examples.rst

autosklearn/estimators.py

@@ -408,7 +408,7 @@ def fit(self, X, y,
         """Fit *auto-sklearn* to given training set (X, y).
 
         Fit both optimizes the machine learning models and builds an ensemble
-        out of them. To disable ensembling, set ``ensemble_size==1``.
+        out of them. To disable ensembling, set ``ensemble_size==0``.
 
         Parameters
         ----------
@@ -512,7 +512,7 @@ def fit(self, X, y,
         """Fit *Auto-sklearn* to given training set (X, y).
 
         Fit both optimizes the machine learning models and builds an ensemble
-        out of them. To disable ensembling, set ``ensemble_size==1``.
+        out of them. To disable ensembling, set ``ensemble_size==0``.
 
         Parameters
         ----------

doc/Makefile

@@ -51,6 +51,8 @@ help:
 clean:
 	rm -rf $(BUILDDIR)/*
 	rm -rf generated
+	rm -rf examples/
+	rm -rf gen_modules/
 
 html:
 	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html

doc/api.rst

@@ -103,3 +103,5 @@ Extension Interfaces
 
 .. autoclass:: autosklearn.pipeline.components.base.AutoSklearnPreprocessingAlgorithm
     :members:
+
+

doc/conf.py

@@ -41,12 +41,37 @@
 extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosummary',
               'sphinx.ext.doctest', 'sphinx.ext.coverage',
               'sphinx.ext.mathjax', 'sphinx.ext.viewcode',
+              'sphinx_gallery.gen_gallery', 'sphinx.ext.autosectionlabel',
+              # sphinx.ext.autosexctionlabel raises duplicate label warnings
+              # because same section headers are used multiple times throughout
+              # the documentation.
               'numpydoc']
 
+
+from sphinx_gallery.sorting import ExplicitOrder, FileNameSortKey
+
 # Configure the extensions
 numpydoc_show_class_members = False
 autosummary_generate = True
 
+# prefix each section label with the name of the document it is in, in order to avoid
+# ambiguity when there are multiple same section labels in different documents.
+autosectionlabel_prefix_document = True
+
+# Sphinx-gallery configuration.
+sphinx_gallery_conf = {
+    # path to the examples
+    'examples_dirs': '../examples',
+    # path where to save gallery generated examples
+    'gallery_dirs': 'examples',
+    #TODO: fix back/forward references for the examples.
+    #'doc_module': ('autosklearn'),
+    #'reference_url': {
+    #    'autosklearn': None
+    #},
+    #'backreferences_dir': 'gen_modules/backreferences'
+}
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
@@ -134,6 +159,7 @@
         ('Releases', 'releases'),
         ('Installation', 'installation'),
         ('Manual', 'manual'),
+        ('Examples', 'examples/index'),
         ('API', 'api'),
         ('Extending', 'extending'),
     ],

doc/index.rst

@@ -47,7 +47,7 @@ Manual
 
 * :ref:`installation`
 * :ref:`manual`
-* :ref:`API`
+* :ref:`api`
 * :ref:`extending`
 
 

doc/manual.rst

@@ -15,13 +15,15 @@ Examples
 *auto-sklearn* comes with the following examples which demonstrate several
 aspects of its usage:
 
-* `Holdout <https://github.com/automl/auto-sklearn/blob/master/example/example_holdout.py>`_
-* `Cross-validation <https://github.com/automl/auto-sklearn/blob/master/example/example_crossvalidation.py>`_
-* `Parallel usage <https://github.com/automl/auto-sklearn/blob/master/example/example_parallel.py>`_
-* `Sequential usage <https://github.com/automl/auto-sklearn/blob/master/example/example_sequential.py>`_
-* `Regression <https://github.com/automl/auto-sklearn/blob/master/example/example_regression.py>`_
-* `Continuous and categorical data <https://github.com/automl/auto-sklearn/blob/master/example/example_feature_types.py>`_
-* `Using custom metrics <https://github.com/automl/auto-sklearn/blob/master/example/example_metrics.py>`_
+* `Holdout <examples/example_holdout.html>`_
+* `Cross-validation <examples/example_crossvalidation.html>`_
+* `Parallel usage <examples/example_parallel.html>`_
+* `Sequential usage <examples/example_sequential.html>`_
+* `Regression <examples/example_regression.html>`_
+* `Continuous and categorical data <examples/example_feature_types.html>`_
+* `Using custom metrics <examples/example_metrics.html>`_
+* `Random search <examples/example_random_search.html>`_
+* `EIPS <examples/example_eips.html>`_
 
 
 Time and memory limits

examples/README.txt

@@ -0,0 +1,9 @@
+:orphan:
+
+.. _examples:
+
+========
+Examples
+========
+
+General introductory examples for *auto-sklearn* can be found here.

example/example_crossvalidation.py → examples/example_crossvalidation.py

@@ -1,4 +1,18 @@
 # -*- encoding: utf-8 -*-
+
+
+"""
+================
+Cross-Validation
+================
+
+In *auto-sklearn* it is possible to use different resampling strategies
+by specifying the arguments ``resampling_strategy`` and
+``resampling_strategy_arguments``. The following example shows how to use
+cross-validation and how to set the folds when instantiating
+``AutoSklearnClassifier``.
+"""
+
 import sklearn.model_selection
 import sklearn.datasets
 import sklearn.metrics

example/example_eips.py → examples/example_eips.py

@@ -1,3 +1,11 @@
+"""
+====
+EIPS
+====
+
+Example description goes here.
+"""
+
 import sklearn.model_selection
 import sklearn.datasets
 import sklearn.metrics

example/example_feature_types.py → examples/example_feature_types.py

@@ -1,4 +1,14 @@
 # -*- encoding: utf-8 -*-
+"""
+=============
+Feature Types
+=============
+
+In *auto-sklearn* it is possible to specify the feature types of a dataset when
+calling the method :meth:`fit() <autosklearn.classification.AutoSklearnClassifier.fit>` by specifying the argument ``feat_type``.
+The following example demonstrates a way it can be done.
+"""
+
 import sklearn.model_selection
 import sklearn.datasets
 import sklearn.metrics

example/example_holdout.py → examples/example_holdout.py

@@ -1,3 +1,15 @@
+"""
+=======
+Holdout
+=======
+
+In *auto-sklearn* it is possible to use different resampling strategies
+by specifying the arguments ``resampling_strategy`` and
+``resampling_strategy_arguments``. The following example shows how to use the
+holdout method as well as set the train-test split ratio when instantiating
+``AutoSklearnClassifier``.
+"""
+
 import sklearn.model_selection
 import sklearn.datasets
 import sklearn.metrics
@@ -16,6 +28,11 @@ def main():
         tmp_folder='/tmp/autosklearn_holdout_example_tmp',
         output_folder='/tmp/autosklearn_holdout_example_out',
         disable_evaluator_output=False,
+        # 'holdout' with 'train_size'=0.67 is the default argument setting
+        # for AutoSklearnClassifier. It is explicitly specified in this example
+        # for demonstrational purpose.
+        resampling_strategy='holdout',
+        resampling_strategy_arguments={'train_size': 0.67}
     )
     automl.fit(X_train, y_train, dataset_name='digits')
 

example/example_metrics.py → examples/example_metrics.py

@@ -1,4 +1,16 @@
 # -*- encoding: utf-8 -*-
+"""
+=======
+Metrics
+=======
+
+*Auto-sklearn* supports various built-in metrics, which can be found in the
+:ref:`metrics section in the API <api:Built-in Metrics>`. However, it is also
+possible to define your own metric and use it to fit and evaluate your model.
+The following examples show how to use built-in and self-defined metrics for a
+classification problem.
+"""
+
 import numpy as np
 
 import sklearn.model_selection
@@ -11,15 +23,24 @@
 
 
 def accuracy(solution, prediction):
-    # function defining accuracy
+    # custom function defining accuracy
     return np.mean(solution == prediction)
 
+def error(solution, prediction):
+    # custom function defining error
+    return np.mean(solution != prediction)
+
 
 def accuracy_wk(solution, prediction, dummy):
-    # function defining accuracy and accepting an additional argument
+    # custom function defining accuracy and accepting an additional argument
     assert dummy is None
     return np.mean(solution == prediction)
 
+def error_wk(solution, prediction, dummy):
+    # custom function defining error and accepting an additional argument
+    assert dummy is None
+    return np.mean(solution != prediction)
+
 
 def main():
 
@@ -72,6 +93,28 @@ def main():
           format(sklearn.metrics.accuracy_score(y_test, predictions),
                  cls._automl._metric.name))
 
+    print("#"*80)
+    print("Use self defined error metric")
+    error_rate = autosklearn.metrics.make_scorer(
+        name='error',
+        score_func=error,
+        optimum=0,
+        greater_is_better=False,
+        needs_proba=False,
+        needs_threshold=False
+    )
+    cls = autosklearn.classification.AutoSklearnClassifier(
+        time_left_for_this_task=60,
+        per_run_time_limit=30,
+        seed=1
+    )
+    cls.fit(X_train, y_train, metric=error_rate)
+
+    cls.predictions = cls.predict(X_test)
+    print("Error rate {:g} using {:s}".
+          format(error_rate(y_test, predictions),
+                 cls._automl._metric.name))
+
     # Third example: Use own accuracy metric with additional argument
     print("#"*80)
     print("Use self defined accuracy with additional argument")
@@ -99,6 +142,31 @@ def main():
         )
     )
 
+    print("#"*80)
+    print("Use self defined error with additional argument")
+    error_rate = autosklearn.metrics.make_scorer(
+        name="error_add",
+        score_func=error_wk,
+        optimum=0,
+        greater_is_better=True,
+        needs_proba=False,
+        needs_threshold=False,
+        dummy=None,
+    )
+    cls = autosklearn.classification.AutoSklearnClassifier(
+        time_left_for_this_task=60,
+        per_run_time_limit=30,
+        seed=1,
+    )
+    cls.fit(X_train, y_train, metric=error_rate)
+
+    predictions = cls.predict(X_test)
+    print(
+        "Error rate {:g} using {:s}".format(
+            error_rate(y_test, predictions),
+            cls._automl._metric.name
+        )
+    )
 
 if __name__ == "__main__":
     main()

example/example_parallel.py → examples/example_parallel.py

@@ -1,4 +1,18 @@
 # -*- encoding: utf-8 -*-
+"""
+====================
+Parallel Usage
+====================
+
+*Auto-sklearn* uses *SMAC* to automatically optimize the hyperparameters of
+the training models. A variant of *SMAC*, called *pSMAC* (parallel SMAC),
+provides a means of running several instances of *auto-sklearn* in a parallel
+mode using several computational resources (detailed information of
+*pSMAC* can be found `here <https://automl.github.io/SMAC3/stable/psmac.html>`_).
+This example shows the necessary steps to configure *auto-sklearn* in
+parallel mode.
+"""
+
 import multiprocessing
 import shutil
 
@@ -62,8 +76,8 @@ def spawn_classifier(seed, dataset_name):
     return spawn_classifier
 
 
-if __name__ == '__main__':
-    
+def main():
+
     X, y = sklearn.datasets.load_digits(return_X_y=True)
     X_train, X_test, y_train, y_test = \
         sklearn.model_selection.train_test_split(X, y, random_state=1)
@@ -106,3 +120,7 @@ def spawn_classifier(seed, dataset_name):
     predictions = automl.predict(X_test)
     print(automl.show_models())
     print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
+
+
+if __name__ == '__main__':
+    main()

example/example_random_search.py → examples/example_random_search.py

@@ -1,3 +1,14 @@
+"""
+=============
+Random Search
+=============
+
+A crucial feature of *auto-sklearn* is automatically optimizing the hyperparameters
+through SMAC, introduced `here <http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf>`_.
+Additionally, it is possible to use `random search <http://www.jmlr.org/papers/v13/bergstra12a.html>`_
+instead of SMAC, as demonstrated in the example below.
+"""
+
 import sklearn.model_selection
 import sklearn.datasets
 import sklearn.metrics
@@ -17,9 +28,7 @@ def get_roar_object_callback(
     runhistory,
     run_id,
 ):
-    """Random online adaptive racing.
-
-    http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf"""
+    """Random online adaptive racing."""
     scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob()
     scenario = Scenario(scenario_dict)
     return ROAR(
@@ -40,9 +49,7 @@ def get_random_search_object_callback(
         runhistory,
         run_id,
 ):
-    """Random search.
-
-    http://www.jmlr.org/papers/v13/bergstra12a.html"""
+    """Random search."""
     scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob()
     scenario_dict['minR'] = len(scenario_dict['instances'])
     scenario_dict['initial_incumbent'] = 'RANDOM'

example/example_regression.py → examples/example_regression.py

@@ -1,11 +1,18 @@
 # -*- encoding: utf-8 -*-
+"""
+==========
+Regression
+==========
+
+The following example shows how to fit a simple regression model with
+*auto-sklearn*.
+"""
 import sklearn.model_selection
 import sklearn.datasets
 import sklearn.metrics
 
 import autosklearn.regression
 
-
 def main():
     X, y = sklearn.datasets.load_boston(return_X_y=True)
     feature_types = (['numerical'] * 3) + ['categorical'] + (['numerical'] * 9)