+path = "../jaqpotpy/test_data/test_data_smiles_regression.csv"
 
 df = pd.read_csv(path).iloc[0:100, :]
 smiles_cols = ["SMILES"]
@@ -24,15 +23,15 @@
     task="regression",
     featurizer=featurizer,
 )
-pre = Preprocess()
 # pre.register_preprocess_class("Standard Scaler", StandardScaler())
 # pre.register_preprocess_class_y("minmax_y", MinMaxScaler())
 
 model = RandomForestRegressor(random_state=42)
-doa_method = Leverage()
-molecularModel_t1 = SklearnModel(
-    dataset=dataset, doa=None, model=model, preprocessor=None, cv=None
-)
+doa_methods = []
+doa_methods.append(Leverage())
+doa_methods.append(MeanVar())
+doa_methods.append(BoundingBox())
+molecularModel_t1 = SklearnModel(dataset=dataset, doa=doa_methods, model=model)
 
 molecularModel_t1.fit()
 # # # print(molecularModel_t1.transformers_y)
@@ -76,17 +75,17 @@
 # print(df_predictions)
 
 
-# # # # Upload locally
-# # jaqpot = Jaqpot(
-# #     base_url="http://localhost.jaqpot.org",
-# #     app_url="http://localhost.jaqpot.org:3000",
-# #     login_url="http://localhost.jaqpot.org:8070",
-# #     api_url="http://localhost.jaqpot.org:8080",
-# #     keycloak_realm="jaqpot-local",
-# #     keycloak_client_id="jaqpot-local-test",
-# # )
+# Upload locally
+jaqpot = Jaqpot(
+    base_url="http://localhost.jaqpot.org",
+    app_url="http://localhost.jaqpot.org:3000",
+    login_url="http://localhost.jaqpot.org:8070",
+    api_url="http://localhost.jaqpot.org:8080",
+    keycloak_realm="jaqpot-local",
+    keycloak_client_id="jaqpot-local-test",
+)
 
-jaqpot = Jaqpot()
+# jaqpot = Jaqpot()
 jaqpot.login()
 molecularModel_t1.deploy_on_jaqpot(
     jaqpot=jaqpot,

diff --git a/jaqpotpy/api/jaqpot_api_client.py b/jaqpotpy/api/jaqpot_api_client.py
@@ -29,10 +29,10 @@
 
 class JaqpotApiClient:
     def __init__(
-            self,
-            base_url=None,
-            api_url=None,
-            create_logs=False,
+        self,
+        base_url=None,
+        api_url=None,
+        create_logs=False,
     ):
         # logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
         self.log = init_logger(
@@ -247,7 +247,7 @@ def _get_dataset_with_polling(self, response):
         try:
             polling2.poll(
                 lambda: self.get_dataset_by_id(dataset_id).status
-                        in ["SUCCESS", "FAILURE"],
+                in ["SUCCESS", "FAILURE"],
                 step=3,
                 timeout=60,
             )

diff --git a/jaqpotpy/api/jaqpot_api_client_builder.py b/jaqpotpy/api/jaqpot_api_client_builder.py
@@ -11,7 +11,7 @@ def build_with_access_token(self, access_token):
 
     def build_with_api_keys(self, api_key, api_secret):
         if api_key is None or api_secret is None:
-            raise ValueError('api_key and api_secret must be set')
+            raise ValueError("api_key and api_secret must be set")
         self.http_client.set_default_header("X-Api-Key", api_key)
         self.http_client.set_default_header("X-Api-Secret", api_secret)
         return self

diff --git a/jaqpotpy/api/openapi/.github/workflows/python.yml b/jaqpotpy/api/openapi/.github/workflows/python.yml
diff --git a/jaqpotpy/api/openapi/.gitignore b/jaqpotpy/api/openapi/.gitignore
diff --git a/jaqpotpy/api/openapi/.gitlab-ci.yml b/jaqpotpy/api/openapi/.gitlab-ci.yml