def computeCrossValidation(self): from sklearn.model_selection import cross_validate X, y = ClassificationModel.preprocessDataCrossValidation( self.args, True) classifier = LogisticRegression.computeModel(X, y, self.args.solver) cv_results = cross_validate(classifier, X, y, cv=self.args.k_fold_cross_validation) if (self.args.print_accuracy): print(cv_results) return cv_results if __name__ == "__main__": parser = ArgumentParser() parser.setBasicArguments() parser.setLogisticRegressionArguments() args = parser.getArguments() model = LogisticRegression(args) if (args.cross_validation == False): model.compute() else: model.computeCrossValidation()