ml viz

code/algviz.py

@@ -0,0 +1,149 @@
+# algviz
+# Generate visualizations of classification, regression, and clustering
+#
+# Author:   Benjamin Bengfort <[email protected]>
+# Created:  Fri Feb 27 13:07:28 2015 -0500
+#
+# Copyright (C) 2015 District Data Labs
+# For license information, see LICENSE.txt
+#
+# ID: algviz.py [] [email protected] $
+
+"""
+Generate visualizations of classification, regression, and clustering
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+import numpy as np
+from sklearn import datasets
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+from sklearn.lda import LDA
+from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+
+from sklearn.cluster import KMeans
+
+rgb_colors = ['#FF0000', '#00FF00', '#0000FF']
+cm_bright  = ListedColormap(rgb_colors)
+
+def visualize_classification(estimator, n_samples=100, n_features=2):
+    # Create the linear dataset and estimator
+    kwargs = {
+        'n_samples': n_samples,
+        'n_features': n_features,
+        'n_classes': 3,
+        'n_redundant': 0,
+        'n_clusters_per_class': 1,
+        'class_sep': 1.22,
+    }
+    X, y = datasets.make_classification(**kwargs)
+
+    # Create the figure
+    fix, axes = plt.subplots()
+
+    # no ticks
+    axes.set_xticks(())
+    axes.set_yticks(())
+    axes.set_ylabel('$x_1$')
+    axes.set_xlabel('$x_0$')
+
+    # Plot the surface
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
+                         np.arange(y_min, y_max, 0.02))
+
+    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    cs = plt.contourf(xx, yy, Z, cmap=cm_bright, alpha=0.3)
+
+    # Plot the points on the grid
+    axes.scatter(X[:,0], X[:,1], c=y, s=30, cmap=cm_bright)
+
+    # Show the plot
+    plt.axis("tight")
+    plt.show()
+
+def visualize_regression(n_samples=100):
+    def f(x):
+        return np.sin(2 * np.pi * x)
+
+    # Generate data
+    X = np.random.uniform(0, 1, size=n_samples)[:,np.newaxis]
+    y = f(X) + np.random.normal(scale=0.3, size=n_samples)[:,np.newaxis]
+
+    # Create the linespace
+    x_plot = np.linspace(0, 1, 100)[:,np.newaxis]
+
+    poly = PolynomialFeatures(degree=6)
+    lreg = LinearRegression()
+
+    pipeline = Pipeline([("polynomial_features", poly),
+                             ("linear_regression", lreg)])
+    pipeline.fit(X, y)
+
+    # Create the figure
+    fix, axes = plt.subplots()
+
+    # no ticks
+    axes.set_xticks(())
+    axes.set_yticks(())
+    axes.set_ylabel('$y$')
+    axes.set_xlabel('$x$')
+
+    # Plot the estimator and the true line
+    axes.plot(x_plot, pipeline.predict(x_plot), color='red', label="estimated")
+    axes.plot(x_plot, f(x_plot), color='green', label='true function')
+
+    # Plot the points
+    axes.scatter(X, y)
+
+    plt.legend(loc="best")
+    plt.show()
+
+def visualize_clustering(n_samples=350, n_centers=3, n_features=2):
+    # Create the data
+    X,y = datasets.make_blobs(n_samples=n_samples, centers=n_centers, n_features=n_features)
+
+    # Create the estimator
+    estimator = KMeans(n_clusters=n_centers, n_init=10)
+    estimator.fit(X)
+
+    centroids = estimator.cluster_centers_
+
+    # Create the figure
+    fig, axes = plt.subplots()
+
+    # Plot the clusters
+    for k, col in zip(xrange(n_centers), rgb_colors):
+        m = estimator.labels_ == k
+        center = centroids[k]
+        # axes.plot(X[m,0], X[m, 1], 'w', markerfacecolor=col, marker='.', markersize=10)
+        axes.plot(center[0], center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=200, alpha=.15)
+
+    # no ticks
+    axes.set_xticks(())
+    axes.set_yticks(())
+    axes.set_ylabel('$x_1$')
+    axes.set_xlabel('$x_0$')
+
+    # Plot the points
+    axes.scatter(X[:,0], X[:,1], c='k')
+
+    plt.show()
+
+if __name__ == '__main__':
+    # visualize_classification(KNeighborsClassifier(n_neighbors=3))
+    # visualize_regression()
+    visualize_clustering()

requirements.txt

@@ -1,4 +1,11 @@
+matplotlib==1.4.3
+mock==1.0.1
+nose==1.3.4
 numpy==1.9.1
+pyparsing==2.0.3
+python-dateutil==2.4.0
+pytz==2014.10
 scikit-learn==0.15.2
 scipy==0.15.1
+six==1.9.0
 wsgiref==0.1.2