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https://github.com/donnemartin/data-science-ipython-notebooks.git
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136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
"""
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Tutorial Diagrams
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-----------------
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This script plots the flow-charts used in the scikit-learn tutorials.
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"""
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import numpy as np
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import pylab as pl
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from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
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def create_base(box_bg = '#CCCCCC',
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arrow1 = '#88CCFF',
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arrow2 = '#88FF88',
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supervised=True):
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fig = pl.figure(figsize=(9, 6), facecolor='w')
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ax = pl.axes((0, 0, 1, 1),
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xticks=[], yticks=[], frameon=False)
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ax.set_xlim(0, 9)
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ax.set_ylim(0, 6)
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patches = [Rectangle((0.3, 3.6), 1.5, 1.8, zorder=1, fc=box_bg),
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Rectangle((0.5, 3.8), 1.5, 1.8, zorder=2, fc=box_bg),
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Rectangle((0.7, 4.0), 1.5, 1.8, zorder=3, fc=box_bg),
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Rectangle((2.9, 3.6), 0.2, 1.8, fc=box_bg),
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Rectangle((3.1, 3.8), 0.2, 1.8, fc=box_bg),
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Rectangle((3.3, 4.0), 0.2, 1.8, fc=box_bg),
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Rectangle((0.3, 0.2), 1.5, 1.8, fc=box_bg),
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Rectangle((2.9, 0.2), 0.2, 1.8, fc=box_bg),
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Circle((5.5, 3.5), 1.0, fc=box_bg),
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Polygon([[5.5, 1.7],
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[6.1, 1.1],
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[5.5, 0.5],
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[4.9, 1.1]], fc=box_bg),
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FancyArrow(2.3, 4.6, 0.35, 0, fc=arrow1,
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width=0.25, head_width=0.5, head_length=0.2),
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FancyArrow(3.75, 4.2, 0.5, -0.2, fc=arrow1,
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width=0.25, head_width=0.5, head_length=0.2),
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FancyArrow(5.5, 2.4, 0, -0.4, fc=arrow1,
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width=0.25, head_width=0.5, head_length=0.2),
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FancyArrow(2.0, 1.1, 0.5, 0, fc=arrow2,
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width=0.25, head_width=0.5, head_length=0.2),
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FancyArrow(3.3, 1.1, 1.3, 0, fc=arrow2,
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width=0.25, head_width=0.5, head_length=0.2),
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FancyArrow(6.2, 1.1, 0.8, 0, fc=arrow2,
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width=0.25, head_width=0.5, head_length=0.2)]
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if supervised:
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patches += [Rectangle((0.3, 2.4), 1.5, 0.5, zorder=1, fc=box_bg),
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Rectangle((0.5, 2.6), 1.5, 0.5, zorder=2, fc=box_bg),
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Rectangle((0.7, 2.8), 1.5, 0.5, zorder=3, fc=box_bg),
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FancyArrow(2.3, 2.9, 2.0, 0, fc=arrow1,
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width=0.25, head_width=0.5, head_length=0.2),
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Rectangle((7.3, 0.85), 1.5, 0.5, fc=box_bg)]
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else:
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patches += [Rectangle((7.3, 0.2), 1.5, 1.8, fc=box_bg)]
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for p in patches:
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ax.add_patch(p)
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pl.text(1.45, 4.9, "Training\nText,\nDocuments,\nImages,\netc.",
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ha='center', va='center', fontsize=14)
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pl.text(3.6, 4.9, "Feature\nVectors",
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ha='left', va='center', fontsize=14)
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pl.text(5.5, 3.5, "Machine\nLearning\nAlgorithm",
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ha='center', va='center', fontsize=14)
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pl.text(1.05, 1.1, "New Text,\nDocument,\nImage,\netc.",
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ha='center', va='center', fontsize=14)
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pl.text(3.3, 1.7, "Feature\nVector",
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ha='left', va='center', fontsize=14)
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pl.text(5.5, 1.1, "Predictive\nModel",
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ha='center', va='center', fontsize=12)
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if supervised:
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pl.text(1.45, 3.05, "Labels",
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ha='center', va='center', fontsize=14)
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pl.text(8.05, 1.1, "Expected\nLabel",
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ha='center', va='center', fontsize=14)
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pl.text(8.8, 5.8, "Supervised Learning Model",
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ha='right', va='top', fontsize=18)
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else:
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pl.text(8.05, 1.1,
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"Likelihood\nor Cluster ID\nor Better\nRepresentation",
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ha='center', va='center', fontsize=12)
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pl.text(8.8, 5.8, "Unsupervised Learning Model",
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ha='right', va='top', fontsize=18)
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def plot_supervised_chart(annotate=False):
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create_base(supervised=True)
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if annotate:
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fontdict = dict(color='r', weight='bold', size=14)
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pl.text(1.9, 4.55, 'X = vec.fit_transform(input)',
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fontdict=fontdict,
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rotation=20, ha='left', va='bottom')
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pl.text(3.7, 3.2, 'clf.fit(X, y)',
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fontdict=fontdict,
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rotation=20, ha='left', va='bottom')
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pl.text(1.7, 1.5, 'X_new = vec.transform(input)',
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fontdict=fontdict,
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rotation=20, ha='left', va='bottom')
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pl.text(6.1, 1.5, 'y_new = clf.predict(X_new)',
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fontdict=fontdict,
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rotation=20, ha='left', va='bottom')
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def plot_unsupervised_chart():
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create_base(supervised=False)
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if __name__ == '__main__':
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plot_supervised_chart(False)
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plot_supervised_chart(True)
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plot_unsupervised_chart()
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pl.show()
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