sklearn.preprocessing.KBinsDiscretizer¶

class sklearn.preprocessing.KBinsDiscretizer(n_bins=5, *, encode='onehot', strategy='quantile', dtype=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.

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.

Attributes

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.

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.

See also

Binarizer

Class used to bin values as 0 or 1 based on a parameter threshold.

Notes

In bin edges for feature i, the first and last values are used only for inverse_transform. During transform, bin edges are extended to:

np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf])

You can combine KBinsDiscretizer with ColumnTransformer if you only want to preprocess part of the features.

KBinsDiscretizer might produce constant features (e.g., when encode = 'onehot' and certain bins do not contain any data). These features can be removed with feature selection algorithms (e.g., VarianceThreshold).

Examples

>>> 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')
>>> 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])	Fit the estimator.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
inverse_transform(Xt)	Transform discretized data back to original feature space.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Discretize the data.

fit(X, y=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.

Returns

self

fit_transform(X, y=None, **fit_params)[source]¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

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(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_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.

transform(X)[source]¶

Discretize the data.

Parameters

Xarray-like of shape (n_samples, n_features)

Data to be discretized.

Returns

Xt{ndarray, sparse matrix}, dtype={np.float32, np.float64}

Data in the binned space. Will be a sparse matrix if self.encode='onehot' and ndarray otherwise.

Examples using sklearn.preprocessing.KBinsDiscretizer¶