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
78
79
|
"""
=====================================================================
Parameter estimation using grid search with a nested cross-validation
=====================================================================
The classifier is optimized by "nested" cross-validation using the
:class:`sklearn.grid_search.GridSearchCV` object on a development set
that comprises only half of the available labeled data.
The performance of the selected hyper-parameters and trained model is
then measured on a dedicated evaluation set that was not used during
the model selection step.
More details on tools available for model selection can be found in the
sections on :ref:`cross_validation` and :ref:`grid_search`.
"""
print __doc__
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.svm import SVC
# Loading the Digits dataset
digits = datasets.load_digits()
# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target
# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_fraction=0.5, random_state=0)
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = [
('precision', precision_score),
('recall', recall_score),
]
for score_name, score_func in scores:
print "# Tuning hyper-parameters for %s" % score_name
print
clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func)
clf.fit(X_train, y_train, cv=5)
print "Best parameters set found on development set:"
print
print clf.best_estimator_
print
print "Grid scores on development set:"
print
for params, mean_score, scores in clf.grid_scores_:
print "%0.3f (+/-%0.03f) for %r" % (
mean_score, scores.std() / 2, params)
print
print "Detailed classification report:"
print
print "The model is trained on the full development set."
print "The scores are computed on the full evaluation set."
print
y_true, y_pred = y_test, clf.predict(X_test)
print classification_report(y_true, y_pred)
print
# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.
|
