sklearn.preprocessing
.OrdinalEncoder¶
-
class
sklearn.preprocessing.
OrdinalEncoder
(*, categories='auto', dtype=<class 'numpy.float64'>, handle_unknown='error', unknown_value=None)[source]¶ Encode categorical features as an integer 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 converted to ordinal integers. This results in a single column of integers (0 to n_categories - 1) per feature.
Read more in the User Guide.
New in version 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 :
categories[i]
holds the categories expected in the ith column. The passed categories should not mix strings and numeric values, and should be sorted in case of numeric values.
The used categories can be found in the
categories_
attribute.- dtypenumber type, default np.float64
Desired dtype of output.
- handle_unknown{‘error’, ‘use_encoded_value’}, default=’error’
When set to ‘error’ an error will be raised in case an unknown categorical feature is present during transform. When set to ‘use_encoded_value’, the encoded value of unknown categories will be set to the value given for the parameter
unknown_value
. Ininverse_transform
, an unknown category will be denoted as None.New in version 0.24.
- unknown_valueint or np.nan, default=None
When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter is required and will set the encoded value of unknown categories. It has to be distinct from the values used to encode any of the categories in
fit
. If set to np.nan, thedtype
parameter must be a float dtype.New in version 0.24.
- Attributes
- categories_list of arrays
The categories of each feature determined during
fit
(in order of the features in X and corresponding with the output oftransform
). This does not include categories that weren’t seen duringfit
.
See also
OneHotEncoder
Performs a one-hot encoding of categorical features.
LabelEncoder
Encodes target labels with values between 0 and
n_classes-1
.
Examples
Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to an ordinal encoding.
>>> from sklearn.preprocessing import OrdinalEncoder >>> enc = OrdinalEncoder() >>> X = [['Male', 1], ['Female', 3], ['Female', 2]] >>> enc.fit(X) OrdinalEncoder() >>> enc.categories_ [array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)] >>> enc.transform([['Female', 3], ['Male', 1]]) array([[0., 2.], [1., 0.]])
>>> enc.inverse_transform([[1, 0], [0, 1]]) array([['Male', 1], ['Female', 2]], dtype=object)
Methods
fit
(X[, y])Fit the OrdinalEncoder to X.
fit_transform
(X[, y])Fit to data, then transform it.
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 to ordinal codes.
-
fit
(X, y=None)[source]¶ Fit the OrdinalEncoder 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
-
fit_transform
(X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
-
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.
- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_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.