plot_classification_probability.py

"""
===============================
Plot classification probability
===============================

Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, as
well as L1 and L2 penalized logistic regression.

The logistic regression is not a multiclass classifier out of the box. As
a result it can identify only the first class.
"""
print __doc__

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD Style.

import pylab as pl
import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:, 0:2]  # we only take the first two features for visualization
y = iris.target

n_features = X.shape[1]

C = 1.0

# Create different classifiers. The logistic regression cannot do
# multiclass out of the box.
classifiers = {
                'L1 logistic': LogisticRegression(C=C, penalty='l1'),
                'L2 logistic': LogisticRegression(C=C, penalty='l2'),
                'Linear SVC': SVC(kernel='linear', C=C, probability=True),
              }

n_classifiers = len(classifiers)

pl.figure(figsize=(3 * 2, n_classifiers * 2))
pl.subplots_adjust(bottom=.2, top=.95)

for index, (name, classifier) in enumerate(classifiers.iteritems()):
    classifier.fit(X, y)

    y_pred = classifier.predict(X)
    classif_rate = np.mean(y_pred.ravel() == y.ravel()) * 100
    print  "classif_rate for %s : %f " % (name, classif_rate)

    # View probabilities=
    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(n_classifiers, n_classes, index * n_classes + k + 1)
        pl.title("Class %d" % k)
        if k == 0:
            pl.ylabel(name)
        imshow_handle = pl.imshow(probas[:, k].reshape((100, 100)),
                                  extent=(3, 9, 1, 5), origin='lower')
        pl.xticks(())
        pl.yticks(())
        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()