def load_data(dirFolder, testRatio, featureKeepRatio=1.0):
classes = sorted(os.listdir(dirFolder))
vocabulary = set()
cMap = {i: classes[i] for i in range(len(classes))}
allDocs = []
for i, dclass in enumerate(classes):
documents = os.listdir(os.path.join(dirFolder, dclass))
np.random.shuffle(documents)
splitPoint = int(testRatio * len(documents))
trainDocs, testDocs = documents[splitPoint:], documents[:splitPoint]
allDocs.append([trainDocs, testDocs])
# Process documents for vocabulary selection
tfidf = TfIdf(os.path.join(dirFolder, dclass), trainDocs,
featureKeepRatio)
selectedWords = tfidf.selectWords()
vocabulary = vocabulary | selectedWords
# Featurize data according to above vocabulary
vocabulary = list(vocabulary)
X_train, Y_train = [], []
X_test, Y_test = [], []
for i, dclass in enumerate(classes):
for j in range(len(allDocs[i])):
for doc in allDocs[i][j]:
processedFile = preprocess.readFile(
os.path.join(os.path.join(dirFolder, dclass), doc))
words = Counter(processedFile)
features = [words.get(w, 0) for w in vocabulary]
if j == 0:
X_train.append(features)
Y_train.append(i)
else:
X_test.append(features)
Y_test.append(i)
return (np.stack(X_train), Y_train), (np.stack(X_test), Y_test)
def init_all_states(self):
self.retrieval = Retrieval(num_ir=NUM_OF_IR, config=self.config)
self.tf_idf = TfIdf(self.config)
# TODO: wait for models
self.cluster_model = joblib.load(self.cluster_md)
self.vec_model = Doc2Vec.load(self.vec_md)
jieba.initialize()
def load_data_word2vec(dirFolder, featureKeepRatio=1.0):
model = gensim.models.keyedvectors.KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin', binary=True)
classes = sorted(os.listdir(dirFolder))
vocabulary = set()
cMap = {i:classes[i] for i in range(len(classes))}
allDocs = []
for i, dclass in enumerate(classes):
documents = os.listdir(os.path.join(dirFolder, dclass))
np.random.shuffle(documents)
allDocs.append(documents)
# Process documents for vocabulary selection
tfidf = TfIdf(os.path.join(dirFolder, dclass), documents, featureKeepRatio)
selectedWords = tfidf.selectWords()
vocabulary = vocabulary | selectedWords
# Featurize data according to above vocabulary
vocabulary = list(vocabulary)
X = []
def getIt(dablu):
try:
return model[dablu]
except:
return np.zeros((300,))
for i, dclass in enumerate(classes):
for doc in allDocs[i]:
processedFile = preprocess.readFile(os.path.join(os.path.join(dirFolder, dclass), doc))
words = list(set(processedFile))
features = [ getIt(w) for w in vocabulary]
X.append(features)
return np.stack(X)
def init_all_states(self):
self.retrieval = Retrieval(num_ir=NUM_OF_IR, config=self.config)
self.tf_idf = TfIdf(self.config)
self.cluster_model = joblib.load(self.cluster_md)
# self.vec_model = Doc2Vec.load(self.vec_md)
# self.vec_model = BertClient()
self.load_stop_words(self.config)
jieba.initialize()
def load_data(dirFolder, featureKeepRatio=1.0):
classes = sorted(os.listdir(dirFolder))
vocabulary = set()
cMap = {i:classes[i] for i in range(len(classes))}
allDocs = []
for i, dclass in enumerate(classes):
documents = os.listdir(os.path.join(dirFolder, dclass))
np.random.shuffle(documents)
allDocs.append(documents)
# Process documents for vocabulary selection
tfidf = TfIdf(os.path.join(dirFolder, dclass), documents, featureKeepRatio)
selectedWords = tfidf.selectWords()
vocabulary = vocabulary | selectedWords
# Featurize data according to above vocabulary
vocabulary = list(vocabulary)
X = []
for i, dclass in enumerate(classes):
for doc in allDocs[i]:
processedFile = preprocess.readFile(os.path.join(os.path.join(dirFolder, dclass), doc))
words = Counter(processedFile)
features = [ words.get(w, 0) for w in vocabulary]
X.append(features)
return np.stack(X)
import datetime
from inverted_index import InvertedIndex
from tf_idf import TfIdf
import pandas as pd
custom_separator = "###"
if __name__ == '__main__':
df=pd.read_csv('../data/stories.csv', sep=',',header=None)
df_head = pd.read_csv('../data/db_books.csv', sep=',')
array_content = df.values
# array_segment = array_content[0:3]
# INVERTED INDEX
print("Init INVERTED INDEX")
begin_time = datetime.datetime.now()
invindex = InvertedIndex(array_content)
invindex.process()
print(datetime.datetime.now() - begin_time)
invindex.saveIndexInDisc()
# TF IDF
print("Init TF IDF")
begin_time = datetime.datetime.now()
tf_idf = TfIdf(array_content)
tf_idf.process()
print(datetime.datetime.now() - begin_time)
tf_idf.saveIndexInDisc()
parser = PreProcess()
parsed_trainning_documents = {}
print('processing...')
for k, v in reader.train.items():
parsed_trainning_documents[k] = parser.process(v)
# Entrada para o tf-idf, devemos anotar os documentos com suas classes.
# Receberá como entrada um array de tuplas: ([tokens], classe)
parsed_trainning_documents_with_classes = []
for k in parsed_trainning_documents.keys():
parsed_trainning_documents_with_classes += [(v, k) for v in parsed_trainning_documents[k]]
# Execução tf-idf
print('generating tf.idf...')
tf_idf_calculator = TfIdf(parsed_trainning_documents_with_classes)
tf_idf_calculator.run()
# testa os parâmetros do knn: métrica de distância e valor de K
for metric in ['cosine', 'euclid']:
for k in range(5, 11, 2):
knn = KNN(tf_idf_calculator.results, k, metric)
# confusion_matrix[A][B] = quantas vezes um documento da classe A foi atribuído à classe B
topics = ['baseball', 'christian', 'guns']
confusion_matrix = {topic:{t:0 for t in topics} for topic in topics}
print_log = False
i = 0
ytrue = []
ypred = []
class Agent:
good_qualified_corpus = set()
def __init__(self):
self.config = config
self.stop_words = ''
self.punctuation_str = ''.join(self.config.punctuation_ls)
self.frequency_domain_dict = frequency_domain.frequency_dict
self.cluster_md = self.config.cluster_model
# self.vec_md = self.config.doc_vector_model
self.init_all_states()
self.fuzzy_weight = 0.2
self.tf_idf_weight = 0.8
self.good_corpus_threshold = 200
self.good_corpus_score = 0.95
def init_all_states(self):
self.retrieval = Retrieval(num_ir=NUM_OF_IR, config=self.config)
self.tf_idf = TfIdf(self.config)
self.cluster_model = joblib.load(self.cluster_md)
# self.vec_model = Doc2Vec.load(self.vec_md)
# self.vec_model = BertClient()
self.load_stop_words(self.config)
jieba.initialize()
def get_utterance_type(self, utterance):
# tmp_vector = self.vec_model.infer_vector(utterance)
# tmp_vector = self.vec_model.encode([utterance])
# label = self.cluster_model.predict(tmp_vector)
# print(label)
# return self.config.cluster_file[label[0]]
return self.config.cluster_file[0]
def record_good_conversations(self, utterance, score_ls, context_ls):
def write_conversations():
localtime = (time.asctime(time.localtime(time.time()))).replace(
' ', '_').replace(':', '-')
with open(self.config.path_of_good_conversation + localtime,
'wb') as wfp:
pickle.dump(Agent.good_qualified_corpus, wfp)
Agent.good_qualified_corpus.clear()
# print(Agent.good_qualified_corpus)
for index in range(len(score_ls)):
if score_ls[index] > self.good_corpus_score:
if context_ls[index][0] and context_ls[index][1]:
# print((utterance, context_ls[index][1]))
Agent.good_qualified_corpus.add(
(utterance, context_ls[index][1]))
# print(len(Agent.good_qualified_corpus))
if len(Agent.good_qualified_corpus) > self.good_corpus_threshold:
record_thread = threading.Thread(target=write_conversations)
record_thread.start()
def random_chose_index(self, score_ls, max_score):
max_score_indexes = []
for i in range(len(score_ls)):
if score_ls[i] == max_score:
max_score_indexes.append(i)
return choice(max_score_indexes)
def load_stop_words(self, config):
with open(config.stop_words, 'rb') as fpr:
self.stop_words = pickle.load(fpr)
def remove_special_words(self, stop_words_ls, input_sentence):
sentence = input_sentence
for special_word in self.config.special_modal_words:
if special_word in sentence:
sentence = sentence.replace(special_word, '')
return sentence
def response_answer(self, reply_msg, max_score):
if type(max_score) is np.ndarray:
final_max_score = max_score[0][0]
else:
final_max_score = max_score
return reply_msg, final_max_score
def get_answer(self, utterance, file_name=None):
try:
utterance = utterance.rstrip(self.punctuation_str)
file_name = self.get_utterance_type(utterance)
self.retrieval.read_indexes(file_name)
context_ls = self.retrieval.search_sentences(
utterance, self.stop_words)
if not context_ls:
return "", 0
utterance_no_stop = self.remove_special_words(
self.stop_words, utterance)
new_context_ls = []
for each_context in context_ls:
ques = self.remove_special_words(self.stop_words,
each_context[0])
ans = self.remove_special_words(self.stop_words,
each_context[1])
if not ques or not ans:
new_context_ls.append((0, 0))
continue
new_context_ls.append((ques, ans))
# print("control!!!!!!!!!!!!!!!!!: {},{}".format(utterance, new_context_ls))
# print(len(new_context_ls))
fuzzy_ratio_ls = fuzzy_matching(utterance_no_stop, new_context_ls)
self.tf_idf.select_model(file_name)
self.tf_idf.predict_tfidf(utterance_no_stop, new_context_ls)
tf_idf_score_ls = self.tf_idf.calculate_distances()
if fuzzy_ratio_ls.count(max(fuzzy_ratio_ls)) > 1:
fuzzy_best_index = self.random_chose_index(
fuzzy_ratio_ls, max(fuzzy_ratio_ls))
else:
fuzzy_best_index = fuzzy_ratio_ls.index(max(fuzzy_ratio_ls))
if tf_idf_score_ls.count(max(tf_idf_score_ls)) > 1:
tftdf_best_index = self.random_chose_index(
tf_idf_score_ls, max(tf_idf_score_ls))
else:
tftdf_best_index = tf_idf_score_ls.index(max(tf_idf_score_ls))
fuzzy_best_content = context_ls[fuzzy_best_index][0].rstrip(
self.punctuation_str)
tfidf_best_content = context_ls[tftdf_best_index][0].rstrip(
self.punctuation_str)
if fuzzy_best_content == utterance or utterance.strip(''.join(
config.special_modal_words)) in fuzzy_best_content:
best_index = fuzzy_best_index
# return context_ls[best_index][1], max(fuzzy_ratio_ls)
return self.response_answer(context_ls[best_index][1],
max(fuzzy_ratio_ls))
if tfidf_best_content == utterance or utterance.strip(''.join(
config.special_modal_words)) in tfidf_best_content:
best_index = tftdf_best_index
# return context_ls[best_index][1], max(tf_idf_score_ls)
return self.response_answer(context_ls[best_index][1],
max(tf_idf_score_ls))
final_score_ls = [(fuzzy_ratio * self.fuzzy_weight +
tf_tdf_score * self.tf_idf_weight)
for fuzzy_ratio, tf_tdf_score in zip(
fuzzy_ratio_ls, tf_idf_score_ls)]
# TODO: find a suitable weight
self.record_good_conversations(utterance, final_score_ls,
context_ls)
max_score = max(final_score_ls)
if final_score_ls.count(max_score) > 1:
best_index = self.random_chose_index(final_score_ls, max_score)
else:
best_index = final_score_ls.index(max_score)
# print("final result:{}".format(context_ls[best_index]))
# print(type(max_score))
return self.response_answer(context_ls[best_index][1], max_score)
except Exception as e:
return "", 0
def test(self, utterance):
answer = self.get_answer(utterance)
return answer
def start_cmd(self):
while True:
utterance = input(">>>")
if utterance.strip() == "exit1":
break
answer, score = self.get_answer(utterance)
print("<<<{}:{}".format(answer, score))
def api(self, utterance):
answer, score = self.get_answer(utterance)
return [answer, score]
def socket_get(self, utterance):
answer, score = self.get_answer(utterance)
# print(answer + '---' + str(score[0][0]))
return answer + '---' + str(score)
import pandas as pd
from preprocessing import InputFrame
from tf_idf import TfIdf
from NMF import LatentFeatures
import cPickle as pk
filename = '../../StateNames.csv'
dataframe = pd.read_csv(filename)
#Run the male preprocessing and model
male_instance = InputFrame(dataframe, 'M')
male_instance.clean_data_from_frame()
male_frame = male_instance.gender_frame
male_tfidf = TfIdf(male_frame)
male_vectors = male_tfidf.tfidf_matrix()
male_nmf = LatentFeatures(male_vectors)
male_matrices = male_nmf.fit_model()
# male_nmf.print_W_H_features(male_tfidf.index, male_tfidf.features, 20)
male_features_dict = male_nmf.latent_features_dict(male_tfidf.index,
male_tfidf.features, 20)
#Store the male model and latent features dictionary
with open('../data/male_nmf.pkl', 'w') as f:
pk.dump(male_nmf, f)
with open('../data/male_latent_features.pkl', 'w') as f:
pk.dump(male_features_dict, f)
def calc_doc_len(doc: Document, tf_idf: TfIdf) -> float:
result = 0
for word in doc.words:
result = result + math.pow(tf_idf.get(word, {}).get(doc, 0), 2)
return math.sqrt(result)
import pandas as pd
from preprocessing import InputFrame
from tf_idf import TfIdf
from NMF import LatentFeatures
import cPickle as pk
filename = '../../StateNames.csv'
dataframe = pd.read_csv(filename)
#Run the male preprocessing and model
male_instance = InputFrame(dataframe,'M')
male_instance.clean_data_from_frame()
male_frame = male_instance.gender_frame
male_tfidf = TfIdf(male_frame)
male_vectors = male_tfidf.tfidf_matrix()
male_nmf = LatentFeatures(male_vectors)
male_matrices = male_nmf.fit_model()
# male_nmf.print_W_H_features(male_tfidf.index, male_tfidf.features, 20)
male_features_dict = male_nmf.latent_features_dict(male_tfidf.index, male_tfidf.features, 20)
#Store the male model and latent features dictionary
with open('../data/male_nmf.pkl','w') as f:
pk.dump(male_nmf,f)
with open('../data/male_latent_features.pkl','w') as f:
pk.dump(male_features_dict,f)
class Agent:
def __init__(self):
self.config = config
self.punctuation_str = ''.join(self.config.punctuation_ls)
self.frequency_domain_dict = frequency_domain.frequency_dict
self.cluster_md = self.config.cluster_model
self.vec_md = self.config.doc_vector_model
self.init_all_states()
self.fuzzy_weight = 0.7
self.tf_idf_weight = 0.3
# self.record_chat_ls = []
def init_all_states(self):
self.retrieval = Retrieval(num_ir=NUM_OF_IR, config=self.config)
self.tf_idf = TfIdf(self.config)
# TODO: wait for models
self.cluster_model = joblib.load(self.cluster_md)
self.vec_model = Doc2Vec.load(self.vec_md)
jieba.initialize()
def select_domain(self, utterance):
utterance_words = [each_word for each_word in jieba.cut(utterance, cut_all=False)]
# print(utterance_words)
for each_word in utterance_words:
if each_word in self.frequency_domain_dict.keys() and len(each_word) > 1:
# print(each_word)
return "domains"
return "xiaohuangji"
def get_utterance_type(self, utterance):
# TODO: wait for models
tmp_vector = self.vec_model.infer_vector(utterance)
label = self.cluster_model.predict(tmp_vector.reshape(1, -1))
# print(label)
return self.config.cluster_file[label[0]]
def record_good_chat(self):
pass # TODO: build a new thread to record conversation whose score is more than 0.95 in interval time
# TODO: by this way we can get a lot of good conversations
def random_chose_index(self, score_ls, max_score):
max_score_indexes = []
for i in range(len(score_ls)):
if score_ls[i] == max_score:
max_score_indexes.append(i)
return choice(max_score_indexes)
def get_answer(self, utterance, file_name=None):
try:
# file_name = self.get_utterance_type(utterance)
utterance = utterance.rstrip(self.punctuation_str)
if not file_name:
file_name = self.select_domain(utterance)
# print(file_name)
# file_name = self.get_utterance_type(utterance)
self.retrieval.read_indexes(file_name)
context_ls = self.retrieval.search_sentences(utterance)
# print(context_ls)
if not context_ls and file_name != "domains":
return "对不起亲，没听懂你说啥，你再重新组织一下语言吧。"
if not context_ls and file_name == "domains":
answer = self.get_answer(utterance, "weibo")
return answer
if file_name == "domains":
fuzzy_ratio_ls = fuzzy_for_domains(utterance, context_ls)
else:
fuzzy_ratio_ls = fuzzy_matching(utterance, context_ls)
# print(fuzzy_ratio_ls)
self.tf_idf.select_model(file_name)
self.tf_idf.predict_tfidf(utterance, context_ls)
tf_idf_score_ls = self.tf_idf.calculate_distances()
if fuzzy_ratio_ls.count(max(fuzzy_ratio_ls)) > 1:
fuzzy_best_index = self.random_chose_index(fuzzy_ratio_ls, max(fuzzy_ratio_ls))
else:
fuzzy_best_index = fuzzy_ratio_ls.index(max(fuzzy_ratio_ls))
if tf_idf_score_ls.count(max(tf_idf_score_ls)) > 1:
tftdf_best_index = self.random_chose_index(tf_idf_score_ls, max(tf_idf_score_ls))
else:
tftdf_best_index = tf_idf_score_ls.index(max(tf_idf_score_ls))
fuzzy_best_content = context_ls[fuzzy_best_index][0].rstrip(self.punctuation_str)
tfidf_best_content = context_ls[tftdf_best_index][0].rstrip(self.punctuation_str)
if fuzzy_best_content == utterance or utterance in fuzzy_best_content:
best_index = fuzzy_best_index
# print(context_ls[best_index][0])
return context_ls[best_index][1]
if tfidf_best_content == utterance or utterance in tfidf_best_content:
best_index = tftdf_best_index
# print(context_ls[best_index][0])
return context_ls[best_index][1]
final_score_ls = [(fuzzy_ratio * self.fuzzy_weight + tf_tdf_score * self.tf_idf_weight) for fuzzy_ratio, tf_tdf_score in
zip(fuzzy_ratio_ls, tf_idf_score_ls)]
# TODO: find a suitable weight
# print(final_score_ls)
if max(final_score_ls) < 0.85 and file_name != "weibo" and file_name != "domains": # TODO: ugly code
# print(max(final_score_ls))
answer = self.get_answer(utterance, "weibo")
return answer
else:
# print(max(final_score_ls))
max_score = max(final_score_ls)
if final_score_ls.count(max_score) > 1:
best_index = self.random_chose_index(final_score_ls, max_score)
else:
best_index = final_score_ls.index(max_score)
# print(context_ls[best_index][0])
return context_ls[best_index][1]
except Exception as e:
return "对不起亲，这个问题实在不晓得呀！"
def get_answer2(self, utterance, file_name=None):
try:
utterance = utterance.rstrip(self.punctuation_str)
file_name = self.get_utterance_type(utterance)
# print(file_name)
self.retrieval.read_indexes(file_name)
context_ls = self.retrieval.search_sentences(utterance)
# print(context_ls)
if not context_ls:
return "", 0
fuzzy_ratio_ls = fuzzy_matching(utterance, context_ls)
self.tf_idf.select_model(file_name)
self.tf_idf.predict_tfidf(utterance, context_ls)
tf_idf_score_ls = self.tf_idf.calculate_distances()
if fuzzy_ratio_ls.count(max(fuzzy_ratio_ls)) > 1:
fuzzy_best_index = self.random_chose_index(fuzzy_ratio_ls, max(fuzzy_ratio_ls))
else:
fuzzy_best_index = fuzzy_ratio_ls.index(max(fuzzy_ratio_ls))
if tf_idf_score_ls.count(max(tf_idf_score_ls)) > 1:
tftdf_best_index = self.random_chose_index(tf_idf_score_ls, max(tf_idf_score_ls))
else:
tftdf_best_index = tf_idf_score_ls.index(max(tf_idf_score_ls))
fuzzy_best_content = context_ls[fuzzy_best_index][0].rstrip(self.punctuation_str)
tfidf_best_content = context_ls[tftdf_best_index][0].rstrip(self.punctuation_str)
if fuzzy_best_content == utterance or utterance in fuzzy_best_content:
best_index = fuzzy_best_index
# print(context_ls[best_index][0])
return context_ls[best_index][1], max(fuzzy_ratio_ls)
if tfidf_best_content == utterance or utterance in tfidf_best_content:
best_index = tftdf_best_index
# print(context_ls[best_index][0])
return context_ls[best_index][1], max(tf_idf_score_ls)
final_score_ls = [(fuzzy_ratio * self.fuzzy_weight + tf_tdf_score * self.tf_idf_weight) for fuzzy_ratio, tf_tdf_score in
zip(fuzzy_ratio_ls, tf_idf_score_ls)]
# TODO: find a suitable weight
max_score = max(final_score_ls)
if final_score_ls.count(max_score) > 1:
best_index = self.random_chose_index(final_score_ls, max_score)
else:
best_index = final_score_ls.index(max_score)
# print(context_ls[best_index][0])
return context_ls[best_index][1], max_score
except Exception as e:
return "", 0
def test(self, utterance):
answer = self.get_answer2(utterance)
return answer
def start(self):
while True:
utterance = input(">>>")
if utterance.strip() == "exit1":
break
answer = self.get_answer2(utterance)
print("<<<{}".format(answer))
def api(self, utterance):
answer, score = self.get_answer2(utterance)
return [answer, score]
