Tutorial Diagrams

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

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Python source code: plot_ML_flow_chart.py

import numpy as np
import pylab as pl
from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow

def create_base(box_bg = '#CCCCCC',
                arrow1 = '#88CCFF',
                arrow2 = '#88FF88',
                supervised=True):
    fig = pl.figure(figsize=(9, 6), facecolor='w')
    ax = pl.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)
        
    pl.text(1.45, 4.9, "Training\nText,\nDocuments,\nImages,\netc.",
            ha='center', va='center', fontsize=14)
    
    pl.text(3.6, 4.9, "Feature\nVectors", 
            ha='left', va='center', fontsize=14)
    
    pl.text(5.5, 3.5, "Machine\nLearning\nAlgorithm",
            ha='center', va='center', fontsize=14)
    
    pl.text(1.05, 1.1, "New Text,\nDocument,\nImage,\netc.",
            ha='center', va='center', fontsize=14)
    
    pl.text(3.3, 1.7, "Feature\nVector", 
            ha='left', va='center', fontsize=14)
    
    pl.text(5.5, 1.1, "Predictive\nModel", 
            ha='center', va='center', fontsize=12)

    if supervised:
        pl.text(1.45, 3.05, "Labels",
                ha='center', va='center', fontsize=14)
    
        pl.text(8.05, 1.1, "Expected\nLabel",
                ha='center', va='center', fontsize=14)
        pl.text(8.8, 5.8, "Supervised Learning Model",
                ha='right', va='top', fontsize=18)

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

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

def plot_unsupervised():
    create_base(supervised=False)


plot_supervised(False)
plot_supervised(True)
plot_unsupervised()
pl.show()