Binarizer

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

Binarize data (set feature values to 0 or 1) according to a threshold

Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1.

Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance.

It can also be used as a pre-processing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting).

Parameters:

thresholdfloat, optional (0.0 by default)

Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices.

copyboolean, optional, default True

Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

Methods

fit(X[, y])	Do nothing and return the estimator unchanged
transform(X[, copy])	Binarize each element of X

See also

binarize

Equivalent function without the estimator API.

Notes

If the input is a sparse matrix, only the non-zero values are subject to update by the Binarizer class.

This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.

Examples

>>> from cuml.preprocessing import Binarizer
>>> import cupy as cp
>>> X = [[ 1., -1.,  2.],
...      [ 2.,  0.,  0.],
...      [ 0.,  1., -1.]]
>>> X = cp.array(X)
>>> transformer = Binarizer().fit(X)  # fit does nothing.
>>> transformer
Binarizer()
>>> transformer.transform(X)
array([[1., 0., 1.],
       [1., 0., 0.],
       [0., 1., 0.]])

fit(

X,

y=None,

) → Binarizer

Do nothing and return the estimator unchanged

This method is just there to implement the usual API and hence work in pipelines.

Parameters:

X{array-like, sparse matrix}

fit_transform(X, y=None, **fit_params)

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:

X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)

yndarray of shape (n_samples,), default=None

Target values.

**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)

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_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, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Same as input features.

get_params(deep=True)

Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

set_params(**params)

Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

transform(X, copy=None)

Binarize each element of X

Parameters:

X{array-like, sparse matrix}, shape [n_samples, n_features]

The data to binarize, element by element.

copybool

Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.