package sklearn

Encode categorical features as a one-hot numeric array.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka 'one-of-K' or 'dummy') encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the ``sparse`` parameter)

By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the `categories` manually.

This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels.

Note: a one-hot encoding of y labels should use a LabelBinarizer instead.

Read more in the :ref:`User Guide <preprocessing_categorical_features>`.

.. versionchanged:: 0.20

Parameters ---------- categories : 'auto' or a list of array-like, default='auto' Categories (unique values) per feature:

• 'auto' : Determine categories automatically from the training data.
• list : ``categoriesi`` holds the categories expected in the ith column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values.

The used categories can be found in the ``categories_`` attribute.

.. versionadded:: 0.20

drop : 'first', 'if_binary' or a array-like of shape (n_features,), default=None Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into a neural network or an unregularized regression.

However, dropping one category breaks the symmetry of the original representation and can therefore induce a bias in downstream models, for instance for penalized linear classification or regression models.

• None : retain all features (the default).
• 'first' : drop the first category in each feature. If only one category is present, the feature will be dropped entirely.
• 'if_binary' : drop the first category in each feature with two categories. Features with 1 or more than 2 categories are left intact.
• array : ``dropi`` is the category in feature ``X:, i`` that should be dropped.

sparse : bool, default=True Will return sparse matrix if set True else will return an array.

dtype : number type, default=np.float Desired dtype of output.

handle_unknown : 'error', 'ignore', default='error' Whether to raise an error or ignore if an unknown categorical feature is present during transform (default is to raise). When this parameter is set to 'ignore' and an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros. In the inverse transform, an unknown category will be denoted as None.

Attributes ---------- categories_ : list of arrays The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of ``transform``). This includes the category specified in ``drop`` (if any).

drop_idx_ : array of shape (n_features,)

• ``drop_idx_i`` is the index in ``categories_i`` of the category to be dropped for each feature.
• ``drop_idx_i = None`` if no category is to be dropped from the feature with index ``i``, e.g. when `drop='if_binary'` and the feature isn't binary.
• ``drop_idx_ = None`` if all the transformed features will be retained.

See Also -------- sklearn.preprocessing.OrdinalEncoder : Performs an ordinal (integer) encoding of the categorical features. sklearn.feature_extraction.DictVectorizer : Performs a one-hot encoding of dictionary items (also handles string-valued features). sklearn.feature_extraction.FeatureHasher : Performs an approximate one-hot encoding of dictionary items or strings. sklearn.preprocessing.LabelBinarizer : Binarizes labels in a one-vs-all fashion. sklearn.preprocessing.MultiLabelBinarizer : Transforms between iterable of iterables and a multilabel format, e.g. a (samples x classes) binary matrix indicating the presence of a class label.

Examples -------- Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to a binary one-hot encoding.

>>> from sklearn.preprocessing import OneHotEncoder

One can discard categories not seen during `fit`:

>>> enc = OneHotEncoder(handle_unknown='ignore') >>> X = ['Male', 1], ['Female', 3], ['Female', 2] >>> enc.fit(X) OneHotEncoder(handle_unknown='ignore') >>> enc.categories_ array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object) >>> enc.transform(['Female', 1], ['Male', 4]).toarray() array([1., 0., 1., 0., 0.], [0., 1., 0., 0., 0.]) >>> enc.inverse_transform([0, 1, 1, 0, 0], [0, 0, 0, 1, 0]) array(['Male', 1], [None, 2], dtype=object) >>> enc.get_feature_names('gender', 'group') array('gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3', dtype=object)

One can always drop the first column for each feature:

>>> drop_enc = OneHotEncoder(drop='first').fit(X) >>> drop_enc.categories_ array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object) >>> drop_enc.transform(['Female', 1], ['Male', 2]).toarray() array([0., 0., 0.], [1., 1., 0.])

Or drop a column for feature only having 2 categories:

>>> drop_binary_enc = OneHotEncoder(drop='if_binary').fit(X) >>> drop_binary_enc.transform(['Female', 1], ['Male', 2]).toarray() array([0., 1., 0., 0.], [1., 0., 1., 0.])

Fit OneHotEncoder to X.

Parameters ---------- X : array-like, shape n_samples, n_features The data to determine the categories of each feature.

y : None Ignored. This parameter exists only for compatibility with :class:`sklearn.pipeline.Pipeline`.

Returns ------- self

Fit OneHotEncoder to X, then transform X.

Equivalent to fit(X).transform(X) but more convenient.

Parameters ---------- X : array-like, shape n_samples, n_features The data to encode.

y : None Ignored. This parameter exists only for compatibility with :class:`sklearn.pipeline.Pipeline`.

Returns ------- X_out : sparse matrix if sparse=True else a 2-d array Transformed input.

Return feature names for output features.

Parameters ---------- input_features : list of str of shape (n_features,) String names for input features if available. By default, 'x0', 'x1', ... 'xn_features' is used.

Returns ------- output_feature_names : ndarray of shape (n_output_features,) Array of feature names.

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

Convert the data back to the original representation.

In case unknown categories are encountered (all zeros in the one-hot encoding), ``None`` is used to represent this category.

Parameters ---------- X : array-like or sparse matrix, shape n_samples, n_encoded_features The transformed data.

Returns ------- X_tr : array-like, shape n_samples, n_features Inverse transformed array.

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Transform X using one-hot encoding.

Parameters ---------- X : array-like, shape n_samples, n_features The data to encode.

Returns ------- X_out : sparse matrix if sparse=True else a 2-d array Transformed input.

Attribute categories_: get value or raise Not_found if None.

Attribute categories_: get value as an option.

Attribute drop_idx_: get value or raise Not_found if None.

Attribute drop_idx_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.