Function assert_all_finite

sklearn/utils/validation.py:189–232

Throw a ValueError if X contains NaN or infinity. Parameters ---------- X : {ndarray, sparse matrix} The input data. allow_nan : bool, default=False If True, do not throw error when `X` contains NaN. estimator_name : str, default=None The estimator name

(
    X,
    *,
    allow_nan=False,
    estimator_name=None,
    input_name="",
)

def assert_all_finite(
    X,
    *,
    allow_nan=False,
    estimator_name=None,
    input_name="",
):
    """Throw a ValueError if X contains NaN or infinity.

    Parameters
    ----------
    X : {ndarray, sparse matrix}
        The input data.

    allow_nan : bool, default=False
        If True, do not throw error when `X` contains NaN.

    estimator_name : str, default=None
        The estimator name, used to construct the error message.

    input_name : str, default=""
        The data name used to construct the error message. In particular
        if `input_name` is "X" and the data has NaN values and
        allow_nan is False, the error message will link to the imputer
        documentation.

    Examples
    --------
    >>> from sklearn.utils import assert_all_finite
    >>> import numpy as np
    >>> array = np.array([1, np.inf, np.nan, 4])
    >>> try:
    ...     assert_all_finite(array)
    ...     print("Test passed: Array contains only finite values.")
    ... except ValueError:
    ...     print("Test failed: Array contains non-finite values.")
    Test failed: Array contains non-finite values.
    """
    _assert_all_finite(
        X.data if sp.issparse(X) else X,
        allow_nan=allow_nan,
        estimator_name=estimator_name,
        input_name=input_name,
    )


def as_float_array(X, *, copy=True, ensure_all_finite=True):