KBinsDiscretizer

class cuml.preprocessing.KBinsDiscretizer(*args, **kwargs)

Bin continuous data into intervals.

Parameters:

n_binsint or array-like, 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.

Attributes:

n_bins_int array, shape (n_features,)

Number of bins per feature. Bins whose width are too small (i.e., <= 1e-8) are removed with a warning.

bin_edges_array of arrays, shape (n_features, )

The edges of each bin. Contain arrays of varying shapes (n_bins_, ) Ignored features will have empty arrays.

Methods

fit(X[, y])	Fit the estimator.
inverse_transform(Xt)	Transform discretized data back to original feature space.
transform(X)	Discretize the data.

See also

cuml.preprocessing.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 cuml.compose.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., sklearn.feature_selection.VarianceThreshold).

Examples

>>> from cuml.preprocessing import KBinsDiscretizer
>>> import cupy as cp
>>> X = [[-2, 1, -4,   -1],
...      [-1, 2, -3, -0.5],
...      [ 0, 3, -2,  0.5],
...      [ 1, 4, -1,    2]]
>>> X = cp.array(X)
>>> 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]], dtype=int32)

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]])

fit(X, y=None) → KBinsDiscretizer

Fit the estimator.

Parameters:

Xnumeric array-like, shape (n_samples, n_features)

Data to be discretized.

yNone

Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

Returns:

self

inverse_transform(Xt) → SparseCumlArray

Transform discretized data back to original feature space.

Note that this function does not regenerate the original data due to discretization rounding.

Parameters:

Xtnumeric array-like, shape (n_sample, n_features)

Transformed data in the binned space.

Returns:

Xinvnumeric array-like

Data in the original feature space.

transform(X) → SparseCumlArray

Discretize the data.

Parameters:

Xnumeric array-like, shape (n_samples, n_features)

Data to be discretized.

Returns:

Xtnumeric array-like or sparse matrix

Data in the binned space.