# -*- coding: utf-8 -*-
"""
Created on Sun Jan 28 19:19:14 2018

@author: Tomokoko
"""

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


dataset = pd.read_csv('Wine.csv')

X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,13].values

from sklearn.preprocessing import StandardScaler 
sc = StandardScaler()
X = sc.fit_transform(X)

from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y, test_size = 0.25, random_state = 0)

from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train,y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)

from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1,X2 = np.meshgrid(np.arange(start = X_set[:,0].min() - 1, stop = X_set[:,0].max() + 1, step = 0.01),
                    np.arange(start = X_set[:,1].min() - 1, stop = X_set[:,1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red','green','yellow')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red','green','orange'))(i), label = j)
plt.legend()
plt.show()    

from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1,X2 = np.meshgrid(np.arange(start = X_set[:,0].min() - 1, stop = X_set[:,0].max() + 1, step = 0.01),
                    np.arange(start = X_set[:,1].min() - 1, stop = X_set[:,1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red','green','yellow')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red','green','orange'))(i), label = j)
plt.legend()
plt.show()   
