def print_precision_recall(classifier, test_dict):
refsets = defaultdict(set)
testsets = defaultdict(set)
for i, (feats, label) in enumerate(test_dict):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print 'pos precision:', precision(refsets['positive'], testsets['positive'])
print 'pos recall:', recall(refsets['positive'], testsets['positive'])
print 'pos F-measure:', f_measure(refsets['positive'], testsets['positive'])
print 'neg precision:', precision(refsets['negative'], testsets['negative'])
print 'neg recall:', recall(refsets['negative'], testsets['negative'])
print 'neg F-measure:', f_measure(refsets['negative'], testsets['negative'])
def evaluate_classifier(featx):
negids = movie_reviews.fileids('neg')
posids = movie_reviews.fileids('pos')
# print(movie_reviews.words(fileids=[negids[0]]))
# exit()
negfeats = [(featx(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
posfeats = [(featx(movie_reviews.words(fileids=[f])), 'pos') for f in posids]
negcutoff = int(len(negfeats) * 3 / 4)
poscutoff = int(len(posfeats) * 3 / 4)
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
print('train on %d instances, test on %d instances' % (len(trainfeats), len(testfeats)))
classifier = NaiveBayesClassifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print('accuracy:', nltk.classify.util.accuracy(classifier, testfeats))
print('pos precision:', precision(refsets['pos'], testsets['pos']))
print('pos recall:', recall(refsets['pos'], testsets['pos']))
print('pos F-measure:', f_measure(refsets['pos'], testsets['pos']))
print('neg precision:', precision(refsets['neg'], testsets['neg']))
print('neg recall:', recall(refsets['neg'], testsets['neg']))
print('neg F-measure:', f_measure(refsets['neg'], testsets['neg']))
classifier.show_most_informative_features()
def findFMetric(self, classifier):
refsets, self.testSets = self.findsets(classifier)
return f_measure(refsets['bullish'],
self.testSets['bullish']), f_measure(
refsets['bearish'],
self.testSets['bearish']), f_measure(
refsets['neutral'], self.testSets['neutral'])
def main():
negfeats = []
posfeats = []
for i, f in enumerate(reviews[0]):
print(f)
if reviews[1][i] == 0:
negfeats.append((word_feats(f.split()), "neg"))
else:
posfeats.append((word_feats(f.split()), "pos"))
testNegfeats = []
testPosfeats = []
for i, f in enumerate(test[0]):
if test[1][i] == 0:
testNegfeats.append((word_feats(f.split()), "neg"))
else:
testPosfeats.append((word_feats(f.split()), "pos"))
trainfeats = negfeats + posfeats
testfeats = testNegfeats + testPosfeats
print('train on %d instances, test on %d instances - Maximum Entropy' %
(len(trainfeats), len(testfeats)))
classifier = MaxentClassifier.train(trainfeats,
'GIS',
trace=0,
encoding=None,
labels=None,
gaussian_prior_sigma=0,
max_iter=1)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
accuracy = nltk.classify.util.accuracy(classifier, testfeats)
pos_precision = precision(refsets['pos'], testsets['pos'])
pos_recall = recall(refsets['pos'], testsets['pos'])
pos_fmeasure = f_measure(refsets['pos'], testsets['pos'])
neg_precision = precision(refsets['neg'], testsets['neg'])
neg_recall = recall(refsets['neg'], testsets['neg'])
neg_fmeasure = f_measure(refsets['neg'], testsets['neg'])
print(pos_recall)
print(neg_recall)
print()
print('')
print('---------------------------------------')
print(' Maximum Entropy ')
print('---------------------------------------')
print('accuracy:', accuracy)
print('precision', (pos_precision + neg_precision) / 2)
print('recall', (pos_recall + neg_recall) / 2)
print('f-measure', (pos_fmeasure + neg_fmeasure) / 2)
def assess_classifier(classifier, test_data, text, max_ent_help=False):
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
numDataSets = len(test_data)
onDataSet = 0
# TN = 0
# TP = 0
# FN = 0
# FP = 0
# enumerate through the test data and classify them
for i, (feats, label) in enumerate(test_data):
refsets[label].add(i)
if max_ent_help:
if maxEntClf.classify(feats):
observed = classifier.classify(feats)
else:
observed = False
else:
observed = classifier.classify(feats)
testsets[observed].add(i)
onDataSet += 1
# if label == observed:
# if observed:
# TP += 1
# else:
# TN += 1
# else:
# if observed:
# FP += 1
# else:
# FN += 1
# printPercentage(onDataSet/numDataSets * 100, "Extracting Features: ")
# precision = TP/(TP+FP)
# recall = TP/(TP+FN)
# f1Score = 2*((precision*recall)/(precision + recall))
# calculate the precisionl, recall, f-measure
laugh_precision = precision(refsets[True], testsets[True])
laugh_recall = recall(refsets[True], testsets[True])
laugh_f1 = f_measure(refsets[True], testsets[True])
non_laugh_precision = precision(refsets[False], testsets[False])
non_laugh_recall = recall(refsets[False], testsets[False])
non_laugh_f1 = f_measure(refsets[False], testsets[False])
acc = nltk.classify.accuracy(classifier, test_data)
return [text, acc, laugh_precision, laugh_recall, laugh_f1, non_laugh_precision, non_laugh_recall, non_laugh_f1]
def main():
results = {'Topic': [], 'Precision': [], 'Recall': [], 'F-measure': []}
print('\nPreparing data...')
(train_set, test_set) = get_train_test_sets('data/content')
print('\nNB classifier training...')
classifier = NaiveBayesClassifier.train(train_set)
print('NB classifier is trained with {}% accuracy'.format(
round(accuracy(classifier, test_set) * 100, 1)))
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
for topic in topics:
results['Topic'].append(topic)
results['Precision'].append(
round(precision(refsets[topic], testsets[topic]) * 100, 1))
results['Recall'].append(
round(recall(refsets[topic], testsets[topic]) * 100, 1))
results['F-measure'].append(
round(f_measure(refsets[topic], testsets[topic]) * 100, 1))
del classifier, train_set, test_set, refsets, testsets
gc.collect()
print(results)
def benchmarking(self, classifier,_test_set,all_f_measure=[],all_precision=[],all_recall=[]):
from nltk import classify
accuracy = classify.accuracy(classifier, _test_set)
print("accuracy:",accuracy)
from nltk.metrics import precision
from nltk.metrics import recall
from nltk.metrics import f_measure
import collections
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(_test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
prec=precision(refsets['class'], testsets['class'])
rec=recall(refsets['class'], testsets['class'])
f1=f_measure(refsets['class'], testsets['class'])
print('precision:', prec)
print('recall:', rec)
print('F-measure:', f1)
all_f_measure.append(f1)
all_precision.append(prec)
all_recall.append(rec)
print('========Show top 10 most informative features========')
classifier.show_most_informative_features(10)
def benchmarking(self,
classifier,
_test_set,
all_f_measure=[],
all_precision=[],
all_recall=[]):
from nltk import classify
accuracy = classify.accuracy(classifier, _test_set)
print("accuracy:", accuracy)
from nltk.metrics import precision
from nltk.metrics import recall
from nltk.metrics import f_measure
import collections
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(_test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
prec = precision(refsets['class'], testsets['class'])
rec = recall(refsets['class'], testsets['class'])
f1 = f_measure(refsets['class'], testsets['class'])
print('precision:', prec)
print('recall:', rec)
print('F-measure:', f1)
all_f_measure.append(f1)
all_precision.append(prec)
all_recall.append(rec)
print('========Show top 10 most informative features========')
classifier.show_most_informative_features(10)
def train_and_score(classifier, train, test):
try:
if classifier.__name__ == 'MaxentClassifier':
clf = classifier.train(train, algorithm='MEGAM')
elif classifier.__name__ == 'DecisionTreeClassifier':
clf = classifier.train(train,
binary=True,
entropy_cutoff=0.8,
depth_cutoff=5,
support_cutoff=3)
else:
clf = classifier.train(train)
except AttributeError:
clf = classifier.train(train)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test):
refsets[label].add(i)
observed = clf.classify(feats)
testsets[observed].add(i)
measures = []
for key in refsets.keys():
measures.append([
precision(refsets[key], testsets[key]),
recall(refsets[key], testsets[key]),
f_measure(refsets[key], testsets[key])
])
return measures
def evaluate_classifier(featx):
stanford_pos_list = list()
stanford_neg_list = list()
with open(stanford_pos, 'r') as stanford_p:
for line in stanford_p:
stanford_pos_list.append(line.strip())
with open(stanford_neg, 'r') as stanford_n:
for line in stanford_n:
stanford_neg_list.append(line.strip())
stanford_p = stanford_pos_list[:5000]
stanford_n = stanford_neg_list[:5000]
negfeats = [(featx(nltk.word_tokenize(line)), 'neg')
for line in stanford_n]
posfeats = [(featx(nltk.word_tokenize(line)), 'pos')
for line in stanford_p]
negcutoff = int(len(negfeats) * 3 / 4)
poscutoff = int(len(posfeats) * 3 / 4)
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
print('train on %d instances, test on %d instances' %
(len(trainfeats), len(testfeats)))
classifier = NaiveBayesClassifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print('accuracy:', nltk.classify.util.accuracy(classifier, testfeats))
print('pos precision:', precision(refsets['pos'], testsets['pos']))
print('pos recall:', recall(refsets['pos'], testsets['pos']))
print('pos F-measure:', f_measure(refsets['pos'], testsets['pos']))
print('neg precision:', precision(refsets['neg'], testsets['neg']))
print('neg recall:', recall(refsets['neg'], testsets['neg']))
print('neg F-measure:', f_measure(refsets['neg'], testsets['neg']))
classifier.show_most_informative_features()
def results(classifier, testing_set, training_set):
now = datetime.now()
# Trains classifier
classifier = classifier.train(training_set)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
tp = 0
fp = 0
tn = 0
fn = 0
# Gets positive/false positives/negatives
for i, (features, label) in enumerate(testing_set):
refsets[label].add(i)
observed = classifier.classify(features)
testsets[observed].add(i)
if label == 'exp' and observed == 'exp':
tp += 1
elif label == 'non' and observed == 'non':
tn += 1
elif label == 'exp' and observed == 'non':
fn += 1
else:
fp += 1
print "Time training: " + str(datetime.now() - now)
print "True Positives: " + str(tp)
print "False Positives: " + str(fp)
print "True Negatives: " + str(tn)
print "False Negatives: " + str(fn)
print 'Explicit Precision: ', precision(refsets['exp'],
testsets['exp'])
print 'Explicit recall: ', recall(refsets['exp'], testsets['exp'])
print 'Explicit F-Score: ', f_measure(refsets['exp'], testsets['exp'])
print 'Non-Explicit Precision: ', precision(refsets['non'],
testsets['non'])
print 'Non-Explicit Recall: ', recall(refsets['non'], testsets['non'])
print 'Non-Explicit F-Score: ', f_measure(refsets['non'],
testsets['non'])
print "Accuracy percent: ", (nltk.classify.accuracy(
classifier, testing_set)) * 100
return classifier
def eval_stats(results):
'''
Compute recall, precision, and f-measure from passed results.
The expected format for results is a dictionary whose keys=<name of article>
and values=tuple (<test category>, <reference category>, <scores>), where:
test=category suggested by classifier, reference=pre-classified gold
category, scores=can be None or dictionary whose keys=category names and
values=matching score for this article.
'''
# Calculate number of correct matches
correct = 0
missed = defaultdict(tuple)
for article_name, (suggested, real, scores) in results.iteritems():
if suggested==real:
correct += 1
else:
missed[article_name] = (suggested, real)
success_ratio = correct / float(len(results))
print "Ratio: %0.3f" % success_ratio
# Print wrong matches
for name, (suggested, real) in missed.iteritems():
print "%s\t%s\t%s" % (name, suggested, real)
# Create sets of references / test classification for evaluation
cat_ref = defaultdict(set)
cat_test= defaultdict(set)
for name, (test_category, ref_category, scores) in results.iteritems():
cat_ref[ref_category].add(name) # gold-tagged categories
cat_test[test_category].add(name) # suggested categories
# Precision, recall, f-measure, support (num of reference articles in
# each category) for each category
print "\nCategory\tPrecision\tRecall\tF-measure\tSupport"
measures = defaultdict(tuple)
for category in cat_ref.keys():
cat_prec = metrics.precision(cat_ref[category], cat_test[category])
cat_rec = metrics.recall(cat_ref[category], cat_test[category])
cat_f = metrics.f_measure(cat_ref[category], cat_test[category])
cat_support = len(cat_ref[category])
measures[category] = (cat_prec, cat_rec, cat_f, cat_support)
print "%s\t%0.3f\t%0.3f\t%0.3f\t%d" % \
(category, cat_prec, cat_rec, cat_f, cat_support)
# Calculate precision, recall, f-measure for entire corpus:
# This is a weighted average of the values of separate categories
# SUM(product of all precisions, product of all supports)/sum(total number of supports)
avg_prec = weighted_average([(cat_measure[0], cat_measure[3]) for \
cat_measure in measures.values()])
avg_rec = weighted_average([(cat_measure[1], cat_measure[3]) for \
cat_measure in measures.values()])
avg_f = weighted_average([(cat_measure[2], cat_measure[3]) for \
cat_measure in measures.values()])
total_support = sum([cat_support[3] for cat_support in measures.values()])
print "%s\t%0.3f\t%0.3f\t%0.3f\t%d" % ("Total", avg_prec, avg_rec, avg_f, total_support)
def evaluate_precision_recall_fmeasure(corpus, category, tagger):
# get a list of the gold standard tags, and the tags set by the tagger.
gold = set(tag_list(corpus.tagged_sents(categories=category)))
test = set(
tag_list(apply_tagger(tagger,
corpus.tagged_sents(categories=category))))
# return the precision and recall of the evaluated model.
return [precision(gold, test), recall(gold, test), f_measure(gold, test)]
def evaluate_features(feature_extractor, N, only_acc=False):
from nltk.corpus import movie_reviews
from nltk.classify import NaiveBayesClassifier as naive
from nltk.classify.util import accuracy
from nltk.metrics import precision, recall, f_measure
from sys import stdout
negative = movie_reviews.fileids('neg')
positive = movie_reviews.fileids('pos')
negfeats = [(feature_extractor(movie_reviews.sents(fileids=[f])), 'neg')
for f in negative]
posfeats = [(feature_extractor(movie_reviews.sents(fileids=[f])), 'pos')
for f in positive]
negtrain, negtest = stratifiedSamples(negfeats, N)
postrain, postest = stratifiedSamples(posfeats, N)
trainfeats = negtrain + postrain
testfeats = negtest + postest
classifier = naive.train(trainfeats)
if only_acc: return accuracy(classifier, testfeats)
print 'accuracy: {}'.format(accuracy(classifier, testfeats))
# Precision, Recall, F-measure
from collections import defaultdict
refsets = defaultdict(set)
testsets = defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print 'pos precision:', precision(refsets['pos'], testsets['pos'])
print 'pos recall:', recall(refsets['pos'], testsets['pos'])
print 'pos F-measure:', f_measure(refsets['pos'], testsets['pos'])
print 'neg precision:', precision(refsets['neg'], testsets['neg'])
print 'neg recall:', recall(refsets['neg'], testsets['neg'])
print 'neg F-measure:', f_measure(refsets['neg'], testsets['neg'])
stdout.flush()
classifier.show_most_informative_features()
return classifier
def evaluate_features(feature_extractor, N, only_acc=False):
from nltk.corpus import movie_reviews
from nltk.classify import NaiveBayesClassifier as naive
from nltk.classify.util import accuracy
from nltk.metrics import precision, recall, f_measure
from sys import stdout
negative = movie_reviews.fileids('neg')
positive = movie_reviews.fileids('pos')
negfeats = [(feature_extractor(movie_reviews.sents(fileids=[f])),
'neg') for f in negative]
posfeats = [(feature_extractor(movie_reviews.sents(fileids=[f])),
'pos') for f in positive]
negtrain, negtest = stratifiedSamples(negfeats, N)
postrain, postest = stratifiedSamples(posfeats, N)
trainfeats = negtrain + postrain
testfeats = negtest + postest
classifier = naive.train(trainfeats)
if only_acc: return accuracy(classifier, testfeats)
print 'accuracy: {}'.format(accuracy(classifier, testfeats))
# Precision, Recall, F-measure
from collections import defaultdict
refsets = defaultdict(set)
testsets = defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print 'pos precision:', precision(refsets['pos'], testsets['pos'])
print 'pos recall:', recall(refsets['pos'], testsets['pos'])
print 'pos F-measure:', f_measure(refsets['pos'], testsets['pos'])
print 'neg precision:', precision(refsets['neg'], testsets['neg'])
print 'neg recall:', recall(refsets['neg'], testsets['neg'])
print 'neg F-measure:', f_measure(refsets['neg'], testsets['neg'])
stdout.flush()
classifier.show_most_informative_features()
return classifier
def calcAllClassesFMeasure(classSet, refsets, testsets):
fSum = 0.0
denominator = 0
for category in classSet:
num = f_measure(refsets[category], testsets[category])
if num is None:
continue
fSum += num
denominator += 1
return fSum/denominator
def calcAllClassesFMeasure(classSet, refsets, testsets):
fSum = 0.0
denominator = 0
for category in classSet:
num = f_measure(refsets[category], testsets[category])
if num is None:
continue
fSum += num
denominator += 1
return fSum / denominator
def evaluate(classifier, evalFeats, labels):
#old eval without cross Validation
try:
print('accuracy: %f' % nltk.classify.util.accuracy(classifier, evalFeats))
except ZeroDivisionError:
print('accuracy: 0')
refsets, testsets = ref_test_sets(classifier, evalFeats)
for label in labels:
ref = refsets[label]
test = testsets[label]
print('%s precision: %f' % (label, precision(ref, test) or 0))
print('%s recall: %f' % (label, recall(ref, test) or 0))
print('%s f-measure: %f' % (label, f_measure(ref, test) or 0))
def word_similarity_dict(self, word):
"""
Return a dictionary mapping from words to 'similarity scores,'
indicating how often these two words occur in the same
context.
"""
word = self._key(word)
word_contexts = set(self._word_to_contexts[word])
scores = {}
for w, w_contexts in self._word_to_contexts.items():
scores[w] = f_measure(word_contexts, set(w_contexts))
return scores
def word_similarity_dict(self, word):
"""
Return a dictionary mapping from words to 'similarity scores,'
indicating how often these two words occur in the same
context.
"""
word = self._key(word)
word_contexts = set(self._word_to_contexts[word])
scores = {}
for w, w_contexts in self._word_to_contexts.items():
scores[w] = f_measure(word_contexts, set(w_contexts))
return scores
def evaluation(test_set, classifier):
"""Evaluate the classifier with the test set. Print the accuracy,
precision, recall and f-measure."""
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print('Accuracy:', accuracy(classifier, test_set))
print('Precision:', precision(refsets['MALE'], testsets['MALE']))
print('Recall:', recall(refsets['MALE'], testsets['MALE']))
print('F Measure:', f_measure(refsets['MALE'], testsets['MALE']))
def assess_classifier(classifier, test_set, text):
accuracy = nltk.classify.accuracy(classifier, test_set)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
pos_pre = precision(refsets['positive'], testsets['positive'])
pos_rec = recall(refsets['positive'], testsets['positive'])
pos_fme = f_measure(refsets['positive'], testsets['positive'])
neg_pre = precision(refsets['negative'], testsets['negative'])
neg_rec = recall(refsets['negative'], testsets['negative'])
neg_fme = f_measure(refsets['negative'], testsets['negative'])
neu_pre = precision(refsets['neutral'], testsets['neutral'])
neu_rec = recall(refsets['neutral'], testsets['neutral'])
neu_fme = f_measure(refsets['negative'], testsets['neutral'])
return [
text, accuracy, pos_pre, pos_rec, pos_fme, neg_pre, neg_rec, neg_fme,
neu_pre, neu_rec, neu_fme
]
def evaluation(test_set, classifier):
"""Evaluate the classifier with the test set. Print the accuracy,
precision, recall and f-measure."""
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (featureset, label) in enumerate(test_set):
refsets[label].add(i)
observed = classifier.classify(featureset)
testsets[observed].add(i)
print('Evaluation Results')
print("\t\t\t{:<20}{:0.2f}".format('classifier accuracy', accuracy(classifier, test_set)))
print("\t\t\t{:<20}{:0.2f}".format('precision male', precision(refsets['male'], testsets['male'])))
print("\t\t\t{:<20}{:0.2f}".format('precision female', precision(refsets['female'], testsets['female'])))
print("\t\t\t{:<20}{:0.2f}".format('recall male', recall(refsets['male'], testsets['male'])))
print("\t\t\t{:<20}{:0.2f}".format('recall female', recall(refsets['female'], testsets['female'])))
print("\t\t\t{:<20}{:0.2f}".format('f_measure male', f_measure(refsets['male'], testsets['male'])))
print("\t\t\t{:<20}{:0.2f}".format('f_measure female', f_measure(refsets['female'], testsets['female'])))
print()
def measure(classifier, testfeats, alpha=0.5):
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
precisions = {}
recalls = {}
f_measures = {}
for label in classifier.labels():
precisions[label] = metrics.precision(refsets[label], testsets[label])
recalls[label] = metrics.recall(refsets[label], testsets[label])
f_measures[label] = metrics.f_measure(refsets[label], testsets[label], alpha)
return precisions, recalls, f_measures
def measure(classifier, testfeats, alpha=0.5):
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
precisions = {}
recalls = {}
f_measures = {}
for label in classifier.labels():
precisions[label] = metrics.precision(refsets[label], testsets[label])
recalls[label] = metrics.recall(refsets[label], testsets[label])
f_measures[label] = metrics.f_measure(refsets[label], testsets[label], alpha)
return precisions, recalls, f_measures
def evaluate_precision_recall_f_measure(self):
'''Evaluate precision, recall and f1 measure'''
scores = dict(prec_pos=[], rec_pos=[], fmeas_pos=[], prec_neg=[], rec_neg=[], fmeas_neg=[])
lfeats = self.label_feats_from_corpus()
for i in range(1, 10):
train_feats, test_feats, nb_classifier = self\
.__get_elements_for_classification(lfeats, train_number=i, classifying=False)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_feats):
refsets[label].add(i)
observed = nb_classifier.classify(feats)
testsets[observed].add(i)
precisions = {}
recalls = {}
f_measure = {}
for label in nb_classifier.labels():
precisions[label] = metrics.precision(
refsets[label], testsets[label]
)
recalls[label] = metrics.recall(refsets[label], testsets[label])
f_measure[label] = metrics.f_measure(
refsets[label], testsets[label]
)
#print nb_classifier.show_most_informative_features(n=20)
scores["prec_pos"].append(precisions["pos"])
scores["prec_neg"].append(precisions["neg"])
scores["rec_pos"].append(recalls["pos"])
scores["rec_neg"].append(recalls["neg"])
scores["fmeas_pos"].append(f_measure["pos"])
scores["fmeas_neg"].append(f_measure["neg"])
scores["prec_pos"] = sum(scores["prec_pos"]) / len(scores["prec_pos"])
scores["prec_neg"] = sum(scores["prec_neg"]) / len(scores["prec_neg"])
scores["rec_pos"] = sum(scores["rec_pos"]) / len(scores["rec_pos"])
scores["rec_neg"] = sum(scores["rec_neg"]) / len(scores["rec_neg"])
scores["fmeas_pos"] = sum(scores["fmeas_pos"]) / len(scores["fmeas_pos"])
scores["fmeas_neg"] = sum(scores["fmeas_neg"]) / len(scores["fmeas_neg"])
return scores
def eval_classifier(self):
'''
Test the model and calculates the metrics of accuracy, precision,
recall and f-measure
'''
test_set = apply_features(self.get_doc_features, self._test_docs, True)
self._accuracy = accuracy(self._classifier, test_set)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = self._classifier.classify(feats)
testsets[observed].add(i)
self.count_categories(self._train_docs)
for cat in self._categories:
self._precision[cat] = precision(refsets[cat], testsets[cat])
self._recall[cat] = recall(refsets[cat], testsets[cat])
self._f_measure[cat] = f_measure(refsets[cat], testsets[cat])
def measuring_accuracy(self):
"""Testing the model *accuracy*"""
print(
"Accuracy:", nltk.classify.accuracy(self.classifier, self.test_set)
)
self.classifier.show_most_informative_features(20)
"""Measuring **Precision,Recall,F-Measure** of a classifier.
Finding **Confusion matrix**"""
actual_set = collections.defaultdict(set)
predicted_set = collections.defaultdict(set)
# cm here refers to confusion matrix
actual_set_cm = []
predicted_set_cm = []
for i, (feature, label) in enumerate(self.test_set):
actual_set[label].add(i)
actual_set_cm.append(label)
predicted_label = self.classifier.classify(feature)
predicted_set[predicted_label].add(i)
predicted_set_cm.append(predicted_label)
for category in self.data.keys():
print(
category,
"precision :",
precision(actual_set[category], predicted_set[category]),
)
print(
category,
"recall :",
recall(actual_set[category], predicted_set[category]),
)
print(
category,
"f-measure :",
f_measure(actual_set[category], predicted_set[category]),
)
confusion_matrix = ConfusionMatrix(actual_set_cm, predicted_set_cm)
print("Confusion Matrix")
print(confusion_matrix)
def precision_recall_f_measure(classifier, test_feats):
refsets = defaultdict(set)
testsets = defaultdict(set)
for i, (feats, label) in enumerate(test_feats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
precisions = {}
recalls = {}
f_measures = {}
for label in classifier.labels():
precisions[label] = precision(refsets[label], testsets[label])
recalls[label] = recall(refsets[label], testsets[label])
f_measures[label] = f_measure(refsets[label], testsets[label])
print("\nPrecision:")
pprint(precisions, width=1)
print("\nRecall")
pprint(recalls, width=1)
print("\nF Measure")
pprint(f_measures, width=1)
#script to validate coding import cPickle as pickle import sys from nltk.metrics import accuracy, ConfusionMatrix, precision, recall, f_measure from collections import defaultdict import classifier if __name__=='__main__': validation_pickle=sys.argv[1] classifier_pickle=sys.argv[2] validation_set=pickle.load(open(validation_pickle, 'rb')) c=pickle.load(open(classifier_pickle, 'rb')) reference=defaultdict(set) observed=defaultdict(set) for i, (tweet, label) in enumerate(validation_set): reference[label].add(i) observation=c.classify(tweet) observed[observation].add(i) print "accuracy: %s" % accuracy(observed, reference) print "pos precision: %s" % precision(reference['positive'], observed['positive']) print "pos recall: %s" % recall(reference['positive'], observed['positive']) print "pos f-measure: %s" % f_measure(reference['positive'], observed['positive']) print "neg precision: %s" % precision(reference['negative'], observed['negative']) print "neg recall: %s" % recall(reference['negative'], observed['negative']) print "neg f-measure: %s" % f_measure(reference['negative'], observed['negative'])
#!/usr/bin/python import nltk from nltk.metrics import precision, recall, f_measure reference = 'DET NN VB DET JJ NN NN IN DET NN'.split() test = 'DET VB VB DET NN NN NN IN DET NN'.split() reference_set = set(reference) test_set = set(test) print "Precision: " print precision(reference_set, test_set) print "\n" print "Recall: " print recall(reference_set, test_set) print "\n" print "F_Measure: " print f_measure(reference_set, test_set)
def cross_fold(instances,
trainf,
testf,
folds=10,
trace=1,
metrics=True,
informative=0):
if folds < 2:
raise ValueError('must have at least 3 folds')
# ensure isn't an exhaustible iterable
instances = list(instances)
# randomize so get an even distribution, in case labeled instances are
# ordered by label
random.shuffle(instances)
l = len(instances)
step = l / folds
if trace:
print 'step %d over %d folds of %d instances' % (step, folds, l)
accuracies = []
precisions = collections.defaultdict(list)
recalls = collections.defaultdict(list)
f_measures = collections.defaultdict(list)
for f in range(folds):
if trace:
print '\nfold %d' % (f + 1)
print '-----%s' % ('-' * len('%s' % (f + 1)))
start = f * step
end = start + step
train_instances = instances[:start] + instances[end:]
test_instances = instances[start:end]
if trace:
print 'training on %d:%d + %d:%d' % (0, start, end, l)
obj = trainf(train_instances)
if trace:
print 'testing on %d:%d' % (start, end)
if metrics:
refsets, testsets = ref_test_sets(obj, test_instances)
for key in set(refsets.keys() + testsets.keys()):
ref = refsets[key]
test = testsets[key]
p = precision(ref, test) or 0
r = recall(ref, test) or 0
f = f_measure(ref, test) or 0
precisions[key].append(p)
recalls[key].append(r)
f_measures[key].append(f)
if trace:
print '%s precision: %f' % (key, p)
print '%s recall: %f' % (key, r)
print '%s f-measure: %f' % (key, f)
accuracy = testf(obj, test_instances)
if trace:
print 'accuracy: %f' % accuracy
accuracies.append(accuracy)
if trace and informative and hasattr(obj,
'show_most_informative_features'):
obj.show_most_informative_features(informative)
if trace:
print '\nmean and variance across folds'
print '------------------------------'
print 'accuracy mean: %f' % (sum(accuracies) / folds)
print 'accuracy variance: %f' % array(accuracies).var()
for key, ps in precisions.iteritems():
print '%s precision mean: %f' % (key, sum(ps) / folds)
print '%s precision variance: %f' % (key, array(ps).var())
for key, rs in recalls.iteritems():
print '%s recall mean: %f' % (key, sum(rs) / folds)
print '%s recall variance: %f' % (key, array(rs).var())
for key, fs in f_measures.iteritems():
print '%s f_measure mean: %f' % (key, sum(fs) / folds)
print '%s f_measure variance: %f' % (key, array(fs).var())
return accuracies, precisions, recalls, f_measures
str(nltk.classify.accuracy(classifier, cv_test)))
foldAccuracies.append(str(nltk.classify.accuracy(classifier, cv_test)))
# most informative feauures
# now get fold stats such as precison, recall, f score
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(cv_test):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
foldPositivePrecisions.append(
str(precision(refsets['spam'], testsets['spam'])))
foldPositiveRecalls.append(str(recall(refsets['spam'], testsets['spam'])))
foldPositiveFScores.append(
str(f_measure(refsets['spam'], testsets['spam'])))
foldNegativePrecisions.append(
str(precision(refsets['ham'], testsets['ham'])))
foldNegativeRecalls.append(str(recall(refsets['ham'], testsets['ham'])))
foldNegativeFScores.append(str(f_measure(refsets['ham'], testsets['ham'])))
print('Positive Precision:', precision(refsets['spam'], testsets['spam']))
print('Positive Recall:', recall(refsets['spam'], testsets['spam']))
print('Positive F1-Score:', f_measure(refsets['spam'], testsets['spam']))
print('Negative Precision:', precision(refsets['ham'], testsets['ham']))
print('Negative Recall:', recall(refsets['ham'], testsets['ham']))
print('Negative F1-Score:', f_measure(refsets['ham'], testsets['ham']))
classifier.show_most_informative_features(5)
total = 0
totalPrecPos = 0
def eval(test_alignments):
f = open(test_alignments, "r")
# initializing our "counters" used for the aggregate scores
sentence_pairs = 0
ibm1_precision_sum, ibm1_recall_sum, ibm1_aer_sum, ibm1_f1_sum = 0, 0, 0, 0
ibm2_precision_sum, ibm2_recall_sum, ibm2_aer_sum, ibm2_f1_sum = 0, 0, 0, 0
for line in f:
sentence_pairs += 1
strs = line.split("\t")
print("-" * 47)
print("Length of foreign sentence: ", len(strs[0].split()))
print(strs[0])
print(strs[1], "\n")
ibm1_aligns = Alignment.fromstring(strs[2])
ibm2_aligns = Alignment.fromstring(strs[3])
hand_aligns = Alignment.fromstring(strs[4])
'''
Evaluate the sentence pair's precisiona and recall by utilizing the
built in ntlk.metrics precision and recall functions. The functions
parameters are the following:
1. Reference ("Gold Standard"): our hand alignments that follow the same format
as the system produced alignments
2. Test: the alignments produced by the model which will be put in
comparison with the hand alignments
'''
ibm1_precision, ibm1_recall, ibm1_aer, ibm1_f1 = precision(hand_aligns, ibm1_aligns), recall(hand_aligns, ibm1_aligns), \
alignment_error_rate(hand_aligns, ibm1_aligns), f_measure(hand_aligns, ibm1_aligns)
ibm2_precision, ibm2_recall, ibm2_aer, ibm2_f1 = precision(hand_aligns, ibm2_aligns), recall(hand_aligns, ibm2_aligns), \
alignment_error_rate(hand_aligns, ibm2_aligns), f_measure(hand_aligns, ibm2_aligns)
# Add it to our aggregate calculations
ibm1_precision_sum += ibm1_precision
ibm1_recall_sum += ibm1_recall
ibm1_aer_sum += ibm1_aer
ibm1_f1_sum += ibm1_f1
ibm2_precision_sum += ibm2_precision
ibm2_recall_sum += ibm2_recall
ibm2_aer_sum += ibm2_aer
ibm2_f1_sum += ibm2_f1
print("IBM1 Precision: ", ibm1_precision, "\t", "IBM2 Precision: ",
ibm2_precision)
print("IBM1 Recall: ", ibm1_recall, "\t", "IBM2 Recall: ", ibm2_recall)
print("IBM1 AER:", ibm1_aer, "\t", "IBM2 AER: ", ibm2_aer)
print("IBM1 F1: ", ibm1_f1, "\t", "IBM2 F1: ", ibm2_f1)
print("-" * 47, "\n")
f.close()
# Prints out the total statistics of the dataset
print("-" * 23, "AVERAGE STATS", "-" * 23)
print("Average IBM1 Precision: ", ibm1_precision_sum / sentence_pairs,
"\t" * 2, "Average IBM2 Precision: ",
ibm2_precision_sum / sentence_pairs)
print("Average IBM1 Recall: ", ibm1_recall_sum / sentence_pairs, "\t" * 2,
"Average IBM2 Recall: ", ibm2_recall_sum / sentence_pairs)
print("Average IBM1 AER:", ibm1_aer_sum / sentence_pairs, "\t" * 2,
"Average IBM2 AER: ", ibm2_aer_sum / sentence_pairs)
print("Average IBM1 F1: ", ibm1_f1_sum / sentence_pairs, "\t" * 2,
"Average IBM2 F1: ", ibm2_f1_sum / sentence_pairs)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
str = 'SINGLE FOLD RESULT ' + '(' + 'linear-svc' + ')'
#training with LinearSVC
classifier = SklearnClassifier(LinearSVC())
classifier.train(trainfeats)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
accuracy = nltk.classify.util.accuracy(classifier, testfeats) * 100
pos_precision = nltk.precision(refsets['pos'], testsets['pos'])
pos_recall = recall(refsets['pos'], testsets['pos'])
pos_fmeasure = f_measure(refsets['pos'], testsets['pos'])
neg_precision = precision(refsets['neg'], testsets['neg'])
neg_recall = recall(refsets['neg'], testsets['neg'])
neg_fmeasure = f_measure(refsets['neg'], testsets['neg'])
print('')
print('---------------------------------------')
print(str)
print('---------------------------------------')
print('accuracy: ', accuracy, '%')
print('precision', (pos_precision + neg_precision) / 2)
print('recall', (pos_recall + neg_recall) / 2)
print('f-measure', (pos_fmeasure + neg_fmeasure) / 2)
########################################################################################
########################################################################################
def evaluate_classifier(featx):
negfeats = [(featx(f), 'neg') for f in word_split(negdata)]
posfeats = [(featx(f), 'pos') for f in word_split(posdata)]
negcutoff = int(len(negfeats) * 3 / 4)
poscutoff = int(len(posfeats) * 3 / 4)
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
# using 3 classifiers
classifier_list = ['nb', 'maxent', 'svm']
for cl in classifier_list:
if cl == 'maxent':
classifierName = 'Maximum Entropy'
classifier = MaxentClassifier.train(trainfeats,
'GIS',
trace=0,
encoding=None,
labels=None,
gaussian_prior_sigma=0,
max_iter=1)
elif cl == 'svm':
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False)
classifier.train(trainfeats)
else:
classifierName = 'Naive Bayes'
classifier = NaiveBayesClassifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
accuracy = nltk.classify.util.accuracy(classifier, testfeats)
pos_precision = precision(refsets['pos'], testsets['pos'])
pos_recall = recall(refsets['pos'], testsets['pos'])
pos_fmeasure = f_measure(refsets['pos'], testsets['pos'])
neg_precision = precision(refsets['neg'], testsets['neg'])
neg_recall = recall(refsets['neg'], testsets['neg'])
neg_fmeasure = f_measure(refsets['neg'], testsets['neg'])
print('')
print('---------------------------------------')
print('SINGLE FOLD RESULT ' + '(' + classifierName + ')')
print('---------------------------------------')
print('accuracy:', accuracy)
print('precision', (pos_precision + neg_precision) / 2)
print('recall', (pos_recall + neg_recall) / 2)
print('f-measure', (pos_fmeasure + neg_fmeasure) / 2)
#classifier.show_most_informative_features()
print('')
## CROSS VALIDATION
trainfeats = negfeats + posfeats
# SHUFFLE TRAIN SET
# As in cross validation, the test chunk might have only negative or only positive data
random.shuffle(trainfeats)
n = 5 # 5-fold cross-validation
for cl in classifier_list:
subset_size = int(len(trainfeats) / n)
accuracy = []
pos_precision = []
pos_recall = []
neg_precision = []
neg_recall = []
pos_fmeasure = []
neg_fmeasure = []
cv_count = 1
for i in range(n):
testing_this_round = trainfeats[i * subset_size:][:subset_size]
training_this_round = trainfeats[:i * subset_size] + trainfeats[
(i + 1) * subset_size:]
if cl == 'maxent':
classifierName = 'Maximum Entropy'
classifier = MaxentClassifier.train(training_this_round,
'GIS',
trace=0,
encoding=None,
labels=None,
gaussian_prior_sigma=0,
max_iter=1)
elif cl == 'svm':
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False)
classifier.train(training_this_round)
else:
classifierName = 'Naive Bayes'
classifier = NaiveBayesClassifier.train(training_this_round)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_this_round):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
cv_accuracy = nltk.classify.util.accuracy(classifier,
testing_this_round)
cv_pos_precision = precision(refsets['pos'], testsets['pos'])
cv_pos_recall = recall(refsets['pos'], testsets['pos'])
cv_pos_fmeasure = f_measure(refsets['pos'], testsets['pos'])
cv_neg_precision = precision(refsets['neg'], testsets['neg'])
cv_neg_recall = recall(refsets['neg'], testsets['neg'])
cv_neg_fmeasure = f_measure(refsets['neg'], testsets['neg'])
accuracy.append(cv_accuracy)
pos_precision.append(cv_pos_precision)
pos_recall.append(cv_pos_recall)
neg_precision.append(cv_neg_precision)
neg_recall.append(cv_neg_recall)
pos_fmeasure.append(cv_pos_fmeasure)
neg_fmeasure.append(cv_neg_fmeasure)
cv_count += 1
print('---------------------------------------')
print('N-FOLD CROSS VALIDATION RESULT ' + '(' + classifierName + ')')
print('---------------------------------------')
print('accuracy:', sum(accuracy) / n)
print('precision',
(sum(pos_precision) / n + sum(neg_precision) / n) / 2)
print('recall', (sum(pos_recall) / n + sum(neg_recall) / n) / 2)
print('f-measure', (sum(pos_fmeasure) / n + sum(neg_fmeasure) / n) / 2)
print('')
#Precison and recall calculation
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
#35% false positives for the pos label.
print 'Positive precision:', precision(refsets['pos'], testsets['pos'])
#98% recall, so very few false negatives
print 'Positive recall:', recall(refsets['pos'], testsets['pos'])
print 'Positive F-measure:', f_measure(refsets['pos'], testsets['pos'])
print 'Negative precision:', precision(refsets['neg'], testsets['neg'])
print 'Negative recall:', recall(refsets['neg'], testsets['neg'])
print 'Negative F-measure:', f_measure(refsets['neg'], testsets['neg'])
#Accuracy
print '\nAccuracy:', nltk.classify.util.accuracy(classifier, testfeats)
classifier.show_most_informative_features()
#print classifier.classify(word_feats('bad'))
#Remove stopwords and present the paragraph words in order to make sense
def summary(words):
return collections.OrderedDict([(word, True) for word in words
if word not in stopwordset])
def f_measure(self):
return f_measure(self._reference, self._test)
print 'Dictionary : ', dictionary.get_name(), '\n'
print ConfusionMatrix(gold_standard,results).pp()
print 'Accuracy: ', accuracy(gold_standard,results)
for c in [0,1,-1]:
print 'Metrics for class ', c
gold = set()
test = set()
for i,x in enumerate(gold_standard):
if x == c:
gold.add(i)
for i,x in enumerate(results):
if x == c:
test.add(i)
print 'Precision: ', precision(gold, test)
print 'Recall : ', recall(gold, test)
print 'F_measure: ', f_measure(gold, test)
print '\n\n'
#################### Sentences classification ##########################
# Not reported in the paper because LIWC doesn't have neutral class
positive_sents = [reli.words_sentence_pos(s) for s in reli.sents(polarity='positive')]
negative_sents = [reli.words_sentence_pos(s) for s in reli.sents(polarity='negative')]
neutral_sents = [reli.words_sentence_pos(s) for s in reli.sents(polarity='neutral')]
print '#########################################################################'
print '###################### Sentences classification #########################'
print '#########################################################################'
classifier, test_feats)
else:
refsets, testsets = scoring.ref_test_sets(classifier, test_feats)
for label in labels:
ref = refsets[label]
test = testsets[label]
if not args.no_precision:
print('%s precision: %f' % (label, precision(ref, test) or 0))
if not args.no_recall:
print('%s recall: %f' % (label, recall(ref, test) or 0))
if not args.no_fmeasure:
print('%s f-measure: %f' % (label, f_measure(ref, test) or 0))
if args.show_most_informative and hasattr(
classifier, 'show_most_informative_features') and not (
args.multi and args.binary) and not args.cross_fold:
print('%d most informative features' % args.show_most_informative)
classifier.show_most_informative_features(args.show_most_informative)
##############
## pickling ##
##############
if not args.no_pickle:
if args.filename:
fname = os.path.expanduser(args.filename)
else:
name = '%s_%s.pickle' % (args.corpus, '_'.join(args.classifier))
refsets, testsets = scoring.multi_ref_test_sets(classifier, test_feats)
else:
refsets, testsets = scoring.ref_test_sets(classifier, test_feats)
for label in labels:
ref = refsets[label]
test = testsets[label]
if not args.no_precision:
print '%s precision: %f' % (label, precision(ref, test) or 0)
if not args.no_recall:
print '%s recall: %f' % (label, recall(ref, test) or 0)
if not args.no_fmeasure:
print '%s f-measure: %f' % (label, f_measure(ref, test) or 0)
if args.show_most_informative and args.algorithm != 'DecisionTree' and not (args.multi and args.binary):
print '%d most informative features' % args.show_most_informative
classifier.show_most_informative_features(args.show_most_informative)
##############
## pickling ##
##############
if not args.no_pickle:
if args.filename:
fname = os.path.expanduser(args.filename)
else:
name = '%s_%s.pickle' % (args.corpus, args.algorithm)
fname = os.path.join(os.path.expanduser('~/nltk_data/classifiers'), name)
trainfeats = negfeats[:4000] + posfeats[:4000]
testfeats = negfeats[4000:] + posfeats[4000:]
print("train on %d instances, test on %d instances" % (len(trainfeats), len(testfeats)))
classifier = NaiveBayesClassifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
# cross validation 3-fold
feats = negfeats + posfeats
M = math.floor(len(feats) / 3)
result = []
for n in range(3):
val_set = feats[n * M :][:M]
train_set = feats[(n + 1) * M :] + feats[: n * M]
classifier = nltk.NaiveBayesClassifier.train(train_set)
result.append("{:.4f}".format(round(nltk.classify.accuracy(classifier, val_set) * 100, 4)))
print("cross_validation:", result)
print("pos precision:", precision(refsets["pos"], testsets["pos"]))
print("pos recall:", recall(refsets["pos"], testsets["pos"]))
print("pos F-measure:", f_measure(refsets["pos"], testsets["pos"]))
print("neg precision:", precision(refsets["neg"], testsets["neg"]))
print("neg recall:", recall(refsets["neg"], testsets["neg"]))
print("neg F-measure:", f_measure(refsets["neg"], testsets["neg"]))
classifier.show_most_informative_features()
def cross_fold(instances, trainf, testf, folds=10, trace=1, metrics=True, informative=0):
if folds < 2:
raise ValueError('must have at least 3 folds')
# ensure isn't an exhaustible iterable
instances = list(instances)
# randomize so get an even distribution, in case labeled instances are
# ordered by label
random.shuffle(instances)
l = len(instances)
step = l / folds
if trace:
print('step %d over %d folds of %d instances' % (step, folds, l))
accuracies = []
precisions = collections.defaultdict(list)
recalls = collections.defaultdict(list)
f_measures = collections.defaultdict(list)
for f in range(folds):
if trace:
print('\nfold %d' % (f+1))
print('-----%s' % ('-'*len('%s' % (f+1))))
start = f * step
end = start + step
train_instances = instances[:start] + instances[end:]
test_instances = instances[start:end]
if trace:
print('training on %d:%d + %d:%d' % (0, start, end, l))
obj = trainf(train_instances)
if trace:
print('testing on %d:%d' % (start, end))
if metrics:
refsets, testsets = ref_test_sets(obj, test_instances)
for key in set(refsets.keys() + testsets.keys()):
ref = refsets[key]
test = testsets[key]
p = precision(ref, test) or 0
r = recall(ref, test) or 0
f = f_measure(ref, test) or 0
precisions[key].append(p)
recalls[key].append(r)
f_measures[key].append(f)
if trace:
print('%s precision: %f' % (key, p))
print('%s recall: %f' % (key, r))
print('%s f-measure: %f' % (key, f))
accuracy = testf(obj, test_instances)
if trace:
print('accuracy: %f' % accuracy)
accuracies.append(accuracy)
if trace and informative and hasattr(obj, 'show_most_informative_features'):
obj.show_most_informative_features(informative)
if trace:
print('\nmean and variance across folds')
print('------------------------------')
print('accuracy mean: %f' % (sum(accuracies) / folds))
print('accuracy variance: %f' % array(accuracies).var())
for key, ps in iteritems(precisions):
print('%s precision mean: %f' % (key, sum(ps) / folds))
print('%s precision variance: %f' % (key, array(ps).var()))
for key, rs in iteritems(recalls):
print('%s recall mean: %f' % (key, sum(rs) / folds))
print('%s recall variance: %f' % (key, array(rs).var()))
for key, fs in iteritems(f_measures):
print('%s f_measure mean: %f' % (key, sum(fs) / folds))
print('%s f_measure variance: %f' % (key, array(fs).var()))
return accuracies, precisions, recalls, f_measures
def avaliate_classifiers(featureSet):
print("Vamos treinar o classificador agora!")
print("\n")
#random.shuffle(featureSet)
#Vai fazer o calculo de recall e precision
# You need to build 2 sets for each classification label:
# a reference set of correct values, and a test set of observed values.
#Os primeiros 6686 + 500(dia 14) tweets sao positivos e resto(6757 + 500(dia 14)) negativo
positive_tweets = featureSet[:7185]
#Misturando as paradas pra nao ficar testando só os mesmos últimos
random.shuffle(positive_tweets)
#print(featureSet[7185])
#Pra pegar 7185 do pos e 7185 do negativo mas o negativo tem 7213
negative_tweets = featureSet[7185:14372]
random.shuffle(negative_tweets)
#Agora vou dividir cada classe em um conjunto de referencia e outro de teste
pos_cutoff = len(positive_tweets)*3/4
neg_cutoff = len(negative_tweets)*3/4
# 75% dos tweets vao pra ser de referencia(treinamento) e o resto pra teste
pos_references = positive_tweets[:pos_cutoff]
pos_tests = positive_tweets[pos_cutoff:]
neg_references = negative_tweets[:neg_cutoff]
neg_tests = negative_tweets[neg_cutoff:]
#COnjunto de treinamento e de testes pra calcular a accuracy
training_set = pos_references + neg_references
testing_set = pos_tests + neg_tests
start_time = time.time()
global classifier
print("Comecou a treina-lo agora!")
#training_set2 = [(t,l) for (t,l,twe) in training_set]
classifier = nltk.NaiveBayesClassifier.train(training_set)
#testing_set2 = [(t,l) for (t,l,twe) in testing_set]
print("Naive Bayes Algo accuracy:", (nltk.classify.accuracy(classifier, testing_set)) * 100)
classifier.show_most_informative_features(30)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
# for i, (feats, label, l) in enumerate(testing_set):
# refsets[label].add(i)
# observed = classifier.classify(feats)
# testsets[observed].add(i)
# print("--"*200)
# print()
# print("Classified as: ",observed)
# print()
# print(l)
# print()
# print("--"*200)
# raw_input("Press any key to continue:")
print 'pos precision:', precision(refsets['pos'], testsets['pos'])
print 'pos recall:', recall(refsets['pos'], testsets['pos'])
print 'pos F-measure:', f_measure(refsets['pos'], testsets['pos'])
print 'neg precision:', precision(refsets['neg'], testsets['neg'])
print 'neg recall:', recall(refsets['neg'], testsets['neg'])
print 'neg F-measure:', f_measure(refsets['neg'], testsets['neg'])
print("--- Classifier executed in %s seconds ---" % (time.time() - start_time))
def avaliate_new_classifier(featureSet):
print("Vamos treinar o classificador agora!")
print("\n")
#random.shuffle(featureSet)
#Cada um tem 197
positive_tweets = featureSet[:196]
#Misturando as paradas pra nao ficar testando só os mesmos últimos
random.shuffle(positive_tweets)
#print(featureSet[7185])
#Pra pegar 7185 do pos e 7185 do negativo mas o negativo tem 7213
negative_tweets = featureSet[196:293]
random.shuffle(negative_tweets)
neutral_tweets = featureSet[293:]
random.shuffle(neutral_tweets)
#Agora vou dividir cada classe em um conjunto de referencia e outro de teste
pos_cutoff = len(positive_tweets)*3/4
neg_cutoff = len(negative_tweets)*3/4
neu_cutoff = len(neutral_tweets)*3/4
# 75% dos tweets vao pra ser de referencia(treinamento) e o resto pra teste
pos_references = positive_tweets[:pos_cutoff]
pos_tests = positive_tweets[pos_cutoff:]
neg_references = negative_tweets[:neg_cutoff]
neg_tests = negative_tweets[neg_cutoff:]
neu_references = neutral_tweets[:neu_cutoff]
neu_tests = neutral_tweets[neu_cutoff:]
#COnjunto de treinamento e de testes pra calcular a accuracy
training_set = pos_references + neg_references + neu_references
testing_set = pos_tests + neg_tests + neu_tests
start_time = time.time()
global classifier
print("Comecou a treina-lo agora!")
#training_set2 = [(t,l) for (t,l,twe) in training_set]
classifier = nltk.NaiveBayesClassifier.train(training_set)
#testing_set2 = [(t,l) for (t,l,twe) in testing_set]
print("Naive Bayes Algo accuracy:", (nltk.classify.accuracy(classifier, testing_set)) * 100)
classifier.show_most_informative_features(30)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print 'pos precision:', precision(refsets['pos'], testsets['pos'])
print 'pos recall:', recall(refsets['pos'], testsets['pos'])
print 'pos F-measure:', f_measure(refsets['pos'], testsets['pos'])
print 'neg precision:', precision(refsets['neg'], testsets['neg'])
print 'neg recall:', recall(refsets['neg'], testsets['neg'])
print 'neg F-measure:', f_measure(refsets['neg'], testsets['neg'])
print 'neutral precision:', precision(refsets['neutral'], testsets['neutral'])
print 'neutral recall:', recall(refsets['neutral'], testsets['neutral'])
print 'neutral F-measure:', f_measure(refsets['neutral'], testsets['neutral'])
print("--- Classifier executed in %s seconds ---" % (time.time() - start_time))
stop = int(len(texts) * args.fraction)
for t in texts[:stop]:
feat = bag_of_words(norm_words(t))
feats.append(feat)
test_feats.append((feat, label))
print "accuracy:", accuracy(classifier, test_feats)
refsets, testsets = scoring.ref_test_sets(classifier, test_feats)
for label in labels:
ref = refsets[label]
test = testsets[label]
print "%s precision: %f" % (label, precision(ref, test) or 0)
print "%s recall: %f" % (label, recall(ref, test) or 0)
print "%s f-measure: %f" % (label, f_measure(ref, test) or 0)
else:
if args.instances == "sents":
texts = categorized_corpus.sents()
total = len(texts)
elif args.instances == "paras":
texts = (itertools.chain(*para) for para in categorized_corpus.paras())
total = len(categorized_corpus.paras)
elif args.instances == "files":
texts = (categorized_corpus.words(fileids=[fid]) for fid in categorized_corpus.fileids())
total = len(categorized_corpus.fileids())
stop = int(total * args.fraction)
feats = (bag_of_words(norm_words(i)) for i in itertools.islice(texts, stop))
label_counts = collections.defaultdict(int)
def validate(self, validation_set):
if self.classifier is None:
raise Exception("self.classifier is None")
reference=defaultdict(set)
observed=defaultdict(set)
observed['neutral']=set()
for i, (tweet, label) in enumerate(validation_set):
reference[label].add(i)
observation=self.classify(tweet)
observed[observation].add(i)
acc=classify.accuracy(self.classifier, observed)
posp=precision(reference['positive'],observed['positive'])
posr=recall(reference['positive'], observed['positive'])
posf=f_measure(reference['positive'], observed['positive'])
negp=precision(reference['negative'],observed['negative'])
negr=recall(reference['negative'], observed['negative'])
negf=f_measure(reference['negative'], observed['negative'])
print "accuracy: %s" % acc
print "pos precision: %s" % posp
print "pos recall: %s" % posr
print "pos f-measure: %s" % posf
print "neg precision: %s" % negp
print "neg recall: %s" % negr
print "neg f-measure: %s" % negf
return (acc, posp, posr, posf, negp, negr, negf)