def _compute_missing_values_in_feature_mask()

in causalml/inference/tree/_tree/_classes.py [0:0]

• 14 lines of code
• 5 McCabe index (conditional complexity)

    def _compute_missing_values_in_feature_mask(self, X, estimator_name=None):
        """Return boolean mask denoting if there are missing values for each feature.

        This method also ensures that X is finite.

        Parameter
        ---------
        X : array-like of shape (n_samples, n_features), dtype=DOUBLE
            Input data.

        estimator_name : str or None, default=None
            Name to use when raising an error. Defaults to the class name.

        Returns
        -------
        missing_values_in_feature_mask : ndarray of shape (n_features,), or None
            Missing value mask. If missing values are not supported or there
            are no missing values, return None.
        """
        estimator_name = estimator_name or self.__class__.__name__
        common_kwargs = dict(estimator_name=estimator_name, input_name="X")

        if not self._support_missing_values(X):
            assert_all_finite(X, **common_kwargs)
            return None

        with np.errstate(over="ignore"):
            overall_sum = np.sum(X)

        if not np.isfinite(overall_sum):
            # Raise a ValueError in case of the presence of an infinite element.
            _assert_all_finite_element_wise(X, xp=np, allow_nan=True, **common_kwargs)

        # If the sum is not nan, then there are no missing values
        if not np.isnan(overall_sum):
            return None

        missing_values_in_feature_mask = _any_isnan_axis0(X)
        return missing_values_in_feature_mask