ch3-SVM-Kernel-on-Iris.py

from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import warnings

def versiontuple(v):
    return tuple(map(int, (v.split("."))))

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):

    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                    alpha=0.8, c=cmap(idx),
                    marker=markers[idx], label=cl)

    # highlight test samples
    if test_idx:
        # plot all samples
        if not versiontuple(np.__version__) >= versiontuple('1.9.0'):
            X_test, y_test = X[list(test_idx), :], y[list(test_idx)]
            warnings.warn('Please update to NumPy 1.9.0 or newer')
        else:
            X_test, y_test = X[test_idx, :], y[test_idx]

        plt.scatter(X_test[:, 0],
                    X_test[:, 1],
                    c='',
                    alpha=1.0,
                    linewidths=1,
                    marker='o',
                    s=55, label='test set')


if __name__ == "__main__":

    from sklearn import datasets
    import numpy as np

    # Loading Iris dataset
    iris = datasets.load_iris()
    X = iris.data[:, [2, 3]]
    y = iris.target
    print('Class labels:', np.unique(y))

    # Splitting data into 70% training and 30% test data:
    from sklearn.cross_validation import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

    # Standardizing the features:
    from sklearn.preprocessing import StandardScaler
    sc = StandardScaler()
    sc.fit(X_train)
    X_train_std = sc.transform(X_train)
    X_test_std = sc.transform(X_test)

    X_combined_std = np.vstack((X_train_std, X_test_std))
    y_combined = np.hstack((y_train, y_test))

    from sklearn.svm import SVC

    # Use low Gamma
    svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)
    svm.fit(X_train_std, y_train)
    plot_decision_regions(X_combined_std, y_combined,
                          classifier=svm, test_idx=range(105,150))
    plt.xlabel('petal length [standardized]')
    plt.ylabel('petal width [standardized]')
    plt.legend(loc='upper left')
    plt.tight_layout()
    # plt.savefig('./figures/support_vector_machine_rbf_iris_1.png', dpi=300)
    plt.show()

    # Use high Gamma
    svm = SVC(kernel='rbf', random_state=0, gamma=100.0, C=1.0)
    svm.fit(X_train_std, y_train)
    plot_decision_regions(X_combined_std, y_combined,
                          classifier=svm, test_idx=range(105, 150))
    plt.xlabel('petal length [standardized]')
    plt.ylabel('petal width [standardized]')
    plt.legend(loc='upper left')
    plt.tight_layout()
    # plt.savefig('./figures/support_vector_machine_rbf_iris_2.png', dpi=300)
    plt.show()