def Ask_Question(question,languege='Persian'):
if(languege=='Persian'):
question = Stemmer_Text(Normalizer_Text(question))
Answers = load_data(pre_path_files+'Persian/ListAnswering')
tokenizer = load_data(pre_path_files+'Persian/Word2Vec/tokenizer')
embedding_matrix = load_data(pre_path_files+'Persian/Word2Vec/embedding_matrix')
embedding_meta_data = {
'tokenizer': tokenizer,
'embedding_matrix': embedding_matrix
}
model_path_persian=glob.glob(pre_path_files+'Persian/Model/*.h5')[0]
model = load_model(model_path_persian)
test_sentence_pairs = []
for i in range(len(Answers)):
test_sentence_pairs.append((question, Stemmer_Text(Normalizer_Text(Answers[i]))))
test_data_x1, test_data_x2, leaks_test = create_test_data(tokenizer, test_sentence_pairs,
siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(model.predict([test_data_x1, test_data_x2, leaks_test], verbose=1).ravel())
results = [z for (x, y), z in zip(test_sentence_pairs, preds)]
out = sorted(range(len(results)), key=lambda k: results[k])
result_list = []
for j in range(5):
result_list.append({'answer': Answers[out[len(out) - 1 - j]],
'score': str(results[out[len(out) - 1 - j]])})
return result_list
elif(languege=='English'):
question = Clean_Text(question)
Answers = load_data(pre_path_files+'English/ListAnswering')
print(Answers)
tokenizer = load_data(pre_path_files+'English/Word2Vec/tokenizer')
embedding_matrix = load_data(pre_path_files+'English/Word2Vec/embedding_matrix')
embedding_meta_data = {
'tokenizer': tokenizer,
'embedding_matrix': embedding_matrix
}
model_path_english=glob.glob(pre_path_files+'English/Word2Vec/*.h5')[0]
model = load_model(model_path_english)
test_sentence_pairs = []
for i in range(len(Answers)):
test_sentence_pairs.append((question, Clean_Text(Answers[i])))
test_data_x1, test_data_x2, leaks_test = create_test_data(tokenizer, test_sentence_pairs,
siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(model.predict([test_data_x1, test_data_x2, leaks_test], verbose=1).ravel())
results = [z for (x, y), z in zip(test_sentence_pairs, preds)]
out = sorted(range(len(results)), key=lambda k: results[k])
result_list = []
for j in range(5):
result_list.append({'answer': Answers[out[len(out) - 1 - j]],
'score': str(results[out[len(out) - 1 - j]])})
return result_list
def output(l1, l2):
test_sentence_pairs = test_sentence_pairs = [(l1, l2)]
#
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, test_sentence_pairs, siamese_config['MAX_SEQUENCE_LENGTH'])
#
preds = list(
model.predict([test_data_x1, test_data_x2, leaks_test],
verbose=1).ravel())
results = [(x, y, z) for (x, y), z in zip(test_sentence_pairs, preds)]
results.sort(key=itemgetter(2), reverse=True)
return results[0][2]
def test(test_pair): model = load_model(best_model_path) test_sentence_pairs = test_pair bias1=random.uniform(0.28123,0.3156) bias2=random.uniform(0.07682,0.09354) test_data_x1, test_data_x2, leaks_test = create_test_data(tokenizer,test_sentence_pairs, siamese_config['MAX_SEQUENCE_LENGTH']) preds = list(model.predict([test_data_x1, test_data_x2, leaks_test], verbose=1).ravel()) results = [(x, y, z) for (x, y), z in zip(test_sentence_pairs, preds)] results.sort(key=itemgetter(2), reverse=True) print(test_pair) print(results[0][2]) return max(min(results[0][2],bias1),bias2)
def get_doc2vec_vectors_train_valid_split(trainingData):
# split the dataset into training and validation datasets
train_x, valid_x, train_y, valid_y = get_train_test_split_of_dataframe(
trainingData, False)
# label encode the target variable
encoder = preprocessing.LabelEncoder()
train_y = encoder.fit_transform(train_y)
valid_y = encoder.fit_transform(valid_y)
sentences1 = train_x['Q1']
sentences2 = train_x['Q2']
is_similar = list(train_y)
sentences1_validate = valid_x['Q1']
sentences2_validate = valid_x['Q2']
is_similar_validate = list(valid_y)
tokenizer, embedding_matrix = word_embed_meta_data(
sentences1 + sentences2, siamese_config['EMBEDDING_DIM'])
embedding_meta_data = {
'tokenizer': tokenizer,
'embedding_matrix': embedding_matrix
}
## creating sentence pairs
sentences_pairs = [(x1, x2) for x1, x2 in zip(sentences1, sentences2)]
del sentences1
del sentences2
sentences_pairs_validate = [
(x1, x2) for x1, x2 in zip(sentences1_validate, sentences2_validate)
]
del sentences1_validate
del sentences2_validate
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, sentences_pairs_validate,
siamese_config['MAX_SEQUENCE_LENGTH'])
test_data_x = [test_data_x1, test_data_x2, leaks_test]
return sentences_pairs, is_similar, test_data_x, is_similar_validate, embedding_meta_data
def single_pred(x, y):
"""
"""
#print(sentences1,sentences2)
from operator import itemgetter
from keras.models import load_model
bst_path = "./stored_model/1589893283/lstm_new.h5"
model = load_model(bst_path)
test_sentence_pairs = [(pre.preprocessing_single_sentence(x),
pre.preprocessing_single_sentence(y))]
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, test_sentence_pairs, siamese_config['MAX_SEQUENCE_LENGTH'])
preds = model.predict([test_data_x1, test_data_x2, leaks_test]).ravel()
print(preds[0])
l = str(preds[0] * 100)
return l
CONFIG.rate_drop_lstm, CONFIG.rate_drop_dense,
CONFIG.activation_function,
CONFIG.validation_split_ratio)
best_model_path = siamese.train_model(sentences_pair,
is_similar,
model_save_directory='./')
#######################
##### Testing #########
#######################
#
# model = load_model(
# r'/Users/caowenli/Desktop/ml_pj/dl/time_series_similarity/checkpoints/1593418708/lstm_128_64_0.17_0.30.h5')
model = load_model(best_model_path)
train_data_x1, train_data_x2 = create_test_data(sentences_pair)
trian_preds = list(
model.predict([train_data_x1, train_data_x1], verbose=1).ravel())
res = []
for i in trian_preds:
if i < 0.5:
res.append(0)
else:
res.append(1)
print(len(res))
print(res)
data['predict'] = np.array(res)
data.to_csv("labeled_train_data.csv", index=None)
test_data = pd.read_csv('test_data.csv')
test_sentences1 = list(test_data['s_link_dir_speed'])
df = pd.read_csv('../data/final_hate_dataset_test.csv', sep='\t')
hs_sentences = list(df['HS'])
cn_sentences = list(df['CN'])
cntype = list(df['CNtype'])
# remove the cn type
# cntype = ['none' for x in cntype]
top_n = 3
tokenizer, embedding_matrix = word_embed_meta_data(hs_sentences + cn_sentences, siamese_config['EMBEDDING_DIM'])
guess = 0
for i in range(len(hs_sentences)):
resp = cn_sentences[i]
hs = hs_sentences[i]
all_pairs = [(hs, cn) for cn in cn_sentences]
test_data_x1, test_data_x2, leaks_test, cntypes = create_test_data(tokenizer, all_pairs, cntype, siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(model.predict([test_data_x1, test_data_x2, leaks_test, cntypes], verbose=1).ravel())
results = [(x, y, z) for (x, y), z in zip(all_pairs, preds)]
results.sort(key=itemgetter(2), reverse=True)
top = results[:top_n]
responses = [x[1] for x in top]
if resp in responses:
print('Guess')
guess += 1
print(hs, resp, top)
print('Accuracy', float(guess)/len(hs_sentences))
CONFIG.activation_function,
CONFIG.validation_split_ratio)
best_model_path = siamese.train_model(sentences_pair,
is_similar,
embedding_meta_data,
model_save_directory='./')
########################
###### Testing #########
########################
model = load_model(best_model_path)
test_sentence_pairs = [
('What can make Physics easy to learn?',
'How can you make physics easy to learn?'),
('How many times a day do a clocks hands overlap?',
'What does it mean that every time I look at the clock the numbers are the same?'
)
]
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, test_sentence_pairs, siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(
model.predict([test_data_x1, test_data_x2, leaks_test], verbose=1).ravel())
results = [(x, y, z) for (x, y), z in zip(test_sentence_pairs, preds)]
results.sort(key=itemgetter(2), reverse=True)
print(results)
df_test = pd.read_csv('data/test_word_char.csv')
sentences_test1 = list(df_test['q1_word'])
sentences_test2 = list(df_test['q2_word'])
## creating sentence pairs
test_sentence_pairs = [(x1, x2)
for x1, x2 in zip(sentences_test1, sentences_test2)]
del sentences_test1
del sentences_test2
#test_sentence_pairs = [('What can make Physics easy to learn?','How can you make physics easy to learn?'),
# ('How many times a day do a clocks hands overlap?','What does it mean that every time I look at the clock the numbers are the same?')]
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, test_sentence_pairs, CONFIG.max_sequence_length,
embedding_index, tfidf_dict)
result = model.predict([test_data_x1, test_data_x2, leaks_test],
verbose=1,
batch_size=1024)
# 提交结果
submit_dir = './submit/'
if not os.path.exists(submit_dir):
os.makedirs(submit_dir)
submit = pd.DataFrame()
submit['y_pre'] = list(result[:, 0])
submit.to_csv(submit_dir + 'result0618_add_features.csv', index=False)
def main():
numberOfRelevantQs = 10
#fileNameList = ['android','english', 'gaming', 'gis', 'mathematica', 'physics', 'programmers', 'stats', 'tex', 'unix', 'webmasters', 'wordpress']
fileNameList = ['stats', 'tex', 'unix', 'webmasters', 'wordpress']
#fileNameList = ['webmasters']
for a in range(len(fileNameList)):
outputFile = open('../data/output/result.txt', 'a')
fileName = str(fileNameList[a])
df_for_file = get_df_from_csv_file(fileName)
train_x, train_y, valid_x, valid_y, embedding_meta_data = get_doc2vec_vectors_train_valid_split(
df_for_file)
model_path = train_model(train_x, train_y, embedding_meta_data,
fileName)
siamese_lstm_model = load_model(model_path)
preds, accuracy = evaluate_model(siamese_lstm_model, valid_x, valid_y)
print('Classification Accuracy for : ' + fileName + ' Siamese LSTM ' +
str(str(accuracy[1])),
file=outputFile)
tokenizer = embedding_meta_data['tokenizer']
corpus = []
corpus_to_query = []
df = pd.read_json('../data/json/' + str(fileName) + '_questions.json',
orient='index')
df['QuestionID'] = df.index
df = df[['QuestionID', 'title', 'body', 'dups']]
numberOfTrainData = df['QuestionID'].size
for i in range(0, numberOfTrainData):
title = review_to_wordlist(df.iloc[i].loc['title'])
body = review_to_wordlist(strip_tags(df.iloc[i].loc['body']))
qid = df.iloc[i].loc['QuestionID']
corpus.append(title)
corpus_to_query.append((str(qid), title, body))
tokenized_corpus = [doc.split(" ") for doc in corpus]
bm25 = BM25Okapi(tokenized_corpus)
foundDupsAll = 0
queriesWithDuplicates = 0
sumOfAveragePrecision = 0.0
precisionAtSum = 0
for i in range(0, numberOfTrainData):
dupids = df.iloc[i].loc['dups']
if (len(dupids) > 0):
test_query = review_to_wordlist(
df.iloc[i].loc['title']) + " " + review_to_wordlist(
strip_tags(df.iloc[i].loc['body']))
tokenized_query = test_query.split(" ")
queriesWithDuplicates += 1
candidate_docs_bm25 = bm25.get_top_n(tokenized_query,
corpus_to_query,
n=10)
topn_similar_docs_as_pairs = []
topn_doc_indexes = []
for p in range(0, len(candidate_docs_bm25)):
candidateQuestion = review_to_wordlist(
candidate_docs_bm25[p][1]) + " " + review_to_wordlist(
strip_tags(candidate_docs_bm25[p][2]))
topn_similar_docs_as_pairs.append(
(test_query, candidateQuestion))
topn_doc_indexes.append(candidate_docs_bm25[p][0])
test_data_x1, test_data_x2, leaks_test = create_test_data(
tokenizer, topn_similar_docs_as_pairs,
siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(
siamese_lstm_model.predict(
[test_data_x1, test_data_x2, leaks_test],
verbose=0).ravel())
results = [
(x, y, z)
for (x, y), z in zip(topn_similar_docs_as_pairs, preds)
]
results = [(a, y, z)
for (x, y, z), a in zip(results, topn_doc_indexes)]
results.sort(key=itemgetter(2), reverse=True)
foundDups = 0
apForQuery = 0.0
#print("Query : " + test_query)
#print("Annotated Dups : " + str(dupids))
#print('Results : ')
for j in range(0, numberOfRelevantQs):
similarDocId = results[j][0]
#similarDocId = candidate_docs_bm25[j][0]
#print(similarDocId)
if (similarDocId in dupids):
foundDups += 1
apForQuery += foundDups / (j + 1)
#print('-------------- Hit !!!-----------------@'+ str(j))
if (foundDups > 0):
sumOfAveragePrecision = sumOfAveragePrecision + (
apForQuery / len(dupids))
precisionAtSum += foundDups / numberOfRelevantQs
foundDupsAll += foundDups
print(str(fileName) + " Train Data Count : " + str(numberOfTrainData),
file=outputFile)
print(str(fileName) + " Relevant Query Count (Test) : " +
str(queriesWithDuplicates),
file=outputFile)
print(str(fileName) + " Found within " + str(numberOfRelevantQs) +
" Count : " + str(foundDupsAll),
file=outputFile)
print(str(fileName) + " Precision @ " + str(numberOfRelevantQs) +
" : " + str(precisionAtSum / queriesWithDuplicates),
file=outputFile)
print(str(fileName) + " MAP : " +
str(sumOfAveragePrecision / queriesWithDuplicates),
file=outputFile)
outputFile.close()
del df
del siamese_lstm_model
del bm25
del corpus
del corpus_to_query
del topn_similar_docs_as_pairs
gc.collect()
