def test_lemmatized_bigrams_with_LSA(corpus_train_data, corpus_test_data,
vocabulary_src, with_stopwords_removal,
use_chi_features, use_raw_tokens,
num_components):
from commons.lemmatizing_tokenizer import LemmaTokenizer
from commons.lemmatizing_tokenizer import RawLemmaTokenizer
from clef_globals import min_df, min_tf, test_set_size, max_labels
from clef_vocabulary_loader import load_vocabulary
from sklearn.decomposition import TruncatedSVD
from scipy import sparse
import numpy
max_ngram_size = 2
if with_stopwords_removal == False:
stopwords_pattern = ''
else:
stopwords_pattern = '_stopwords'
if use_chi_features == False:
chi_features_pattern = ''
else:
chi_features_pattern = '_chi'
if use_raw_tokens == False:
raw_tokens_pattern = ''
tokenizer = LemmaTokenizer()
else:
raw_tokens_pattern = '_raw'
tokenizer = RawLemmaTokenizer()
# load vocabulary
vocabulary_tbl_name = 'clef_2010_{0}{1}_lemmas{2}_bigrams{3}_df{4}_tf{5}'.format(
vocabulary_src, raw_tokens_pattern, chi_features_pattern,
stopwords_pattern, min_df, min_tf)
vocabulary = load_vocabulary(vocabulary_tbl_name)
# generate tfidf vectors
corpus_train_tfidf_vectors = vectorize_corpus(corpus_train_data['corpus'],
tokenizer, vocabulary,
max_ngram_size)
corpus_test_tfidf_vectors = vectorize_corpus(corpus_test_data['corpus'],
tokenizer, vocabulary,
max_ngram_size)
# apply LSA
#print numpy.max(corpus_train_tfidf_vectors)
#print numpy.min(corpus_train_tfidf_vectors)
lsa = TruncatedSVD(n_components=num_components)
lsa.fit(corpus_train_tfidf_vectors)
#corpus_train_tfidf_vectors = numpy.dot(corpus_train_tfidf_vectors,pca.components_.transpose())
corpus_train_tfidf_vectors = lsa.transform(corpus_train_tfidf_vectors)
corpus_test_tfidf_vectors = lsa.transform(corpus_test_tfidf_vectors)
# classify & evaluate
results = classify(corpus_train_tfidf_vectors, corpus_train_data['labels'],
corpus_test_tfidf_vectors, corpus_test_data['labels'],
test_set_size, max_labels)
print 'LSA ^', vocabulary_tbl_name, ' --> ', 'precision ', results[
'precision'], 'recall ', results['recall'], 'f1 ', results['f1']
def test_lemmatized_wiktionary_google_bigrams(corpus_train_data,
corpus_test_data, vocabulary_src,
with_stopwords_removal,
use_chi_features,
use_raw_tokens):
from commons.lemmatizing_tokenizer import LemmaTokenizer
from commons.lemmatizing_tokenizer import RawLemmaTokenizer
from clef_globals import min_df, min_tf, test_set_size, max_labels
from clef_vocabulary_loader import load_common_vocabulary
max_ngram_size = 2
if with_stopwords_removal == False:
stopwords_pattern = ''
else:
stopwords_pattern = '_stopwords'
if use_chi_features == False:
chi_features_pattern = ''
else:
chi_features_pattern = '_chi'
if use_raw_tokens == False:
raw_tokens_pattern = ''
tokenizer = LemmaTokenizer()
else:
raw_tokens_pattern = '_raw'
tokenizer = RawLemmaTokenizer()
# load vocabulary
vocabulary_tbl_name1 = 'clef_2010_{0}{1}_lemmas{2}_unigrams{3}_df{4}_tf{5}'.format(
vocabulary_src, raw_tokens_pattern, chi_features_pattern,
stopwords_pattern, min_df, min_tf)
vocabulary_tbl_name2 = 'clef_2010_{0}{1}_lemmas_bigrams{3}_df{4}_tf{5}'.format(
vocabulary_src, raw_tokens_pattern, chi_features_pattern,
stopwords_pattern, min_df, min_tf)
vocabulary_tbl_intersect = 'wiktionary_google_bigrams_vw'
vocabulary = load_common_vocabulary(vocabulary_tbl_name1,
vocabulary_tbl_name2,
vocabulary_tbl_intersect, 'lemma')
# generate tfidf vectors
corpus_train_tfidf_vectors = vectorize_corpus(corpus_train_data['corpus'],
tokenizer, vocabulary,
max_ngram_size)
corpus_test_tfidf_vectors = vectorize_corpus(corpus_test_data['corpus'],
tokenizer, vocabulary,
max_ngram_size)
# classify & evaluate
results = classify(corpus_train_tfidf_vectors, corpus_train_data['labels'],
corpus_test_tfidf_vectors, corpus_test_data['labels'],
test_set_size, max_labels)
print vocabulary_tbl_name1, '^', vocabulary_tbl_name2, '^', vocabulary_tbl_intersect, ' --> ', 'precision ', results[
'precision'], 'recall ', results['recall'], 'f1 ', results['f1']
def build_raw_lemmatized_bigrams_stopwords_vocabulary(corpus,stop_words,):
from ng20_globals import min_tf,min_df
from commons.lemmatizing_tokenizer import RawLemmaTokenizer
tokenizer = RawLemmaTokenizer()
max_ngram_size = 2
vocabulary = build_vocabulary(corpus,tokenizer,stop_words,max_ngram_size,min_df,min_tf)
# save to DB
tbl_name = 'ng20_raw_lemmas_bigrams_stopwords_df{0}_tf{1}'.format(min_df,min_tf)
save_vocabulary(vocabulary,tbl_name)
print 'done '+tbl_name
def test_lemmatized_bigrams_unigrams(bigrams_src, corpus_train_data,
corpus_test_data, label_names,
with_stopwords_removal, use_chi_features,
use_raw_tokens):
import numpy as np
from commons.lemmatizing_tokenizer import LemmaTokenizer
from commons.lemmatizing_tokenizer import RawLemmaTokenizer
from ng20_globals import max_labels, min_df, min_tf
from ng20_vocabulary_loader import load_common_vocabulary_extend_unigrams
max_ngram_size = 2
if with_stopwords_removal == False:
stopwords_pattern = ''
else:
stopwords_pattern = '_stopwords'
if use_chi_features == False:
chi_features_pattern = ''
else:
chi_features_pattern = '_chi'
if use_raw_tokens == False:
raw_tokens_pattern = ''
tokenizer = LemmaTokenizer()
else:
raw_tokens_pattern = '_raw'
tokenizer = RawLemmaTokenizer()
# load vocabulary
vocabulary_tbl_name = 'ng20{0}_lemmas{1}_bigrams{2}_df{3}_tf{4}'.format(
raw_tokens_pattern, chi_features_pattern, stopwords_pattern, min_df,
min_tf)
if len(bigrams_src) == 1:
vocabulary_tbl_intersect = '{0}_bigrams'.format(bigrams_src[0])
else:
vocabulary_tbl_intersect = '{0}_'.format(bigrams_src[0])
for i in range(len(bigrams_src) - 1):
vocabulary_tbl_intersect = '{0}{1}_'.format(
vocabulary_tbl_intersect, bigrams_src[i + 1])
vocabulary_tbl_intersect = '{0}bigrams_vw'.format(
vocabulary_tbl_intersect)
vocabulary = load_common_vocabulary_extend_unigrams(
vocabulary_tbl_name, vocabulary_tbl_intersect, 'lemma')
print 'done loading vocabulary'
# generate tfidf vectors
vectorizer, corpus_train_tfidf_vectors = vectorize_corpus(
corpus_train_data['corpus'], tokenizer, vocabulary, max_ngram_size)
_, corpus_test_tfidf_vectors = vectorize_corpus(corpus_test_data['corpus'],
tokenizer, vocabulary,
max_ngram_size)
# classify & evaluate
results = classify(corpus_train_tfidf_vectors, corpus_train_data['labels'],
corpus_test_tfidf_vectors, corpus_test_data['labels'],
max_labels)
print_top_feature_names(
results['features_weights'],
np.asarray(vectorizer.get_feature_names()),
vocabulary_tbl_name + '_' + vocabulary_tbl_intersect, label_names)
print vocabulary_tbl_name, '^', vocabulary_tbl_intersect, '(extended unigrams) --> ', 'accuracy ', results[
'accuracy'] #print vocabulary_tbl_name,'^',vocabulary_tbl_intersect,'(extended unigrams) --> ','precision ',results['precision'],'recall ',results['recall'],'f1 ',results['f1']
def test_lemmatized_bigrams_with_LSA(corpus_train_data, corpus_test_data,
label_names, with_stopwords_removal,
use_chi_features, use_raw_tokens,
num_components):
import numpy as np
from commons.lemmatizing_tokenizer import LemmaTokenizer
from commons.lemmatizing_tokenizer import RawLemmaTokenizer
from ng20_globals import max_labels, min_df, min_tf
from ng20_vocabulary_loader import load_vocabulary
from sklearn.decomposition import TruncatedSVD
max_ngram_size = 2
if with_stopwords_removal == False:
stopwords_pattern = ''
else:
stopwords_pattern = '_stopwords'
if use_chi_features == False:
chi_features_pattern = ''
else:
chi_features_pattern = '_chi'
if use_raw_tokens == False:
raw_tokens_pattern = ''
tokenizer = LemmaTokenizer()
else:
raw_tokens_pattern = '_raw'
tokenizer = RawLemmaTokenizer()
# load vocabulary
vocabulary_tbl_name = 'ng20{0}_lemmas{1}_bigrams{2}_df{3}_tf{4}'.format(
raw_tokens_pattern, chi_features_pattern, stopwords_pattern, min_df,
min_tf)
vocabulary = load_vocabulary(vocabulary_tbl_name)
# generate tfidf vectors
vectorizer, corpus_train_tfidf_vectors = vectorize_corpus(
corpus_train_data['corpus'], tokenizer, vocabulary, max_ngram_size)
_, corpus_test_tfidf_vectors = vectorize_corpus(corpus_test_data['corpus'],
tokenizer, vocabulary,
max_ngram_size)
# apply LSA
#print numpy.max(corpus_train_tfidf_vectors)
#print numpy.min(corpus_train_tfidf_vectors)
lsa = TruncatedSVD(n_components=num_components)
lsa.fit(corpus_train_tfidf_vectors)
#corpus_train_tfidf_vectors = numpy.dot(corpus_train_tfidf_vectors,pca.components_.transpose())
corpus_train_tfidf_vectors = lsa.transform(corpus_train_tfidf_vectors)
corpus_test_tfidf_vectors = lsa.transform(corpus_test_tfidf_vectors)
# classify & evaluate
results = classify(corpus_train_tfidf_vectors, corpus_train_data['labels'],
corpus_test_tfidf_vectors, corpus_test_data['labels'],
max_labels)
print_top_feature_names(results['features_weights'],
np.asarray(vectorizer.get_feature_names()),
vocabulary_tbl_name, 'lsa_' + label_names)
print 'LSA ^', vocabulary_tbl_name, ' --> ', 'precision ', results[
'precision'], 'recall ', results['recall'], 'f1 ', results['f1']
def run_experiment(corpus_train_data,
corpus_test_data,
label_names,
with_stopwords_removal=False,
use_chi_features=False,
preprocess_tokens='raw',
ngram_size=1,
knowledge_set=None):
import numpy as np
from commons.lemmatizing_tokenizer import RawTokenizer
from commons.lemmatizing_tokenizer import RawLemmaTokenizer
from commons.stemming_tokenizer import RawStemmingTokenizer
from ng20_globals import max_labels, min_df, min_tf
from ng20_vocabulary_loader import load_vocabulary
from ng20_vocabulary_loader import load_common_vocabulary
from commons.globals import knowledge_dic
if with_stopwords_removal == False:
stopwords_pattern = ''
else:
stopwords_pattern = '_stopwords'
if use_chi_features == False:
chi_features_pattern = ''
else:
chi_features_pattern = '_chi'
if preprocess_tokens == 'lemmatize':
raw_tokens_pattern = '_raw_lemmas'
tokenizer = RawLemmaTokenizer()
elif preprocess_tokens == 'stem':
raw_tokens_pattern = '_raw_stems'
tokenizer = RawStemmingTokenizer()
else:
raw_tokens_pattern = '_raw'
tokenizer = RawTokenizer()
if ngram_size == 1:
ngram_pattern = '_unigrams'
elif ngram_size == 2:
ngram_pattern = '_bigrams'
# load vocabulary
if knowledge_set != None:
vocabulary_tbl_name1 = 'ng20{0}{1}_unigrams{2}_df{3}_tf{4}'.format(
raw_tokens_pattern, chi_features_pattern, stopwords_pattern,
min_df, min_tf)
vocabulary_tbl_name2 = 'ng20{0}{1}_bigrams{2}_df{3}_tf{4}'.format(
raw_tokens_pattern, chi_features_pattern, stopwords_pattern,
min_df, min_tf)
vocabulary_tbl_intersect = knowledge_dic[knowledge_set.lower()]
vocabulary = load_common_vocabulary(vocabulary_tbl_name1,
vocabulary_tbl_name2,
vocabulary_tbl_intersect, 'stem')
vocabulary_tbl_name = vocabulary_tbl_name1, '^', vocabulary_tbl_name2, '^', vocabulary_tbl_intersect
else:
vocabulary_tbl_name = 'ng20{0}{1}{2}{3}_df{4}_tf{5}'.format(
raw_tokens_pattern, chi_features_pattern, ngram_pattern,
stopwords_pattern, min_df, min_tf)
vocabulary = load_vocabulary(vocabulary_tbl_name)
# generate tfidf vectors
vectorizer, corpus_train_tfidf_vectors = vectorize_corpus(
corpus_train_data['corpus'], tokenizer, vocabulary, ngram_size)
_, corpus_test_tfidf_vectors = vectorize_corpus(corpus_test_data['corpus'],
tokenizer, vocabulary,
ngram_size)
# classify & evaluate
results = classify_cv(corpus_train_tfidf_vectors,
corpus_train_data['labels'],
corpus_test_tfidf_vectors,
corpus_test_data['labels'], max_labels)
print_top_feature_names(results['features_weights'],
np.asarray(vectorizer.get_feature_names()),
vocabulary_tbl_name, label_names)
print vocabulary_tbl_name, ' --> ', 'accuracy', results[
'accuracy'] #print vocabulary_tbl_name,' --> ','precision ',results['precision'],'recall ',results['recall'],'f1 ',results['f1']
