def test_linear_regression_noreg(Xtrain, ytrain): result = [] w = lr.linear_regression_noreg(Xtrain, ytrain) result.append('[TEST LinearRegressionNonReg]' + str(len(w)) + ",") result.append('[TEST LinearRegressionNonReg]' + weights_to_string(w)) return result
from linear_regression import linear_regression_noreg, linear_regression_invertible, regularized_linear_regression, tune_lambda, mean_absolute_error, mapping_data from data_loader import data_processing_linear_regression import numpy as np import pandas as pd filename = 'winequality-white.csv' print("\n======== Question 1.1 and Question 1.2 ========") Xtrain, ytrain, Xval, yval, Xtest, ytest = data_processing_linear_regression( filename, False, False, 0) w = linear_regression_noreg(Xtrain, ytrain) print("dimensionality of the model parameter is ", w.shape, ".", sep="") print("model parameter is ", np.array_str(w)) mae = mean_absolute_error(w, Xtrain, ytrain) print("MAE on train is %.5f" % mae) mae = mean_absolute_error(w, Xval, yval) print("MAE on val is %.5f" % mae) mae = mean_absolute_error(w, Xtest, ytest) print("MAE on test is %.5f" % mae) print("\n======== Question 1.3 ========") Xtrain, ytrain, Xval, yval, Xtest, ytest = data_processing_linear_regression( filename, True, False, 0) w = linear_regression_invertible(Xtrain, ytrain) print("dimensionality of the model parameter is ", w.shape, ".", sep="") print("model parameter is ", np.array_str(w)) mae = mean_absolute_error(w, Xtrain, ytrain) print("MAE on train is %.5f" % mae) mae = mean_absolute_error(w, Xval, yval) print("MAE on val is %.5f" % mae) mae = mean_absolute_error(w, Xtest, ytest)