nonLinearRegression.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv("FuelConsumption.csv")

cdf = df[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_COMB', 'CO2EMISSIONS']]
msk = np.random.rand(len(df)) < 0.8
train = cdf[msk]
test = cdf[~msk]

from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model

train_x = np.asanyarray(train[['ENGINESIZE']])
train_y = np.asanyarray(train[['CO2EMISSIONS']])

test_x = np.asanyarray(test[['ENGINESIZE']])
test_y = np.asanyarray(test[['CO2EMISSIONS']])

# fit_transform takes our x values, and output a list of our data
# raised from power of 0 to power of 2 (since we set the degree
# of our polynomial to 2).
# poly = PolynomialFeatures(degree=2)
# train_x_poly = poly.fit_transform(train_x)
# # print(train_x_poly)
#
# clf = linear_model.LinearRegression()
# train_y_ = clf.fit(train_x_poly, train_y)
# The coefficients
# print ('Coefficients: ', clf.coef_)
# print ('Intercept: ',clf.intercept_)
# plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
# XX = np.arange(0.0, 10.0, 0.1)
# yy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)
# plt.plot(XX, yy, '-r' )
# plt.xlabel("Engine size")
# plt.ylabel("Emission")
# plt.show()

# test_x_poly = poly.fit_transform(test_x)
# test_y_ = clf.predict(test_x_poly)
#
# print("Mean absolute error: %.2f" % np.mean(np.absolute(test_y_ - test_y)))
# print("Residual sum of squares (MSE): %.2f" % np.mean((test_y_ - test_y) ** 2))
# print("R2-score: %.2f" % r2_score(test_y_ , test_y) )
poly = PolynomialFeatures(degree=3)
train_x_poly = poly.fit_transform(train_x)
# print(train_x_poly)

clf = linear_model.LinearRegression()
train_y_ = clf.fit(train_x_poly, train_y)

from sklearn.metrics import r2_score

test_x_poly = poly.fit_transform(test_x)
test_y_ = clf.predict(test_x_poly)

print("Mean absolute error: %.2f" % np.mean(np.absolute(test_y_ - test_y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((test_y_ - test_y) ** 2))
print("R2-score: %.2f" % r2_score(test_y_, test_y))