Pretty printing the show_models() dictionary

Author: sagar-kaushik

examples/20_basic/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

examples/20_basic/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

examples/20_basic/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

examples/20_basic/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

examples/40_advanced/example_get_pipeline_components.py

@@ -14,6 +14,8 @@
 the sklearn models. This example illustrates how to interact
 with the sklearn components directly, in this case a PCA preprocessor.
 """
+from pprint import pprint
+
 import sklearn.datasets
 import sklearn.metrics
 
@@ -63,7 +65,7 @@
 # to construct ensembles in a post-hoc fashion. The ensemble is a linear
 # weighting of all models constructed during the hyperparameter optimization.
 # This prints the final ensemble. It is a dictionary where ``model_id`` of
-# each model is a key, and its value is a dictionary containing information
+# each model is a key, and value is a dictionary containing information
 # of that model. A model's dict contains its ``'model_id'``, ``'rank'``,
 # ``'cost'``, ``'ensemble_weight'``, and the model itself. The model is
 # given by the ``'data_preprocessor'``, ``'feature_preprocessor'``,
@@ -72,7 +74,7 @@
 # model is stored in the ``'estimators'`` list in the dict, along with the
 # ``'voting_model'``.
 
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 ###########################################################################
 # Report statistics about the search

examples/40_advanced/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

examples/60_search/example_random_search.py

@@ -12,6 +12,7 @@
 as yet another alternative optimizatino strategy.
 Both examples are intended to show how the optimization strategy in *auto-sklearn* can be adapted.
 """  # noqa (links are too long)
+from pprint import pprint
 
 import sklearn.model_selection
 import sklearn.datasets
@@ -75,7 +76,7 @@ def get_roar_object_callback(
 print('#' * 80)
 print('Results for ROAR.')
 # Print the final ensemble constructed by auto-sklearn via ROAR.
-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.
@@ -129,7 +130,7 @@ def get_random_search_object_callback(
 print('Results for random search.')
 
 # Print the final ensemble constructed by auto-sklearn via random search.
-print(automl.show_models())
+pprint(automl.show_models(), indent=4)
 
 # Print statistics about the auto-sklearn run such as number of
 # iterations, number of models failed with a time out.

examples/60_search/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

examples/60_search/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.

examples/80_extending/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)

examples/80_extending/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)

examples/80_extending/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)

examples/80_extending/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)