sklearn.preprocessing
.KBinsDiscretizer¶
- class sklearn.preprocessing.KBinsDiscretizer(n_bins=5, *, encode='onehot', strategy='quantile', dtype=None, subsample='warn', random_state=None)[source]¶
Bin continuous data into intervals.
Read more in the User Guide.
New in version 0.20.
- Parameters:
- n_binsint or array-like of shape (n_features,), default=5
The number of bins to produce. Raises ValueError if
n_bins < 2
.- encode{‘onehot’, ‘onehot-dense’, ‘ordinal’}, default=’onehot’
Method used to encode the transformed result.
‘onehot’: Encode the transformed result with one-hot encoding and return a sparse matrix. Ignored features are always stacked to the right.
‘onehot-dense’: Encode the transformed result with one-hot encoding and return a dense array. Ignored features are always stacked to the right.
‘ordinal’: Return the bin identifier encoded as an integer value.
- strategy{‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’
Strategy used to define the widths of the bins.
‘uniform’: All bins in each feature have identical widths.
‘quantile’: All bins in each feature have the same number of points.
‘kmeans’: Values in each bin have the same nearest center of a 1D k-means cluster.
For an example of the different strategies see: Demonstrating the different strategies of KBinsDiscretizer.
- dtype{np.float32, np.float64}, default=None
The desired data-type for the output. If None, output dtype is consistent with input dtype. Only np.float32 and np.float64 are supported.
New in version 0.24.
- subsampleint or None, default=’warn’
Maximum number of samples, used to fit the model, for computational efficiency. Defaults to 200_000 when
strategy='quantile'
and toNone
whenstrategy='uniform'
orstrategy='kmeans'
.subsample=None
means that all the training samples are used when computing the quantiles that determine the binning thresholds. Since quantile computation relies on sorting each column ofX
and that sorting has ann log(n)
time complexity, it is recommended to use subsampling on datasets with a very large number of samples.Changed in version 1.3: The default value of
subsample
changed fromNone
to200_000
whenstrategy="quantile"
.Changed in version 1.5: The default value of
subsample
changed fromNone
to200_000
whenstrategy="uniform"
orstrategy="kmeans"
.- random_stateint, RandomState instance or None, default=None
Determines random number generation for subsampling. Pass an int for reproducible results across multiple function calls. See the
subsample
parameter for more details. See Glossary.New in version 1.1.
- Attributes:
- bin_edges_ndarray of ndarray of shape (n_features,)
The edges of each bin. Contain arrays of varying shapes
(n_bins_, )
Ignored features will have empty arrays.- n_bins_ndarray of shape (n_features,), dtype=np.int_
Number of bins per feature. Bins whose width are too small (i.e., <= 1e-8) are removed with a warning.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- 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
Binarizer
Class used to bin values as
0
or1
based on a parameterthreshold
.
Notes
For a visualization of discretization on different datasets refer to Feature discretization. On the effect of discretization on linear models see: Using KBinsDiscretizer to discretize continuous features.
In bin edges for feature
i
, the first and last values are used only forinverse_transform
. During transform, bin edges are extended to:np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf])
You can combine
KBinsDiscretizer
withColumnTransformer
if you only want to preprocess part of the features.KBinsDiscretizer
might produce constant features (e.g., whenencode = 'onehot'
and certain bins do not contain any data). These features can be removed with feature selection algorithms (e.g.,VarianceThreshold
).Examples
>>> from sklearn.preprocessing import KBinsDiscretizer >>> X = [[-2, 1, -4, -1], ... [-1, 2, -3, -0.5], ... [ 0, 3, -2, 0.5], ... [ 1, 4, -1, 2]] >>> est = KBinsDiscretizer( ... n_bins=3, encode='ordinal', strategy='uniform', subsample=None ... ) >>> est.fit(X) KBinsDiscretizer(...) >>> Xt = est.transform(X) >>> Xt array([[ 0., 0., 0., 0.], [ 1., 1., 1., 0.], [ 2., 2., 2., 1.], [ 2., 2., 2., 2.]])
Sometimes it may be useful to convert the data back into the original feature space. The
inverse_transform
function converts the binned data into the original feature space. Each value will be equal to the mean of the two bin edges.>>> est.bin_edges_[0] array([-2., -1., 0., 1.]) >>> est.inverse_transform(Xt) array([[-1.5, 1.5, -3.5, -0.5], [-0.5, 2.5, -2.5, -0.5], [ 0.5, 3.5, -1.5, 0.5], [ 0.5, 3.5, -1.5, 1.5]])
Methods
fit
(X[, y, sample_weight])Fit the estimator.
fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Get output feature names.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
Transform discretized data back to original feature space.
set_fit_request
(*[, sample_weight])Request metadata passed to the
fit
method.set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Discretize the data.
- fit(X, y=None, sample_weight=None)[source]¶
Fit the estimator.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Data to be discretized.
- yNone
Ignored. This parameter exists only for compatibility with
Pipeline
.- sample_weightndarray of shape (n_samples,)
Contains weight values to be associated with each sample. Only possible when
strategy
is set to"quantile"
.New in version 1.3.
- Returns:
- selfobject
Returns the instance itself.
- 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_feature_names_out(input_features=None)[source]¶
Get output feature names.
- 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 the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]
.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_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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(Xt)[source]¶
Transform discretized data back to original feature space.
Note that this function does not regenerate the original data due to discretization rounding.
- Parameters:
- Xtarray-like of shape (n_samples, n_features)
Transformed data in the binned space.
- Returns:
- Xinvndarray, dtype={np.float32, np.float64}
Data in the original feature space.
- set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') KBinsDiscretizer [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- set_output(*, transform=None)[source]¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame outputNone
: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.
- 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.
Examples using sklearn.preprocessing.KBinsDiscretizer
¶
Release Highlights for scikit-learn 1.2
Time-related feature engineering
Poisson regression and non-normal loss
Tweedie regression on insurance claims
Demonstrating the different strategies of KBinsDiscretizer
Target Encoder’s Internal Cross fitting
Using KBinsDiscretizer to discretize continuous features