sklearn.preprocessing
.OneHotEncoder¶
- class sklearn.preprocessing.OneHotEncoder(*, categories='auto', drop=None, sparse=True, dtype=<class 'numpy.float64'>, handle_unknown='error')[source]¶
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 User Guide.
- 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 :
categories[i]
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.New in version 0.20.
- drop{‘first’, ‘if_binary’} or an 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 :
drop[i]
is the category in featureX[:, i]
that should be dropped.
New in version 0.21: The parameter
drop
was added in 0.21.Changed in version 0.23: The option
drop='if_binary'
was added in 0.23.- sparsebool, default=True
Will return sparse matrix if set True else will return an array.
- dtypenumber type, default=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 indrop
(if any).- drop_idx_array of shape (n_features,)
drop_idx_[i]
is the index incategories_[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 indexi
, e.g. whendrop='if_binary'
and the feature isn’t binary.drop_idx_ = None
if all the transformed features will be retained.
Changed in version 0.23: Added the possibility to contain
None
values.- n_features_in_int
Number of features seen during fit.
New in version 1.0.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
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.
LabelBinarizer
Binarizes labels in a one-vs-all fashion.
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_out(['gender', 'group']) array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'], ...)
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.]])
Methods
fit
(X[, y])Fit OneHotEncoder to X.
fit_transform
(X[, y])Fit OneHotEncoder to X, then transform X.
get_feature_names
([input_features])DEPRECATED: get_feature_names is deprecated in 1.0 and will be removed in 1.2.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_params
([deep])Get parameters for this estimator.
Convert the data back to the original representation.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Transform X using one-hot encoding.
- fit(X, y=None)[source]¶
Fit OneHotEncoder to X.
- Parameters
- Xarray-like of shape (n_samples, n_features)
The data to determine the categories of each feature.
- yNone
Ignored. This parameter exists only for compatibility with
Pipeline
.
- Returns
- self
Fitted encoder.
- fit_transform(X, y=None)[source]¶
Fit OneHotEncoder to X, then transform X.
Equivalent to fit(X).transform(X) but more convenient.
- Parameters
- Xarray-like of shape (n_samples, n_features)
The data to encode.
- yNone
Ignored. This parameter exists only for compatibility with
Pipeline
.
- Returns
- X_out{ndarray, sparse matrix} of shape (n_samples, n_encoded_features)
Transformed input. If
sparse=True
, a sparse matrix will be returned.
- get_feature_names(input_features=None)[source]¶
DEPRECATED: get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
Return feature names for output features.
- Parameters
- input_featureslist of str of shape (n_features,)
String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.
- Returns
- output_feature_namesndarray of shape (n_output_features,)
Array of feature names.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters
- input_featuresarray-like of str or None, default=None
Input features.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then names are generated:[x0, x1, ..., x(n_features_in_)]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns
- feature_names_outndarray of str objects
Transformed feature names.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- inverse_transform(X)[source]¶
Convert the data back to the original representation.
When unknown categories are encountered (all zeros in the one-hot encoding),
None
is used to represent this category. If the feature with the unknown category has a dropped category, the dropped category will be its inverse.- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_encoded_features)
The transformed data.
- Returns
- X_trndarray of shape (n_samples, n_features)
Inverse transformed array.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.