Eddie Bergman: Merge pull request #1379 from automl/v0.14.4

automl · Jan 25, 2022 · e2d270e · e2d270e
1 parent 1828f2c
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diff --git a/master/.buildinfo b/master/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 13a06760a87ac8d7b659b4aa6efbd203
+config: ba5b8e6ad6e1c8d0fcc11a6163f03902
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/master/_downloads/16fb4037a0b549292dbf3df70af70372/example_classification.ipynb b/master/_downloads/16fb4037a0b549292dbf3df70af70372/example_classification.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.classification"
+        "from pprint import pprint\n\nimport sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.classification"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

diff --git a/master/_downloads/1a053e45a20a2c15032411b9fee890a3/example_sequential.py b/master/_downloads/1a053e45a20a2c15032411b9fee890a3/example_sequential.py
@@ -8,6 +8,7 @@
 sequentially. The example below shows how to first fit the models and build the
 ensembles afterwards.
 """
+from pprint import pprint
 
 import sklearn.model_selection
 import sklearn.datasets
@@ -48,7 +49,7 @@
 # Print the final ensemble constructed by auto-sklearn
 # ====================================================
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 ############################################################################
 # Get the Score of the final ensemble

diff --git a/master/_downloads/23ae4950352edc8dd9ea5443ba77886b/example_extending_regression.py b/master/_downloads/23ae4950352edc8dd9ea5443ba77886b/example_extending_regression.py
@@ -6,6 +6,7 @@
 The following example demonstrates how to create a new regression
 component for using in auto-sklearn.
 """
+from pprint import pprint
 
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
@@ -137,4 +138,4 @@ def get_hyperparameter_search_space(dataset_properties=None):
 # =====================================
 y_pred = reg.predict(X_test)
 print("r2 score: ", sklearn.metrics.r2_score(y_pred, y_test))
-print(reg.show_models())
+pprint(reg.show_models(), indent=4)
diff --git a/master/_downloads/2991959d1e025c5f9f27e3b4d3265a81/example_multilabel_classification.ipynb b/master/_downloads/2991959d1e025c5f9f27e3b4d3265a81/example_multilabel_classification.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\n\nimport sklearn.datasets\nimport sklearn.metrics\nfrom sklearn.utils.multiclass import type_of_target\n\nimport autosklearn.classification"
+        "import numpy as np\nfrom pprint import pprint\n\nimport sklearn.datasets\nimport sklearn.metrics\nfrom sklearn.utils.multiclass import type_of_target\n\nimport autosklearn.classification"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "# Using reuters multilabel dataset -- https://www.openml.org/d/40594\nX, y = sklearn.datasets.fetch_openml(data_id=40594, return_X_y=True, as_frame=False)\n\n# fetch openml downloads a numpy array with TRUE/FALSE strings. Re-map it to\n# integer dtype with ones and zeros\n# This is to comply with Scikit-learn requirement:\n# \"Positive classes are indicated with 1 and negative classes with 0 or -1.\"\n# More information on: https://scikit-learn.org/stable/modules/multiclass.html\ny[y == 'TRUE'] = 1\ny[y == 'FALSE'] = 0\ny = y.astype(np.int)\n\n# Using type of target is a good way to make sure your data\n# is properly formatted\nprint(f\"type_of_target={type_of_target(y)}\")\n\nX_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n    X, y, random_state=1\n)"
+        "# Using reuters multilabel dataset -- https://www.openml.org/d/40594\nX, y = sklearn.datasets.fetch_openml(data_id=40594, return_X_y=True, as_frame=False)\n\n# fetch openml downloads a numpy array with TRUE/FALSE strings. Re-map it to\n# integer dtype with ones and zeros\n# This is to comply with Scikit-learn requirement:\n# \"Positive classes are indicated with 1 and negative classes with 0 or -1.\"\n# More information on: https://scikit-learn.org/stable/modules/multiclass.html\ny[y == 'TRUE'] = 1\ny[y == 'FALSE'] = 0\ny = y.astype(int)\n\n# Using type of target is a good way to make sure your data\n# is properly formatted\nprint(f\"type_of_target={type_of_target(y)}\")\n\nX_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n    X, y, random_state=1\n)"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

diff --git a/master/_downloads/2f293025f5b75af329925f2c3ff58c06/example_sequential.ipynb b/master/_downloads/2f293025f5b75af329925f2c3ff58c06/example_sequential.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn.model_selection\nimport sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.classification"
+        "from pprint import pprint\n\nimport sklearn.model_selection\nimport sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.classification"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

diff --git a/master/_downloads/34d1d3a5e629625f2f22f39d7c3a650e/example_interpretable_models.py b/master/_downloads/34d1d3a5e629625f2f22f39d7c3a650e/example_interpretable_models.py
@@ -7,6 +7,8 @@
 The following example shows how to inspect the models which *auto-sklearn*
 optimizes over and how to restrict them to an interpretable subset.
 """
+from pprint import pprint
+
 import autosklearn.classification
 import sklearn.datasets
 import sklearn.metrics
@@ -70,7 +72,7 @@
 # Print the final ensemble constructed by auto-sklearn
 # ====================================================
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 ###########################################################################
 # Get the Score of the final ensemble

diff --git a/master/_downloads/42784b4d4739b840b1eeb6159bf08f4e/example_regression.py b/master/_downloads/42784b4d4739b840b1eeb6159bf08f4e/example_regression.py
@@ -7,6 +7,8 @@
 The following example shows how to fit a simple regression model with
 *auto-sklearn*.
 """
+from pprint import pprint
+
 import sklearn.datasets
 import sklearn.metrics
 
@@ -43,7 +45,7 @@
 # Print the final ensemble constructed by auto-sklearn
 # ====================================================
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 #####################################
 # Get the Score of the final ensemble

diff --git a/master/_downloads/49577cbea6f814a2c9a5167f35be2814/example_multioutput_regression.py b/master/_downloads/49577cbea6f814a2c9a5167f35be2814/example_multioutput_regression.py
@@ -8,6 +8,7 @@
 *auto-sklearn*.
 """
 import numpy as numpy
+from pprint import pprint
 
 from sklearn.datasets import make_regression
 from sklearn.metrics import r2_score
@@ -46,7 +47,7 @@
 # Print the final ensemble constructed by auto-sklearn
 # ====================================================
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 ###########################################################################
 # Get the Score of the final ensemble

diff --git a/master/_downloads/515ab036d01801cb08e4878be1aef556/example_extending_classification.py b/master/_downloads/515ab036d01801cb08e4878be1aef556/example_extending_classification.py
@@ -6,6 +6,7 @@
 The following example demonstrates how to create a new classification
 component for using in auto-sklearn.
 """
+from pprint import pprint
 
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
@@ -149,4 +150,4 @@ def get_hyperparameter_search_space(dataset_properties=None):
 
 y_pred = clf.predict(X_test)
 print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test))
-print(clf.show_models())
+pprint(clf.show_models(), indent=4)
diff --git a/master/_downloads/54625156807f9d21fa14b78034b1df5c/example_successive_halving.py b/master/_downloads/54625156807f9d21fa14b78034b1df5c/example_successive_halving.py
@@ -14,7 +14,7 @@
 To get the BOHB algorithm, simply import Hyperband and use it as the intensification strategy.
 
 """  # noqa (links are too long)
-
+from pprint import pprint
 
 import sklearn.model_selection
 import sklearn.datasets
@@ -110,7 +110,7 @@ def get_smac_object(
 )
 automl.fit(X_train, y_train, dataset_name='breast_cancer')
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 predictions = automl.predict(X_test)
 # Print statistics about the auto-sklearn run such as number of
 # iterations, number of models failed with a time out.
@@ -143,7 +143,7 @@ def get_smac_object(
 automl.fit(X_train, y_train, dataset_name='breast_cancer')
 
 # Print the final ensemble constructed by auto-sklearn.
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 automl.refit(X_train, y_train)
 predictions = automl.predict(X_test)
 # Print statistics about the auto-sklearn run such as number of
@@ -177,7 +177,7 @@ def get_smac_object(
 automl.fit(X_train, y_train, dataset_name='breast_cancer')
 
 # Print the final ensemble constructed by auto-sklearn.
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 automl.refit(X_train, y_train)
 predictions = automl.predict(X_test)
 # Print statistics about the auto-sklearn run such as number of
@@ -208,7 +208,7 @@ def get_smac_object(
 automl.fit(X_train, y_train, dataset_name='breast_cancer')
 
 # Print the final ensemble constructed by auto-sklearn.
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 predictions = automl.predict(X_test)
 # Print statistics about the auto-sklearn run such as number of
 # iterations, number of models failed with a time out.
@@ -245,7 +245,7 @@ def get_smac_object(
 automl.fit(X_train, y_train, dataset_name='breast_cancer')
 
 # Print the final ensemble constructed by auto-sklearn.
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 predictions = automl.predict(X_test)
 # Print statistics about the auto-sklearn run such as number of
 # iterations, number of models failed with a time out.

diff --git a/master/_downloads/5f58bfc8fd90dc731fa26fb9996d2aa5/example_interpretable_models.ipynb b/master/_downloads/5f58bfc8fd90dc731fa26fb9996d2aa5/example_interpretable_models.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import autosklearn.classification\nimport sklearn.datasets\nimport sklearn.metrics"
+        "from pprint import pprint\n\nimport autosklearn.classification\nimport sklearn.datasets\nimport sklearn.metrics"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

diff --git a/master/_downloads/60abb5e0c4b0861f5ecbe4ae9c2e51dd/example_extending_data_preprocessor.py b/master/_downloads/60abb5e0c4b0861f5ecbe4ae9c2e51dd/example_extending_data_preprocessor.py
@@ -5,6 +5,7 @@
 
 The following example demonstrates how to turn off data preprocessing step in auto-skearn.
 """
+from pprint import pprint
 
 import autosklearn.classification
 import autosklearn.pipeline.components.data_preprocessing
@@ -89,4 +90,4 @@ def get_hyperparameter_search_space(dataset_properties=None):
 
 y_pred = clf.predict(X_test)
 print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test))
-print(clf.show_models())
+pprint(clf.show_models(), indent=4)