scikit-learn
diff --git a/‎doc/modules/classes.rst
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diff --git a/‎doc/modules/preprocessing.rst
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diff --git a/‎doc/whats_new/_contributors.rst
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diff --git a/‎doc/whats_new/v0.20.rst
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diff --git a/‎examples/ensemble/plot_feature_transformation.py
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diff --git a/‎sklearn/feature_extraction/dict_vectorizer.py
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diff --git a/‎sklearn/preprocessing/__init__.py
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@@ -1198,6 +1198,7 @@ Model validation
    preprocessing.MinMaxScaler
    preprocessing.Normalizer
    preprocessing.OneHotEncoder
+   preprocessing.CategoricalEncoder
    preprocessing.PolynomialFeatures
    preprocessing.QuantileTransformer
    preprocessing.RobustScaler
 
@@ -455,47 +455,87 @@ Such features can be efficiently coded as integers, for instance
 ``[0, 1, 3]`` while ``["female", "from Asia", "uses Chrome"]`` would be
 ``[1, 2, 1]``.
 
-Such integer representation can not be used directly with scikit-learn estimators, as these
-expect continuous input, and would interpret the categories as being ordered, which is often
-not desired (i.e. the set of browsers was ordered arbitrarily).
-
-One possibility to convert categorical features to features that can be used
-with scikit-learn estimators is to use a one-of-K or one-hot encoding, which is
-implemented in :class:`OneHotEncoder`.  This estimator transforms each
-categorical feature with ``m`` possible values into ``m`` binary features, with
-only one active.
+To convert categorical features to such integer codes, we can use the
+:class:`CategoricalEncoder`. When specifying that we want to perform an
+ordinal encoding, the estimator transforms each categorical feature to one
+new feature of integers (0 to n_categories - 1)::
+
+    >>> enc = preprocessing.CategoricalEncoder(encoding='ordinal')
+    >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
+    >>> enc.fit(X)  # doctest: +ELLIPSIS
+    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
+              encoding='ordinal', handle_unknown='error')
+    >>> enc.transform([['female', 'from US', 'uses Safari']])
+    array([[ 0.,  1.,  1.]])
+
+Such integer representation can, however, not be used directly with all
+scikit-learn estimators, as these expect continuous input, and would interpret
+the categories as being ordered, which is often not desired (i.e. the set of
+browsers was ordered arbitrarily).
+
+Another possibility to convert categorical features to features that can be used
+with scikit-learn estimators is to use a one-of-K, also known as one-hot or
+dummy encoding.
+This type of encoding is the default behaviour of the :class:`CategoricalEncoder`.
+The :class:`CategoricalEncoder` then transforms each categorical feature with
+``n_categories`` possible values into ``n_categories`` binary features, with
+one of them 1, and all others 0.
 
 Continuing the example above::
 
-  >>> enc = preprocessing.OneHotEncoder()
-  >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])  # doctest: +ELLIPSIS
-  OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,
-         handle_unknown='error', n_values='auto', sparse=True)
-  >>> enc.transform([[0, 1, 3]]).toarray()
-  array([[ 1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.]])
-
-By default, how many values each feature can take is inferred automatically from the dataset.
-It is possible to specify this explicitly using the parameter ``n_values``.
-There are two genders, three possible continents and four web browsers in our
-dataset.
-Then we fit the estimator, and transform a data point.
-In the result, the first two numbers encode the gender, the next set of three
-numbers the continent and the last four the web browser.
-
-Note that, if there is a possibility that the training data might have missing categorical
-features, one has to explicitly set ``n_values``. For example,
-
-    >>> enc = preprocessing.OneHotEncoder(n_values=[2, 3, 4])
-    >>> # Note that there are missing categorical values for the 2nd and 3rd
-    >>> # features
-    >>> enc.fit([[1, 2, 3], [0, 2, 0]])  # doctest: +ELLIPSIS
-    OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,
-           handle_unknown='error', n_values=[2, 3, 4], sparse=True)
-    >>> enc.transform([[1, 0, 0]]).toarray()
-    array([[ 0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.]])
+  >>> enc = preprocessing.CategoricalEncoder()
+  >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
+  >>> enc.fit(X)  # doctest: +ELLIPSIS
+  CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
+            encoding='onehot', handle_unknown='error')
+  >>> enc.transform([['female', 'from US', 'uses Safari'],
+  ...                ['male', 'from Europe', 'uses Safari']]).toarray()
+  array([[ 1.,  0.,  0.,  1.,  0.,  1.],
+         [ 0.,  1.,  1.,  0.,  0.,  1.]])
+
+By default, the values each feature can take is inferred automatically
+from the dataset and can be found in the ``categories_`` attribute::
+
+    >>> enc.categories_
+    [array(['female', 'male'], dtype=object), array(['from Europe', 'from US'], dtype=object), array(['uses Firefox', 'uses Safari'], dtype=object)]
+
+It is possible to specify this explicitly using the parameter ``categories``.
+There are two genders, four possible continents and four web browsers in our
+dataset::
+
+    >>> genders = ['female', 'male']
+    >>> locations = ['from Africa', 'from Asia', 'from Europe', 'from US']
+    >>> browsers = ['uses Chrome', 'uses Firefox', 'uses IE', 'uses Safari']
+    >>> enc = preprocessing.CategoricalEncoder(categories=[genders, locations, browsers])
+    >>> # Note that for there are missing categorical values for the 2nd and 3rd
+    >>> # feature
+    >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
+    >>> enc.fit(X) # doctest: +ELLIPSIS
+    CategoricalEncoder(categories=[...],
+              dtype=<... 'numpy.float64'>, encoding='onehot',
+              handle_unknown='error')
+    >>> enc.transform([['female', 'from Asia', 'uses Chrome']]).toarray()
+    array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.]])
+
+If there is a possibility that the training data might have missing categorical
+features, it can often be better to specify ``handle_unknown='ignore'`` instead
+of setting the ``categories`` manually as above. When
+``handle_unknown='ignore'`` is specified and unknown categories are encountered
+during transform, no error will be raised but the resulting one-hot encoded
+columns for this feature will be all zeros
+(``handle_unknown='ignore'`` is only supported for one-hot encoding)::
+
+    >>> enc = preprocessing.CategoricalEncoder(handle_unknown='ignore')
+    >>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
+    >>> enc.fit(X) # doctest: +ELLIPSIS
+    CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
+              encoding='onehot', handle_unknown='ignore')
+    >>> enc.transform([['female', 'from Asia', 'uses Chrome']]).toarray()
+    array([[ 1.,  0.,  0.,  0.,  0.,  0.]])
+
 
 See :ref:`dict_feature_extraction` for categorical features that are represented
-as a dict, not as integers.
+as a dict, not as scalars.
 
 .. _imputation:
 
 
@@ -149,3 +149,5 @@
 .. _Neeraj Gangwar: http://neerajgangwar.in
 
 .. _Arthur Mensch: https://amensch.fr
+
+.. _Joris Van den Bossche: https://github.com/jorisvandenbossche
@@ -45,6 +45,17 @@ Classifiers and regressors
   Naive Bayes classifier described in Rennie et al. (2003).
   By :user:`Michael A. Alcorn <airalcorn2>`.
 
+Preprocessing
+
+- Added :class:`preprocessing.CategoricalEncoder`, which allows to encode
+  categorical features as a numeric array, either using a one-hot (or
+  dummy) encoding scheme or by converting to ordinal integers.
+  Compared to the existing :class:`OneHotEncoder`, this new class handles
+  encoding of all feature types (also handles string-valued features) and
+  derives the categories based on the unique values in the features instead of
+  the maximum value in the features.
+  By :user:`Vighnesh Birodkar <vighneshbirodkar>` and `Joris Van den Bossche`_.
+
 Model evaluation
 
 - Added the :func:`metrics.balanced_accuracy_score` metric and a corresponding
 
@@ -34,7 +34,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,
                               GradientBoostingClassifier)
-from sklearn.preprocessing import OneHotEncoder
+from sklearn.preprocessing import CategoricalEncoder
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import roc_curve
 from sklearn.pipeline import make_pipeline
@@ -62,7 +62,7 @@
 
 # Supervised transformation based on random forests
 rf = RandomForestClassifier(max_depth=3, n_estimators=n_estimator)
-rf_enc = OneHotEncoder()
+rf_enc = CategoricalEncoder()
 rf_lm = LogisticRegression()
 rf.fit(X_train, y_train)
 rf_enc.fit(rf.apply(X_train))
@@ -72,7 +72,7 @@
 fpr_rf_lm, tpr_rf_lm, _ = roc_curve(y_test, y_pred_rf_lm)
 
 grd = GradientBoostingClassifier(n_estimators=n_estimator)
-grd_enc = OneHotEncoder()
+grd_enc = CategoricalEncoder()
 grd_lm = LogisticRegression()
 grd.fit(X_train, y_train)
 grd_enc.fit(grd.apply(X_train)[:, :, 0])
 
@@ -39,7 +39,8 @@ class DictVectorizer(BaseEstimator, TransformerMixin):
     However, note that this transformer will only do a binary one-hot encoding
     when feature values are of type string. If categorical features are
     represented as numeric values such as int, the DictVectorizer can be
-    followed by OneHotEncoder to complete binary one-hot encoding.
+    followed by :class:`sklearn.preprocessing.CategoricalEncoder` to complete
+    binary one-hot encoding.
 
     Features that do not occur in a sample (mapping) will have a zero value
     in the resulting array/matrix.
@@ -88,8 +89,8 @@ class DictVectorizer(BaseEstimator, TransformerMixin):
     See also
     --------
     FeatureHasher : performs vectorization using only a hash function.
-    sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features
-      encoded as columns of integers.
+    sklearn.preprocessing.CategoricalEncoder : handles nominal/categorical
+      features encoded as columns of arbitrary data types.
     """
 
     def __init__(self, dtype=np.float64, separator="=", sparse=True,
 
@@ -22,6 +22,7 @@
 from .data import minmax_scale
 from .data import quantile_transform
 from .data import OneHotEncoder
+from .data import CategoricalEncoder
 
 from .data import PolynomialFeatures
 
@@ -46,6 +47,7 @@
     'QuantileTransformer',
     'Normalizer',
     'OneHotEncoder',
+    'CategoricalEncoder',
     'RobustScaler',
     'StandardScaler',
     'add_dummy_feature',