from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
from matplotlib.colors import ListedColormap
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#importing the dataset
dataset = pd.read_csv('data/Social_Network_Ads.csv')
X = dataset.iloc[:,[2,3]].values
y = dataset.iloc[:,4].values

# creating different datasets. one for training and another to test the model
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.25,random_state = 0)

#scaling features for precise answer
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)

#fitting classifier to the dataset
classifier = SVC(kernel = 'rbf', random_state = 0) #rbf takes the data to a higher dimension and separates classes from each other
classifier.fit(X_train,y_train)

#now we predict the values of y based on X_test
y_pred = classifier.predict(X_test)

#create the confusion matrix to check the accuracy of the results.
cm = confusion_matrix(y_test,y_pred)

#visualization of the traning set

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 = 0.25, 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('SVM with rbf kernel (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.savefig('rbf_SVM.png')

#visualization of the test set
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 = 0.25, 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('SVM with rbf kernel (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.savefig('rbf_SVM_pred.png')
