def getVideos(root):
''' get all videos in the root directory and all its subdirectories or the
root if it is a video file'''
videos = []
if path.isfile(root):
dirpath, nameWithExt = path.split(root)
name, ext = path.splitext(nameWithExt)
if ext[1:] in common.videoTypes:
videos.append(
common.videoToDict(dirpath, name, common.tokenize(name)))
else:
raise Exception('File is not a known video format')
else:
for (dirpath, dirname, filenames) in os.walk(root):
# get all files with extension from videoTypes, leading dot is
# stripped from the extension
for f in filenames:
name, ext = path.splitext(f)
if ext[1:] in common.videoTypes:
videos.append(common.videoToDict(
dirpath, name, common.tokenize(name)))
if len(videos) == 0:
raise Exception(
'No file with known video format found in the directory')
return videos
def transform(self, documents):
documents = [tokenize(d) for d in documents]
documents = [d[:self.max_page_size] for d in documents]
documents = [' '.join(d) for d in documents]
if self.encoding_type in ['tfidf', 'count', 'binary']:
return self.vectorizer.transform(documents).toarray()
if self.encoding_type == 'lda':
documents_tokenized = [tokenize(i) for i in documents]
other_corpus = [
self.common_dictionary.doc2bow(i) for i in documents_tokenized
]
results = []
for i in other_corpus:
result = self.vectorizer[i]
result = vectorize_topic_models(result, self.num_of_topics)
results.append(result)
return np.array(results)
if self.encoding_type in ['doc2vec']:
documents_tokenized = [tokenize(i) for i in documents]
results = []
for i in documents_tokenized:
if i:
try:
results.append(self.vectorizer[i][0])
except KeyError:
results.append([0 for _ in range(self.encoding_size)])
else:
results.append([0 for _ in range(self.encoding_size)])
return np.array(results)
if self.encoding_type in ['fasttext']:
documents_clean = [clean_text(i) for i in documents]
results = []
for i in documents_clean:
if i:
results.append(self.vectorizer.get_sentence_vector(i))
# results.append(self.vectorizer[i])
else:
results.append(
np.array([0 for _ in range(self.encoding_size)]))
return np.array(results)
def convert_format(self):
sentences = []
words = pkl.load(open("LM_corpura//%s//%s" % (cfg['lm_corpus'], cfg['corpus__dict_file']), 'rb'))
word_dict = dict([(word, key) for key, word in enumerate(words, 1)])
#word_dict = common.get_word_dict(self.conf['index2word_path'])
if self.name == "pos_tagging":
tags = set([tag for word, tag in treebank.tagged_words()])
tag_index = {tag: idx for idx, tag in enumerate(tags, 1)}
for sentence in treebank.tagged_sents():
sent_words = [(word_dict[common.tokenize(w)], tag_index[t])
if common.tokenize(w) in word_dict else (0, tag_index[t])
for w, t in sentence]
sentences.append(sent_words)
return sentences
def read_data(self, tagged_sentences):
features = []
tags = []
for sentence in tagged_sentences:
sent_words = [
self.word_dict[common.tokenize(w)]
if common.tokenize(w) in self.word_dict else len(self.words)
for w, t in sentence
]
sent_tags = [t for w, t in sentence]
sent_representations = self.lm.predict(np.asarray(sent_words))
sent_representations = np.squeeze(sent_representations, axis=1)
features.append(sent_representations)
tags.append(sent_tags)
all_tags = set(np.concatenate(tags))
tags_enum = [(tag, idx) for idx, tag in enumerate(all_tags)]
tag_dict = dict(tags_enum)
tags = [[tag_dict[tag] for tag in sent_tags] for sent_tags in tags]
#features = np.concatenate(features)
tags = np.asarray(tags)
tags = keras.utils.to_categorical(tags)
X_train, X_test, y_train, y_test = train_test_split(features,
tags,
test_size=0.2,
random_state=42)
for size in [0.01, 0.05, 0.1, 0.2]:
print("Using %d examples" % (int(X_train.shape[0] * size)))
svm = SVC(kernel='linear')
svm.fit(X_train[:int(X_train.shape[0] * size)],
y_train[:int(X_train.shape[0] * size)])
score = svm.score(X_test, y_test)
print("for %.2f%% of the data: %.2f%% accuracy" %
(100 * size, 100 * score))
def process_html(r_text, r_time, url, timestamp, file_name):
if r_text:
record = generate_link_dict(url)
soup = BeautifulSoup(r_text, 'lxml')
new_links = [i['href'] for i in soup.find_all('a', href=True)]
new_abs_links = [i for i in new_links if is_link_external(i, record['netloc'])]
record['page_external_links'] = str(new_abs_links)
record['request_time'] = r_time
record['request_timestamp'] = timestamp
meta_data = get_meta_info_from_html(r_text)
page_text = get_text_from_html(r_text)
record['html_char_len'] = len(r_text)
record['text_char_len'] = len(page_text)
record['meta_char_len'] = len(meta_data)
record['html_word_len'] = len(tokenize(r_text))
record['text_word_len'] = len(tokenize(page_text))
record['meta_word_len'] = len(tokenize(meta_data))
with open(f'{dir_loc}/all_html_chunks/{file_name}.txt', 'a') as f:
f.write(f'{url}{sep_char}{str(r_text).replace(sep_char, "")}' + "\n")
with open(f'{dir_loc}/all_meta_chunks/{file_name}.txt', 'a') as f:
f.write(f'{url}{sep_char}{str(meta_data).replace(sep_char, "")}' + "\n")
with open(f'{dir_loc}/all_text_chunks/{file_name}.txt', 'a') as f:
f.write(f'{url}{sep_char}{str(page_text).replace(sep_char, "")}' + "\n")
record['file_name'] = str(file_name)
record_df = pd.DataFrame.from_dict([record])
record_df = record_df.set_index('url')
while True:
try:
with sqlite3.connect(f'{dir_loc}/dbs/{db_name}') as conn_disk:
record_df.to_sql('websites', conn_disk, if_exists='append', index=True)
break
except sqlite3.OperationalError:
time.sleep(5)
print('db locked')
def train(self, corpus_filename: str):
word_freq = {}
p = Path(corpus_filename)
with open(corpus_filename) as f:
parenstack = []
words_pos = []
line_number = 1
for line in f:
line_number += 1
tokens = common.tokenize(line)
tokens_in_node = []
for token in tokens:
if token == '(':
parenstack.append('(')
tokens_in_node = []
elif token == ')':
parenstack.pop()
if len(tokens_in_node) == 2:
words_pos.append(tuple(tokens_in_node))
pos, word = tokens_in_node
if not word in word_freq:
word_freq[word] = {}
word_freq[word][pos] = 1
else:
if not pos in word_freq[word]:
word_freq[word][pos] = 1
else:
word_freq[word][pos] += 1
tokens_in_node = []
else:
tokens_in_node.append(token)
p = Path(self.PICKLE_FILE)
with p.open('wb') as output_file:
pickle.dump(word_freq, output_file, pickle.HIGHEST_PROTOCOL)
print('辞書を読み込みました:', DICT_PATH)
tfidf = models.TfidfModel.load(TFIDF_MODEL_PATH)
print('TF-IDFモデルを読み込みました:', TFIDF_MODEL_PATH)
clf = joblib.load(SVC_MODEL_PATH)
print('SVM学習モデルを読み込みました:', SVC_MODEL_PATH)
print('')
print('予測したいニュースタイトルを入力してください...')
print('')
try:
for line in sys.stdin:
title = line.rstrip('\r\n')
documents = [tokenize(title)]
bow_corpus = [dictionary.doc2bow(doc) for doc in documents]
tfidf_corpus = tfidf[bow_corpus]
X = [
matutils.corpus2dense([corpus], num_terms=len(dictionary)).T[0]
for corpus in tfidf_corpus
]
result = clf.predict(X)[0]
print('-----')
print('入力:', title)
print('予測:', CATEGORIES[result])
print('')
except KeyboardInterrupt:
print('===== 終了 =====')
def test_normalize():
suffixsymbs = {
'high': '++',
'medium': '+~',
'low': '+-',
'positive': '+',
'negative': '-'
}
suffixsyns = {
'high': 'high',
'hi': 'high',
'bright': 'high',
'Bright': 'high',
'bri': 'high',
'br': 'high',
'(high)': 'high',
'medium': 'medium',
'med': 'medium',
'intermediate': 'medium',
'int': 'medium',
'(medium)': 'medium',
'low': 'low',
'lo': 'low',
'LO': 'low',
'dim': 'low',
'di': 'low',
'(low)': 'low',
'positive': 'positive',
'negative': 'negative'
}
gate_mappings = {
'Alexa350': 'http://purl.obolibrary.org/obo/PR_001',
'Alexa750': 'http://purl.obolibrary.org/obo/PR_002',
'Annexin': 'http://purl.obolibrary.org/obo/PR_003',
'B220-_live': 'http://purl.obolibrary.org/obo/PR_004',
'CCR7': 'http://purl.obolibrary.org/obo/PR_005',
'CD14': 'http://purl.obolibrary.org/obo/PR_006',
'CD16': 'http://purl.obolibrary.org/obo/PR_007',
'CD19': 'http://purl.obolibrary.org/obo/PR_008',
'CD20': 'http://purl.obolibrary.org/obo/PR_009',
'CD21': 'http://purl.obolibrary.org/obo/PR_010',
'CD24': 'http://purl.obolibrary.org/obo/PR_011',
'CD27': 'http://purl.obolibrary.org/obo/PR_012',
'CD3': 'http://purl.obolibrary.org/obo/PR_013',
'CD33': 'http://purl.obolibrary.org/obo/PR_014',
'CD38': 'http://purl.obolibrary.org/obo/PR_015',
'CD4': 'http://purl.obolibrary.org/obo/PR_016',
'CD44': 'http://purl.obolibrary.org/obo/PR_017',
'CD45RA': 'http://purl.obolibrary.org/obo/PR_018',
'CD4_T_cells': 'http://purl.obolibrary.org/obo/PR_019',
'CD56': 'http://purl.obolibrary.org/obo/PR_020',
'CD69': 'http://purl.obolibrary.org/obo/PR_021',
'CD8': 'http://purl.obolibrary.org/obo/PR_022',
'CD94': 'http://purl.obolibrary.org/obo/PR_023',
'CXCR5': 'http://purl.obolibrary.org/obo/PR_024',
'doublet_excluded': 'http://purl.obolibrary.org/obo/PR_025',
'ICOS': 'http://purl.obolibrary.org/obo/PR_026',
'IFNg': 'http://purl.obolibrary.org/obo/PR_027',
'IL2': 'http://purl.obolibrary.org/obo/PR_028',
'live': 'http://purl.obolibrary.org/obo/PR_029',
'Live_cells': 'http://purl.obolibrary.org/obo/PR_030',
'Lymph': 'http://purl.obolibrary.org/obo/PR_031',
'Lymphocytes': 'http://purl.obolibrary.org/obo/PR_032',
'lymphocytes': 'http://purl.obolibrary.org/obo/PR_033',
'Michael': 'http://purl.obolibrary.org/obo/PR_034',
'NP_tet': 'http://purl.obolibrary.org/obo/PR_035',
'PD1': 'http://purl.obolibrary.org/obo/PR_036',
'Robert': 'http://purl.obolibrary.org/obo/PR_037',
'singlets': 'http://purl.obolibrary.org/obo/PR_038',
'small_lymphocyte': 'http://purl.obolibrary.org/obo/PR_039',
'SSC': 'http://purl.obolibrary.org/obo/PR_040',
'TNFa': 'http://purl.obolibrary.org/obo/PR_041',
'Uninfected': 'http://purl.obolibrary.org/obo/PR_042',
'viable': 'http://purl.obolibrary.org/obo/PR_043',
}
special_gates = {
'Michael': {
'Ontology ID': 'PR:034',
'Synonyms': 'mike, mickey, mick',
'Toxic Synonym': 'mikey'
},
'Robert': {
'Ontology ID': 'PR:037',
'Synonyms': 'rob, bob, bert',
'Toxic Synonym': 'bobert'
}
}
preferred = {
'http://purl.obolibrary.org/obo/PR_001': 'Axexa350',
'http://purl.obolibrary.org/obo/PR_002': 'Alexa750',
'http://purl.obolibrary.org/obo/PR_003': 'Annexin',
'http://purl.obolibrary.org/obo/PR_004': 'B220-_live',
'http://purl.obolibrary.org/obo/PR_005': 'CCR7',
'http://purl.obolibrary.org/obo/PR_006': 'CD14',
'http://purl.obolibrary.org/obo/PR_007': 'CD16',
'http://purl.obolibrary.org/obo/PR_008': 'CD19',
'http://purl.obolibrary.org/obo/PR_009': 'CD20',
'http://purl.obolibrary.org/obo/PR_010': 'CD21',
'http://purl.obolibrary.org/obo/PR_011': 'CD24',
'http://purl.obolibrary.org/obo/PR_012': 'CD27',
'http://purl.obolibrary.org/obo/PR_013': 'CD3',
'http://purl.obolibrary.org/obo/PR_014': 'CD33',
'http://purl.obolibrary.org/obo/PR_015': 'CD38',
'http://purl.obolibrary.org/obo/PR_016': 'CD4',
'http://purl.obolibrary.org/obo/PR_017': 'CD44',
'http://purl.obolibrary.org/obo/PR_018': 'CD45RA',
'http://purl.obolibrary.org/obo/PR_019': 'CD4_T_cells',
'http://purl.obolibrary.org/obo/PR_020': 'CD56',
'http://purl.obolibrary.org/obo/PR_021': 'CD69',
'http://purl.obolibrary.org/obo/PR_022': 'CD8',
'http://purl.obolibrary.org/obo/PR_023': 'CD94',
'http://purl.obolibrary.org/obo/PR_024': 'CXCR5',
'http://purl.obolibrary.org/obo/PR_025': 'doublet_excluded',
'http://purl.obolibrary.org/obo/PR_026': 'ICOS',
'http://purl.obolibrary.org/obo/PR_027': 'IFNg',
'http://purl.obolibrary.org/obo/PR_028': 'IL2',
'http://purl.obolibrary.org/obo/PR_029': 'live',
'http://purl.obolibrary.org/obo/PR_030': 'Live_cells',
'http://purl.obolibrary.org/obo/PR_031': 'Lymph',
'http://purl.obolibrary.org/obo/PR_032': 'Lymphocytes',
'http://purl.obolibrary.org/obo/PR_033': 'lymphocytes',
'http://purl.obolibrary.org/obo/PR_035': 'NP_tet',
'http://purl.obolibrary.org/obo/PR_036': 'PD1',
'http://purl.obolibrary.org/obo/PR_038': 'singlets',
'http://purl.obolibrary.org/obo/PR_039': 'small_lymphocyte',
'http://purl.obolibrary.org/obo/PR_040': 'SSC',
'http://purl.obolibrary.org/obo/PR_041': 'TNFa',
'http://purl.obolibrary.org/obo/PR_042': 'Uninfected',
}
reported = 'CD14-CD56-CD3+CD4+CD8-CD45RA+CCR7+'
tokenized = tokenize('LaJolla', suffixsymbs, suffixsyns, reported)
assert tokenized == [
'CD14-', 'CD56-', 'CD3+', 'CD4+', 'CD8-', 'CD45RA+', 'CCR7+'
]
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:006-', 'PR:020-', 'PR:013+', 'PR:016+', 'PR:022-', 'PR:018+',
'PR:005+'
]
assert preferized == [
'CD14-', 'CD56-', 'CD3+', 'CD4+', 'CD8-', 'CD45RA+', 'CCR7+'
]
reported = 'CD3-, CD19+, CD20-, CD27hi, CD38hi'
tokenized = tokenize('Emory', suffixsymbs, suffixsyns, reported)
assert tokenized == ['CD3-', 'CD19+', 'CD20-', 'CD27++', 'CD38++']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:013-', 'PR:008+', 'PR:009-', 'PR:012++', 'PR:015++'
]
assert preferized == ['CD3-', 'CD19+', 'CD20-', 'CD27++', 'CD38++']
reported = 'CD3-/CD19+/CD20lo/CD38hi/CD27hi'
tokenized = tokenize('IPIRC', suffixsymbs, suffixsyns, reported)
assert tokenized == ['CD3-', 'CD19+', 'CD20+-', 'CD38++', 'CD27++']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:013-', 'PR:008+', 'PR:009+-', 'PR:015++', 'PR:012++'
]
assert preferized == ['CD3-', 'CD19+', 'CD20+-', 'CD38++', 'CD27++']
reported = 'CD21hi/CD24int'
tokenized = tokenize('Watson', suffixsymbs, suffixsyns, reported)
assert tokenized == ['CD21++', 'CD24+~']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:010++', 'PR:011+~']
assert preferized == ['CD21++', 'CD24+~']
reported = 'Annexin negative'
tokenized = tokenize('Ltest', suffixsymbs, suffixsyns, reported)
assert tokenized == ['Annexin-']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:003-']
assert preferized == ['Annexin-']
reported = 'CD3+ AND CD4+ AND small lymphocyte'
tokenized = tokenize('VRC', suffixsymbs, suffixsyns, reported)
assert tokenized == ['CD3+', 'CD4+', 'small_lymphocyte']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:013+', 'PR:016+', 'PR:039']
assert preferized == ['CD3+', 'CD4+', 'small_lymphocyte']
reported = 'Lymphocytes and CD8+ and NP tet+'
tokenized = tokenize('Ertl', suffixsymbs, suffixsyns, reported)
assert tokenized == ['Lymphocytes', 'CD8+', 'NP_tet+']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:032', 'PR:022+', 'PR:035+']
assert preferized == ['Lymphocytes', 'CD8+', 'NP_tet+']
reported = 'Activated T: viable/singlets/Lymph/CD3+'
tokenized = tokenize('Stanford', suffixsymbs, suffixsyns, reported)
assert tokenized == ['viable', 'singlets', 'Lymph', 'CD3+']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:043', 'PR:038', 'PR:031', 'PR:013+']
assert preferized == ['!viable', 'singlets', 'Lymph', 'CD3+']
# TODO: Is this right?
reported = 'CD14-CD33-/CD3-/CD16+CD56+/CD94+'
tokenized = tokenize('Stanford', suffixsymbs, suffixsyns, reported)
assert tokenized == ['CD14-', 'CD33-', 'CD3-', 'CD16+', 'CD56+', 'CD94+']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:006-', 'PR:014-', 'PR:013-', 'PR:007+', 'PR:020+', 'PR:023+'
]
assert preferized == ['CD14-', 'CD33-', 'CD3-', 'CD16+', 'CD56+', 'CD94+']
# TODO: Is this right?
reported = 'Live cells/CD4 T cells/CD4+ CD45RA-/Uninfected/SSC low'
tokenized = tokenize('Mayo', suffixsymbs, suffixsyns, reported)
assert tokenized == [
'Live_cells', 'CD4_T_cells', 'CD4+', 'CD45RA-', 'Uninfected', 'SSC+-'
]
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:030', 'PR:019', 'PR:016+', 'PR:018-', 'PR:042', 'PR:040+-'
]
assert preferized == [
'Live_cells', 'CD4_T_cells', 'CD4+', 'CD45RA-', 'Uninfected', 'SSC+-'
]
reported = 'B220- live,doublet excluded,CD4+ CD44highCXCR5highPD1high,ICOS+'
tokenized = tokenize('New York Influenza', suffixsymbs, suffixsyns,
reported)
assert tokenized == [
'B220-_live', 'doublet_excluded', 'CD4+', 'CD44++', 'CXCR5++', 'PD1++',
'ICOS+'
]
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:004', 'PR:025', 'PR:016+', 'PR:017++', 'PR:024++', 'PR:036++',
'PR:026+'
]
assert preferized == [
'B220-_live', 'doublet_excluded', 'CD4+', 'CD44++', 'CXCR5++', 'PD1++',
'ICOS+'
]
reported = 'lymphocytes/singlets/live/CD19-CD14-/CD3+/CD8+/CD69+IFNg+IL2+TNFa+'
tokenized = tokenize('New York Influenza', suffixsymbs, suffixsyns,
reported)
assert tokenized == [
'lymphocytes', 'singlets', 'live', 'CD19-', 'CD14-', 'CD3+', 'CD8+',
'CD69+', 'IFNg+', 'IL2+', 'TNFa+'
]
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == [
'PR:033', 'PR:038', 'PR:029', 'PR:008-', 'PR:006-', 'PR:013+',
'PR:022+', 'PR:021+', 'PR:027+', 'PR:028+', 'PR:041+'
]
assert preferized == [
'lymphocytes', 'singlets', 'live', 'CD19-', 'CD14-', 'CD3+', 'CD8+',
'CD69+', 'IFNg+', 'IL2+', 'TNFa+'
]
reported = 'Alexa350 (high) + Alexa750 (medium)'
tokenized = tokenize('Modeling Viral', suffixsymbs, suffixsyns, reported)
assert tokenized == ['Alexa350++', 'Alexa750+~']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:001++', 'PR:002+~']
assert preferized == ['Axexa350++', 'Alexa750+~']
reported = 'TNFa+IFNg-'
tokenized = tokenize('Flow Cytometry Analysis', suffixsymbs, suffixsyns,
reported)
assert tokenized == ['TNFa+', 'IFNg-']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:041+', 'PR:027-']
assert preferized == ['TNFa+', 'IFNg-']
reported = 'Mikeyhigh/RobLO/Alexa350 (high)/CD33+ý'
tokenized = tokenize('Some Project', suffixsymbs, suffixsyns, reported)
assert tokenized == ['Mikey++', 'Rob+-', 'Alexa350++', 'CD33+-']
preferized, ontologized = normalize(tokenized, gate_mappings,
special_gates, preferred,
suffixsymbs.values())
assert ontologized == ['PR:034++', 'PR:037+-', 'PR:001++', 'PR:014+-']
assert preferized == ['Michael++', 'Robert+-', 'Axexa350++', 'CD33+-']
def train(self, corpus_filename: str):
"""Calculates emission and transition model.
t means tag, w means word.
Emission: P(w[i] | t[i]) = C(t[i], w[i]) / C(t[i])
Transition: P(t[i] | t[i-1]) = C(t[i-1], t[i]) / C(t[i-1])
Where C(t) counts the occurrences of t
"""
model = {}
pos_words = {}
p = Path(corpus_filename)
with open(corpus_filename) as f:
parenstack = [];
bigrams = {}
bigrams[self.END_STR] = {}
word_counts = {}
for line in f:
tokens = common.tokenize(line)
tokens_in_node = []
for token in tokens:
if token == '(':
parenstack.append('(')
tokens_in_node = []
elif token == 'S':
prev = self.START_STR
elif token == ')':
parenstack.pop()
if not parenstack:
if not prev in bigrams[self.END_STR]:
bigrams[self.END_STR][prev] = 1
else:
bigrams[self.END_STR][prev] += 1
elif len(tokens_in_node) == 2:
pos, word = tokens_in_node
if pos != "-NONE-":
if not pos in bigrams:
bigrams[pos] = {}
if not prev in bigrams[pos]:
bigrams[pos][prev] = 1
else:
bigrams[pos][prev] += 1
prev = pos
if not pos in pos_words:
pos_words[pos] = {}
pos_words[pos][word] = 1
else:
if not word in pos_words[pos]:
pos_words[pos][word] = 1
else:
pos_words[pos][word] += 1
tokens_in_node = []
if not word in word_counts:
word_counts[word] = 1
else:
word_counts[word] += 1
else:
tokens_in_node.append(token)
# normalize transition model
for pos, d in bigrams.items():
total = sum(d.values())
for prev, count in d.items():
bigrams[pos][prev] = count / total
# rare words are counted as a single word
pos_words2 = copy.deepcopy(pos_words)
for pos, d in pos_words.items():
for word, count in d.items():
if count <= self.UNKNOWN_TRESHOLD:
pos_words2[pos].pop(word)
if not self.UNKNOWN_STR in pos_words2[pos]:
pos_words2[pos][self.UNKNOWN_STR] = 1
else:
pos_words2[pos][self.UNKNOWN_STR] += 1
pos_words = pos_words2
# normalize emission model
for pos, d in pos_words.items():
total = sum(d.values())
for word, count in d.items():
pos_words[pos][word] = count / total
model["bigrams"] = bigrams
model["pos_words"] = pos_words
p = Path(self.PICKLE_FILE)
with p.open('wb') as output_file:
pickle.dump(model, output_file, pickle.HIGHEST_PROTOCOL)
def fit(self, documents):
documents = [tokenize(d) for d in documents]
documents = [d[:self.max_page_size] for d in documents]
documents = [' '.join(d) for d in documents]
if self.encoding_type in ['tfidf', 'count', 'binary']:
if self.encoding_type == 'tfidf':
self.vectorizer = CountVectorizer(
ngram_range=(self.min_n_gram, self.max_n_gram),
max_features=self.max_vocab_size,
binary=False,
max_df=self.max_df,
analyzer=self.tokenizer_level)
self.vectorizer.fit(documents)
if self.encoding_type == 'count':
self.vectorizer = CountVectorizer(
ngram_range=(self.min_n_gram, self.max_n_gram),
max_features=self.max_vocab_size,
binary=False,
max_df=self.max_df,
analyzer=self.tokenizer_level)
self.vectorizer.fit(documents)
if self.encoding_type == 'binary':
self.vectorizer = CountVectorizer(
ngram_range=(self.min_n_gram, self.max_n_gram),
max_features=self.max_vocab_size,
binary=False,
max_df=self.max_df,
analyzer=self.tokenizer_level)
self.vectorizer.fit(documents)
with open(self.save_file_loc, 'wb') as f:
pickle.dump(self.vectorizer, f)
if self.encoding_type == 'lda':
documents_tokenized = [tokenize(i) for i in documents]
self.common_dictionary = Dictionary(documents_tokenized)
common_corpus = [
self.common_dictionary.doc2bow(text)
for text in documents_tokenized
]
self.vectorizer = ldamodel.LdaModel(common_corpus,
id2word=self.common_dictionary,
num_topics=self.num_of_topics,
passes=self.vectorizer_epochs)
self.vectorizer.save(self.save_file_loc)
if self.encoding_type == 'doc2vec':
tagged_documents = [
TaggedDocument(tokenize(doc), [i])
for i, doc in enumerate(documents)
]
self.vectorizer = Doc2Vec(tagged_documents,
vector_size=self.encoding_size,
window=2,
min_count=1,
workers=4,
epochs=self.vectorizer_epochs,
max_vocab_size=100000)
self.vectorizer.delete_temporary_training_data(
keep_doctags_vectors=True, keep_inference=True)
self.vectorizer.save(self.save_file_loc)
if self.encoding_type == 'fasttext':
with open(self.fasttext_training_file_location, 'w') as f:
for i in documents:
f.write(clean_text(i) + '\n')
self.vectorizer = fasttext.train_unsupervised(
self.fasttext_training_file_location,
model=self.fasttext_algorithm,
dim=self.encoding_size)
self.vectorizer.save_model(self.save_file_loc)
parser = ArgumentParser()
parser.add_argument('-d', '--data', default='data')
args = parser.parse_args()
in_dir = join(args.data, 'raw/blogs/')
in_paths = glob(in_dir + "*xml")
out_dir = join(args.data, 'proc/blogs/')
x = []
y = []
for path in in_paths:
if 'male' not in path:
continue
xml = read_xml(path)
posts = xml.xpath(".//post/text()")
is_female = 'female' in path
for post in posts:
encoding = tokenize(post)
if encoding.shape[0] <= min_sequence_length:
continue
if encoding.shape[0] < sequence_length:
encoding = pad(encoding, sequence_length)
label = 1 if is_female else 0
x.append(encoding[:sequence_length])
y.append(label)
makedirs(out_dir, exist_ok=True)
np.save(join(out_dir, 'x.npy'), np.vstack(x))
np.save(join(out_dir, 'y.npy'), np.array(y))
from gensim import corpora, models, matutils
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.externals import joblib
from sklearn.svm import SVC
from common import CATEGORIES, DICT_PATH, TFIDF_MODEL_PATH, SVC_MODEL_PATH, tokenize, load_documents
titles, labels = load_documents()
print('教師データを読み込みました: titles=%s labels=%s' % (len(titles), len(labels)))
print('')
documents = [tokenize(title) for title in titles]
print('形態素解析によりトークン化しました: ドキュメント数=%s' % (len(documents)))
print('')
dictionary = corpora.Dictionary(documents)
dictionary.filter_extremes(no_above=0.7)
print('辞書を作成しました: ユニークトークン数=%s' % (len(dictionary)))
print('')
dictionary.save(DICT_PATH)
print('辞書を保存しました:', DICT_PATH)
print('')
bow_corpus = [dictionary.doc2bow(doc) for doc in documents]
print('BOWコーパスを作成しました:', bow_corpus[0])
print('')
tfidf = models.TfidfModel(bow_corpus)
print('TF-IDFモデルを作成しました')
print('')
def tokenizer(text):
tokens = tokenize(text)
tokens = stemwords(tokens)
return tokens
['\nFrom:', '\nTo:', '- Original Message -', '-----------']
]
reply_indices = [index for index in reply_indices if index > -1]
if len(reply_indices) > 0: # remove older messages, signatures, etc.
text = text[:min(reply_indices) - 5]
lines = text.split('\n')
non_quote_lines = [line for line in lines
if not line.startswith('>')] # remove quotes
text = '\n'.join(non_quote_lines)
for name in names: # remove own name as indicator for gender (e.g. in signature)
if len(name) > 2:
text = sub(name, '', text, flags=IGNORECASE)
text = text.replace(
'?', ''
) # replace question mark, as it is also used for unknown characters and redacted text
encoding = tokenize(text)
if encoding.shape[0] <= min_sequence_length:
continue
if encoding.shape[0] < sequence_length:
encoding = pad(encoding, sequence_length)
x.append(encoding[:sequence_length])
label = 1 if 'female' in gender else 0
y.append(label)
checksums.append(checksum)
x = np.vstack(x)
y = np.array(y)
makedirs(out_dir, exist_ok=True)
np.save(join(out_dir, 'x'), x)
np.save(join(out_dir, 'y'), y)
def predict(text):
x = tokenize(text)
with graph.as_default():
probability = model.predict(np.array([x], ))[0][0]
return probability
# a dictionary of the form { term: { docid: freq } }
term_dictionary = defaultdict(Counter)
all_doc_ids = sorted(map(int, os.listdir(input_directory)))
doc_size = dict()
for doc_id in all_doc_ids:
print "Trying to index doc %s..." % doc_id
filepath = os.path.join(input_directory, str(doc_id))
with open(filepath) as input_file:
document_content = input_file.read()
unique_terms = 0
for term in tokenize(document_content):
if doc_id not in term_dictionary[term]:
unique_terms += 1
term_dictionary[term][doc_id] += 1
doc_size[doc_id] = unique_terms
# Formats the posting list for a specific term
# - input: a posting of the form { doc_id: freq, doc_id: freq }
# - return: a formatted posting string with "doc_id:freq doc_id:freq"
def format_posting_list(posting):
sorted_doc_ids = sorted(posting)
posting_strings = []
def extract_grammar(filename: str, args):
with open(filename, "r") as corpus_file:
pos_stack = []
paren_stack = []
words_pos = defaultdict(dd)
prev = None
root = None
rules = []
terminals = set()
for line in corpus_file:
tokens = common.tokenize(line)
for token in tokens:
if token == "(":
paren_stack.append("(")
elif token == ")":
while True:
if not paren_stack:
return "bad"
else:
el = paren_stack.pop()
if pos_stack:
pos_stack.pop()
if el == "(":
break
if not paren_stack:
save_rule(root, rules)
pos_stack = []
paren_stack = []
prev = None
root = None
else:
if prev == "(":
node = Node(token)
if pos_stack:
pos_stack[-1].children.append(node)
else:
root = node
pos_stack.append(node)
elif prev != "-NONE-":
# previous token was a POS, token can only be a terminal
terminals.add(prev)
words_pos[token][prev] += 1
prev = token
# probabilities from frequencies
for word, d in words_pos.items():
total = sum(d.values())
for pos, count in d.items():
words_pos[word][pos] = count / total
MOST_COMMON_COUNT = 1000
sorted_rules = Counter(rules).most_common()
for rule in sorted_rules[:MOST_COMMON_COUNT]:
same_lhs = filter(lambda r: r[0].lhs == rule[0].lhs, sorted_rules)
total = sum([r[1] for r in same_lhs])
rule[0].prob = rule[1] / total
grammar_rules = [rule[0] for rule in sorted_rules[:MOST_COMMON_COUNT]]
grammar = Grammar(grammar_rules, terminals, words_pos)
if args.mode == "pre":
print("{:>5} | {}".format("count", "rule"))
for rule in sorted_rules[:MOST_COMMON_COUNT]:
print("{:>5} | {}".format(rule[1], rule[0]))
sum_first = sum(
[rule[1] for rule in sorted_rules[:MOST_COMMON_COUNT]])
sum_rest = sum(
[rule[1] for rule in sorted_rules[MOST_COMMON_COUNT:]])
print("{} rules total:".format(len(rules)))
print("{:>8} (first {})".format(sum_first, MOST_COMMON_COUNT))
print("{:>8} (rest)".format(sum_rest))
print("{:.3f}% coverage".format(100.0 * sum_first / len(rules)))
assert len(rules) == sum_first + sum_rest
else:
for rule in grammar.rules:
print(rule)
p = Path(PICKLE_FILE)
with p.open('wb') as output_file:
pickle.dump(grammar, output_file, pickle.HIGHEST_PROTOCOL)
return grammar
def main():
# Define command-line parameters
parser = argparse.ArgumentParser(
description='Normalize cell population descriptions')
parser.add_argument(
'excluded',
type=argparse.FileType('r'),
help='a TSV file with experiment accessions to be ignored')
parser.add_argument(
'scale',
type=argparse.FileType('r'),
help='a TSV file with the value scale (e.g. high, low, negative)')
parser.add_argument(
'mappings',
type=argparse.FileType('r'),
help='a TSV file which maps gate labels to ontology ids/keywords')
parser.add_argument(
'special',
type=argparse.FileType('r'),
help='a TSV file containing extra information about a subset of gates')
parser.add_argument(
'preferred',
type=argparse.FileType('r'),
help='a TSV file which maps ontology ids to preferred labels')
parser.add_argument('cells',
type=argparse.FileType('r'),
help='an OWL file for the Cell Ontology')
parser.add_argument('source',
type=argparse.FileType('r'),
help='the source data TSV file')
parser.add_argument('output', type=str, help='the output TSV file')
# Parse command-line parameters
args = parser.parse_args()
# Load the contents of the file given by the command-line parameter args.excluded
# These are the experiments we should ignore when reading from the source file
excluded_experiments = set()
rows = csv.DictReader(args.excluded, delimiter='\t')
for row in rows:
excluded_experiments.add(row['Experiment Accession'])
# Load the contents of the file given by the command-line parameter args.scale.
# This defines the suffix synonyms and symbols for various scaling indicators,
# which must be noted during parsing
rows = csv.DictReader(args.scale, delimiter='\t')
suffixsymbs, suffixsyns = extract_suffix_syns_symbs_maps(rows)
# Load the contents of the file given by the command-line parameter args.mappings.
# This file associates gate laels with the ontology ids / keywords with which we populate the
# 'Gating mapped to ontologies' column of the output file.
rows = csv.DictReader(args.mappings, delimiter='\t')
gate_mappings = {}
for row in rows:
gate_mappings[row['Label']] = row['Ontology ID']
# Load the contents of the file given by the command-line parameter args.special.
# This file (similary to the args.mapping file) associates certain gate labels with ontology ids
# but also contains additional information regarding these gates.
rows = csv.DictReader(args.special, delimiter='\t')
special_gates = {}
for row in rows:
special_gates[row['Label']] = {
'Ontology ID': row['Ontology ID'],
'Synonyms': row['Synonyms'],
'Toxic Synonym': row['toxic synonym']
}
# Load the contents of the file given by the command-line parameter args.preferred.
# This file associates ontology ids with preferred gate labels (i.e. pr#PRO-short-label).
rows = csv.DictReader(args.preferred, delimiter='\t')
preferred = {}
for row in rows:
preferred[row['Ontology ID']] = row['Preferred Label']
# Load the contents of the file given by args.cells. This is an OWL file in XML format. We first
# parse it using python's xml library, and then call update_iri_maps_from_owl
# to retrieve the maps: synonym_iris, iri_labels, iri_gates, and iri_parents
tree = ET.parse(args.cells)
iri_gates, iri_parents, iri_labels, synonym_iris = update_iri_maps_from_owl(
tree)
# Finally, load the contents of the source file, process each row and write the processed row
# to a new file.
rows = csv.DictReader(args.source, delimiter='\t')
with open(args.output, 'w') as output:
w = csv.writer(output, delimiter='\t', lineterminator='\n')
# Write the header row:
output_fieldnames = [
'NAME', 'STUDY_ACCESSION', 'EXPERIMENT_ACCESSION',
'POPULATION_NAME_REPORTED', 'CL term', 'CL ID', 'CL definition',
'extra', 'POPULATION_DEFNITION_REPORTED',
'Population preferred name', 'Gating tokenized',
'Gating mapped to ontologies', 'Gating preferred definition',
'Conflicts', 'Conflict type'
]
w.writerow(output_fieldnames)
conflict_count = 0
symbols = suffixsymbs.values()
for row in rows:
# Ignore any rows describing excluded experiments.
if row['EXPERIMENT_ACCESSION'] in excluded_experiments:
continue
# Tokenize and normalize the population name:
extra = row['extra'].strip()
tokenized_gates = tokenize('Standard', suffixsymbs, suffixsyns,
extra)
preferized_gates, ontologized_gates = normalize(
tokenized_gates, gate_mappings, special_gates, preferred,
symbols)
# Determine the population preferred name:
preferred_name = row['CL term'] or ''
if preferred_name and preferized_gates:
preferred_name += ' & ' + ', '.join(preferized_gates)
row['Population preferred name'] = preferred_name
# Determine the CL definition:
population_gates = []
cell_type = re.sub('^CL:', 'http://purl.obolibrary.org/obo/CL_',
row['CL ID'])
if cell_type and cell_type in iri_gates:
for gate in iri_gates[cell_type]:
preferred_label = preferred.get(gate['kind'])
if preferred_label:
population_gates.append(
preferred_label + get_iri_levels()[gate['level']])
row['CL definition'] = ', '.join(population_gates)
# These will be needed later for determining conflicts:
extra_gates = preferized_gates.copy()
cell_gates = population_gates + preferized_gates
# Tokenize and normalize the reported population definition, first removing any surrounding
# quotation marks:
reported = row['POPULATION_DEFNITION_REPORTED'].strip('"').strip(
"'")
tokenized_gates = tokenize(row['NAME'], suffixsymbs, suffixsyns,
reported)
row['Gating tokenized'] = ', '.join(tokenized_gates)
preferized_gates, ontologized_gates = normalize(
tokenized_gates, gate_mappings, special_gates, preferred,
symbols)
row['Gating mapped to ontologies'] = ', '.join(ontologized_gates)
row['Gating preferred definition'] = ', '.join(preferized_gates)
# Determine the conflicts:
conflict_type = ''
conflicts = []
for population_gate in cell_gates:
for definition_gate in preferized_gates:
pgate, plevel = split_gate(population_gate, symbols)
dgate, dlevel = split_gate(definition_gate, symbols)
ppos = plevel != '-'
dpos = dlevel != '-'
if pgate == dgate and ppos != dpos:
conflicts.append(population_gate + '/' + dlevel)
if population_gate in extra_gates:
conflict_type = 'conflict with extra'
else:
conflict_type = 'conflict with CL definition'
if len(conflicts) > 0:
print(conflicts)
conflict_count += 1
row['Conflicts'] = ', '.join(conflicts)
row['Conflict type'] = conflict_type
# Explicitly reference output_fieldnames here to make sure that the order in which the data
# is written to the file matches the header order.
w.writerow([row[fn] for fn in output_fieldnames])
print('Conflicts:', conflict_count)
def parse_query(query):
"""Parses a query into { term: freq } mapping"""
return Counter(tokenize(query))