import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score

########################################################################

# load data
cars = pd.read_csv('auto-mpg.data.txt',header=None, sep='\s+')

# extract power and weight as data matrix X
X = cars.iloc[:, [3,4]].values

# extract origin (0:Non-U.S. / 1:U.S.) as target vector y
y = [1 if o == 1 else 0 for o in cars.iloc[:, 7].values]

# split into training data (80%) and test data (20%)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=0)

# fit logistic regression model on training data
lr = LogisticRegression()
lr.fit(X_train, y_train)

# output intercept and coefficients
lr.intercept_ # [-3.82184907]
lr.coef_ # [-0.00600983, 0.00178002]

# perform prediction and state whether it is correct
correct = 0
incorrect = 0
y_predicted = lr.predict(X_test)
for i in range(0, len(y_test)):
    print("Car with " + str(X_test[i, 0]) + " hp and weight "+ str(X_test[i,1]) + " lbs")

    print("Predicted class: " + str(y_test[i]) + " / Correct class: "+ str(y_predicted[i]) + "\n")
    if (y_test[i] == y_predicted[i]):
        correct = correct + 1
    else:
        incorrect = incorrect + 1
        
print(str(correct) + " classified correctly") # 64 classified correctly
print(str(incorrect) + " classified incorrectly") # 15 classified incorrectly

print("===============")
########################################################################
# Computing Quality Measures

# generate confusion matrix
conf = confusion_matrix(y_true=y_test, y_pred=y_predicted)
print("confusion matrix ", conf)

# compute accuracy
acc_score =accuracy_score(y_true=y_test, y_pred=y_predicted) # 0.7468354430379747
print("accuracy ", acc_score)

# compute precision
prec_score = precision_score(y_true=y_test, y_pred=y_predicted) # 0.7796610169491526
print("precision ", prec_score)

# compute recall
recall = recall_score(y_true=y_test, y_pred=y_predicted) # 0.8679
print("recall ", recall)

# compute f1
f1 = f1_score(y_true=y_test, y_pred=y_predicted) # 0.8214285714285715
print("f1 ", f1)

########################################################################