def _check_input()

in econml/sklearn_extensions/linear_model.py [0:0]

• 53 lines of code
• 19 McCabe index (conditional complexity)

    def _check_input(self, X, y, sample_weight, freq_weight, sample_var):
        """Check dimensions and other assertions."""

        if X is None:
            X = np.empty((y.shape[0], 0))
        if self.fit_intercept:
            X = add_constant(X, has_constant='add')

        # set default values for None
        if sample_weight is None:
            sample_weight = np.ones(y.shape[0])
        if freq_weight is None:
            freq_weight = np.ones(y.shape[0])
        if sample_var is None:
            sample_var = np.zeros(y.shape)

        # check freq_weight should be integer and should be accompanied by sample_var
        if np.any(np.not_equal(np.mod(freq_weight, 1), 0)):
            raise AttributeError("Frequency weights must all be integers for inference to be valid!")
        if sample_var.ndim < 2:
            if np.any(np.equal(freq_weight, 1) & np.not_equal(sample_var, 0)):
                warnings.warn(
                    "Variance was set to non-zero for an observation with freq_weight=1! "
                    "sample_var represents the variance of the original observations that are "
                    "summarized in this sample. Hence, cannot have a non-zero variance if only "
                    "one observations was summarized. Inference will be invalid!")
            elif np.any(np.not_equal(freq_weight, 1) & np.equal(sample_var, 0)):
                warnings.warn(
                    "Variance was set to zero for an observation with freq_weight>1! "
                    "sample_var represents the variance of the original observations that are "
                    "summarized in this sample. If it's zero, please use sample_wegiht instead "
                    "to reflect the weight for each individual sample!")
        else:
            if np.any(np.equal(freq_weight, 1) & np.not_equal(np.sum(sample_var, axis=1), 0)):
                warnings.warn(
                    "Variance was set to non-zero for an observation with freq_weight=1! "
                    "sample_var represents the variance of the original observations that are "
                    "summarized in this sample. Hence, cannot have a non-zero variance if only "
                    "one observations was summarized. Inference will be invalid!")
            elif np.any(np.not_equal(freq_weight, 1) & np.equal(np.sum(sample_var, axis=1), 0)):
                warnings.warn(
                    "Variance was set to zero for an observation with freq_weight>1! "
                    "sample_var represents the variance of the original observations that are "
                    "summarized in this sample. If it's zero, please use sample_wegiht instead "
                    "to reflect the weight for each individual sample!")

        # check array shape
        assert (X.shape[0] == y.shape[0] == sample_weight.shape[0] ==
                freq_weight.shape[0] == sample_var.shape[0]), "Input lengths not compatible!"
        if y.ndim >= 2:
            assert (y.ndim == sample_var.ndim and
                    y.shape[1] == sample_var.shape[1]), "Input shapes not compatible: {}, {}!".format(
                y.shape, sample_var.shape)

        # weight X and y and sample_var
        weighted_X = X * np.sqrt(sample_weight).reshape(-1, 1)
        if y.ndim < 2:
            weighted_y = y * np.sqrt(sample_weight)
            sample_var = sample_var * sample_weight
        else:
            weighted_y = y * np.sqrt(sample_weight).reshape(-1, 1)
            sample_var = sample_var * (sample_weight.reshape(-1, 1))
        return weighted_X, weighted_y, freq_weight, sample_var