def make_classifier_builder(args):
if isinstance(args.classifier, basestring):
algos = [args.classifier]
else:
algos = args.classifier
for algo in algos:
if algo not in classifier_choices:
raise ValueError('classifier %s is not supported' % algo)
classifier_train_args = []
for algo in algos:
classifier_train_kwargs = {}
if algo == 'DecisionTree':
classifier_train = DecisionTreeClassifier.train
classifier_train_kwargs['binary'] = False
classifier_train_kwargs['entropy_cutoff'] = args.entropy_cutoff
classifier_train_kwargs['depth_cutoff'] = args.depth_cutoff
classifier_train_kwargs['support_cutoff'] = args.support_cutoff
classifier_train_kwargs['verbose'] = args.trace
elif algo == 'NaiveBayes':
classifier_train = NaiveBayesClassifier.train
elif algo == 'Scikits':
classifier_train = ScikitsClassifier.train
else:
if algo != 'Maxent':
classifier_train_kwargs['algorithm'] = algo
if algo == 'MEGAM':
megam.config_megam()
classifier_train = MaxentClassifier.train
classifier_train_kwargs['max_iter'] = args.max_iter
classifier_train_kwargs['min_ll'] = args.min_ll
classifier_train_kwargs['min_lldelta'] = args.min_lldelta
classifier_train_kwargs['trace'] = args.trace
classifier_train_args.append((algo, classifier_train, classifier_train_kwargs))
def trainf(train_feats):
classifiers = []
for algo, classifier_train, train_kwargs in classifier_train_args:
if args.trace:
print 'training %s classifier' % algo
classifiers.append(classifier_train(train_feats, **train_kwargs))
if len(classifiers) == 1:
return classifiers[0]
else:
return AvgProbClassifier(classifiers)
return trainf
#return lambda(train_feats): classifier_train(train_feats, **classifier_train_kwargs)
def maximum_entropy(train_set, test_set):
print "--- nltk.classify.maximum_entropy ---"
from nltk.classify import megam
megam.config_megam()
classifier = nltk.classify.MaxentClassifier.train(train_set, "megam")
print "Overall accuracy:", accuracy(classifier, test_set)
classifier.show_most_informative_features(10)
def __init__(self, train_sents):
train_set = []
for tagged_sent in train_sents:
untagged_sent = nltk.tag.untag(tagged_sent)
history = []
for i, (word, tag) in enumerate(tagged_sent):
featureset = npchunk_features(untagged_sent, i, history)
train_set.append( (featureset, tag) )
history.append(tag)
from nltk.classify import megam
megam.config_megam(bin='/cs/fs/home/hxiao/code/megam_i686.opt')
self.classifier = nltk.MaxentClassifier.train(
train_set, algorithm='megam', trace=0)
def __init__(self, train_sents):
train_set = []
for tagged_sent in train_sents:
untagged_sent = nltk.tag.untag(tagged_sent)
history = []
for i, (word, tag) in enumerate(tagged_sent):
featureset = npchunk_features(untagged_sent, i, history)
train_set.append((featureset, tag))
history.append(tag)
from nltk.classify import megam
megam.config_megam(bin='/cs/fs/home/hxiao/code/megam_i686.opt')
self.classifier = nltk.MaxentClassifier.train(train_set,
algorithm='megam',
trace=0)
def main():
megam.config_megam(megam_path)
feature_extractor = BigramFeatureExtractor()
training_documents = []
training_documents.append((['muy', 'buena', 'comida', 'hola'], 'pos'))
training_documents.append((['muy', 'mala', 'comida'], 'neg'))
training_documents.append((['muy', 'mala', 'comida', 'hola'], 'neg'))
training_documents.append((['buena', 'comida'], 'pos'))
training_set = nltk.classify.util.apply_features(feature_extractor.extract,
training_documents)
classifier = nltk.MaxentClassifier.train(training_set,
algorithm='megam',
explicit=False,
bernoulli=True,
model='binary')
classifier.show_most_informative_features()
def make_classifier_builder(args):
if isinstance(args.classifier, basestring):
algos = [args.classifier]
else:
algos = args.classifier
for algo in algos:
if algo not in classifier_choices:
raise ValueError('classifier %s is not supported' % algo)
classifier_train_args = []
for algo in algos:
classifier_train_kwargs = {}
if algo == 'DecisionTree':
classifier_train = DecisionTreeClassifier.train
classifier_train_kwargs['binary'] = False
classifier_train_kwargs['entropy_cutoff'] = args.entropy_cutoff
classifier_train_kwargs['depth_cutoff'] = args.depth_cutoff
classifier_train_kwargs['support_cutoff'] = args.support_cutoff
classifier_train_kwargs['verbose'] = args.trace
elif algo == 'NaiveBayes':
classifier_train = NaiveBayesClassifier.train
elif algo == 'Svm':
classifier_train = SvmClassifier.train
elif algo.startswith('sklearn.'):
# TODO: support many options for building an estimator pipeline
pipe = [('classifier', make_sklearn_classifier(algo, args))]
if args.tfidf:
if args.trace:
print 'using tfidf transformer with norm %s' % args.penalty
pipe.insert(0, ('tfidf', TfidfTransformer(norm=args.penalty)))
sparse = pipe[-1][1].__class__.__name__ not in dense_classifiers
if not sparse and args.trace:
print 'using dense matrix'
if args.value_type == 'bool' and not args.tfidf:
dtype = bool
elif args.value_type == 'int' and not args.tfidf:
dtype = int
else:
dtype = float
if args.trace:
print 'using dtype %s' % dtype.__name__
classifier_train = scikitlearn.SklearnClassifier(Pipeline(pipe), dtype=dtype, sparse=sparse).train
else:
if algo != 'Maxent':
classifier_train_kwargs['algorithm'] = algo
if algo == 'MEGAM':
megam.config_megam()
classifier_train = MaxentClassifier.train
classifier_train_kwargs['max_iter'] = args.max_iter
classifier_train_kwargs['min_ll'] = args.min_ll
classifier_train_kwargs['min_lldelta'] = args.min_lldelta
classifier_train_kwargs['trace'] = args.trace
classifier_train_args.append((algo, classifier_train, classifier_train_kwargs))
def trainf(train_feats):
classifiers = []
for algo, classifier_train, train_kwargs in classifier_train_args:
if args.trace:
print 'training %s classifier' % algo
classifiers.append(classifier_train(train_feats, **train_kwargs))
if len(classifiers) == 1:
return classifiers[0]
else:
return AvgProbClassifier(classifiers)
return trainf
def __init__(self, n_folds, corpus_size, fold_number, remove_stop_words, use_unigrams, use_unigrams_frequency, use_bigrams, use_all_bigrams, min_word_length, remove_duplicated_chars, process_negation, stem, transform_lower_case, remove_punctuation_marks, remove_accents, lemma, adjectives, allprepro, out_of_domain_test, proportion_of_positives):
super(CrossValidatedMegamMaxEntClassifier, self).__init__(n_folds, corpus_size, fold_number, remove_stop_words, use_unigrams, use_unigrams_frequency, use_bigrams, use_all_bigrams, min_word_length, remove_duplicated_chars, process_negation, stem, transform_lower_case, remove_punctuation_marks, remove_accents, lemma, adjectives, allprepro, out_of_domain_test, proportion_of_positives)
megam.config_megam(megam_path)
import nltk
from nltk.classify import megam
megam.config_megam('/Users/arlogb/ext_sources/megam_0.92/')
#We will use the nltk NaiveBayesClassifer
def tset(extractor, tok):
"""function wrapping the apply_feature function. Should pass a
feature extracting function which returns a featureset - dict mapping features to
feature values. Tok are tokens which extractor will be applied to."""
trainset = nltk.classify.apply_features(extractor, tok)
return trainset
def trainclassifier(trainset):
return nltk.NaiveBayesClassifier.train(trainset)
def NBCtrain(labeled_featuresets, estimator=nltk.ELEProbDist):
"""A copy of the nltk.NaiveBayesClassifer.train(...)
method to allow inspection of what the method is actually doing
and how long it's taking"""
"""
@param labeled_featuresets: A list of classified featuresets,
i.e., a list of tuples C{(featureset, label)}.
"""
label_freqdist = nltk.FreqDist()
feature_freqdist = nltk.defaultdict(nltk.FreqDist)
feature_values = nltk.defaultdict(set)
fnames = set()
print 'There are ' + str(len(labeled_featuresets)) + ' labeled featuresets'
# Count up how many times each feature value occured, given
# the label and featurename.
print 'Counting feature value occurence'
help="use rules that are applied after the maxent algorithm")
parser.add_argument("--classifier",
help="classifying algorithm to use",
choices=["maxent", "svm", "dt", "rf"],
default="svm")
args = parser.parse_args()
inputdir = args.inputdirdevel if args.testdir == "devel" else args.inputdirtest
train_file = "features/features_megan_train_" + str(args.version) + ".txt"
test_file = "features/features_megan_" + args.testdir + "_" + str(
args.version) + ".txt"
if args.classifier == "maxent":
# Train
megam.config_megam("src/megam_i686.opt")
train = megam.call_megam(["multiclass", train_file])
with open("features/weights", "w") as f:
f.write(train)
# Prediction
predictions_text = megam.call_megam(
["-predict", "features/weights", "multiclass", test_file])
predictions = [
y.split("\t")[0] for y in predictions_text.split("\n")
if len(y) > 1
]
else: # sklearn classifiers
# Read train files
with open(train_file, 'r') as f:
self.old_stdout.flush()
self.old_stderr.flush()
sys.stdout, sys.stderr = self._stdout, self._stderr
def __exit__(self, exc_type, exc_value, traceback):
self._stdout.flush()
self._stderr.flush()
sys.stdout = self.old_stdout
sys.stderr = self.old_stderr
try:
import nltk.classify.megam as megam
import nltk.classify.maxent as maxent
with RedirectStdStreams(stdout=sys.stderr):
megam.config_megam()
#megam.config_megam(bin="path/to/megam")
use_megam = True
except:
print >> sys.stderr, "ERROR: megam not found, configure it before using this"
print >> sys.stderr, "using default nltk maxent"
use_megam = False
class MaxentLearner:
"""
wrapper to nltk version of max entropy classifier
TODO: forces to use megam, could use a parameter for other algorithms.
return_type: whether to return the "label" (a string) or an orange "value"
"""
self.old_stdout.flush()
self.old_stderr.flush()
sys.stdout, sys.stderr = self._stdout, self._stderr
def __exit__(self, exc_type, exc_value, traceback):
self._stdout.flush()
self._stderr.flush()
sys.stdout = self.old_stdout
sys.stderr = self.old_stderr
try:
import nltk.classify.megam as megam
import nltk.classify.maxent as maxent
with RedirectStdStreams(stdout=sys.stderr):
megam.config_megam()
#megam.config_megam(bin="path/to/megam")
use_megam = True
except:
print >> sys.stderr, "ERROR: megam not found, configure it before using this"
print >> sys.stderr, "using default nltk maxent"
use_megam = False
class MaxentLearner:
"""
wrapper to nltk version of max entropy classifier
TODO: forces to use megam, could use a parameter for other algorithms.
return_type: whether to return the "label" (a string) or an orange "value"
"""
def make_classifier_builder(args):
if isinstance(args.classifier, basestring):
algos = [args.classifier]
else:
algos = args.classifier
for algo in algos:
if algo not in classifier_choices:
raise ValueError("classifier %s is not supported" % algo)
classifier_train_args = []
for algo in algos:
classifier_train_kwargs = {}
if algo == "DecisionTree":
classifier_train = DecisionTreeClassifier.train
classifier_train_kwargs["binary"] = False
classifier_train_kwargs["entropy_cutoff"] = args.entropy_cutoff
classifier_train_kwargs["depth_cutoff"] = args.depth_cutoff
classifier_train_kwargs["support_cutoff"] = args.support_cutoff
classifier_train_kwargs["verbose"] = args.trace
elif algo == "NaiveBayes":
classifier_train = NaiveBayesClassifier.train
elif algo == "Svm":
classifier_train = SvmClassifier.train
elif algo.startswith("sklearn."):
# TODO: support many options for building an estimator pipeline
pipe = [("classifier", make_sklearn_classifier(algo, args))]
tfidf = getattr(args, "tfidf", None)
penalty = getattr(args, "penalty", None)
if tfidf and penalty:
if args.trace:
print("using tfidf transformer with norm %s" % penalty)
pipe.insert(0, ("tfidf", TfidfTransformer(norm=penalty)))
sparse = pipe[-1][1].__class__.__name__ not in dense_classifiers
if not sparse and args.trace:
print("using dense matrix")
value_type = getattr(args, "value_type", "bool")
if value_type == "bool" and not tfidf:
dtype = bool
elif value_type == "int" and not tfidf:
dtype = int
else:
dtype = float
if args.trace:
print("using dtype %s" % dtype.__name__)
classifier_train = scikitlearn.SklearnClassifier(Pipeline(pipe), dtype=dtype, sparse=sparse).train
else:
if algo != "Maxent":
classifier_train_kwargs["algorithm"] = algo
if algo == "MEGAM":
megam.config_megam()
classifier_train = MaxentClassifier.train
classifier_train_kwargs["max_iter"] = args.max_iter
classifier_train_kwargs["min_ll"] = args.min_ll
classifier_train_kwargs["min_lldelta"] = args.min_lldelta
classifier_train_kwargs["trace"] = args.trace
classifier_train_args.append((algo, classifier_train, classifier_train_kwargs))
def trainf(train_feats):
classifiers = []
for algo, classifier_train, train_kwargs in classifier_train_args:
if args.trace:
print("training %s classifier" % algo)
classifiers.append(classifier_train(train_feats, **train_kwargs))
if len(classifiers) == 1:
return classifiers[0]
else:
return AvgProbClassifier(classifiers)
return trainf
import nltk
from nltk.classify import megam
megam.config_megam("/Users/arlogb/ext_sources/megam_0.92/")
# We will use the nltk NaiveBayesClassifer
def tset(extractor, tok):
"""function wrapping the apply_feature function. Should pass a
feature extracting function which returns a featureset - dict mapping features to
feature values. Tok are tokens which extractor will be applied to."""
trainset = nltk.classify.apply_features(extractor, tok)
return trainset
def trainclassifier(trainset):
return nltk.NaiveBayesClassifier.train(trainset)
def NBCtrain(labeled_featuresets, estimator=nltk.ELEProbDist):
"""A copy of the nltk.NaiveBayesClassifer.train(...)
method to allow inspection of what the method is actually doing
and how long it's taking"""
"""
@param labeled_featuresets: A list of classified featuresets,
i.e., a list of tuples C{(featureset, label)}.
"""
label_freqdist = nltk.FreqDist()
feature_freqdist = nltk.defaultdict(nltk.FreqDist)
feature_values = nltk.defaultdict(set)
fnames = set()
print "There are " + str(len(labeled_featuresets)) + " labeled featuresets"
import ipdb
ipdb.set_trace()
for clf in clfs:
evaluate_cross_validataion(clf, data, target, 5)
import ipdb
ipdb.set_trace()
if not os.path.exists(MULTI_CLASSIFIER_NAME):
st = str("/usr/local/bin/megam")
import ipdb
ipdb.set_trace()
config_megam(st)
rwords = reuters_high_info_words()
featdet = lambda words: bag_of_words_in_set(words, rwords)
multi_train_feats, multi_test_feats = reuters_train_test_feats(featdet)
trainf = lambda train_feats: MaxentClassifier.train(train_feats, algorithm="megam", trace=0, max_iter=10)
# labelset = set(reuters.categories())
labelset = set(list(publics.keys()))
classifiers = train_binary_classifiers(trainf, multi_train_feats, labelset)
len(classifiers)
multi_classifier = MultiBinaryClassifier(*classifiers.items())
output = open(MULTI_CLASSIFIER_NAME, "wb")
pickle.dump(multi_classifier, output)
##########################
from nltk.data import path as nltk_data_path
nltk_data_location = os.getenv('NLTK_DATA_PATH')
if nltk_data_location is not None:
nltk_data_path.append(nltk_data_location)
# EXTERNAL LIBRARIES
##########################
# NOTE Set this directory to wherever megam, MITIE, Stanford NER and SENNA are
# located.
EXTLIB_DIR = '/Data/nlp/utilities/'
#TODO this might need a try/except as well:
MEGAM_DIR = EXTLIB_DIR + 'megam_0.92/'
try:
megam.config_megam(MEGAM_DIR + 'megam.opt')
except:
print("megam is not installed or not configured correctly.")
MITIE_DIR = EXTLIB_DIR + 'MITIE/'
MITIE_LIB_DIR = MITIE_DIR + 'mitielib/'
sys.path.append(MITIE_LIB_DIR)
try:
import mitie
except:
print("To use the pre-trained MITIE model, you will need to install the " +
"MITIE Python wrapper.")
try:
stanford_ner_path = os.environ.get('CLASSPATH')
except:
import optparse
import sys
import bleu_smooth
from nltk.classify.megam import call_megam, parse_megam_weights,config_megam
optparser = optparse.OptionParser()
optparser.add_option("-k", "--kbest-list", dest="train", default="data/train.100best", help="100-best translation lists")
optparser.add_option("-d", "--dev_kbest-list", dest="dev", default="data/dev+test.100best", help="100-best translation lists")
optparser.add_option("-r", "--reference", dest="reference", default="data/train.ref", help="Target language reference sentences")
(opts, _) = optparser.parse_args()
lm = tm1 = -0.92
tm2 = -1
megam_features = []
samples = []
sent_features = []
sign = lambda x: (1, -1)[x<0]
config_megam("/usr/local/bin/")
#Read Reference Translation for Training
ref = [line.strip().split() for line in open(opts.reference)]
#Read Candidate Translations for Training
all_hyps = [pair.split(' ||| ') for pair in open(opts.train)]
num_sents = len(all_hyps) / 100
bleu_score_per_sent = []
for s in xrange(0, num_sents):
del bleu_score_per_sent[:]
del sent_features[:]
del samples[:]
empty=0
hyps_for_one_sent = all_hyps[s * 100:s * 100 + 100]
#compute BLEU+1 for label and read/compute feature values
for (num, hyp, feats) in hyps_for_one_sent:
untranslated=0