ADD example on how to use feat_type

Author: KEggensperger

autosklearn/estimators.py

@@ -388,7 +388,7 @@ def fit(self, X, y,
             List of str of `len(X.shape[1])` describing the attribute type.
             Possible types are `Categorical` and `Numerical`. `Categorical`
             attributes will be automatically One-Hot encoded. The values
-            used for a categorical attribute must be integers, obtainde for
+            used for a categorical attribute must be integers, obtained for
             example by `sklearn.preprocessing.LabelEncoder
             <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
 

doc/manual.rst

@@ -20,6 +20,7 @@ aspects of its usage:
 * `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>`_
 
 Time and memory limits
 ======================
@@ -64,7 +65,7 @@ For a full list please have a look at the source code (in `autosklearn/pipeline/
   * `Regressors <https://github.com/automl/auto-sklearn/tree/master/autosklearn/pipeline/components/regression>`_
   * `Preprocessors <https://github.com/automl/auto-sklearn/tree/master/autosklearn/pipeline/components/feature_preprocessing>`_
 
-Turning of preprocessing
+Turning off preprocessing
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 Preprocessing in *auto-sklearn* is divided into data preprocessing and

example/example_feature_types.py

@@ -0,0 +1,57 @@
+# -*- encoding: utf-8 -*-
+import sklearn.model_selection
+import sklearn.datasets
+import sklearn.metrics
+
+import autosklearn.classification
+
+try:
+    import openml
+except ImportError:
+    print("#"*80 + """
+    To run this example you need to install openml-python:
+
+    git+https://github.com/renatopp/liac-arff
+    # OpenML is currently not on pypi, use an old version to not depend on
+    # scikit-learn 0.18
+    requests
+    xmltodict
+    git+https://github.com/renatopp/liac-arff
+    git+https://github.com/openml/""" +
+    "openml-python@0b9009b0436fda77d9f7c701bd116aff4158d5e1\n""" +
+          "#"*80)
+    raise
+
+
+def main():
+    # Load adult dataset from openml.org, see https://www.openml.org/t/2117
+    openml.config.apikey = '610344db6388d9ba34f6db45a3cf71de'
+
+    task = openml.tasks.get_task(2117)
+    train_indices, test_indices = task.get_train_test_split_indices()
+    X, y = task.get_X_and_y()
+
+    X_train = X[train_indices]
+    y_train = y[train_indices]
+    X_test = X[test_indices]
+    y_test = y[test_indices]
+
+    dataset = task.get_dataset()
+    _, _, categorical_indicator = dataset.\
+        get_data(target=task.target_name, return_categorical_indicator=True)
+
+    # Create feature type list from openml.org indicator and run autosklearn
+    feat_type = ['categorical' if ci else 'numerical'
+                 for ci in categorical_indicator]
+
+    cls = autosklearn.classification.\
+        AutoSklearnClassifier(time_left_for_this_task=120,
+                              per_run_time_limit=30)
+    cls.fit(X_train, y_train, feat_type=feat_type)
+
+    predictions = cls.predict(X_test)
+    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
+
+
+if __name__ == "__main__":
+    main()