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()
