def main(unused_args):
myconfig = Config()
w2v = WordEmbedding.Word2Vec(myconfig)
start_time = time.time()
w2v.loadWordFile("word_table_merge")
end_time = time.time()
sys.stderr.write(' %.2f' % (end_time - start_time) +
' seconds escaped...\n')
trainnames = getFileNames("data/")
trainnames.sort()
print(trainnames)
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
configproto = tf.ConfigProto()
configproto.gpu_options.allow_growth = True
configproto.allow_soft_placement = True
with tf.Session(config=configproto) as sess:
filenum = len(trainnames)
lstm_model = createLstmModel(myconfig, w2v)
init = tf.global_variables_initializer()
sess.run(init)
for i in range(myconfig.max_epoch):
run_epoch(sess, lstm_model, w2v, trainnames[i % filenum], i + 1)
def readIntoBST():
tree = None
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line on the file
while content:
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
tree = bst.Insert(tree, n)
content = ef.readline()
print("Done")
return tree
def readIntoBTree(max):
tree = btree.BTree([], max_data=max)
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line on the file
while content:
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on btree
btree.Insert(tree, n)
btree.Print
content = ef.readline()
print("Done")
return tree
def main(unused_args):
with tf.device('/cpu:0'):
myconfig = Config()
w2v = WordEmbedding.Word2Vec(myconfig)
start_time = time.time()
w2v.loadWordFile(myconfig.wordTablePath)
end_time = time.time()
sys.stderr.write(' %.2f' % (end_time - start_time) +
' seconds escaped...\n')
trainnames = getFileNames(myconfig.trainFileDir)
#按照文件顺序读取数据
trainnames.sort()
print("train file list:\n")
for i in trainnames:
print(i)
os.environ["CUDA_VISIBLE_DEVICES"] = myconfig.CUDA_VISIBLE_DEVICES
configproto = tf.ConfigProto()
#configproto.gpu_options.allow_growth = True
configproto.allow_soft_placement = True
configproto.log_device_placement = True
timeList = []
with tf.Session(config=configproto) as sess:
filenum = len(trainnames)
lstm_models = createLstmModel(myconfig)
init = tf.global_variables_initializer()
sess.run(init)
var_list = tf.global_variables()
print_variable(var_list)
#loader = tf.train.Saver()
#loader.restore(sess, "models/lstmp_imp_refine_100")
steps = 0
for i in range(myconfig.max_epoch):
steps, elapsed_time = run_epoch(sess, lstm_models, w2v,
trainnames[i % filenum], i + 1,
steps, myconfig.saveModelEvery,
myconfig.modelDir,
myconfig.modelName,
len(myconfig.gpu_list))
timeList.append(elapsed_time)
all = 0
for epoch, t in enumerate(timeList):
print("epoch:{} used time:{}".format(epoch, t))
all += t
print("run {} epoch use time:{}".format(len(timeList), all))
def buildBST(T, file):
"""
Read through the text file to retrieve each word and its embedding. If a
word starts with an alphabetic character, create a WordEmbedding object
for that word and its embedding and insert it in its proper place in the
BST.
"""
for line in file:
word_line = line.split(' ')
word = word_line[0]
embedding = word_line[1:]
embedding = [float(i) for i in embedding]
if word[0].isalpha():
word_emb_object = WordEmbedding.WordEmbedding(word, embedding)
T = insert(T, word_emb_object)
return T
def main(unused_args):
myconfig = Config()
w2v = WordEmbedding.Word2Vec(myconfig)
start_time = time.time()
w2v.loadWordFile("word_table_merge")
end_time = time.time()
sys.stderr.write(' %.2f' % (end_time - start_time) +
' seconds escaped...\n')
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
configproto = tf.ConfigProto()
configproto.gpu_options.allow_growth = True
configproto.allow_soft_placement = True
with tf.Session(config=configproto) as sess:
model = createModel(myconfig, w2v)
loader = tf.train.Saver()
loader.restore(sess, "models/qrnn_imp_refine_009")
decode(sess, model, w2v, 'src_code/input.demo')
def main(unused_args):
myconfig = Config()
w2v = WordEmbedding.Word2Vec(myconfig)
start_time = time.time()
w2v.loadWordFile("word_table_merge")
end_time = time.time()
sys.stderr.write(' %.2f'%(end_time-start_time) + ' seconds escaped...\n')
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
configproto = tf.ConfigProto()
configproto.gpu_options.allow_growth = True
configproto.allow_soft_placement = True
with tf.Session(config=configproto) as sess:
#w2v.loadEmbeddings(sess,'word_embedding.tensorflow')
#end_time = time.time()
#sys.stderr.write(' %.2f'%(end_time-start_time) + ' seconds escaped...\n')
lstm_model = createLstmModel(myconfig,w2v)
loader = tf.train.Saver()
loader.restore(sess, "models/lstmp_tw_refine_199")
decode(sess, lstm_model, w2v, 'data/test.txt')
import Datasets
import BugReportPreprocessing
import SourceCodePreprocessing
import rVSM
import WordEmbedding
import TokenMatch
import BugRecency
import Evaluation
data_set = Datasets.zxing
BugReportPreprocessing.main(data_set)
SourceCodePreprocessing.main(data_set)
rVSM.main(data_set)
TokenMatch.main(data_set)
WordEmbedding.main(data_set)
BugRecency.main(data_set)
print('---------------------------------------------------')
Evaluation.main(data_set)
from tensorflow.keras.models import load_model
import Prepare
import WordEmbedding
import SiameseModel
word_embedding_file = 'glove_vector_dict_300d.pickle'
model_file = 'SRA_trained_model.h5'
word_embedding = WordEmbedding(word_embedding_file)
input_ = Prepare(data_frame)
label_map = {0: 'correct', 1: 'incorrect', 2: 'contradictory'}
model = SiameseModel(word_embedding, input_)
model.load_weights(model_file, by_name=False, skip_mismatch=False)
predictions = model.predict(input_.premise, input_.hypothesis)
predicted_labels = [label_map[np.argmax(p)] for p in predictions]
BST_tree = None
start_time = time.time()
BST_tree = BST.buildBST(BST_tree, words_file)
end_time = time.time()
print('Binary Search Tree stats:')
num_nodes = BST.numNodes(BST_tree)
print('Number of nodes: ' + str(num_nodes))
height = BST.height(BST_tree)
print('Height: ' + str(height))
total_time = end_time - start_time
print('Running time for binary search tree construction: ' +
str(round(total_time, 6)) + ' seconds.\n')
if compare_words == 'y':
start_time = time.time()
similarity = WordEmbedding.findSimilarityC(BST_tree, user_words)
end_time = time.time()
print('Word similarities found:')
print('Similarity [' + user_words[0] + ',' + user_words[1] + '] = ' +
str(round(similarity, 4)))
print()
total_time = end_time - start_time
print('Running time for binary search tree query processing: ' +
str(round(total_time, 6)) + ' seconds.\n')
elif compare_words == 'n':
print('Reading word file to determine similarities\n')
start_time = time.time()
similarities = WordEmbedding.findSimilarityA(BST_tree, word_pairs_file)
end_time = time.time()
print('Word similarities found:')
def readIntoHashLP(size, choice):
h = htlp.HashTableLP(size)
if choice == "1":
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line on the file
for i in range(10000):
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
h.insertS(n)
content = ef.readline()
elif choice == "2":
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line
for i in range(10000):
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
h.insertAscii(n)
content = ef.readline()
elif choice == "3":
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line
for i in range(10000):
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
h.insertPAscii(n)
content = ef.readline()
elif choice == "4":
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line on the file
for i in range(10000):
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
h.insertSAscii(n)
content = ef.readline()
elif choice == "5":
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line on the file
for i in range(10000):
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
h.insertRecursive(n)
content = ef.readline()
elif choice == "6":
with open(embed_file, encoding="utf8") as ef:
content = ef.readline()
#goes through every line on the file
cnt = 0
for i in range(10000):
word = content.split()
#creates Word Embedding with information from file
n = WordEmbedding.WordEmbedding(word[0], word[1:])
#inserts on bst
h.insertFE(n)
cnt += 1
if cnt % 10000 == 0:
print(cnt)
content = ef.readline()
print("Done")
return h
def train():
w2v_path = r'C:\Users\fkarl\PycharmProjects\Image2SequenceFiles\w2vModel\en.wiki.bpe.op200000.d300.w2v.bin'
w2v_model = WordEmbedding.EmbeddingModel(300, w2v_path, True, True)
# restrict to newer data for sake of training time
YEAR_START = 2010
YEAR_END = 2017
# produce model
model = Models.get_stock_pred_model(None, HEADER_LENGTH, SENT_LENGTH)
model.summary()
epochs = 10
for i in range(epochs):
print('EPOCH: ' + str(i) + ' of ' + str(epochs))
# Outer Loop for yearly validation
for year in range(YEAR_START, YEAR_END):
# load data for year
print('Year: ' + str(year))
df_year_path = path_to_df + str(year)
df_paths_per_month = [
os.path.join(df_year_path, f) for f in os.listdir(df_year_path)
if os.path.isfile(os.path.join(df_year_path, f))
]
print(df_paths_per_month)
rand_int = random.randint(0, len(df_paths_per_month) - 1)
val_df = df_paths_per_month[rand_int]
df_paths_per_month.remove(val_df)
with open(val_df, 'rb') as f:
data_frame = pickle.loads(f.read())
data_frame = refine_dataframe(data_frame)
Y_val = data_frame['BA'].values # take only Boeing stock prices
Y_val = get_targets(
Y_val
) # make some adjustment to the targets; interested in large deviations.
print('Validation Set size', Counter(list(Y_val)))
headers = data_frame['abstract'].map(str).map(str.lower).map(
nltk.word_tokenize).values # tokenise
embeddings = [
np.array(w2v_model.get_embeddings(sent)) for sent in headers
]
padded_headders = keras.preprocessing.sequence.pad_sequences(
embeddings,
maxlen=HEADER_LENGTH,
dtype='float32',
padding='post',
truncating='post',
value=0.0)
X_headders_val = np.array(padded_headders)
X_sents_val = get_refined_news(data_frame, w2v_model)
assert (len(X_headders_val) == len(Y_val))
# Inner loop for training
for data_frame_path in df_paths_per_month:
with open(data_frame_path, 'rb') as f:
data_frame = pickle.loads(f.read())
data_frame = refine_dataframe(data_frame)
Y = data_frame['BA'].values
Y = get_targets(Y)
print('Training Set size', Counter(list(Y)))
headers = data_frame['abstract'].map(str).map(str.lower).map(
nltk.word_tokenize).values
embeddings = [
np.array(w2v_model.get_embeddings(sent))
for sent in headers
]
padded_headders = keras.preprocessing.sequence.pad_sequences(
embeddings,
maxlen=HEADER_LENGTH,
dtype='float32',
padding='post',
truncating='post',
value=0.0)
X_headders = np.array(padded_headders)
X_sents = get_refined_news(data_frame, w2v_model)
assert (len(Y) == len(X_headders))
assert (len(headers) == len(X_headders))
print('*** Training *** on ' + data_frame_path + ' with ' +
str(len(Y)) + ' samples')
model.fit([X_headders, X_sents],
Y,
batch_size=16,
validation_split=0.05,
verbose=2,
epochs=3)
model.save('stock_pred_model.h5')
print('*** Validation ***')
predictions = model.predict([X_headders_val, X_sents_val],
batch_size=64)
rounded_predicitons = [
np.round(elem[0]) for elem in predictions
] # could also use conservative_results = [1 if elem >= TOL else 0 for elem in predictions]
print('TestResults', Counter(rounded_predicitons))
print(
metrics.precision_recall_fscore_support(
Y_val, rounded_predicitons))
fpr, tpr, threshold = metrics.roc_curve(
Y_val, [elem[0] for elem in predictions])
roc_auc = metrics.auc(fpr, tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % roc_auc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()