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)
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)
print ""
print ""
pipeline = Pipeline(tokenizer, tagger, prefilters, postfilters)
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:
# adjust all path
originalFile = map(lambda path: get_project_dir() + "resources/tweeti/" + path + ".tsv", file)
conllFile = map(lambda path: get_project_dir() + "resources/conll/" + path + ".conll", file)
# timer used for timing
timer = Timer()
# classifiers to test
classifiers = {"ShortTextClassifier": ShortTextClassifier(), "SVMClassifier": SVMClassifier(), "Bayes": BayesianClassifier()}
#classifiers = {"LinearClassifier": LinearClassifier()}
#classifiers = {"Bayes": BayesianClassifier()}
# loading and processing data set
timer.start()
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
(acc, conf_matr, prec, recall, f_measure) = validator.validate(classifiers[classifierName], numOfBins)
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:"
print "" print "" pipeline = Pipeline( tokenizer, tagger, prefilters, postfilters ) 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]