def test_build_extraction_dataset():
if os.path.exists(os.path.join(TMP_DIR, 'extraction.data')):
os.remove(os.path.join(TMP_DIR, 'extraction.data'))
d.build_extraction_dataset(os.path.join(EMAILS_DIR, 'P'),
os.path.join(TMP_DIR, 'extraction.data'), 1)
test_data = SparseDataSet(os.path.join(TMP_DIR, 'extraction.data'),
labelsColumn=-1)
# the result is a loadable signature extraction dataset
# 32 comes from 3 emails in emails/P folder, 11 lines checked to be
# a signature, one email has only 10 lines
eq_(test_data.size(), 32)
eq_(len(features('')), test_data.numFeatures)
def test_build_extraction_dataset():
if os.path.exists(os.path.join(TMP_DIR, 'extraction.data')):
os.remove(os.path.join(TMP_DIR, 'extraction.data'))
d.build_extraction_dataset(os.path.join(EMAILS_DIR, 'P'),
os.path.join(TMP_DIR,
'extraction.data'), 1)
test_data = SparseDataSet(os.path.join(TMP_DIR, 'extraction.data'),
labelsColumn=-1)
# the result is a loadable signature extraction dataset
# 32 comes from 3 emails in emails/P folder, 11 lines checked to be
# a signature, one email has only 10 lines
eq_(test_data.size(), 32)
eq_(len(features('')), test_data.numFeatures)
def train(classifier, train_data_filename, save_classifier_filename=None):
'''Trains and saves classifier so that it could be easily loaded later.'''
data = SparseDataSet(train_data_filename, labelsColumn=-1)
classifier.train(data)
if save_classifier_filename:
classifier.save(save_classifier_filename)
return classifier
def load(saved_classifier_filename, train_data_filename):
"""Loads saved classifier.
Classifier should be loaded with the same data it was trained against
"""
train_data = SparseDataSet(train_data_filename, labelsColumn=-1)
classifier = init()
classifier.load(saved_classifier_filename, train_data)
return classifier
def train_test(ds_path):
data = SparseDataSet(ds_path)
g, c, fold = 0.25, 128, 2
##################################################
#### This part of the code does Does statistical
#### feature selection ...
#labels = np.array([int(n) for n in data.labels.L])
#ranks = rank_feat(data.getMatrix().T, labels)
#ranks = [(abs(r),i) for i, r in enumerate(ranks)]
#ranks.sort()
#ranks.reverse()
#feats = [f[1] for f in ranks]
#data.keepFeatures(feats[:2662])
data.attachKernel('gaussian', gamma = g)
s=SVM(C=c)
r = s.cv(data, numFolds=fold)
o = open(ds_path+'.pkl', 'wb')
pickle.dump(r, o)
o.close();
print ds_path
def train_test(ds_path):
data = SparseDataSet(ds_path)
g, c, fold = 0.25, 128, 2
##################################################
#### This part of the code does Does statistical
#### feature selection ...
#labels = np.array([int(n) for n in data.labels.L])
#ranks = rank_feat(data.getMatrix().T, labels)
#ranks = [(abs(r),i) for i, r in enumerate(ranks)]
#ranks.sort()
#ranks.reverse()
#feats = [f[1] for f in ranks]
#data.keepFeatures(feats[:2662])
data.attachKernel('gaussian', gamma=g)
s = SVM(C=c)
r = s.cv(data, numFolds=fold)
o = open(ds_path + '.pkl', 'wb')
pickle.dump(r, o)
o.close()
print ds_path
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L = labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree = 5)
self.svminstance.train(data)
def is_signature_line(line, sender, classifier):
'''Checks if the line belongs to signature. Returns True or False.'''
data = SparseDataSet([build_pattern(line, features(sender))])
return classifier.decisionFunc(data, 0) > 0
from PyML import SparseDataSet, SVM
__author__ = 'basir'
data = SparseDataSet('data/heartSparse.data', labelsColumn=-1)
svm = SVM()
res = svm.cv(data, 5)
for fold in res:
print fold
print res
# print data
# help(sequenceData.spectrum_data)
def trainforTD(self, datalist, labelslist):
data = SparseDataSet(datalist, L=labelslist)
self.svminstance.train(data)
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L=labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree=5)
self.svminstance.train(data)
def predict(self, datalist):
data = SparseDataSet(datalist)
results = self.svminstance.test(data)
return results.getPredictedLabels()[0]
