grid_search.py

""" Grid Search
"""

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
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 sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from matplotlib.colors import ListedColormap


def main():
    # Importing the dataset
    dataset = pd.read_csv('Social_Network_Ads.csv')
    X = dataset.iloc[:, [2, 3]].values
    y = dataset.iloc[:, 4].values

    # Splitting the dataset into the Training set and Test set
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

    # Feature Scaling
    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)

    # Fitting Kernel SVM to the Training set
    classifier = SVC(kernel='rbf', random_state=0)
    classifier.fit(X_train, y_train)

    # Predicting the Test set results
    y_pred = classifier.predict(X_test)

    # Making the Confusion Matrix
    cm = confusion_matrix(y_test, y_pred)

    # Applying k-Fold Cross Validation
    accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=10)
    accuracies.mean()
    accuracies.std()

    # Applying Grid Search to find the best model and the best parameters
    parameters = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']},
                  {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]
    grid_search = GridSearchCV(estimator=classifier,
                               param_grid=parameters,
                               scoring='accuracy',
                               cv=10,
                               n_jobs=-1)
    grid_search = grid_search.fit(X_train, y_train)
    best_accuracy = grid_search.best_score_
    best_parameters = grid_search.best_params_

    # Visualising the Training set results
    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')))
    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('Kernel SVM (Training set)')
    plt.xlabel('Age')
    plt.ylabel('Estimated Salary')
    plt.legend()
    plt.show()

    # Visualising the Test set results
    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')))
    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('Kernel SVM (Test set)')
    plt.xlabel('Age')
    plt.ylabel('Estimated Salary')
    plt.legend()
    plt.show()


if __name__ == '__main__':
    main()