def fit()

in svinfer/linear_model/logistic_regression.py [0:0]

• 16 lines of code
• 2 McCabe index (conditional complexity)

    def fit(self, data):
        assert isinstance(data, AbstractProcessor)
        x, y = data.prepare_xy(self.x_columns, self.y_column, self.fit_intercept)
        beta_est, success = LogisticRegression._get_coefficients(
            x, y, self.x_s2, data.run_query
        )
        if not success:
            logging.warning("optimization does not converge!")
        var_est = LogisticRegression._get_covariance(
            beta_est, x, y, self.x_s2, data.run_query
        )
        self.success = success
        self.beta = beta_est
        self.beta_vcov = var_est
        self.beta_standarderror = np.sqrt(np.diag(var_est))
        return self