Ravin Kohli: [RELEASE] Release v0.2 (automl#448)

master/_sources/examples/20_basics/example_image_classification.rst.txt

@@ -35,22 +35,22 @@ Image Classification
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz
-      0%|          | 0/26421880 [00:00<?, ?it/s]      0%|          | 32768/26421880 [00:00<01:36, 273782.78it/s]      0%|          | 65536/26421880 [00:00<01:36, 272085.67it/s]      0%|          | 131072/26421880 [00:00<01:06, 395103.56it/s]      1%|          | 229376/26421880 [00:00<00:46, 559676.03it/s]      2%|1         | 491520/26421880 [00:00<00:22, 1139362.33it/s]      4%|3         | 950272/26421880 [00:00<00:12, 2040207.80it/s]      7%|7         | 1933312/26421880 [00:00<00:06, 4028864.97it/s]     15%|#4        | 3833856/26421880 [00:00<00:02, 7755299.71it/s]     26%|##6       | 6946816/26421880 [00:01<00:01, 13355008.56it/s]     38%|###7      | 9994240/26421880 [00:01<00:00, 17024900.52it/s]     50%|####9     | 13107200/26421880 [00:01<00:00, 19655030.10it/s]     61%|######1   | 16220160/26421880 [00:01<00:00, 21480086.91it/s]     72%|#######2  | 19070976/26421880 [00:01<00:00, 22104367.53it/s]     84%|########3 | 22183936/26421880 [00:01<00:00, 23166077.29it/s]     96%|#########5| 25264128/26421880 [00:01<00:00, 23905682.06it/s]    100%|##########| 26421880/26421880 [00:01<00:00, 14285093.32it/s]
+      0%|          | 0/26421880 [00:00<?, ?it/s]      0%|          | 65536/26421880 [00:00<01:10, 371580.66it/s]      1%|          | 196608/26421880 [00:00<00:33, 778605.91it/s]      2%|1         | 491520/26421880 [00:00<00:19, 1299282.40it/s]      6%|5         | 1474560/26421880 [00:00<00:06, 3870397.79it/s]     15%|#4        | 3833856/26421880 [00:00<00:02, 8266135.19it/s]     32%|###1      | 8323072/26421880 [00:00<00:01, 18011894.83it/s]     46%|####5     | 12025856/26421880 [00:00<00:00, 23031717.05it/s]     61%|######    | 15990784/26421880 [00:01<00:00, 24150658.44it/s]     76%|#######6  | 20185088/26421880 [00:01<00:00, 28592387.84it/s]     91%|#########1| 24150016/26421880 [00:01<00:00, 31471992.29it/s]    100%|##########| 26421880/26421880 [00:01<00:00, 19592712.38it/s]
     Extracting ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz
-      0%|          | 0/29515 [00:00<?, ?it/s]    100%|##########| 29515/29515 [00:00<00:00, 251659.09it/s]    100%|##########| 29515/29515 [00:00<00:00, 250831.51it/s]
+      0%|          | 0/29515 [00:00<?, ?it/s]    100%|##########| 29515/29515 [00:00<00:00, 337445.40it/s]
     Extracting ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
-      0%|          | 0/4422102 [00:00<?, ?it/s]      1%|          | 32768/4422102 [00:00<00:16, 272250.69it/s]      1%|1         | 65536/4422102 [00:00<00:16, 271395.90it/s]      3%|2         | 131072/4422102 [00:00<00:10, 395117.54it/s]      5%|5         | 229376/4422102 [00:00<00:07, 559442.46it/s]     10%|9         | 425984/4422102 [00:00<00:04, 943693.86it/s]     20%|##        | 884736/4422102 [00:00<00:01, 1912211.37it/s]     39%|###9      | 1736704/4422102 [00:00<00:00, 3590465.29it/s]     79%|#######8  | 3473408/4422102 [00:00<00:00, 7006166.70it/s]    100%|##########| 4422102/4422102 [00:00<00:00, 4543942.11it/s]
+      0%|          | 0/4422102 [00:00<?, ?it/s]      1%|1         | 65536/4422102 [00:00<00:11, 374268.70it/s]      4%|4         | 196608/4422102 [00:00<00:05, 776151.02it/s]     11%|#1        | 491520/4422102 [00:00<00:02, 1315738.98it/s]     37%|###7      | 1638400/4422102 [00:00<00:00, 4396323.24it/s]     87%|########6 | 3833856/4422102 [00:00<00:00, 8251018.47it/s]    100%|##########| 4422102/4422102 [00:00<00:00, 6247373.62it/s]
     Extracting ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
-      0%|          | 0/5148 [00:00<?, ?it/s]    100%|##########| 5148/5148 [00:00<00:00, 28225198.68it/s]
+      0%|          | 0/5148 [00:00<?, ?it/s]    100%|##########| 5148/5148 [00:00<00:00, 19647203.81it/s]
     Extracting ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Pipeline CS:
@@ -86,13 +86,11 @@ Image Classification
      ________________________________________ 
     Configuration(values={
       'image_augmenter:GaussianBlur:use_augmenter': False,
-      'image_augmenter:GaussianNoise:sigma_offset': 2.16870839870273,
-      'image_augmenter:GaussianNoise:use_augmenter': True,
+      'image_augmenter:GaussianNoise:use_augmenter': False,
       'image_augmenter:RandomAffine:use_augmenter': False,
-      'image_augmenter:RandomCutout:p': 0.4136881910603881,
-      'image_augmenter:RandomCutout:use_augmenter': True,
+      'image_augmenter:RandomCutout:use_augmenter': False,
       'image_augmenter:Resize:use_augmenter': False,
-      'image_augmenter:ZeroPadAndCrop:percent': 0.426471061733336,
+      'image_augmenter:ZeroPadAndCrop:percent': 0.4845537861730235,
       'normalizer:__choice__': 'NoNormalizer',
     })
 
@@ -173,7 +171,7 @@ Image Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.630 seconds)
+   **Total running time of the script:** ( 0 minutes  5.416 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_image_classification.py:

master/_sources/examples/20_basics/example_tabular_classification.rst.txt

@@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f11a36ddcd0>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7efe1fa97520>
 
 
 
@@ -166,20 +166,21 @@ Print the final ensemble performance
  .. code-block:: none
 
     {'accuracy': 0.8728323699421965}
-    |    | Preprocessing                                                                   | Estimator                                                 |   Weight |
-    |---:|:--------------------------------------------------------------------------------|:----------------------------------------------------------|---------:|
-    |  0 | None                                                                            | CBLearner                                                 |     0.68 |
-    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.28 |
-    |  2 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PCA       | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
-    |  3 | None                                                                            | ETLearner                                                 |     0.02 |
+    |    | Preprocessing                                                                              | Estimator                                                    |   Weight |
+    |---:|:-------------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
+    |  0 | None                                                                                       | CBLearner                                                    |     0.5  |
+    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,FastICA            | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.22 |
+    |  2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,PowerTransformer,Nystroem | embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.14 |
+    |  3 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,PCA                  | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.12 |
+    |  4 | None                                                                                       | RFLearner                                                    |     0.02 |
     autoPyTorch results:
             Dataset name: Australian
             Optimisation Metric: accuracy
             Best validation score: 0.8713450292397661
-            Number of target algorithm runs: 24
-            Number of successful target algorithm runs: 22
+            Number of target algorithm runs: 22
+            Number of successful target algorithm runs: 21
             Number of crashed target algorithm runs: 0
-            Number of target algorithms that exceeded the time limit: 2
+            Number of target algorithms that exceeded the time limit: 1
             Number of target algorithms that exceeded the memory limit: 0
 
 
@@ -189,7 +190,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  24.323 seconds)
+   **Total running time of the script:** ( 5 minutes  35.766 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

master/_sources/examples/20_basics/example_tabular_regression.rst.txt

@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f123bc83070>
+    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7efeaf590eb0>
 
 
 
@@ -159,21 +159,22 @@ Print the final ensemble performance
 
  .. code-block:: none
 
-    {'r2': 0.9407884171054208}
+    {'r2': 0.9412847640085195}
     |    | Preprocessing                                                                                    | Estimator                                                       |   Weight |
     |---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
-    |  0 | None                                                                                             | CBLearner                                                       |     0.44 |
-    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.42 |
+    |  0 | None                                                                                             | CBLearner                                                       |     0.46 |
+    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.4  |
     |  2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.1  |
-    |  3 | None                                                                                             | LGBMLearner                                                     |     0.04 |
+    |  3 | None                                                                                             | LGBMLearner                                                     |     0.02 |
+    |  4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
     autoPyTorch results:
-            Dataset name: e1ae53e5-0685-11ed-882e-b1ae9262a945
+            Dataset name: d21a1548-0700-11ed-884a-abcc1edca430
             Optimisation Metric: r2
-            Best validation score: 0.8670098636440993
-            Number of target algorithm runs: 23
-            Number of successful target algorithm runs: 22
-            Number of crashed target algorithm runs: 0
-            Number of target algorithms that exceeded the time limit: 1
+            Best validation score: 0.8669094525651709
+            Number of target algorithm runs: 21
+            Number of successful target algorithm runs: 20
+            Number of crashed target algorithm runs: 1
+            Number of target algorithms that exceeded the time limit: 0
             Number of target algorithms that exceeded the memory limit: 0
 
 
@@ -183,7 +184,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  42.804 seconds)
+   **Total running time of the script:** ( 6 minutes  2.017 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

master/_sources/examples/20_basics/example_time_series_forecasting.rst.txt

@@ -150,7 +150,7 @@ Search for an ensemble of machine learning algorithms
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  3.754 seconds)
+   **Total running time of the script:** ( 0 minutes  59.130 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_time_series_forecasting.py:

master/_sources/examples/20_basics/sg_execution_times.rst.txt

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**12:17.511** total execution time for **examples_20_basics** files:
+**12:42.329** total execution time for **examples_20_basics** files:
 
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)           | 05:42.804 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)           | 06:02.017 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``)   | 05:24.323 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``)   | 05:35.766 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 01:03.754 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 00:59.130 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)       | 00:06.630 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)       | 00:05.416 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+