Sagar Kaushik: Changes show_models() function to return a dictionary of models in ensemble (#1321)

Author: Github Actions

development/_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)"
       ]
     },
     {

development/_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

development/_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)

development/_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"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

development/_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)"
       ]
     },
     {

development/_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

development/_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

development/_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

development/_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)

development/_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.

development/_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)"
       ]
     },
     {

development/_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)

development/_downloads/650cd19a8dfee813a7d7fb5b90341ed3/example_classification.py

@@ -7,6 +7,8 @@
 The following example shows how to fit a simple classification model with
 *auto-sklearn*.
 """
+from pprint import pprint
+
 import sklearn.datasets
 import sklearn.metrics
 
@@ -42,7 +44,7 @@
 # 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

development/_downloads/6dea5849db1f35abbefe123cd0eb49fd/example_extending_data_preprocessor.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import autosklearn.classification\nimport autosklearn.pipeline.components.data_preprocessing\nimport sklearn.metrics\nfrom ConfigSpace.configuration_space import ConfigurationSpace\nfrom autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm\nfrom autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split"
+        "from pprint import pprint\n\nimport autosklearn.classification\nimport autosklearn.pipeline.components.data_preprocessing\nimport sklearn.metrics\nfrom ConfigSpace.configuration_space import ConfigurationSpace\nfrom autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm\nfrom autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "y_pred = clf.predict(X_test)\nprint(\"accuracy: \", sklearn.metrics.accuracy_score(y_pred, y_test))\nprint(clf.show_models())"
+        "y_pred = clf.predict(X_test)\nprint(\"accuracy: \", sklearn.metrics.accuracy_score(y_pred, y_test))\npprint(clf.show_models(), indent=4)"
       ]
     }
   ],

development/_downloads/77f2a85d2877495effe09ede1c7a1d31/example_regression.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.regression\nimport matplotlib.pyplot as plt"
+        "from pprint import pprint\n\nimport sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.regression\nimport matplotlib.pyplot as plt"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

development/_downloads/89647a1665eba015b7197cfe70420e4d/example_multilabel_classification.py

@@ -8,6 +8,7 @@
 `here <https://scikit-learn.org/stable/modules/multiclass.html>`_.
 """
 import numpy as np
+from pprint import pprint
 
 import sklearn.datasets
 import sklearn.metrics
@@ -65,7 +66,7 @@
 # Print the final ensemble constructed by auto-sklearn
 # ====================================================
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 ############################################################################
 # Print statistics about the auto-sklearn run

development/_downloads/a23bc40b83b60b7e97a3eb188a82ad24/example_extending_preprocessor.py

@@ -7,6 +7,7 @@
 discriminant analysis (LDA) algorithm from sklearn and use it as a preprocessor
 in auto-sklearn.
 """
+from pprint import pprint
 
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter
@@ -130,4 +131,4 @@ 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)

development/_downloads/a7f5a98ecf82e30e146cc9bfe484c397/example_get_pipeline_components.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"
       ]
     },
     {
@@ -87,7 +87,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Report the models found by Auto-Sklearn\n\nAuto-sklearn uses\n`Ensemble Selection <https://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf>`_\nto construct ensembles in a post-hoc fashion. The ensemble is a linear\nweighting of all models constructed during the hyperparameter optimization.\nThis prints the final ensemble. It is a list of tuples, each tuple being\nthe model weight in the ensemble and the model itself.\n\n"
+        "## Report the models found by Auto-Sklearn\n\nAuto-sklearn uses\n`Ensemble Selection <https://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf>`_\nto construct ensembles in a post-hoc fashion. The ensemble is a linear\nweighting of all models constructed during the hyperparameter optimization.\nThis prints the final ensemble. It is a dictionary where ``model_id`` of\neach model is a key, and value is a dictionary containing information\nof that model. A model's dict contains its ``'model_id'``, ``'rank'``,\n``'cost'``, ``'ensemble_weight'``, and the model itself. The model is\ngiven by the ``'data_preprocessor'``, ``'feature_preprocessor'``,\n``'regressor'/'classifier'`` and ``'sklearn_regressor'/'sklearn_classifier'``\nentries. But for the ``'cv'`` resampling strategy, the same for each cv\nmodel is stored in the ``'estimators'`` list in the dict, along with the\n``'voting_model'``.\n\n"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "print(automl.show_models())"
+        "pprint(automl.show_models(), indent=4)"
       ]
     },
     {

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from ConfigSpace.configuration_space import ConfigurationSpace\nfrom ConfigSpace.hyperparameters import CategoricalHyperparameter, \\\n    UniformIntegerHyperparameter, UniformFloatHyperparameter\n\nimport sklearn.metrics\nimport autosklearn.classification\nimport autosklearn.pipeline.components.classification\nfrom autosklearn.pipeline.components.base \\\n    import AutoSklearnClassificationAlgorithm\nfrom autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, \\\n    PREDICTIONS\n\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split"
+        "from pprint import pprint\n\nfrom ConfigSpace.configuration_space import ConfigurationSpace\nfrom ConfigSpace.hyperparameters import CategoricalHyperparameter, \\\n    UniformIntegerHyperparameter, UniformFloatHyperparameter\n\nimport sklearn.metrics\nimport autosklearn.classification\nimport autosklearn.pipeline.components.classification\nfrom autosklearn.pipeline.components.base \\\n    import AutoSklearnClassificationAlgorithm\nfrom autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, \\\n    PREDICTIONS\n\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "y_pred = clf.predict(X_test)\nprint(\"accuracy: \", sklearn.metrics.accuracy_score(y_pred, y_test))\nprint(clf.show_models())"
+        "y_pred = clf.predict(X_test)\nprint(\"accuracy: \", sklearn.metrics.accuracy_score(y_pred, y_test))\npprint(clf.show_models(), indent=4)"
       ]
     }
   ],