class StatementClassifier(object):
def __init__(self, train=False, cv=True, folds=5, trained_model_name="trained_model.pkl", feat_index_name="feature_index.pkl", stored_tagset="tagset.pkl"):
self.trained_model_name = trained_model_name
self.feat_index_name = feat_index_name
self.stored_tagset = stored_tagset
self.cv = cv
self.folds = folds
self.fp = FeatureProcessing()
if train:
print >>sys.stderr, "Statement classifier initialized for training."
if self.cv:
print >>sys.stderr, "Cross-validation will be done"
else:
self.classifier = pickle.load(open(self.trained_model_name, "rb"))
feat_index = pickle.load(open(self.feat_index_name, "rb"))
self.tagset = pickle.load(open(self.stored_tagset, "rb"))
self.fp.feat_index = feat_index
print >>sys.stderr, "Stored model loaded. Statement classifier initialized for prediction."
def predict(self, testfile_name):
test_data = [x.strip() for x in codecs.open(testfile_name, "r", "utf-8")]
filter_feature = get_filter()
if len(test_data) == 0:
return []
X = numpy.asarray([self.fp.featurize(clause, filter_feature) for clause in test_data])
predictions = [self.tagset[ind] for ind in self.classifier.predict(X)]
return zip(predictions, test_data)
def train(self, trainfile_name):
print >>sys.stderr, "Reading data.."
train_data = [tuple(x.strip().split("\t")) for x in codecs.open(trainfile_name, "r", "utf-8")]
shuffle(train_data)
filter_feature = get_filter()
train_labels, train_clauses = zip(*train_data)
train_labels = [tl.lower() for tl in train_labels]
print >>sys.stderr, "Indexing features.."
self.fp.index_data(train_clauses, filter_feature)
X = numpy.asarray([self.fp.featurize(clause, filter_feature) for clause in train_clauses])
tagset = list(set(train_labels))
tag_index = {l:i for (i, l) in enumerate(tagset)}
Y = numpy.asarray([[tag_index[label]] for label in train_labels])
classifier = OneVsRestClassifier(SVC(kernel='linear'))
if self.cv:
print >>sys.stderr, "Starting Cross-validation for %d folds.."%(self.folds)
y = [l[0] for l in Y]
scores = cross_validation.cross_val_score(classifier, X, y, cv=self.folds, scoring='f1_weighted')
print >>sys.stderr, "Scores:", scores
print >>sys.stderr, "Average: %0.4f (+/- %0.4f)"%(scores.mean(), scores.std() * 2)
print >>sys.stderr, "Starting training.."
classifier.fit(X, Y)
pickle.dump(classifier, open(self.trained_model_name, "wb"))
pickle.dump(self.fp.feat_index, open(self.feat_index_name, "wb"))
pickle.dump(tagset, open(self.stored_tagset, "wb"))
print >>sys.stderr, "Done"
import sys
import codecs
import pickle
from features import FeatureProcessing
trainfile_name = sys.argv[1]
outfile_name = sys.argv[2]
fp = FeatureProcessing()
train_data = [
tuple(x.strip().split("\t"))
for x in codecs.open(trainfile_name, "r", "utf-8")
]
train_labels, train_clauses = zip(*train_data)
print >> sys.stderr, "Indexing features.."
fp.index_data(train_clauses)
feats = [fp.featurize(clause) for clause in train_clauses]
pickle.dump((feats, train_labels, fp.feat_index), open(outfile_name, "wb"))
import sys
import codecs
import pickle
from features import FeatureProcessing
trainfile_name = sys.argv[1]
outfile_name = sys.argv[2]
fp = FeatureProcessing()
train_data = [tuple(x.strip().split("\t")) for x in codecs.open(trainfile_name, "r", "utf-8")]
train_labels, train_clauses = zip(*train_data)
print >>sys.stderr, "Indexing features.."
fp.index_data(train_clauses)
feats = [fp.featurize(clause) for clause in train_clauses]
pickle.dump((feats, train_labels, fp.feat_index), open(outfile_name, "wb"))
