Function binarize

sklearn/preprocessing/_data.py:2236–2292

Boolean thresholding of array-like or scipy.sparse matrix. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to binarize, element by element. scipy.sparse

(X, *, threshold=0.0, copy=True)

    prefer_skip_nested_validation=True,
)
def binarize(X, *, threshold=0.0, copy=True):
    """Boolean thresholding of array-like or scipy.sparse matrix.

    Read more in the :ref:`User Guide <preprocessing_binarization>`.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The data to binarize, element by element.
        scipy.sparse matrices should be in CSR or CSC format to avoid an
        un-necessary copy.

    threshold : float, default=0.0
        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.

    copy : bool, default=True
        If False, try to avoid a copy and binarize in place.
        This is not guaranteed to always work in place; e.g. if the data is
        a numpy array with an object dtype, a copy will be returned even with
        copy=False.

    Returns
    -------
    X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
        The transformed data.

    See Also
    --------
    Binarizer : Performs binarization using the Transformer API
        (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`).

    Examples
    --------
    >>> from sklearn.preprocessing import binarize
    >>> X = [[0.4, 0.6, 0.5], [0.6, 0.1, 0.2]]
    >>> binarize(X, threshold=0.5)
    array([[0., 1., 0.],
           [1., 0., 0.]])
    """
    X = check_array(X, accept_sparse=["csr", "csc"], force_writeable=True, copy=copy)
    if sparse.issparse(X):
        if threshold < 0:
            raise ValueError("Cannot binarize a sparse matrix with threshold < 0")
        cond = X.data > threshold
        not_cond = np.logical_not(cond)
        X.data[cond] = 1
        X.data[not_cond] = 0
        X.eliminate_zeros()
    else:
        xp, _, device = get_namespace_and_device(X)
        float_dtype = _find_matching_floating_dtype(X, threshold, xp=xp)
        cond = xp.astype(X, float_dtype, copy=False) > threshold
        not_cond = xp.logical_not(cond)
        X[cond] = 1
        X[not_cond] = 0
    return X