def build_words(questions, configuration):
config = configuration
# Build an empty dictionary for words
word_dict = dict.Dictionary()
word_dict.initialize_unknown_token()
# Placeholder for returned dataset
x = []
for question in questions:
# Temporary row
row = []
for token in question.tokens:
# First add "new" word to dict
token_id = word_dict.add(token)
# Add token_id to the data
row.append(token_id)
# If padding is enabled - fill sequence with unknown tokens
if config.use_word_padding:
while len(row) < config.max_sequence_length:
row.append(word_dict.get_index(constants.unknown_token))
# Add new row to the data
x.append(row)
# Transform x to numpy array
x = np.array(x)
# Set configuration value for word_dict
config.word_dict = word_dict
config.word_dict.add("competing")
config.word_dict.add("compete")
config.word_dict.add("activities")
config.word_dict.add("activity")
config.word_dict.add("year’s")
config.word_dict.add("r&d")
config.word_dict.add("t&e")
config.word_dict.add("get")
config.word_dict.add("located")
config.word_dict.add("segment")
config.word_dict.add("minimum")
config.word_dict.add("maximum")
return x
def main():
parser = argparse.ArgumentParser(
description='Clasifying a sentence with LSTM.')
parser.add_argument('-m',
'--model',
required=True,
help='the path to the model file.')
parser.add_argument('-s',
'--source-sentence',
required=True,
help='the source sentence to classify')
parser.add_argument('-t',
'--target-sentence',
required=True,
help='the target sentence to classify')
parser.add_argument('-d',
'--dictionary',
required=True,
help='the dictionary/vocabulary.')
parser.add_argument('-g',
'--gpuid',
required=False,
default=-1,
help='the ID of the GPU to use.')
args = parser.parse_args()
deviceid = -1
if int(args.gpuid) > -1 and torch.cuda.is_available():
deviceid = int(args.gpuid)
print('Using GPU ' + str(deviceid))
torch.cuda.set_device(deviceid)
if os.path.exists(args.dictionary):
dictionary = Dict.Dictionary(os.path.realpath(args.dictionary))
dictionary.load_dictionary()
data_pr = DataPreprocessor(dictionary)
else:
print("Path not found: ", args.dictionary)
exit(1)
if not os.path.exists(args.model):
print("Path not found: ", args.model)
exit(1)
print(
test(args.source_sentence, args.target_sentence, data_pr, args.model,
deviceid))
def build_types(questions, type, configuration):
config = configuration
y = []
# Build an empty dictionary for words
label_dict = dict.Dictionary()
print("label_dict")
print(label_dict.idx2str)
for question in questions:
if type == "cnn_question":
# Fill dictionary with values
label_dict.add(question.question_type)
else:
# Fill dictionary with values
label_dict.add(question.answer_type)
config.label_dict = label_dict
# Build label data
for question in questions:
row = []
for label in config.label_dict.idx2str:
if type == "cnn_question":
if label == question.question_type:
row.append(1)
else:
row.append(0)
else:
if label == question.answer_type:
row.append(1)
else:
row.append(0)
y.append(row)
y = np.array(y)
return y
def build_words(questions, configuration):
config = configuration
# Build an empty dictionary for words
word_dict = dict.Dictionary()
print("Dict")
print(word_dict.idx2str)
# Placeholder for returned dataset
x = []
for question in questions:
# Temporary row
row = []
for token in question.tokens:
# First add "new" word to dict
token_id = word_dict.add(token)
# Add token_id to the data
row.append(token_id)
# If padding is enabled - fill sequence with unknown tokens
if config.use_word_padding:
while len(row) < config.max_sequence_length:
row.append(word_dict.get_index(constants.unknown_token))
# Add new row to the data
x.append(row)
# Transform x to numpy array
x = np.array(x)
# Set configuration value for word_dict
config.word_dict = word_dict
return x
def build_labels(questions, configuration):
config = configuration
# Build an empty dictionary for words
label_dict = dict.Dictionary()
print("label_dict")
print(label_dict.idx2str)
# Add unknown label marker if label padding is enabled
if config.use_label_padding:
label_dict.add(constants.unknown_label)
# Placeholder for returned labels
y = []
for question in questions:
# Temporary row
row = []
for label in question.semantic_labels:
# First add "new" word to dict
label_id = label_dict.add(label)
# Add token_id to the data
row.append(label_id)
# Add new row to the data
y.append(row)
# Transform y to numpy array
y = np.array(y)
# Set configuration value for word_dict
config.label_dict = label_dict
return y
def __init__(self, words_filename, min_word_len, board_size):
self.dictionary = dictionary.Dictionary(words_filename, min_word_len,
MAX_WORD_LEN)
self.size = board_size
self.gen_board()
def rest_api_call(question):
config = Variables()
constant = constants
words = [
'<UNK>', 'how', 'is', 'our', 'financial', 'performance', 'versus',
'analyst', 'estimates', 'which', 'market', 'units', 'generate', 'the',
'most', 'cloud', 'revenue', 'have', 'had', 'highest', 'growth',
'rates', 'region', 'generates', 'countries', 'generated', 'service',
'country', 'has', 'in', 'past', 'half', 'year', 'unit', 'least',
'organization', 'last', 'quarter', 'location', 'for', 'locations',
'greater', 'than', 'ten', 'million', 'show', 'all', 'that', 'profits',
'bigger', '1', 'are', 'top', 'regarding', 'many', 'more', '10',
'percent', 'software', '5', 'on-premise', 'was', 'what', 'two',
'quarters', 'during', 'us', 'development', 'of', 'throughout', 'three',
'margin', 'product', 'objects', 'products', 'rolling', 'four',
'germany', 'rate', 'compared', 'to', 'oracle', 'tell', 'me', 'key',
'driver', 'who', 'does', 'cost', 'structure', 'united', 'states',
'look', 'like', 'travel', 'and', 'entertainment', 'expenses', 'much',
'license', 'over', 'hana', 'per', 'quarterly', 'services', 'offerings',
'total', 'profit', 'first', 'north', 'america', 'this', 'years',
'trend', 'apj', 'evolved', 'biggest', 'based', 'on', 'with', 'emea',
'share', 'decreasing', 'digits', 'compare', 'gross', 'between',
'france', 'italy', 'categories', 'marketing', 'costs', 'differences',
'austria', 'calculate', 'each', 'stake', '2016', 'display', 'high',
'percentage', 'difference', 'influencer', 'operating', 'dach',
'lowest', 'earned', 'across', 'company', 'q1', 'worst', 'bottom', '3',
'relation', 'impact', 'looks', 'traffic', 'same', 'indonesia',
'revenues', 'higher', 'figures', 'one', 'billion', 'dollars', 'great',
'britain', 'china', 'comparison', 'regions', 'americas', 'double',
'digit', 'do', 'not', 'achieved', 'rising', 'increasing', 'poland',
'number', 'a', 'lower', 'whole', 'sap', 'saudi', 'arabia',
'profitability', 'japan', 'spend', 'croatia', 'research', 'denmark',
'latin', 'annually', 'hardware', 'monthly', 'run', '4', 'spain',
'india', 'europe', 'strongest', 'split', 'by', 'sector', 'profitable',
'business', 'margins', 'switzerland', 'belonging', 'south', 'numbers',
'runs', 'were', 'best', 'performing', 'their', 'gains', '2017',
'distribution', 'africa', 'stagnating', 'month', '2', 'organizations',
'having', 'positive', 'every', 's4', '2015', '2014', 'combined', 'q2',
'caused', 'erp', 'usa', 'canada', 'q3', 'q4', 'five', 'deals',
'pipeline', 'facebook', 'dollar', 'sales', 'declining', 'salesperson',
'closed', 'salespersons', 'salesmen', 'forecast', 'employee', 'sale',
'michael', 'scott', 'employees', 'fulfilled', 'quotas', 'quota',
'goal', 'did', 'reach', 'brazil', 'current', 'quote', 'close', 'ratio',
'target', 'opportunities', 'currently', 'bookings', 'average', 'deal',
'customers', 'we', 'companies', 'consider', 'buy', 'boardroom', 'want',
'thinks', 'about', 'buying', 'missed', 'next', 'volume', 'bought',
'new', 'won', 'as', 'well', 'ariba', 'concur', 'amount', 'salesman',
'sold', 'licences', 'often', 'months', 'digital', 'r3', 'led',
'contracts', 'resulted', 'end', 'fourth', 'since', 'licenses',
'within', 'selling', 'argentina', 'now', 'john', 'schneider', 'six',
'contract', 'volkswagen', 'sealed', '80', 'values', 'resigned', 'from',
'6', 'customer', 'loyalty', 'nestle', 'volumes', '250', 'person',
'russia', 'leads', 'turned', 'into', 'sweden', 'items', 'opportunity',
'stage', 'booking', 'weakest', 'related', 'holds', 'promises',
'promising', 'iot', 'mexico', 'asia', 'keeps', 'his', 'promise',
'keep', 'analytics', 'analytic', 'conversion', 'responsible',
'thyssen', 'krupp', 'account', 'executive', 'persons', 'lost',
'booked', 'starting', 'its', 'working', 'employed', 'open', 'or',
'representatives', 'norway', 'name', 'representative', 'todd',
'packer', 'hr', 'successfactors', 'audi', 'australia', 'porsche',
'peru', 'worldwide', 'seven', 'names', 'fifty', 'an', 'at&t',
'verizon', 'samsung', 'headcount', 'people', 'israel', 'fte', 'palo',
'alto', 'department', 'developed', 'at', 'fully', 'loaded', 'plan',
'2019', 'satisfaction', 'happiest', 'below', '95', 'dwight', 'schrute',
'managing', 'managed', 'wage', 'managers', 'salary', 'position',
'level', 'newtown', 'square', 'early', 'talents', 'walldorf',
'learning', 'completions', 'online', 'courses', 'completed', 'hired',
'fluctuation', 'thousand', 'women', 'management', 'positions',
'leadership', 'manager', 'leader', 'men', 'managerial',
'responsibility', 'tasks', 'growing', 'strong', 'fewest', 'external',
'workforce', 'consulting', 'non', 'billable', 'fastest', 'job', 'role',
'diversity', 'gender', 'among', 'workers', 'hirings', 'so', 'far',
'joined', 'salaries', 'incomes', 'students', 'bangalore', 'seattle',
'paris', '100', 'less', 'beijing', 'finance', 'administration',
'innovation', 'human', 'resources', 'leaders', 'leading', 'frequent',
'hold', 'grouped', 'age', 'movement', 'bill', 'mcdermott', 'greece',
'subordinates', 'finished', 'jimmy', 'kimmel', 'andy', 'bernard',
'where', 'departments', 'available', 'developer', 'vacant',
'advertisement', 'earn', 'money', 'lot', 'christian', 'klein', 'luka',
'mueller', 'head', 'been', 'signed', 'unlimited', 'conditions', 'mee',
'possibilities', 'training', 'sessions', 'offered', 'nordics',
'singapore', 'none', '50000', 'older', '30', '50', 'l3', 'threatening',
'competitors', 'competitor', 'threats', 'main', 'ibm', 'microsoft',
'also', 'facing', 'gaining', 'momentum', 'against', 'netsuite',
'fiercest', 'workday', 'salesforce', 'in-memory', 'database',
'investments', 'earnings', 'budget', 'list', 'dangerous', 'carries',
'invested', 'boosting', 'better', 'stock', 'price', 'evaluation',
'index', 'exchange', 'google', 'january', 'december', 'may', 'right',
'doing', 'prices', 'introduction', '8', 'value', 'capitalization',
'today', 'increased', 'saps', 'latest', 'ibms', 'indices', 'apple',
'evaluate', 'mercedes', 'competing', 'compete', 'activities',
'activity', 'year’s', 'r&d', 't&e', 'get', 'located', 'segment',
'minimum', 'maximum'
]
labels = [
'o', 'mea', 'cmp', 'res', 'argm', 'b_where', 'i_where', 'oper', 'grpby'
]
# Build an empty dictionary for words and labels
word_dict = dict.Dictionary()
word_dict.initialize_unknown_token()
label_dict = dict.Dictionary()
for word in words:
word_dict.add(word)
config.word_dict = word_dict
for label in labels:
label_dict.add(label)
config.label_dict = label_dict
# Load word embeddings that map words in some language to high-dimensional vectors
emb = embeddings.load(path=constant.data_folder,
file=config.embedding_data,
configuration=config)
# Transpose embedding keys to corresponding keys in dictionary
num_embeddings = embeddings.transpose(embeddings=emb, configuration=config)
# Get only numeric vectors of embeddings
vector_embeddings = embeddings.get_vectors(num_embeddings,
configuration=config)
# Build Tensorflow Graph
tag_graph = tf.Graph()
with tag_graph.as_default():
# Build Seq2Tag Model
# Create model and load parameters.
tagger = model.SRLModel(config, vector_embeddings,
config.label_dict.size())
tagger.build()
# Start Tensorflow Session
tag_sess = tf.Session(graph=tag_graph)
with tag_sess.as_default():
with tag_graph.as_default():
tf.global_variables_initializer().run()
tags = get_labels(sentence=question,
session=tag_sess,
tagger=tagger,
configuration=config)
return tags
def main():
''' read arguments from the command line and initiate the training.
'''
parser = argparse.ArgumentParser(
description='Train an LSTM sentence-pair classifier.')
parser.add_argument(
'-d',
'--data-folder',
required=True,
help='the folder containing the train, test, dev sets.')
parser.add_argument('-s',
'--source-ext',
required=False,
default='src',
help='the extension of the source files.')
parser.add_argument('-t',
'--target-ext',
required=False,
default='mt',
help='the extension of the target files.')
parser.add_argument('-l',
'--labels-ext',
required=False,
default='hter',
help='the extension of the labels files.')
parser.add_argument('-b',
'--batch-size',
required=False,
default=64,
help='the batch size.')
parser.add_argument('-a',
'--attention-type',
required=False,
default=None,
help='the attention type: \'dot\', \'rte\', \'None\'.')
parser.add_argument('-m',
'--model-folder',
required=False,
default='models',
help='the directory to save the models')
parser.add_argument('-g',
'--gpuid',
required=False,
default=-1,
help='the ID of the GPU to use.')
args = parser.parse_args()
source_train_filename = os.path.join(os.path.realpath(args.data_folder),
'train.' + args.source_ext)
target_train_filename = os.path.join(os.path.realpath(args.data_folder),
'train.' + args.target_ext)
source_dev_filename = os.path.join(os.path.realpath(args.data_folder),
'dev.' + args.source_ext)
target_dev_filename = os.path.join(os.path.realpath(args.data_folder),
'dev.' + args.target_ext)
source_test_filename = os.path.join(os.path.realpath(args.data_folder),
'test.' + args.source_ext)
target_test_filename = os.path.join(os.path.realpath(args.data_folder),
'test.' + args.target_ext)
labels_train = os.path.join(os.path.realpath(args.data_folder),
'train.' + args.labels_ext)
labels_dev = os.path.join(os.path.realpath(args.data_folder),
'dev.' + args.labels_ext)
labels_test = os.path.join(os.path.realpath(args.data_folder),
'test.' + args.labels_ext)
if not os.path.exists(labels_test):
labels_test = None
data_dict_file = os.path.join(os.path.realpath(args.data_folder),
'data.dict')
#labels_dict_file = os.path.join(os.path.realpath(args.data_folder), 'ter.dict')
dictionary_data = Dict.Dictionary(data_dict_file)
dictionary_data.load_dictionary()
#dictionary_labels = Dict.Labels(labels_dict_file)
#dictionary_labels.load_dictionary()
train_data = DP.DataLD(source_train_filename, target_train_filename,
labels_train,
dictionary_data) #, dictionary_labels)
dev_data = DP.DataLD(source_dev_filename, target_dev_filename, labels_dev,
dictionary_data) #, dictionary_labels)
test_data = DP.DataLD(source_test_filename, target_test_filename,
labels_test, dictionary_data) #, dictionary_labels)
device = torch.device("cuda:" +
str(args.gpuid) if torch.cuda.is_available()
and int(args.gpuid) > -1 else "cpu")
train(train_data, dev_data, test_data, int(args.batch_size), device,
args.attention_type, args.model_folder)
def pre_processing(self):
processed_questions = []
# Build an empty dictionary for question types
question_type_dict = dict.Dictionary()
# Build an empty dictionary for answer types
answer_type_dict = dict.Dictionary()
# Build an empty dictionary for table names
table_dict = dict.Dictionary()
for item in self.questions:
# Extract the id of the question in the dataset
question_id = item[0]
# Extract the questions from loaded inputs and convert to lower case
question = item[1]
question = question.lower()
# Extract the table name lower case
# and add table name to dictionary
table_name = item[2]
table_name = table_name.lower()
table_dict.add(table_name)
# Extract the question type from loaded inputs and convert to lower case
# and add question type to dictionary
question_type = item[3]
question_type = question_type.lower()
question_type_dict.add(question_type)
# Extract the answer type from loaded inputs and convert to lower case
# and add answer type to dictionary
answer_type = item[4]
answer_type = answer_type.lower()
answer_type_dict.add(answer_type)
# Extract the target labels from loaded inputs and convert to lower case
semantic_labels = item[5]
semantic_labels = semantic_labels.lower()
# Tokenize Questions and Labels
question_tokens, semantic_label_tokens = self.token_generator(
question, semantic_labels)
# Receive POS Tag List for tokens
pos = self.pos_generator(question_tokens)
# Get list of unique labels
# We do not use a dictionary because "<UNK>", "o", "<s>", "</s>" would automatically be added when initializing the dict
for token in semantic_label_tokens:
if not self.config.semantic_labels.__contains__(token):
self.config.semantic_labels.append(token)
if len(question_tokens) != len(semantic_label_tokens):
print("Error: Question", str(question_id),
"has different lengths!")
# Create Question Object
q = Question(question_id, question, question_tokens, pos, "ner",
table_name, question_type, answer_type,
semantic_label_tokens, None)
processed_questions.append(q)
# print(q.id, q.tokens, q.semantic_labels)
# Call the method to find the longest sequence
self.max_sequence_length(processed_questions)
self.config.table_name_dict = table_dict
self.config.question_types_dict = question_type_dict
self.config.answer_types_dict = answer_type_dict
return processed_questions
def text_pre_processing(self):
processed_questions = []
# Build an empty dictionary for question types
question_type_dict = dict.Dictionary()
# Build an empty dictionary for answer types
answer_type_dict = dict.Dictionary()
for item in self.questions:
# Extract the id of the question in the dataset
question_id = item[0]
# Extract the questions from loaded inputs and convert to lower case
question = item[1]
question = question.lower()
# Extract the question type from loaded inputs and convert to lower case
# and add question type to dictionary
question_type = item[2]
question_type = question_type.lower()
question_type_dict.add(question_type)
# Extract the answer type from loaded inputs and convert to lower case
# and add answer type to dictionary
answer_type = item[3]
answer_type = answer_type.lower()
answer_type_dict.add(answer_type)
# Extract the target labels from loaded inputs and convert to lower case
semantic_labels = item[4]
semantic_labels = semantic_labels.lower()
# Tokenize Questions and Labels
question_tokens, semantic_label_tokens = self.token_generator(
question, semantic_labels)
# Receive POS Tag List for tokens
pos = self.pos_generator(question_tokens)
# Get list of unique labels
for token in semantic_label_tokens:
if not self.config.semantic_labels.__contains__(token):
self.config.semantic_labels.append(token)
# Create Question Object
q = Question(question_id, question, question_tokens, pos, "ner",
question_type, answer_type, semantic_label_tokens)
processed_questions.append(q)
# print(q.id)
# print(q.question)
# print(q.question_type)
# print(q.answer_type)
# print()
# Call the method to find the longest sequence
self.max_sequence_length(processed_questions)
self.config.question_types_dict = question_type_dict
self.config.answer_types_dict = answer_type_dict
return processed_questions
