# -*- coding: utf-8 -*-
"""
Created on Wed Dec 25 09:02:13 2019

@author: Aditya Saxena
"""

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

dataset = pd.read_csv('Lithium-ion-battery-dataset.csv')
X = dataset.iloc[:, [6,7,8,9,13,14]].values
y = dataset.iloc[:, 5].values
X = np.nan_to_num(X)

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

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components = 2)
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)

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','blue')))
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','blue'))(i), label =j)
plt.title('Logistic Regression(Test)')
plt.xlabel('LDA1')
plt.ylabel('LDA2')
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','blue')))
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','blue'))(i), label =j)
plt.title('Logistic Regression(Train)')
plt.xlabel('LDA1')
plt.ylabel('LDA2')
plt.legend()
plt.show()  



