1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
|
"""
===============================
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()
|
