def polynomialRegressionCV(obj,X,y,numberoffolds,degree,label):
rmseList = []
degreeList = []
for degree in range(1,degree+1):
model = make_pipeline(PolynomialFeatures(degree), obj)
rmse = callCrossValPoly(model, X, y, numberoffolds, degree, label)
degreeList.append(degree)
rmseList.append(rmse)
#print('%s (Polynomial) - The best RMSE obtained is: %.4f for Degree: %d' % (label,minRMSE(rmseList),rmseList.index(minRMSE(rmseList))+1))
Plots.linePlot(degreeList, rmseList, 'Degree_of_Polynomial', 'RMSE', 'Degree VS RMSE', 'red', 'PolyRegressionCV'+label)
def polynomialRegression(obj,X_train,y_train,X_test,y_test, degree, label):
rmseList = []
degreeList = []
for degree in range(1,degree+1):
model = make_pipeline(PolynomialFeatures(degree), obj)
model.fit(X_train, y_train)
pred=model.predict(X_test)
rmse=rootMeanSquareError(pred, y_test)
degreeList.append(degree)
rmseList.append(rmse)
print("%s (Polynomial) - Root Mean Squared Error for degree %d: %.4f" % (label,degree,rmse))
#print('Variance score (Polynomial): %.4f' % model.score(X_test, y_test))
print('%s (Polynomial) - The best RMSE obtained is %.4f for degree: %d' % (label,minRMSE(rmseList),rmseList.index(minRMSE(rmseList))+1))
Plots.linePlot(degreeList, rmseList, 'Degree_of_Polynomial', 'RMSE', 'Degree VS RMSE', 'blue', 'PolyRegression'+label)
