def test_pipeline(pipeline):
"""
Support function used to test a pipeline using the specified testSet
"""
if not isinstance(pipeline, Pipeline):
raise ValueError("pipeline must be an instance of Pipeline")
timer.start()
if not useConllFile:
labeled_featuresets = read_tweets_file(originalFile, pipeline).values()
else:
labeled_featuresets = read_conll_file(originalFile, conllFile,
pipeline).values()
validator = CrossValidator(labeled_featuresets)
print "Elapsed time for data set processing: %.0fs\n" % (timer.stop() /
1000)
# test the classifiers
for classifierName in classifiers:
timer.start()
print "- %s " % classifierName,
print "accuracy: %f" % validator.validate(classifiers[classifierName],
numOfBins)[0]
print " Elapsed time: %.0fs\n" % (timer.stop() / 1000)
file = ["tweeti-b", "tweeti-b.dev"]
if not args.n:
# Load standard tweet file
trainingfile = map(lambda path: args.datasetpath + path + ".tsv", file)
labeled_featuresets = read_tweets_file(trainingfile, pipeline).values()
else:
# If the not adapter filter has to be used, the program has to load the *.conll files instead
# the conll files must be in the same dataset path specified by the user.
trainingfile = map(lambda path: args.datasetpath + path + ".tsv", file)
conllfile = map(lambda path: args.datasetpath + path + ".conll", file)
labeled_featuresets = read_conll_file(trainingfile, conllfile,
pipeline).values()
if not args.predict:
############ Cross Validation
validator = CrossValidator(labeled_featuresets)
timer.start()
(acc, conf_matr, prec, recall,
f_measure) = validator.validate(classifier, args.v)
print "Accuracy: %f" % acc
print "Confusion Matrix:"
for prec_label in conf_matr:
for real_label in conf_matr[prec_label]:
print "\tPredicted: " + prec_label + "\tReal: " + real_label + "\t" + str(
conf_matr[prec_label][real_label])
print "Precision:"
for label in prec:
print "\t" + label + ": %f" % prec[label]
print "Recall:"
for label in recall:
