Added fig_code from https://github.com/jakevdp/sklearn_pycon2015, which is used in the scikit-learn notebooks.

Author: donnemartin

scikit-learn/fig_code/ML_flow_chart.py

@@ -0,0 +1,135 @@
+"""
+Tutorial Diagrams
+-----------------
+
+This script plots the flow-charts used in the scikit-learn tutorials.
+"""
+
+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_chart(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_chart():
+    create_base(supervised=False)
+
+
+if __name__ == '__main__':
+    plot_supervised_chart(False)
+    plot_supervised_chart(True)
+    plot_unsupervised_chart()
+    pl.show()
+
+

scikit-learn/fig_code/__init__.py

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+from .data import *
+from .figures import *
+
+from .sgd_separator import plot_sgd_separator
+from .linear_regression import plot_linear_regression
+from .helpers import plot_iris_knn

scikit-learn/fig_code/__init__.py~

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+from .sgd_separator import plot_sgd_separator
+from .linear_regression import plot_linear_regression
+from .ML_flow_chart import plot_supervised_chart, plot_unsupervised_chart
+from .helpers import plot_iris_knn

scikit-learn/fig_code/data.py

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+import numpy as np
+
+
+def linear_data_sample(N=40, rseed=0, m=3, b=-2):
+    rng = np.random.RandomState(rseed)
+
+    x = 10 * rng.rand(N)
+    dy = m / 2 * (1 + rng.rand(N))
+    y = m * x + b + dy * rng.randn(N)
+
+    return (x, y, dy)
+
+
+def linear_data_sample_big_errs(N=40, rseed=0, m=3, b=-2):
+    rng = np.random.RandomState(rseed)
+
+    x = 10 * rng.rand(N)
+    dy = m / 2 * (1 + rng.rand(N))
+    dy[20:25] *= 10
+    y = m * x + b + dy * rng.randn(N)
+
+    return (x, y, dy)
+
+
+def sample_light_curve(phased=True):
+    from astroML.datasets import fetch_LINEAR_sample
+    data = fetch_LINEAR_sample()
+    t, y, dy = data[18525697].T
+
+    if phased:
+        P_best = 0.580313015651
+        t /= P_best
+
+    return (t, y, dy)
+    
+
+def sample_light_curve_2(phased=True):
+    from astroML.datasets import fetch_LINEAR_sample
+    data = fetch_LINEAR_sample()
+    t, y, dy = data[10022663].T
+
+    if phased:
+        P_best = 0.61596079804
+        t /= P_best
+
+    return (t, y, dy)
+