def sentence_tags(sentence):
"""
1文の形態素をリストで返す
"""
nlp = NLP()
tokens = nlp.rm(nlp.tokens(sentence))
tags = nlp.tags(tokens)
tags = [elem[1] for elem in tags]
return tags
def __init__(self, configFile, db=None):
self.configFile = configFile
self.db = db
self.qwd = QWD(configFile)
UrlUtils.init(configFile)
Validation.init(configFile)
NLP.init(configFile)
def __init__(self):
rospy.init_node('speech_node', anonymous=True)
self.pub_command = rospy.Publisher('ui_command', UI, queue_size=10)
self.pub_feedback = rospy.Publisher('ui_feedback', UI_feedback, queue_size=10)
self.pub_floor= rospy.Publisher('lift', lift, queue_size=10)
self.r = sr.Recognizer()
self.mic = sr.Microphone(device_index = 2, sample_rate = 44100, chunk_size = 512)
self.r.dynamic_energy_threshold = True
with self.mic as source:
self.r.adjust_for_ambient_noise(source)
self.nlp = NLP()
print('Speech node ready')
def get_json(self, lang, sentences):
'''
Explanation: Creates object of class NLP to get the list of sentences in appropriate tagged JSON format
Parameters:
lang: Language of the list of sentences
sentences: List of string represeting the sentences
Return:
JSON object containing annotated values
'''
nlp = NLP(lang, sentences)
return nlp.get_dependencies()
def get_corpus_from_node(self, node):
'''
Retrieve a set of text file paths from category graph node
return a [] of texts path
'''
texts = []
for myfile in os.listdir(self.categories[node]["path"]):
if myfile.endswith(".txt"):
path=os.path.join(self.categories[node]["path"], myfile)
with codecs.open (path, "r", "utf-8") as myfile:
text=myfile.read().replace('\n', '')
result=NLP(text).get_clean_text()
texts.append(result.encode("utf-8"))
return texts
def __init__(self, lang='en', **kwargs):
"""
Do not use this object for anything else besides training models because normalization of documents
takes a while.
TODO: in an actual solution documents should be normalized and those normalized documents should be stored
TODO: as corpus for fast loading when initiating a training procedure.
:param lang: str
:param kwargs: dict
"""
self.datasets = kwargs.get('datasets', DATASETS)
self.nlp = NLP(lang)
self.tagged_docs = []
self.__init_tagged_docs(
) # The dataset is small enough to be loaded in RAM
def __init__(self):
WXBot.__init__(self)
self.tuling_key = ""
self.robot_switch = True
self.nlp = NLP()
self.reply_list = ['温', '司尚春', '亲爱的']
try:
cf = ConfigParser.ConfigParser()
cf.read('conf.ini')
self.tuling_key = cf.get('main', 'key')
except Exception:
pass
print 'tuling_key:', self.tuling_key
def classifier(features, labels):
features_copy = features.copy()
labels_copy = labels.copy()
features_copy = NLP.text_to_numeric(features_copy)
results = NLP.test(features_copy, labels)
betterFM = 0
for classifier in results:
metrics = results[classifier]
if (metrics['F1']['avg'] > betterFM):
betterFM = metrics['F1']['avg']
return betterFM
def call_find_template_set(arg_json, arg_nlp, arg_templates):
examples = LabeledExample.read(arg_json)
indices = [e.index for e in examples.itervalues()]
natural_language = {i: NLP.read(arg_nlp, i) for i in indices}
word_problems = [WordProblem(examples[i], natural_language[i])
for i in indices]
templates = [wp.extract_template() for wp in word_problems]
unique = list()
wp_template_map = dict()
for wp in word_problems:
template = wp.template
wp_index = wp.labeled_example.index
found_template = False
for unique_i, u in enumerate(unique):
if template == u:
wp_template_map[wp_index] = unique_i
found_template = True
break
if not found_template:
unique.append(template)
wp_template_map[wp_index] = len(unique) - 1
print('{} total and {} unique templates'.format(len(templates),
len(unique)))
with open(arg_templates, 'wt') as f_handle:
out_json = {'templates': [t.to_json() for t in unique],
'wp_template_map': wp_template_map}
f_handle.write(json.dumps(out_json))
def call_extract_features(arg_json, arg_nlp, arg_templates, arg_parameters):
examples = LabeledExample.read(arg_json)
indices = [e.index for e in examples.itervalues()]
natural_language = {i: NLP.read(arg_nlp, i) for i in indices}
word_problems = [WordProblem(examples[i], natural_language[i])
for i in indices]
with open(arg_templates, 'rt') as f_handle:
raw = f_handle.read()
parsed = json.loads(raw)
unique_templates = [Template.from_json(j) for j in parsed['templates']]
# TODO(Eric): using only 2 word problems for testing
unique_templates = unique_templates[:2]
word_problems = word_problems[:2]
feature_extractor = FeatureExtractor(unique_templates, word_problems)
derivations = initialize_partial_derivations_for_all_templates(
word_problems[0], unique_templates)
derivation = derivations[0]
while not derivation.is_complete():
derivation = derivation.all_ways_to_fill_next_slot()[0]
print(feature_extractor.extract(derivation))
print(derivation)
class Datasets:
def __init__(self, lang='en', **kwargs):
"""
Do not use this object for anything else besides training models because normalization of documents
takes a while.
TODO: in an actual solution documents should be normalized and those normalized documents should be stored
TODO: as corpus for fast loading when initiating a training procedure.
:param lang: str
:param kwargs: dict
"""
self.datasets = kwargs.get('datasets', DATASETS)
self.nlp = NLP(lang)
self.tagged_docs = []
self.__init_tagged_docs(
) # The dataset is small enough to be loaded in RAM
def __init_tagged_docs(self):
for dataset_name in self.datasets:
dataset = initialize_dataset(dataset_name)
for doc, labels in dataset.get_documents_labels():
self.tagged_docs.append(
TaggedDocument(words=self.nlp.normalize_text(doc),
tags=labels))
def __iter__(self):
for tagged_document in self.tagged_docs:
yield tagged_document
class Doc2VecEmbedding:
def __init__(self, lang='en'):
self.lang = lang
self.model = None
self.nlp = NLP(self.lang)
def __get_model_fpath(self):
return join(MODELS_DIR, 'doc2vec_%s.model' % self.lang)
def load(self):
self.model = Doc2Vec.load(self.__get_model_fpath())
def fit(self, **kwargs):
logging.basicConfig(format='%(levelname)s : %(message)s',
level=logging.INFO)
logging.root.level = logging.INFO
datasets = Datasets()
params = DOC2VEC_PARAMS
self.model = Doc2Vec(datasets.tagged_docs, **params)
self.model.save(self.__get_model_fpath())
def vectorize(self, text):
tokens = self.nlp.normalize_text(text)
return self.model.infer_vector(tokens, steps=256)
def find_most_similar_docs(self, text, topn=10):
vector = self.vectorize(text)
return self.model.docvecs.most_similar(positive=[vector], topn=topn)
def main(args):
# Load configuration
config = Configuration(args.yaml_path)
print("Loading Probase...")
probase = Probase(config)
print("Loading dataset...")
dataset = Data(config)
print("Loading NLP utility...")
nlp = NLP('en')
print("Loading feature extractor...")
features = Feature(config, probase, nlp=nlp)
print("Extracting vector features")
features.extract_vector_features(dataset)
print("Extracting statistical vector features")
features.extract_statistical_features(dataset)
print("Evaluating clasifiers")
ev = Evaluation(config, dataset)
ev.full_evaluation(features.X, features.y)
def __init__(self, name):
self.name = name
self.ai = GCAI(qad={
'你是谁': '我是' + self.name,
self.name: '干什么',
})
self.voice = Voice(VOICE_REGS)
self.nlp = NLP(NLP_REGS)
def __init__(self, provider, output) -> None:
super().__init__()
self.provider = provider
self.output = output
self.nlp = NLP(NameProvider("gn_csv/mena.csv"),
TownProvider("gn_csv/GNKU.csv"),
GeoProvider("gn_csv/geograficky_nazov.csv"))
self.cache = Cache(100000, output)
def server(config):
print('Compiling assets...')
compile_assets()
print('Loading NLP models...')
app.nlp = NLP(config)
return app
def on_message(client, userdata, message):
message_payload = json.loads(message.payload.decode())
message_type = message_payload['type']
if message_type == SPEECH_TRIGGER:
time.sleep(2)
# speech recognition
speech_success, text = recognize()
if speech_success:
print("output of speech recognition:", text)
# natural language processing
nlp = NLP()
nlp_success, meaning = nlp.parse(text)
if nlp_success:
print("output of natural language processing:", meaning)
if meaning == 'sunrise':
pi_data = json.dumps({'type': TIME_SET, 'nature': SUNRISE})
app_data = json.dumps({
'type':
SPEAK,
'say':
speech_messages['WAKEUP_SUNRISE']
})
else:
pi_data = json.dumps({
'type': TIME_SET,
'nature': AT,
'time': meaning
})
hour = str(int(meaning.split(' ')[1].split(':')[0]))
if hour == '0':
hour = 'midnight'
minute = str(int(meaning.split(' ')[1].split(':')[1]))
if minute == '0':
minute = ''
app_data = json.dumps({
'type':
SPEAK,
'say':
speech_messages['WAKEUP_TIME'] + str(hour) + ' ' +
str(minute)
})
client.publish(TO_PI, pi_data)
client.publish(TO_APP, app_data)
def analyze(self, lyrics_result, stats_result, analyze_threshold):
if not self.data['lyrics']:
self.data['lyrics'] = self.parse_lyrics(lyrics_result)
self.data['stats'] = self.parse_stats(stats_result)
artists_to_analyze = self.divide_artists_to_analyze()
nlp = NLP(artists=artists_to_analyze,
analyze_threshold=analyze_threshold)
try:
results = nlp.start()
except Exception as e:
print(
'Error: To be able to analyze lyrics, start Zemberek app with `StartGrpcServer` parameter!\n\n'
)
else:
processed_stats = nlp.process_stats(results)
for stat in processed_stats:
self.data['stats'].append(stat)
self.save('stats', stats_result)
print('Analyzes saved!')
def call_count_unique(arg_json, arg_unique, arg_nlp):
examples = LabeledExample.read(arg_json)
templates = list()
for index in arg_unique:
example = examples[index]
natural_language = NLP.read(arg_nlp, index)
wp = WordProblem(example, natural_language)
templates.append(wp.extract_template())
print(len(set(templates)))
print(json.dumps([t.to_json() for t in templates]))
def __init__(self, *args, **kwargs):
self.nlp_helper = NLP()
self.recognizer = sr.Recognizer()
self.microphone = sr.Microphone()
self.label_text = ""
Page.__init__(self, *args, **kwargs)
frame = tk.Frame(self, bg="gray")
frame.pack(fill="x", expand=False)
self.label = tk.Label(frame, text=self.label_text, font=LARGE_FONT)
button = tk.Button(frame,
text="Main View",
fg="white",
bg="gray",
width=15,
height=7,
command=lambda: self.button_action)
self.label.place(in_=frame, x=0, y=0, relwidth=1, relheight=1)
self.label.pack(side="top", fill="both", expand=True)
button.pack(side="bottom")
def word_count_similarity(path, speech_type, speech_id):
"""
1文同士の類似度を, 1文の単語数に着目して計算する
non-similarity = t番目の文の単語数 / (t-1)番目の文の単語数
non-similarityをsin波のように出力する
non-similarityの値が大きいほど類似度は低い
"""
file_path = f"{path}/{speech_type}/{speech_id}.txt"
sentences = preprocess(file_path)
nlp = NLP()
tokens_list = [nlp.rm(nlp.tokens(s)) for s in sentences]
word_counts = [len(token) for token in tokens_list]
if 0 in word_counts:
word_counts.remove(0)
similarities = [0.0]
for idx in range(len(word_counts) - 1):
if int(word_counts[idx]) != 0:
sim = round(int(word_counts[idx]) / int(word_counts[idx - 1]), 2)
similarities.append(sim)
return similarities
class TestNLP(TestCase):
def setUp(self):
self.NLP = NLP()
def test_translate_whitespace(self):
original = ' look hobo '
test = 'look hobo'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def test_eat_mushroom(self):
original = 'eat mushroom'
translated = self.NLP.translate(original)
self.assertEqual(translated, translated)
def test_translate_whitespace_2(self):
original = 'look hobo'
test = 'look hobo'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def test_translate_verbs(self):
original = ' view hobo '
test = 'look hobo'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def test_translate_verbs_2(self):
original = 'view dog'
test = 'look dog'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def test_translate_2(self):
original = 'eat hobo'
test = 'eat hobo'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def test_remove_preps(self):
original = ' view the about hobo '
test = 'look hobo'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def test_remove_articles(self):
original = ' view a albatross '
test = 'look albatross'
translated = self.NLP.translate(original)
self.assertEqual(test, translated)
def get_dict(features, labels, percentage_words=0.1, iterations=50):
real_features = features.copy()
words_label = USES_MULTI.words_label(features, labels)
number_words = len(words_label)
number_selected_words = int(round(number_words * percentage_words))
distinct_labels = list(set(labels))
number_selected_words_label = int(number_selected_words /
len(distinct_labels))
features_score = USES_MULTI.feature_score(words_label, labels)
candidates = {}
for label in features_score:
positive_candidates = features_score[
label][:number_selected_words_label]
candidates.update(positive_candidates)
negative_candidates = features_score[label][
-number_selected_words_label:]
candidates.update(negative_candidates)
results = {}
actual_iteration = 0
while (actual_iteration < iterations):
actual_candidates = USES_MULTI.random_items(
candidates, number_selected_words)
filtered_features = NLP.filter_features(features,
actual_candidates)
try:
#pprint(actual_iteration)
FMeasure = USES_MULTI.classifier(filtered_features, labels)
#pprint(FMeasure)
except Exception as e:
#pprint(e)
FMeasure = 0
results.update({FMeasure: actual_candidates})
actual_iteration = actual_iteration + 1
results = sorted(results.items(), key=lambda x: x[0], reverse=True)
best_FM = results[0][0]
dict_words = results[0][1]
#print('Best: ', best_FM)
return dict_words
def call_print(arg_json, arg_index, arg_nlp):
examples = LabeledExample.read(arg_json)
example = examples[arg_index]
natural_language = NLP.read(arg_nlp, arg_index)
wp = WordProblem(example, natural_language)
wp.extract_template()
print(wp)
print('questions: {}'
.format([(s.as_text(), s.object_of_sentence())
for s in wp.nlp.questions().itervalues()]))
print('commands: {}'
.format([(s.as_text(), s.object_of_sentence())
for s in wp.nlp.commands().itervalues()]))
class MainView(Page):
label_text = ""
def __init__(self, *args, **kwargs):
self.nlp_helper = NLP()
self.recognizer = sr.Recognizer()
self.microphone = sr.Microphone()
self.label_text = ""
Page.__init__(self, *args, **kwargs)
frame = tk.Frame(self, bg="gray")
frame.pack(fill="x", expand=False)
self.label = tk.Label(frame, text=self.label_text, font=LARGE_FONT)
button = tk.Button(frame,
text="Main View",
fg="white",
bg="gray",
width=15,
height=7,
command=lambda: self.button_action)
self.label.place(in_=frame, x=0, y=0, relwidth=1, relheight=1)
self.label.pack(side="top", fill="both", expand=True)
button.pack(side="bottom")
def button_action(self):
self.label_text = "Listening..."
self.label.config(text=self.label_text)
input = self.nlp_helper.listen(self.recognizer, self.microphone)
if input != "Nothing transcribed":
result = self.nlp_helper.predict_class(input)
self.label_text = result
self.label.config(text=self.label_text)
else:
self.label_text = input
self.label.config(text=self.label_text)
def run(self, problem):
nlp = NLP(fg=problem.fg,
x0=problem.var_init,
h=problem.h,
lb=problem.var_bounds_l,
ub=problem.var_bounds_u)
solver = Solver(ftol=self.ftol,
gtol=self.gtol,
maxiter=self.maxiter,
verbose=False,
record=False)
res = solver.solve(nlp)
res.H = None # Memory issues ;)
res.g = None
return res
def getKey(content):
tokens = NLP.getMorphology(content)
if tokens is None:
return content
for token in tokens:
if token is None:
break
if u'名词' == token['wtype']:
return token['word']
return content
def call_fold(arg_testfold, arg_numfolds, arg_foldoutput,
arg_json, arg_nlp, arg_templates, arg_parameters):
examples = LabeledExample.read(arg_json)
indices = [e.index for e in examples.itervalues()][:5] # TODO just 5 for testing
natural_language = {i: NLP.read(arg_nlp, i) for i in indices}
word_problems = [WordProblem(examples[i], natural_language[i])
for i in indices]
fold_indices = make_fold_indices(arg_numfolds, len(word_problems))
test_indices = fold_indices.pop(arg_testfold)
train_indices = list()
for per_fold in fold_indices:
train_indices.extend(per_fold)
with open(arg_templates, 'rt') as f_handle:
raw = f_handle.read()
parsed = json.loads(raw)
unique_templates = [Template.from_json(j) for j in parsed['templates']]
wp_template_map = {int(k): v
for k, v in parsed['wp_template_map'].iteritems()}
train_wps = [word_problems[i] for i in train_indices]
train_templates_indices = list({wp_template_map[wp.labeled_example.index]
for wp in train_wps})
remap_templates = {wp.labeled_example.index:
train_templates_indices.index(
wp_template_map[wp.labeled_example.index])
for wp in train_wps}
train_templates = [unique_templates[i] for i in train_templates_indices]
feature_extractor = FeatureExtractor(train_templates, train_wps)
classifier = optimize_parameters(feature_extractor, train_wps,
train_templates, remap_templates)
with open(arg_parameters, 'wt') as f_handle:
f_handle.write(json.dumps(classifier.to_json()))
correct = 0
for test_i in test_indices:
test_wp = word_problems[test_i]
correct += classifier.solve(test_wp)
print('{} correct out of {}'.format(correct, len(test_indices)))
def isMatched(title, key):
keywords = NLP.getKeywords(title)
if keywords is None:
# XXX: Should it return true?
return True
for keyword in keywords:
if keyword is None:
# XXX: Should it return true?
return True
if key in keyword['keyword']:
return True
print '"', title, '" doesn\'t match "', key, '"'
return False
def main():
last_update_id = None
if not os.path.isfile("proc_data/proc_data.pkl"):
preproc()
if not os.path.isfile("model/encoder.h5"):
preproc()
df = pd.read_pickle("proc_data/proc_data.pkl")
encoder = load_model("model/encoder.h5")
with open("word_vectors/word_indx.csv", 'rb') as outfile:
word_index = pickle.load(outfile)
with open("word_vectors/embedding_matrix.csv", 'rb') as outfile:
embedding_matrix = pickle.load(outfile)
nlp = NLP(df, encoder, word_index, embedding_matrix, 200, 200)
print("The bot is now active")
while True:
updates = get_updates(last_update_id)
if len(updates["result"]) > 0:
last_update_id = get_last_update_id(updates) + 1
handle_updates(updates, nlp)
time.sleep(0.5)
def get_dict(features, labels, max_words=5):
real_features = features.copy()
real_labels = labels.copy()
words_label = USES_MULTI.words_label(features, labels)
number_words = len(words_label)
if (number_words < max_words):
max_words = number_words
features_score = ALTER_USES.feature_score(words_label, labels)
results = {}
number_words = 1
while (number_words <= max_words):
dict_words = ALTER_USES.build_dict(features, labels,
features_score, number_words)
filtered_features = NLP.filter_features(features, dict_words)
try:
#pprint(number_words)
FMeasure = USES_MULTI.classifier(filtered_features, labels)
#pprint(FMeasure)
except Exception as e:
#pprint(e)
FMeasure = 0
results.update({FMeasure: dict_words})
number_words = number_words + 1
results = sorted(results.items(), key=lambda x: x[0], reverse=True)
best_FM = results[0][0]
dict_words = results[0][1]
#print('Best FM: ', best_FM)
return dict_words
def execute(self, string):
#print(queries.index(string))
a = NLP(string)
a.namedEntityRecognition()
a.replaceContractions()
a.lemmatize()
print("Lemmatized query:", a.lowercase_query)
a.tokenize()
a.removePunctAndStop()
a.replaceRelations()
a.replaceAttr()
a.reconstruct()
a.replaceOperators()
a.replaceSynAttr()
a.replaceSynCommon()
#print (a.lowercase_query)
a.andOr()
a.unknownAttr()
a.relationSearch()
a.negationCheck()
a.removeDuplicates()
a.cleaningSelectList()
#print(a.SELECT)
#print(a.WHERE)
#print("Unique relation ",a.unique_attribute_relation)
#print("Common relation ",a.common_attribute_relation)
b = QueryConstruction(a.SELECT, a.WHERE, a.unique_attribute_relation, a.common_attribute_relation)
b.checkJoin()
b.constructSelectPart()
check = b.constructFromPart()
if check is True:
b.constructWherePart()
#print(b.final_query)
return b.final_query
import gensim
import pandas as pd
from nlp import NLP
from dataset_utils import *
import jieba
combined_dir = '../dataset/combined/'
assembled_combined_csv = pd.read_csv(combined_dir + 'assembled_combined.csv', quoting=3)
train_dataset, test_dataset = NLP.divide_train_test(assembled_combined_csv, '2016-02-01')
# date and label list
class NLPWithGensim:
@classmethod
def documents_to_tfidf_corpus(cls, documents):
news_data = documents.values
tokenized_news_list = []
for daily_news in news_data:
filtered_daily_news = []
filtered_daily_news = [x for x in daily_news if str(type(x)).find('float') == -1]
filtered_daily_news = ' '.join(filtered_daily_news)
filtered_daily_news = list(jieba.cut(filtered_daily_news))
tokenized_news_list.append(filtered_daily_news)
dic = gensim.corpora.Dictionary(tokenized_news_list)
corpus = [dic.doc2bow(text) for text in tokenized_news_list]
tfidf = gensim.models.TfidfModel(corpus)
class TweetAnalyser(BaseAnalyser):
def __init__(self):
self.nlp = NLP()
self.txt_utils = TextUtils()
self.usr_words = {} # to temporary store users with tagged words
self.users = {}
self.result = None
def add_users(self, users):
for u in users:
# store tagged tweets of the current user
if u.tweets:
self.users[u.id] = u.screen_name
self.usr_words[u.id] = self.get_tagged_words(u.tweets, ['N'])
def get_tagged_words(self, tweets, tags):
""" Return a list of all the tagged words of tweets
tweets: list of TwitterMessage objects
[TwitterMessage, TwitterMessage]
tags : list with tag that must be filtered on
['N', 'V']
"""
words = []
for tweet in tweets:
tagged_tweet = self.tag_tweet(tweet)
if tagged_tweet:
for tagged_word in tagged_tweet:
if tagged_word[1] in tags:
words.append(tagged_word)
return words
def tag_tweet(self, tweet):
if self.nlp.detect_language(tweet.text):
return self.nlp.tag(self.txt_utils.tokenize(tweet.text), self.nlp.detect_language(tweet.text))
return None
def analyse(self):
word_count = {}
word_users = {} # contains a list of tuples (usr, timesused)
data = {}
# Build word_count and word_users
data['message'] = "Building word_count and word_users"
self.notifyAll(data)
for u_id in self.usr_words:
word_fd = nltk.FreqDist(word for (word, tag) in self.usr_words[u_id])
for word in word_fd:
if word_count.has_key(word):
word_count[word] += word_fd[word]
word_users[word].append((u_id, word_fd[word]))
else:
word_count[word] = word_fd[word]
word_users[word] = [(u_id, word_fd[word])]
data['message'] = "Filtering word_count and word_users on the times a word is used by a user in comparison with the other users of this word."
self.notifyAll(data)
# Filter the users of a word on the times a word is used
for word in word_count:
avg_usg = word_count[word]/float(len(word_users[word]))
lower_limit = avg_usg - 0.25 * avg_usg
for i, user in enumerate(word_users[word]):
if user[1] < lower_limit:
word_count[word] -= user[1]
clusters = []
words = word_count.keys()
data['message'] = "Comparing word users and clusters words and users if the group of users of both word match enough with each other."
self.notifyAll(data)
# Compare word_users and if they are similar combine to a clusters
for i in range(len(words)):
cluster_words = [words[i]]
cluster_users = []
users_a = [user[0] for user in word_users[words[i]]]
cluster_users.extend(users_a)
# Now compare the users of word[i] with the users of all other words
for j in range((i+1), len(words)):
users_b = [user[0] for user in word_users[words[j]]]
intersect_len = len(set(users_a).intersection(set(users_b)))
# Check if user groups of two words are very similar
if intersect_len < 0.75 * len(users_a) or intersect_len < 0.75 * len(users_b):
continue
# They are very similar
cluster_words.append(words[j])
cluster_users = set(cluster_users).union(users_b)
# We don't want clusters with one word, and especially not clusters with one user.
if len(cluster_users) == 1 or len(cluster_words) == 1:
continue
# Check if the cluster is not a subcluster (of words) of a previous cluster. Then we can skip the subcluster
in_previous_cluster = False
for cluster in clusters:
if len(set(cluster_words)) == len(set(cluster[0]).intersection(set(cluster_words))):
in_previous_cluster = True
break
if in_previous_cluster:
continue
# Everything ok, so replace user ids with screennames in result
screennames = []
for user in cluster_users:
screennames.append("@" + self.users[user])
# Save found cluster
data['message'] = self.cluster_to_string((cluster_words, screennames))
self.notifyAll(data)
clusters.append((cluster_words, screennames))
# Sort by users per cluster
sorted_clusters = sorted(clusters, cmp=lambda x,y: cmp(len(x[1]), len(y[1])), reverse=True)
self.result = sorted_clusters
return sorted_clusters
def cluster_to_string(self, cluster):
r = ""
for word in cluster[0]:
r+= " " + word
r+="\n"
for username in cluster[1]:
r += " " + str(username)
r+="\n"
return r
def result_to_string(self):
"""Returns a printable version of the results"""
r = ""
for cluster in self.result:
r += self.cluster_to_string(cluster) + "\n"
return r
def __init__(self):
self.nlp = NLP()
self.txt_utils = TextUtils()
self.usr_words = {} # to temporary store users with tagged words
self.users = {}
self.result = None
