data-science-ipython-notebooks/scikit-learn/fig_code/figures.py

import numpy as np
import matplotlib.pyplot as plt
import warnings


def plot_venn_diagram():
    fig, ax = plt.subplots(subplot_kw=dict(frameon=False, xticks=[], yticks=[]))
    ax.add_patch(plt.Circle((0.3, 0.3), 0.3, fc='red', alpha=0.5))
    ax.add_patch(plt.Circle((0.6, 0.3), 0.3, fc='blue', alpha=0.5))
    ax.add_patch(plt.Rectangle((-0.1, -0.1), 1.1, 0.8, fc='none', ec='black'))
    ax.text(0.2, 0.3, '$x$', size=30, ha='center', va='center')
    ax.text(0.7, 0.3, '$y$', size=30, ha='center', va='center')
    ax.text(0.0, 0.6, '$I$', size=30)
    ax.axis('equal')


def plot_example_decision_tree():
    fig = plt.figure(figsize=(10, 4))
    ax = fig.add_axes([0, 0, 0.8, 1], frameon=False, xticks=[], yticks=[])
    ax.set_title('Example Decision Tree: Animal Classification', size=24)

    def text(ax, x, y, t, size=20, **kwargs):
        ax.text(x, y, t,
                ha='center', va='center', size=size,
                bbox=dict(boxstyle='round', ec='k', fc='w'), **kwargs)

    text(ax, 0.5, 0.9, "How big is\nthe animal?", 20)
    text(ax, 0.3, 0.6, "Does the animal\nhave horns?", 18)
    text(ax, 0.7, 0.6, "Does the animal\nhave two legs?", 18)
    text(ax, 0.12, 0.3, "Are the horns\nlonger than 10cm?", 14)
    text(ax, 0.38, 0.3, "Is the animal\nwearing a collar?", 14)
    text(ax, 0.62, 0.3, "Does the animal\nhave wings?", 14)
    text(ax, 0.88, 0.3, "Does the animal\nhave a tail?", 14)

    text(ax, 0.4, 0.75, "> 1m", 12, alpha=0.4)
    text(ax, 0.6, 0.75, "< 1m", 12, alpha=0.4)

    text(ax, 0.21, 0.45, "yes", 12, alpha=0.4)
    text(ax, 0.34, 0.45, "no", 12, alpha=0.4)

    text(ax, 0.66, 0.45, "yes", 12, alpha=0.4)
    text(ax, 0.79, 0.45, "no", 12, alpha=0.4)

    ax.plot([0.3, 0.5, 0.7], [0.6, 0.9, 0.6], '-k')
    ax.plot([0.12, 0.3, 0.38], [0.3, 0.6, 0.3], '-k')
    ax.plot([0.62, 0.7, 0.88], [0.3, 0.6, 0.3], '-k')
    ax.plot([0.0, 0.12, 0.20], [0.0, 0.3, 0.0], '--k')
    ax.plot([0.28, 0.38, 0.48], [0.0, 0.3, 0.0], '--k')
    ax.plot([0.52, 0.62, 0.72], [0.0, 0.3, 0.0], '--k')
    ax.plot([0.8, 0.88, 1.0], [0.0, 0.3, 0.0], '--k')
    ax.axis([0, 1, 0, 1])


def visualize_tree(estimator, X, y, boundaries=True,
                   xlim=None, ylim=None):
    estimator.fit(X, y)

    if xlim is None:
        xlim = (X[:, 0].min() - 0.1, X[:, 0].max() + 0.1)
    if ylim is None:
        ylim = (X[:, 1].min() - 0.1, X[:, 1].max() + 0.1)

    x_min, x_max = xlim
    y_min, y_max = ylim
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, alpha=0.2, cmap='rainbow')
    plt.clim(y.min(), y.max())

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow')
    plt.axis('off')

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)        
    plt.clim(y.min(), y.max())
    
    # Plot the decision boundaries
    def plot_boundaries(i, xlim, ylim):
        if i < 0:
            return

        tree = estimator.tree_
        
        if tree.feature[i] == 0:
            plt.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k')
            plot_boundaries(tree.children_left[i],
                            [xlim[0], tree.threshold[i]], ylim)
            plot_boundaries(tree.children_right[i],
                            [tree.threshold[i], xlim[1]], ylim)
        
        elif tree.feature[i] == 1:
            plt.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k')
            plot_boundaries(tree.children_left[i], xlim,
                            [ylim[0], tree.threshold[i]])
            plot_boundaries(tree.children_right[i], xlim,
                            [tree.threshold[i], ylim[1]])
            
    if boundaries:
        plot_boundaries(0, plt.xlim(), plt.ylim())


def plot_tree_interactive(X, y):
    from sklearn.tree import DecisionTreeClassifier

    def interactive_tree(depth=1):
        clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
        visualize_tree(clf, X, y)

    from IPython.html.widgets import interact
    return interact(interactive_tree, depth=[1, 5])


def plot_kmeans_interactive(min_clusters=1, max_clusters=6):
    from IPython.html.widgets import interact
    from sklearn.metrics.pairwise import euclidean_distances
    from sklearn.datasets.samples_generator import make_blobs

    with warnings.catch_warnings():
        warnings.filterwarnings('ignore')

        X, y = make_blobs(n_samples=300, centers=4,
                          random_state=0, cluster_std=0.60)

        def _kmeans_step(frame=0, n_clusters=4):
            rng = np.random.RandomState(2)
            labels = np.zeros(X.shape[0])
            centers = rng.randn(n_clusters, 2)

            nsteps = frame // 3

            for i in range(nsteps + 1):
                old_centers = centers
                if i < nsteps or frame % 3 > 0:
                    dist = euclidean_distances(X, centers)
                    labels = dist.argmin(1)

                if i < nsteps or frame % 3 > 1:
                    centers = np.array([X[labels == j].mean(0)
                                        for j in range(n_clusters)])
                    nans = np.isnan(centers)
                    centers[nans] = old_centers[nans]


            # plot the data and cluster centers
            plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='rainbow',
                        vmin=0, vmax=n_clusters - 1);
            plt.scatter(old_centers[:, 0], old_centers[:, 1], marker='o',
                        c=np.arange(n_clusters),
                        s=200, cmap='rainbow')
            plt.scatter(old_centers[:, 0], old_centers[:, 1], marker='o',
                        c='black', s=50)

            # plot new centers if third frame
            if frame % 3 == 2:
                for i in range(n_clusters):
                    plt.annotate('', centers[i], old_centers[i], 
                                 arrowprops=dict(arrowstyle='->', linewidth=1))
                plt.scatter(centers[:, 0], centers[:, 1], marker='o',
                            c=np.arange(n_clusters),
                            s=200, cmap='rainbow')
                plt.scatter(centers[:, 0], centers[:, 1], marker='o',
                            c='black', s=50)

            plt.xlim(-4, 4)
            plt.ylim(-2, 10)

            if frame % 3 == 1:
                plt.text(3.8, 9.5, "1. Reassign points to nearest centroid",
                         ha='right', va='top', size=14)
            elif frame % 3 == 2:
                plt.text(3.8, 9.5, "2. Update centroids to cluster means",
                         ha='right', va='top', size=14)

    
    return interact(_kmeans_step, frame=[0, 50],
                    n_clusters=[min_clusters, max_clusters])


def plot_image_components(x, coefficients=None, mean=0, components=None,
                          imshape=(8, 8), n_components=6, fontsize=12):
    if coefficients is None:
        coefficients = x
        
    if components is None:
        components = np.eye(len(coefficients), len(x))
        
    mean = np.zeros_like(x) + mean
        

    fig = plt.figure(figsize=(1.2 * (5 + n_components), 1.2 * 2))
    g = plt.GridSpec(2, 5 + n_components, hspace=0.3)

    def show(i, j, x, title=None):
        ax = fig.add_subplot(g[i, j], xticks=[], yticks=[])
        ax.imshow(x.reshape(imshape), interpolation='nearest')
        if title:
            ax.set_title(title, fontsize=fontsize)

    show(slice(2), slice(2), x, "True")

    approx = mean.copy()
    show(0, 2, np.zeros_like(x) + mean, r'$\mu$')
    show(1, 2, approx, r'$1 \cdot \mu$')

    for i in range(0, n_components):
        approx = approx + coefficients[i] * components[i]
        show(0, i + 3, components[i], r'$c_{0}$'.format(i + 1))
        show(1, i + 3, approx,
             r"${0:.2f} \cdot c_{1}$".format(coefficients[i], i + 1))
        plt.gca().text(0, 1.05, '$+$', ha='right', va='bottom',
                       transform=plt.gca().transAxes, fontsize=fontsize)

    show(slice(2), slice(-2, None), approx, "Approx")


def plot_pca_interactive(data, n_components=6):
    from sklearn.decomposition import PCA
    from IPython.html.widgets import interact

    pca = PCA(n_components=n_components)
    Xproj = pca.fit_transform(data)

    def show_decomp(i=0):
        plot_image_components(data[i], Xproj[i],
                              pca.mean_, pca.components_)
    
    interact(show_decomp, i=(0, data.shape[0] - 1));