3.4.8.17. Tutorial Diagrams

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

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import numpy as np
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 = {"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)


if __name__ == "__main__":
    plot_supervised_chart(False)
    plot_supervised_chart(True)
    plot_unsupervised_chart()
    plt.show()