nabenabe0928: [refactor] Fix SparseMatrixType --> spmatrix and add is…

…pandas (#397)
automl · Mar 2, 2022 · be4dd8f · be4dd8f
1 parent b9cc98b
commit be4dd8f
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Showing 31 changed files with 193 additions and 194 deletions.
diff --git a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
diff --git a/development/_images/sphx_glr_example_plot_over_time_001.png b/development/_images/sphx_glr_example_plot_over_time_001.png
diff --git a/development/_images/sphx_glr_example_plot_over_time_thumb.png b/development/_images/sphx_glr_example_plot_over_time_thumb.png
diff --git a/development/_images/sphx_glr_example_visualization_001.png b/development/_images/sphx_glr_example_visualization_001.png
diff --git a/development/_images/sphx_glr_example_visualization_thumb.png b/development/_images/sphx_glr_example_visualization_thumb.png
diff --git a/development/_sources/examples/20_basics/example_image_classification.rst.txt b/development/_sources/examples/20_basics/example_image_classification.rst.txt
@@ -85,24 +85,23 @@ Image Classification
     Pipeline Random Config:
      ________________________________________ 
     Configuration(values={
-      'image_augmenter:GaussianBlur:sigma_min': 2.128854080819082,
-      'image_augmenter:GaussianBlur:sigma_offset': 1.685286865327408,
-      'image_augmenter:GaussianBlur:use_augmenter': True,
-      'image_augmenter:GaussianNoise:use_augmenter': False,
+      'image_augmenter:GaussianBlur:use_augmenter': False,
+      'image_augmenter:GaussianNoise:sigma_offset': 2.016605350658349,
+      'image_augmenter:GaussianNoise:use_augmenter': True,
       'image_augmenter:RandomAffine:use_augmenter': False,
-      'image_augmenter:RandomCutout:p': 0.8505129226214392,
+      'image_augmenter:RandomCutout:p': 0.7728298341875102,
       'image_augmenter:RandomCutout:use_augmenter': True,
       'image_augmenter:Resize:use_augmenter': False,
-      'image_augmenter:ZeroPadAndCrop:percent': 0.3503163793523587,
-      'normalizer:__choice__': 'NoNormalizer',
+      'image_augmenter:ZeroPadAndCrop:percent': 0.4990785598329858,
+      'normalizer:__choice__': 'ImageNormalizer',
     })
 
     Fitting the pipeline...
     ________________________________________
             ImageClassificationPipeline
     ________________________________________
     0-) normalizer: 
-            NoNormalizer
+            ImageNormalizer
 
     1-) preprocessing: 
             EarlyPreprocessing
@@ -174,7 +173,7 @@ Image Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.887 seconds)
+   **Total running time of the script:** ( 0 minutes  8.510 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_image_classification.py:

diff --git a/development/_sources/examples/20_basics/example_tabular_classification.rst.txt b/development/_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 0x7f7e22bfd7f0>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f444a4d0d90>
 
 
 
@@ -181,9 +181,9 @@ Print the final ensemble performance
             Optimisation Metric: accuracy
             Best validation score: 0.8713450292397661
             Number of target algorithm runs: 23
-            Number of successful target algorithm runs: 21
+            Number of successful target algorithm runs: 20
             Number of crashed target algorithm runs: 2
-            Number of target algorithms that exceeded the time limit: 0
+            Number of target algorithms that exceeded the time limit: 1
             Number of target algorithms that exceeded the memory limit: 0
 
 
@@ -193,7 +193,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  28.953 seconds)
+   **Total running time of the script:** ( 5 minutes  31.215 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

diff --git a/development/_sources/examples/20_basics/example_tabular_regression.rst.txt b/development/_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 0x7f7ebdf86d90>
+    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f44e625f430>
 
 
 
@@ -167,12 +167,12 @@ Print the final ensemble performance
     |  2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.1  |
     |  3 | None                                                                                             | LGBMLearner                                                     |     0.04 |
     autoPyTorch results:
-            Dataset name: 4d60e332-964c-11ec-87e7-95e9f01c72a4
+            Dataset name: 6c9e9506-9a70-11ec-87d5-474e93987d34
             Optimisation Metric: r2
             Best validation score: 0.8670098636440993
-            Number of target algorithm runs: 27
-            Number of successful target algorithm runs: 25
-            Number of crashed target algorithm runs: 1
+            Number of target algorithm runs: 24
+            Number of successful target algorithm runs: 23
+            Number of crashed target algorithm runs: 0
             Number of target algorithms that exceeded the time limit: 1
             Number of target algorithms that exceeded the memory limit: 0
 
@@ -183,7 +183,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  34.491 seconds)
+   **Total running time of the script:** ( 5 minutes  33.512 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

diff --git a/development/_sources/examples/20_basics/sg_execution_times.rst.txt b/development/_sources/examples/20_basics/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**11:10.330** total execution time for **examples_20_basics** files:
+**11:13.237** total execution time for **examples_20_basics** files:
 
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:34.491 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)         | 05:33.512 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:28.953 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``) | 05:31.215 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:06.887 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)     | 00:08.510 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/...opment/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt b/...opment/_sources/examples/40_advanced/example_custom_configuration_space.rst.txt
@@ -163,7 +163,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7e22069580>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f4449514820>
 
 
 
@@ -194,24 +194,27 @@ Print the final ensemble performance
 
  .. code-block:: none
 
-    {'accuracy': 0.8497109826589595}
+    {'accuracy': 0.838150289017341}
     |    | Preprocessing                                                                                    | Estimator                                                    |   Weight |
     |---:|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|---------:|
-    |  0 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,PolynomialFeatures       | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential       |     0.56 |
-    |  1 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,RobustScaler,KitchenSink        | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential       |     0.16 |
-    |  2 | None                                                                                             | RFLearner                                                    |     0.08 |
-    |  3 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,PowerTransformer,Nystroem       | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.06 |
-    |  4 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,TruncSVD                 | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
-    |  5 | None                                                                                             | ETLearner                                                    |     0.04 |
-    |  6 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,QuantileTransformer,KitchenSink       | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential       |     0.02 |
-    |  7 | None                                                                                             | LGBMLearner                                                  |     0.02 |
-    |  8 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
+    |  0 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,PolynomialFeatures       | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential       |     0.2  |
+    |  1 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,PowerTransformer,Nystroem       | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.16 |
+    |  2 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,RobustScaler,KitchenSink        | embedding,MLPBackbone,FullyConnectedHead,nn.Sequential       |     0.12 |
+    |  3 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,MinMaxScaler,TruncSVD                 | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.1  |
+    |  4 | None                                                                                             | RFLearner                                                    |     0.1  |
+    |  5 | None                                                                                             | SVMLearner                                                   |     0.1  |
+    |  6 | None                                                                                             | ETLearner                                                    |     0.06 |
+    |  7 | None                                                                                             | LGBMLearner                                                  |     0.04 |
+    |  8 | None                                                                                             | KNNLearner                                                   |     0.04 |
+    |  9 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
+    | 10 | SimpleImputer,Variance Threshold,MinorityCoalescer,OneHotEncoder,NoScaler,PolynomialFeatures     | no embedding,ResNetBackbone,FullyConnectedHead,nn.Sequential |     0.02 |
+    | 11 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,MLPBackbone,FullyConnectedHead,nn.Sequential    |     0.02 |
     autoPyTorch results:
-            Dataset name: d0cbe6ed-964f-11ec-87e7-95e9f01c72a4
+            Dataset name: ebc28c6f-9a73-11ec-87d5-474e93987d34
             Optimisation Metric: accuracy
             Best validation score: 0.8596491228070176
-            Number of target algorithm runs: 18
-            Number of successful target algorithm runs: 16
+            Number of target algorithm runs: 17
+            Number of successful target algorithm runs: 15
             Number of crashed target algorithm runs: 2
             Number of target algorithms that exceeded the time limit: 0
             Number of target algorithms that exceeded the memory limit: 0
@@ -272,7 +275,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7e218d7b80>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f444970b6d0>
 
 
 
@@ -315,13 +318,13 @@ Print the final ensemble performance
     |  7 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.06 |
     |  8 | SimpleImputer,Variance Threshold,NoCoalescer,NoEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential    |     0.04 |
     autoPyTorch results:
-            Dataset name: 3850871f-9650-11ec-87e7-95e9f01c72a4
+            Dataset name: 533c0306-9a74-11ec-87d5-474e93987d34
             Optimisation Metric: accuracy
             Best validation score: 0.8596491228070176
-            Number of target algorithm runs: 19
+            Number of target algorithm runs: 18
             Number of successful target algorithm runs: 13
             Number of crashed target algorithm runs: 5
-            Number of target algorithms that exceeded the time limit: 1
+            Number of target algorithms that exceeded the time limit: 0
             Number of target algorithms that exceeded the memory limit: 0
 
 
@@ -331,7 +334,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  39.929 seconds)
+   **Total running time of the script:** ( 5 minutes  46.242 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_custom_configuration_space.py:

diff --git a/development/_sources/examples/40_advanced/example_parallel_n_jobs.rst.txt b/development/_sources/examples/40_advanced/example_parallel_n_jobs.rst.txt
@@ -36,14 +36,14 @@ with AutoPyTorch
 
  .. code-block:: none
 
-    {'accuracy': 0.8786127167630058}
+    {'accuracy': 0.8670520231213873}
     autoPyTorch results:
-            Dataset name: ec2cb869-964e-11ec-87e7-95e9f01c72a4
+            Dataset name: 0be6cebd-9a73-11ec-87d5-474e93987d34
             Optimisation Metric: accuracy
             Best validation score: 0.8713450292397661
-            Number of target algorithm runs: 50
-            Number of successful target algorithm runs: 38
-            Number of crashed target algorithm runs: 9
+            Number of target algorithm runs: 42
+            Number of successful target algorithm runs: 33
+            Number of crashed target algorithm runs: 6
             Number of target algorithms that exceeded the time limit: 3
             Number of target algorithms that exceeded the memory limit: 0
 
@@ -121,7 +121,7 @@ with AutoPyTorch
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  36.610 seconds)
+   **Total running time of the script:** ( 5 minutes  26.745 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_parallel_n_jobs.py:

diff --git a/development/_sources/examples/40_advanced/example_plot_over_time.rst.txt b/development/_sources/examples/40_advanced/example_plot_over_time.rst.txt
@@ -89,9 +89,9 @@ Task Definition
 
  .. code-block:: none
 
-    [1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1 0 0
-     0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 0 0 0 0 1 0
-     0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0]
+    [0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 0 1
+     1 1 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1
+     1 0 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0]
 
 
 
@@ -121,7 +121,7 @@ API Instantiation and Searching
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f7e228dc4c0>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f444956be50>
 
 
 
@@ -186,7 +186,7 @@ _, ax = plt.subplots()  <=== You can feed it to post-process the figure.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  16.573 seconds)
+   **Total running time of the script:** ( 2 minutes  16.294 seconds)
 
 
 .. _sphx_glr_download_examples_40_advanced_example_plot_over_time.py: