#K Nearest Neighbors classification
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the data set with pandas and taking the necessary variables
dataset=pd.read_csv('Social_Network_Ads.csv')
x=dataset.iloc[:,[2,3]].values
y=dataset.iloc[:,4].values
#Splitting the dataset into train and test set
from sklearn.model_selection import train_test_split
xTrain,yTrain,xTest,yTest=train_test_split(x,y,test_size=0.25,random_state=0)
#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
xTrain=sc.fit_transform(xTrain)
xTest=sc.transform(xTest)
#Fitting the kNN classifier
from sklearn.neighbors import KNeighborsClassifier
classifier= KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)
classifier.fit(xTrain,yTrain)
#Predicting the test set results
yPred=classifier.predict(xTest)
#Building confusion matrix
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(yTest,yPred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = xTrain, yTrain
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')))
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'))(i), label = j)
plt.title('KNN (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = xTest,yTest
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')))
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'))(i), label = j)
plt.title('KNN (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()