adding example to view posterior class probabilities when using LogisticRegression or SVC

git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@696 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8

examples/plot_classification_probability.py

+# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
+# License: BSD Style.
+
+# $Id$
+
+import pylab as pl
+import numpy as np
+
+from scikits.learn.logistic import LogisticRegression
+from scikits.learn.svm import SVC
+from scikits.learn import datasets
+
+iris = datasets.load_iris()
+X = iris.data[:, :2] # we only take the first two features for visualization
+y = iris.target
+
+n_features = X.shape[1]
+
+C = 1.0
+
+# Create classifier (any of the following 3)
+classifier = LogisticRegression(C=C, penalty='l1')
+classifier = LogisticRegression(C=C, penalty='l2')
+classifier = SVC(kernel='linear', C=C, probability=True)
+
+classifier.fit(X, y)
+
+y_pred = classifier.predict(X)
+classif_rate = np.mean(y_pred.ravel() == y.ravel()) * 100
+print  "classif_rate : %f " % classif_rate
+
+# ======================
+# = View probabilities =
+# ======================
+pl.figure()
+xx = np.linspace(3,9,100)
+yy = np.linspace(1,5,100).T
+xx, yy = np.meshgrid(xx, yy)
+Xfull = np.c_[xx.ravel(),yy.ravel()]
+probas = classifier.predict_proba(Xfull)
+n_classes = np.unique(y_pred).size
+for k in range(n_classes):
+    pl.subplot(1, n_classes, k + 1)
+    pl.title("Class %d" % k)
+    imshow_handle = pl.imshow(probas[:,k].reshape((100, 100)), extent=(3, 9, 1, 5), origin='lower')
+    pl.hold(True)
+    idx = (y_pred == k)
+    if idx.any(): pl.scatter(X[idx,0], X[idx,1], marker='o', c='k')
+
+ax = pl.axes([0.15,0.04,0.7,0.05])
+pl.title("Probability")
+pl.colorbar(imshow_handle, cax=ax, orientation='horizontal')
+
+pl.show()