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


#Read data in
df = pd.read_csv("FuelConsumptionCo2.csv")

# take a look at the dataset
df.head()
#Summarize the data
df.describe()

#Select some specific features
cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]
cdf.head(9)

"""
# Plot each of the specified features
plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')
plt.xlabel("FUELCONSUMPTION_COMB")
plt.ylabel("Emission")
plt.show()

#Again
plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')
plt.xlabel("Engine size")
plt.ylabel("Emission")
plt.show()
"""

#Create train and test dataset 80% for train 20% for test
msk = np.random.rand(len(df)) < 0.8
train = cdf[msk]
test = cdf[~msk]

"""
plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
plt.xlabel("Engine size")
plt.ylabel("Emission")
plt.show()
"""

from sklearn import linear_model
regr = linear_model.LinearRegression()
train_x = np.asanyarray(train[['ENGINESIZE']])
train_y = np.asanyarray(train[['CO2EMISSIONS']])
regr.fit (train_x, train_y)

print (regr)
# The coefficients
print ('Coefficients: ', regr.coef_)
print ('Intercept: ',regr.intercept_)

# Plot the fit line over the data.
plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')
plt.xlabel("Engine size")
plt.ylabel("Emission")
plt.show()

# Calculate the error to measure accuracy of the model.

from sklearn.metrics import r2_score

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

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