svm_iris.py

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from sklearn import svm, datasets

pdf = PdfPages("iris_svm.pdf")

def make_meshgrid(x, y, h=0.02):
    """Create a mesh of points to plot in

    Parameters
    ----------
    x: data to base x-axis meshgrid on
    y: data to base y-axis meshgrid on
    h: stepsize for meshgrid, optional

    Returns
    -------
    xx, yy : ndarray
    """
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    return xx, yy


def plot_contours(ax, clf, xx, yy, **params):
    """Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    """
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out


# import some data to play with
iris = datasets.load_iris()
num_features = len(iris.data[0])

for i in range(num_features):
    for j in range(num_features):
        if i < j:
            # take the two features.
            X = iris.data[:, [i,j]]
            y = iris.target
            feature_i = iris['feature_names'][i]
            feature_j = iris['feature_names'][j]
            for C in (0.1, 1):
                # we create an instance of SVM and fit out data. We do not scale our
                # data since we want to plot the support vectors

                models = (
                    svm.SVC(kernel="linear", C=C),
                    svm.LinearSVC(C=C, max_iter=10000),
                    svm.SVC(kernel="rbf", gamma=0.7, C=C),
                    svm.SVC(kernel="poly", degree=3, gamma="auto", C=C),
                )
                models = (clf.fit(X, y) for clf in models)

                # title for the plots
                titles = (
                    "SVC with linear kernel",
                    "LinearSVC (linear kernel)",
                    "SVC with RBF kernel",
                    "SVC with polynomial (degree 3) kernel",
                )

                # Set-up 2x2 grid for plotting.
                fig, sub = plt.subplots(2, 2)
                plt.subplots_adjust(wspace=0.4, hspace=0.4)

                X0, X1 = X[:, 0], X[:, 1]
                xx, yy = make_meshgrid(X0, X1)

                for clf, title, ax in zip(models, titles, sub.flatten()):
                    plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)
                    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors="k")
                    ax.set_xlim(xx.min(), xx.max())
                    ax.set_ylim(yy.min(), yy.max())
                    ax.set_xlabel(feature_i)
                    ax.set_ylabel(feature_j)
                    ax.set_xticks(())
                    ax.set_yticks(())
                    ax.set_title(title)

                plt.show()

                plt.savefig(feature_i + "_" + feature_j + ".png")
                pdf.savefig(fig)

pdf.close()