Computes l1-penalized logistic regression score.
        Parameters
        ----------
        category : str
            category name to score

        Returns
        -------
            (coefficient array, accuracy, majority class baseline accuracy)

def get_logistic_regression_coefs_l1(self, category,
                                         clf=LassoCV(alphas=[0.1, 0.001],
                                                     max_iter=10000,
                                                     n_jobs=-1)):
        ''' Computes l1-penalized logistic regression score.
        Parameters
        ----------
        category : str
            category name to score

        Returns
        -------
            (coefficient array, accuracy, majority class baseline accuracy)
        '''
        try:
            from sklearn.cross_validation import cross_val_predict
        except:
            from sklearn.model_selection import cross_val_predict
        y = self._get_mask_from_category(category)
        y_continuous = self._get_continuous_version_boolean_y(y)
        # X = TfidfTransformer().fit_transform(self._X)
        X = self._X

        clf.fit(X, y_continuous)
        y_hat = (cross_val_predict(clf, X, y_continuous) > 0)
        acc, baseline = self._get_accuracy_and_baseline_accuracy(y, y_hat)
        clf.fit(X, y_continuous)
        return clf.coef_, acc, baseline