def predict(self, predict_data_lines):
if self.predict_queue is None:
self.predict_queue = PathContextReader.PathContextReader(word_to_index=self.word_to_index,
path_to_index=self.path_to_index,
target_word_to_index=self.target_word_to_index,
config=self.config, is_evaluating=True)
self.predict_placeholder = self.predict_queue.get_input_placeholder()
self.predict_top_words_op, self.predict_top_values_op, self.predict_original_names_op, \
self.attention_weights_op, self.predict_source_string, self.predict_path_string, self.predict_path_target_string, self.predict_code_vectors = \
self.build_test_graph(self.predict_queue.get_filtered_batches(), normalize_scores=True)
self.initialize_session_variables(self.sess)
self.saver = tf.train.Saver()
self.load_model(self.sess)
code_vectors = []
results = []
for batch in common.split_to_batches(predict_data_lines, 1):
top_words, top_scores, original_names, attention_weights, source_strings, path_strings, target_strings, batch_code_vectors = self.sess.run(
[self.predict_top_words_op, self.predict_top_values_op, self.predict_original_names_op,
self.attention_weights_op, self.predict_source_string, self.predict_path_string,
self.predict_path_target_string, self.predict_code_vectors],
feed_dict={self.predict_placeholder: batch})
top_words, original_names = common.binary_to_string_matrix(top_words), common.binary_to_string_matrix(
original_names)
# Flatten original names from [[]] to []
attention_per_path = self.get_attention_per_path(source_strings, path_strings, target_strings,
attention_weights)
original_names = [w for l in original_names for w in l]
results.append((original_names[0], top_words[0], top_scores[0], attention_per_path))
if self.config.EXPORT_CODE_VECTORS:
code_vectors.append(batch_code_vectors)
if len(code_vectors) > 0:
code_vectors = np.vstack(code_vectors)
return results, code_vectors
def predict_dataset(self):
if self.predict_queue is None:
self.predict_queue = PathContextReader.PathContextReader(word_to_index=self.word_to_index,
path_to_index=self.path_to_index,
target_word_to_index=self.target_word_to_index,
config=self.config, is_evaluating=True)
self.predict_placeholder = self.predict_queue.get_input_placeholder()
self.predict_top_words_op, self.predict_top_values_op, self.predict_original_names_op, \
self.attention_weights_op, self.predict_source_string, self.predict_path_string, self.predict_path_target_string = \
self.build_test_graph(self.predict_queue.get_filtered_batches(), normalize_scores=True)
self.initialize_session_variables(self.sess)
self.saver = tf.train.Saver()
self.load_model(self.sess)
self.predict_data_lines = common.load_file_lines(self.config.TEST_PATH)
with open(self.config.OUTPUT_FILE, 'a+') as output_file:
batch_num = 0
for batch in common.split_to_batches(self.predict_data_lines, self.config.TEST_BATCH_SIZE):
batch_num += 1
top_words, top_scores, original_names, attention_weights, source_strings, path_strings, target_strings = self.sess.run(
[self.predict_top_words_op, self.predict_top_values_op, self.predict_original_names_op,
self.attention_weights_op, self.predict_source_string, self.predict_path_string,
self.predict_path_target_string],
feed_dict={self.predict_placeholder: batch})
top_words, original_names = common.binary_to_string_matrix(top_words), common.binary_to_string_matrix(original_names)
original_names = [w for l in original_names for w in l]
for res_index in range(len(original_names)):
output_file.write("%s;" % (original_names[res_index],))
output_file.write(";".join(top_words[res_index]))
output_file.write("\n")
print("Finished batch %s with %s elements" % (batch_num, len(original_names)))
def evaluate(self) -> Optional[ModelEvaluationResults]:
eval_start_time = time.time()
if self.eval_reader is None:
self.eval_reader = PathContextReader(
vocabs=self.vocabs,
model_input_tensors_former=_TFEvaluateModelInputTensorsFormer(
),
config=self.config,
estimator_action=EstimatorAction.Evaluate)
input_iterator = tf.compat.v1.data.make_initializable_iterator(
self.eval_reader.get_dataset())
self.eval_input_iterator_reset_op = input_iterator.initializer
input_tensors = input_iterator.get_next()
self.eval_top_words_op, self.eval_top_values_op, self.eval_original_names_op, _, _, _, _, _, _, \
self.eval_code_vectors = self._build_tf_test_graph(input_tensors)
self.saver = tf.compat.v1.train.Saver()
if self.config.MODEL_LOAD_PATH and not self.config.TRAIN_DATA_PATH_PREFIX:
self._initialize_session_variables()
self._load_inner_model(self.sess)
if self.config.RELEASE:
release_name = self.config.MODEL_LOAD_PATH + '.release'
self.log('Releasing model, output model: %s' % release_name)
self.saver.save(self.sess, release_name)
return None # FIXME: why do we return none here?
with open('log.txt', 'w') as log_output_file:
if self.config.EXPORT_CODE_VECTORS:
code_vectors_file = open(
self.config.TEST_DATA_PATH + '.vectors', 'w')
total_predictions = 0
total_prediction_batches = 0
subtokens_evaluation_metric = SubtokensEvaluationMetric(
partial(common.filter_impossible_names,
self.vocabs.target_vocab.special_words))
topk_accuracy_evaluation_metric = TopKAccuracyEvaluationMetric(
self.config.TOP_K_WORDS_CONSIDERED_DURING_PREDICTION,
partial(common.get_first_match_word_from_top_predictions,
self.vocabs.target_vocab.special_words))
start_time = time.time()
self.sess.run(self.eval_input_iterator_reset_op)
self.log('Starting evaluation')
# Run evaluation in a loop until iterator is exhausted.
# Each iteration = batch. We iterate as long as the tf iterator (reader) yields batches.
try:
while True:
top_words, top_scores, original_names, code_vectors = self.sess.run(
[
self.eval_top_words_op, self.eval_top_values_op,
self.eval_original_names_op, self.eval_code_vectors
], )
# shapes:
# top_words: (batch, top_k); top_scores: (batch, top_k)
# original_names: (batch, ); code_vectors: (batch, code_vector_size)
top_words = common.binary_to_string_matrix(
top_words) # (batch, top_k)
original_names = common.binary_to_string_list(
original_names) # (batch,)
self._log_predictions_during_evaluation(
zip(original_names, top_words), log_output_file)
topk_accuracy_evaluation_metric.update_batch(
zip(original_names, top_words))
subtokens_evaluation_metric.update_batch(
zip(original_names, top_words))
total_predictions += len(original_names)
total_prediction_batches += 1
if self.config.EXPORT_CODE_VECTORS:
self._write_code_vectors(code_vectors_file,
code_vectors)
if total_prediction_batches % self.config.NUM_BATCHES_TO_LOG_PROGRESS == 0:
elapsed = time.time() - start_time
# start_time = time.time()
self._trace_evaluation(total_predictions, elapsed)
except tf.errors.OutOfRangeError:
pass # reader iterator is exhausted and have no more batches to produce.
self.log('Done evaluating, epoch reached')
log_output_file.write(
str(topk_accuracy_evaluation_metric.topk_correct_predictions) +
'\n')
if self.config.EXPORT_CODE_VECTORS:
code_vectors_file.close()
elapsed = int(time.time() - eval_start_time)
self.log("Evaluation time: %sH:%sM:%sS" %
((elapsed // 60 // 60), (elapsed // 60) % 60, elapsed % 60))
return ModelEvaluationResults(
topk_acc=topk_accuracy_evaluation_metric.topk_correct_predictions,
subtoken_precision=subtokens_evaluation_metric.precision,
subtoken_recall=subtokens_evaluation_metric.recall,
subtoken_f1=subtokens_evaluation_metric.f1)
def evaluate(self):
eval_start_time = time.time()
if self.eval_queue is None:
self.eval_queue = PathContextReader.PathContextReader(word_to_index=self.word_to_index,
path_to_index=self.path_to_index,
target_word_to_index=self.target_word_to_index,
config=self.config, is_evaluating=True)
self.eval_placeholder = self.eval_queue.get_input_placeholder()
self.eval_top_words_op, self.eval_top_values_op, self.eval_original_names_op, _, _, _, _, self.eval_code_vectors = \
self.build_test_graph(self.eval_queue.get_filtered_batches())
self.saver = tf.train.Saver()
if self.config.LOAD_PATH and not self.config.TRAIN_PATH:
self.initialize_session_variables(self.sess)
self.load_model(self.sess)
if self.config.RELEASE:
release_name = self.config.LOAD_PATH + '.release'
print('Releasing model, output model: %s' % release_name )
self.saver.save(self.sess, release_name )
return None
if self.eval_data_lines is None:
print('Loading test data from: ' + self.config.TEST_PATH)
self.eval_data_lines = common.load_file_lines(self.config.TEST_PATH)
print('Done loading test data')
with open('log.txt', 'w') as output_file:
if self.config.EXPORT_CODE_VECTORS:
code_vectors_file = open(self.config.TEST_PATH + '.vectors', 'w')
num_correct_predictions = np.zeros(self.topk)
total_predictions = 0
total_prediction_batches = 0
true_positive, false_positive, false_negative = 0, 0, 0
start_time = time.time()
for batch in common.split_to_batches(self.eval_data_lines, self.config.TEST_BATCH_SIZE):
top_words, top_scores, original_names, code_vectors = self.sess.run(
[self.eval_top_words_op, self.eval_top_values_op, self.eval_original_names_op, self.eval_code_vectors],
feed_dict={self.eval_placeholder: batch})
top_words, original_names = common.binary_to_string_matrix(top_words), common.binary_to_string_matrix(
original_names)
# Flatten original names from [[]] to []
original_names = [w for l in original_names for w in l]
num_correct_predictions = self.update_correct_predictions(num_correct_predictions, output_file,
zip(original_names, top_words))
true_positive, false_positive, false_negative = self.update_per_subtoken_statistics(
zip(original_names, top_words),
true_positive, false_positive, false_negative)
total_predictions += len(original_names)
total_prediction_batches += 1
if self.config.EXPORT_CODE_VECTORS:
self.write_code_vectors(code_vectors_file, code_vectors)
if total_prediction_batches % self.num_batches_to_log == 0:
elapsed = time.time() - start_time
# start_time = time.time()
self.trace_evaluation(output_file, num_correct_predictions, total_predictions, elapsed, len(self.eval_data_lines))
print('Done testing, epoch reached')
output_file.write(str(num_correct_predictions / total_predictions) + '\n')
if self.config.EXPORT_CODE_VECTORS:
code_vectors_file.close()
elapsed = int(time.time() - eval_start_time)
precision, recall, f1 = self.calculate_results(true_positive, false_positive, false_negative)
print("Evaluation time: %sH:%sM:%sS" % ((elapsed // 60 // 60), (elapsed // 60) % 60, elapsed % 60))
del self.eval_data_lines
self.eval_data_lines = None
return num_correct_predictions / total_predictions, precision, recall, f1
