def bench_scikit(X, Y):
"""
bench with scikit-learn bindings on libsvm
"""
import scikits.learn
from scikits.learn.svm import SVC
gc.collect()
# start time
tstart = datetime.now()
clf = SVC(kernel='rbf')
clf.fit(X, Y).predict(X)
delta = (datetime.now() - tstart)
# stop time
scikit_results.append(delta.seconds + delta.microseconds / mu_second)
def bench_scikit(X, Y):
"""
bench with scikit-learn bindings on libsvm
"""
import scikits.learn
from scikits.learn.svm import SVC
gc.collect()
# start time
tstart = datetime.now()
clf = SVC(kernel='rbf')
clf.fit(X, Y).predict(X)
delta = (datetime.now() - tstart)
# stop time
scikit_results.append(delta.seconds + delta.microseconds/mu_second)
def test_SVMModelField():
X = [[0 ,0],[1, 1]]
y = [0, 1]
svm = SVM()
clf = SVC()
clf.fit(X,y)
a1 = clf.predict([[2.,2.]])
#print clf
#print a1
svm.classifier = clf
svm.save(safe=True)
s = SVM.objects.first()
#print s.classifier
a2 = s.classifier.predict([[2., 2.]])
#print a2
assert a1 == a2
<<<<<<< HEAD
rfe = RFE(estimator = SVC(kernel="linear",C=1), n_features = 10, percentage =
0.1)
anova_filter = UnivariateFilter(SelectKBest(k=10), f_classif)
clf = SVC(kernel="linear",C=1)
y_pred_rfe = []
y_pred_univ = []
y_true = []
for train, test in StratifiedKFold(y, 2):
Xtrain, ytrain, Xtest, ytest = X[train], y[train], X[test], y[test]
### Fit and predict rfe
support = rfe.fit(X[train], y[train]).support_
y_pred_rfe.append(clf.fit(X[train,support],y[train]).predict(
X[test,support]))
### Fit and predict univariate feature selection
xr = anova_filter.fit(Xtrain, ytrain).transform(Xtrain)
y_pred_univ.append(clf.fit(Xtrain[:,anova_filter.support_],ytrain).predict(
Xtest[:,anova_filter.support_]))
y_true.append(ytest)
y_pred_univ = np.concatenate(y_pred_univ)
y_true = np.concatenate(y_true)
classif_rate_univ = np.mean(y_pred_univ == y_true) * 100
print "Classification rate univariate: %f" % classif_rate_univ
=======
svc = SVC(kernel="linear", C=1)
rfe = RFE(estimator=svc, n_features=30, percentage=0.1)
rfe.fit(X, y)
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from __future__ import division
import os
import logging
import pickle
import numpy as np
from scikits.learn.svm import SVC
from string import punctuation
from operator import itemgetter
logging.basicConfig(level=logging.DEBUG)
lab_train, vec_train, lab_test, vec_test = [
pickle.load(open(file)) for file in [
'labels_training.pik', 'vectors_training.pik', 'labels_test.pik',
'vectors_test.pik'
]
]
logging.info("Data loaded")
cat_train = list(set(lab_train))
cat_test = list(set(lab_test))
assert cat_test == cat_train
lab_train = [cat_train.index(l) for l in lab_train]
lab_test = [cat_test.index(l) for l in lab_test]
clf = SVC(kernel='rbf')
clf.fit(vec_train, lab_train)
pickle.dump(clf, open('classifier.pik', 'wb'))
from scikits.learn.svm import SVC
from string import punctuation
from operator import itemgetter
logging.basicConfig(level=logging.DEBUG)
lab_train, vec_train , lab_test, vec_test = [pickle.load(open(file))
for file
in ['labels_training.pik',
'vectors_training.pik',
'labels_test.pik',
'vectors_test.pik']]
logging.info("Data loaded")
cat_train = list(set(lab_train))
cat_test = list(set(lab_test))
assert cat_test == cat_train
lab_train = [cat_train.index(l) for l in lab_train]
lab_test = [cat_test.index(l) for l in lab_test]
clf = SVC(kernel='rbf')
clf.fit(vec_train, lab_train)
pickle.dump(clf,open('classifier.pik','wb'))
import numpy as np X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1, 2, 2]) from scikits.learn.svm import SVC clf = SVC() clf.fit(X, y) print clf.predict([[-0.8, -1]])
