Tutorial Diagrams

This script plots the flow-charts used in the scikit-learn tutorials.

import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow


def create_base(box_bg='#CCCCCC',
                arrow1='#88CCFF',
                arrow2='#88FF88',
                supervised=True):
    fig = plt.figure(figsize=(9, 6), facecolor='w')
    ax = plt.axes((0, 0, 1, 1),
                  xticks=[], yticks=[], frameon=False)
    ax.set_xlim(0, 9)
    ax.set_ylim(0, 6)

    patches = [Rectangle((0.3, 3.6), 1.5, 1.8, zorder=1, fc=box_bg),
               Rectangle((0.5, 3.8), 1.5, 1.8, zorder=2, fc=box_bg),
               Rectangle((0.7, 4.0), 1.5, 1.8, zorder=3, fc=box_bg),

               Rectangle((2.9, 3.6), 0.2, 1.8, fc=box_bg),
               Rectangle((3.1, 3.8), 0.2, 1.8, fc=box_bg),
               Rectangle((3.3, 4.0), 0.2, 1.8, fc=box_bg),

               Rectangle((0.3, 0.2), 1.5, 1.8, fc=box_bg),

               Rectangle((2.9, 0.2), 0.2, 1.8, fc=box_bg),

               Circle((5.5, 3.5), 1.0, fc=box_bg),

               Polygon([[5.5, 1.7],
                        [6.1, 1.1],
                        [5.5, 0.5],
                        [4.9, 1.1]], fc=box_bg),

               FancyArrow(2.3, 4.6, 0.35, 0, fc=arrow1,
                          width=0.25, head_width=0.5, head_length=0.2),

               FancyArrow(3.75, 4.2, 0.5, -0.2, fc=arrow1,
                          width=0.25, head_width=0.5, head_length=0.2),

               FancyArrow(5.5, 2.4, 0, -0.4, fc=arrow1,
                          width=0.25, head_width=0.5, head_length=0.2),

               FancyArrow(2.0, 1.1, 0.5, 0, fc=arrow2,
                          width=0.25, head_width=0.5, head_length=0.2),

               FancyArrow(3.3, 1.1, 1.3, 0, fc=arrow2,
                          width=0.25, head_width=0.5, head_length=0.2),

               FancyArrow(6.2, 1.1, 0.8, 0, fc=arrow2,
                          width=0.25, head_width=0.5, head_length=0.2)]

    if supervised:
        patches += [Rectangle((0.3, 2.4), 1.5, 0.5, zorder=1, fc=box_bg),
                    Rectangle((0.5, 2.6), 1.5, 0.5, zorder=2, fc=box_bg),
                    Rectangle((0.7, 2.8), 1.5, 0.5, zorder=3, fc=box_bg),
                    FancyArrow(2.3, 2.9, 2.0, 0, fc=arrow1,
                               width=0.25, head_width=0.5, head_length=0.2),
                    Rectangle((7.3, 0.85), 1.5, 0.5, fc=box_bg)]
    else:
        patches += [Rectangle((7.3, 0.2), 1.5, 1.8, fc=box_bg)]

    for p in patches:
        ax.add_patch(p)

    plt.text(1.45, 4.9, "Training\nText,\nDocuments,\nImages,\netc.",
             ha='center', va='center', fontsize=14)

    plt.text(3.6, 4.9, "Feature\nVectors",
             ha='left', va='center', fontsize=14)

    plt.text(5.5, 3.5, "Machine\nLearning\nAlgorithm",
             ha='center', va='center', fontsize=14)

    plt.text(1.05, 1.1, "New Text,\nDocument,\nImage,\netc.",
             ha='center', va='center', fontsize=14)

    plt.text(3.3, 1.7, "Feature\nVector",
             ha='left', va='center', fontsize=14)

    plt.text(5.5, 1.1, "Predictive\nModel",
             ha='center', va='center', fontsize=12)

    if supervised:
        plt.text(1.45, 3.05, "Labels",
                 ha='center', va='center', fontsize=14)

        plt.text(8.05, 1.1, "Expected\nLabel",
                 ha='center', va='center', fontsize=14)
        plt.text(8.8, 5.8, "Supervised Learning Model",
                 ha='right', va='top', fontsize=18)

    else:
        plt.text(8.05, 1.1,
                 "Likelihood\nor Cluster ID\nor Better\nRepresentation",
                 ha='center', va='center', fontsize=12)
        plt.text(8.8, 5.8, "Unsupervised Learning Model",
                 ha='right', va='top', fontsize=18)


def plot_supervised_chart(annotate=False):
    create_base(supervised=True)
    if annotate:
        fontdict = dict(color='r', weight='bold', size=14)
        plt.text(1.9, 4.55, 'X = vec.fit_transform(input)',
                 fontdict=fontdict,
                 rotation=20, ha='left', va='bottom')
        plt.text(3.7, 3.2, 'clf.fit(X, y)',
                 fontdict=fontdict,
                 rotation=20, ha='left', va='bottom')
        plt.text(1.7, 1.5, 'X_new = vec.transform(input)',
                 fontdict=fontdict,
                 rotation=20, ha='left', va='bottom')
        plt.text(6.1, 1.5, 'y_new = clf.predict(X_new)',
                 fontdict=fontdict,
                 rotation=20, ha='left', va='bottom')


def plot_unsupervised_chart():
    create_base(supervised=False)

Suggested course of action in a machine learning problem when there are labels.

plot_supervised_chart(False)

Same graph with name of the function to use with the scikit-learn API.

plot_supervised_chart(True)

Suggested course of action in a machine learning problem when there is no label.

plot_unsupervised_chart()