pca.py

""" Principal Component Analysis (PCA)
"""

# 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.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from matplotlib.colors import ListedColormap


def main():
    # Importing the dataset
    dataset = pd.read_csv('Wine.csv')
    X = dataset.iloc[:, 0:13].values
    y = dataset.iloc[:, 13].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=0.2, random_state=0)

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

    # Applying 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_

    # Fitting Logistic Regression to the Training set
    classifier = LogisticRegression(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)

    # 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', '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 (Training set)')
    plt.xlabel('PC1')
    plt.ylabel('PC2')
    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', '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 set)')
    plt.xlabel('PC1')
    plt.ylabel('PC2')
    plt.legend()
    plt.show()


if __name__ == '__main__':
    main()