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import tensorflow as tf
import numpy as np
import time
from typing import Dict, Optional, List, Iterable
from collections import Counter
from functools import partial
from path_context_reader import PathContextReader, ModelInputTensorsFormer, ReaderInputTensors, EstimatorAction
from common import common
from vocabularies import VocabType
from config import Config
from model_base import Code2VecModelBase, ModelEvaluationResults, ModelPredictionResults
tf.compat.v1.disable_eager_execution()
class Code2VecModel(Code2VecModelBase):
def __init__(self, config: Config):
self.sess = tf.compat.v1.Session()
self.saver = None
self.eval_reader = None
self.eval_input_iterator_reset_op = None
self.predict_reader = None
# self.eval_placeholder = None
self.predict_placeholder = None
self.eval_top_words_op, self.eval_top_values_op, self.eval_original_names_op, self.eval_code_vectors = None, None, None, None
self.predict_top_words_op, self.predict_top_values_op, self.predict_original_names_op = None, None, None
self.vocab_type_to_tf_variable_name_mapping: Dict[VocabType, str] = {
VocabType.Token: 'WORDS_VOCAB',
VocabType.Target: 'TARGET_WORDS_VOCAB',
VocabType.Path: 'PATHS_VOCAB'
}
super(Code2VecModel, self).__init__(config)
def train(self):
self.log('Starting training')
start_time = time.time()
batch_num = 0
sum_loss = 0
multi_batch_start_time = time.time()
num_batches_to_save_and_eval = max(int(self.config.train_steps_per_epoch * self.config.SAVE_EVERY_EPOCHS), 1)
train_reader = PathContextReader(vocabs=self.vocabs,
model_input_tensors_former=_TFTrainModelInputTensorsFormer(),
config=self.config, estimator_action=EstimatorAction.Train)
input_iterator = tf.compat.v1.data.make_initializable_iterator(train_reader.get_dataset())
input_iterator_reset_op = input_iterator.initializer
input_tensors = input_iterator.get_next()
optimizer, train_loss = self._build_tf_training_graph(input_tensors)
self.saver = tf.compat.v1.train.Saver(max_to_keep=self.config.MAX_TO_KEEP)
self.log('Number of trainable params: {}'.format(
np.sum([np.prod(v.get_shape().as_list()) for v in tf.compat.v1.trainable_variables()])))
for variable in tf.compat.v1.trainable_variables():
self.log("variable name: {} -- shape: {} -- #params: {}".format(
variable.name, variable.get_shape(), np.prod(variable.get_shape().as_list())))
self._initialize_session_variables()
if self.config.MODEL_LOAD_PATH:
self._load_inner_model(self.sess)
self.sess.run(input_iterator_reset_op)
time.sleep(1)
self.log('Started reader...')
# run evaluation in a loop until iterator is exhausted.
try:
while True:
# Each iteration = batch. We iterate as long as the tf iterator (reader) yields batches.
batch_num += 1
# Actual training for the current batch.
_, batch_loss = self.sess.run([optimizer, train_loss])
sum_loss += batch_loss
if batch_num % self.config.NUM_BATCHES_TO_LOG_PROGRESS == 0:
self._trace_training(sum_loss, batch_num, multi_batch_start_time)
# Uri: the "shuffle_batch/random_shuffle_queue_Size:0" op does not exist since the migration to the new reader.
# self.log('Number of waiting examples in queue: %d' % self.sess.run(
# "shuffle_batch/random_shuffle_queue_Size:0"))
sum_loss = 0
multi_batch_start_time = time.time()
if batch_num % num_batches_to_save_and_eval == 0:
epoch_num = int((batch_num / num_batches_to_save_and_eval) * self.config.SAVE_EVERY_EPOCHS)
model_save_path = self.config.MODEL_SAVE_PATH + '_iter' + str(epoch_num)
self.save(model_save_path)
self.log('Saved after %d epochs in: %s' % (epoch_num, model_save_path))
evaluation_results = self.evaluate()
evaluation_results_str = (str(evaluation_results).replace('topk', 'top{}'.format(
self.config.TOP_K_WORDS_CONSIDERED_DURING_PREDICTION)))
self.log('After {nr_epochs} epochs -- {evaluation_results}'.format(
nr_epochs=epoch_num,
evaluation_results=evaluation_results_str
))
except tf.errors.OutOfRangeError:
pass # The reader iterator is exhausted and have no more batches to produce.
self.log('Done training')
if self.config.MODEL_SAVE_PATH:
self._save_inner_model(self.config.MODEL_SAVE_PATH)
self.log('Model saved in file: %s' % self.config.MODEL_SAVE_PATH)
elapsed = int(time.time() - start_time)
self.log("Training time: %sH:%sM:%sS\n" % ((elapsed // 60 // 60), (elapsed // 60) % 60, elapsed % 60))
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)
if self.saver is None:
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('results.txt', 'w') as log_output_file, open('predicted_words.txt', 'w') as log_predicted_words:
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,)
if self.config.printScores:
self._log_prediction_scores_during_evaluation(zip(original_names, top_words, top_scores)
, log_output_file)
self._log_predicted_words_during_evaluation(zip(original_names, top_words), log_predicted_words)
else:
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 _build_tf_training_graph(self, input_tensors):
# Use `_TFTrainModelInputTensorsFormer` to access input tensors by name.
input_tensors = _TFTrainModelInputTensorsFormer().from_model_input_form(input_tensors)
# shape of (batch, 1) for input_tensors.target_index
# shape of (batch, max_contexts) for others:
# input_tensors.path_source_token_indices, input_tensors.path_indices,
# input_tensors.path_target_token_indices, input_tensors.context_valid_mask
with tf.compat.v1.variable_scope('model'):
tokens_vocab = tf.compat.v1.get_variable(
self.vocab_type_to_tf_variable_name_mapping[VocabType.Token],
shape=(self.vocabs.token_vocab.size, self.config.TOKEN_EMBEDDINGS_SIZE), dtype=tf.float32,
initializer=tf.compat.v1.initializers.variance_scaling(scale=1.0, mode='fan_out',
distribution="uniform"))
targets_vocab = tf.compat.v1.get_variable(
self.vocab_type_to_tf_variable_name_mapping[VocabType.Target],
shape=(self.vocabs.target_vocab.size, self.config.TARGET_EMBEDDINGS_SIZE), dtype=tf.float32,
initializer=tf.compat.v1.initializers.variance_scaling(scale=1.0, mode='fan_out',
distribution="uniform"))
attention_param = tf.compat.v1.get_variable(
'ATTENTION',
shape=(self.config.CODE_VECTOR_SIZE, 1), dtype=tf.float32)
paths_vocab = tf.compat.v1.get_variable(
self.vocab_type_to_tf_variable_name_mapping[VocabType.Path],
shape=(self.vocabs.path_vocab.size, self.config.PATH_EMBEDDINGS_SIZE), dtype=tf.float32,
initializer=tf.compat.v1.initializers.variance_scaling(scale=1.0, mode='fan_out',
distribution="uniform"))
code_vectors, _ = self._calculate_weighted_contexts(
tokens_vocab, paths_vocab, attention_param, input_tensors.path_source_token_indices,
input_tensors.path_indices, input_tensors.path_target_token_indices, input_tensors.context_valid_mask)
logits = tf.matmul(code_vectors, targets_vocab, transpose_b=True)
batch_size = tf.cast(tf.shape(input_tensors.target_index)[0], dtype=tf.float32)
loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=tf.reshape(input_tensors.target_index, [-1]),
logits=logits)) / batch_size
optimizer = tf.compat.v1.train.AdamOptimizer().minimize(loss)
return optimizer, loss
def _calculate_weighted_contexts(self, tokens_vocab, paths_vocab, attention_param, source_input, path_input,
target_input, valid_mask, is_evaluating=False):
source_word_embed = tf.nn.embedding_lookup(params=tokens_vocab, ids=source_input) # (batch, max_contexts, dim)
path_embed = tf.nn.embedding_lookup(params=paths_vocab, ids=path_input) # (batch, max_contexts, dim)
target_word_embed = tf.nn.embedding_lookup(params=tokens_vocab, ids=target_input) # (batch, max_contexts, dim)
context_embed = tf.concat([source_word_embed, path_embed, target_word_embed],
axis=-1) # (batch, max_contexts, dim * 3)
if not is_evaluating:
context_embed = tf.nn.dropout(context_embed, rate=1 - self.config.DROPOUT_KEEP_RATE)
flat_embed = tf.reshape(context_embed, [-1, self.config.context_vector_size]) # (batch * max_contexts, dim * 3)
transform_param = tf.compat.v1.get_variable(
'TRANSFORM', shape=(self.config.context_vector_size, self.config.CODE_VECTOR_SIZE), dtype=tf.float32)
flat_embed = tf.tanh(tf.matmul(flat_embed, transform_param)) # (batch * max_contexts, dim * 3)
contexts_weights = tf.matmul(flat_embed, attention_param) # (batch * max_contexts, 1)
batched_contexts_weights = tf.reshape(
contexts_weights, [-1, self.config.MAX_CONTEXTS, 1]) # (batch, max_contexts, 1)
mask = tf.math.log(valid_mask) # (batch, max_contexts)
mask = tf.expand_dims(mask, axis=2) # (batch, max_contexts, 1)
batched_contexts_weights += mask # (batch, max_contexts, 1)
attention_weights = tf.nn.softmax(batched_contexts_weights, axis=1) # (batch, max_contexts, 1)
batched_embed = tf.reshape(flat_embed, shape=[-1, self.config.MAX_CONTEXTS, self.config.CODE_VECTOR_SIZE])
code_vectors = tf.reduce_sum(tf.multiply(batched_embed, attention_weights), axis=1) # (batch, dim * 3)
return code_vectors, attention_weights
def _build_tf_test_graph(self, input_tensors, normalize_scores=False):
with tf.compat.v1.variable_scope('model', reuse=self.get_should_reuse_variables()):
tokens_vocab = tf.compat.v1.get_variable(
self.vocab_type_to_tf_variable_name_mapping[VocabType.Token],
shape=(self.vocabs.token_vocab.size, self.config.TOKEN_EMBEDDINGS_SIZE),
dtype=tf.float32, trainable=False)
targets_vocab = tf.compat.v1.get_variable(
self.vocab_type_to_tf_variable_name_mapping[VocabType.Target],
shape=(self.vocabs.target_vocab.size, self.config.TARGET_EMBEDDINGS_SIZE),
dtype=tf.float32, trainable=False)
attention_param = tf.compat.v1.get_variable(
'ATTENTION', shape=(self.config.context_vector_size, 1),
dtype=tf.float32, trainable=False)
paths_vocab = tf.compat.v1.get_variable(
self.vocab_type_to_tf_variable_name_mapping[VocabType.Path],
shape=(self.vocabs.path_vocab.size, self.config.PATH_EMBEDDINGS_SIZE),
dtype=tf.float32, trainable=False)
targets_vocab = tf.transpose(targets_vocab) # (dim * 3, target_word_vocab)
# Use `_TFEvaluateModelInputTensorsFormer` to access input tensors by name.
input_tensors = _TFEvaluateModelInputTensorsFormer().from_model_input_form(input_tensors)
# shape of (batch, 1) for input_tensors.target_string
# shape of (batch, max_contexts) for the other tensors
code_vectors, attention_weights = self._calculate_weighted_contexts(
tokens_vocab, paths_vocab, attention_param, input_tensors.path_source_token_indices,
input_tensors.path_indices, input_tensors.path_target_token_indices,
input_tensors.context_valid_mask, is_evaluating=True)
scores = tf.matmul(code_vectors, targets_vocab) # (batch, target_word_vocab)
topk_candidates = tf.nn.top_k(scores, k=tf.minimum(
self.config.TOP_K_WORDS_CONSIDERED_DURING_PREDICTION, self.vocabs.target_vocab.size))
top_indices = topk_candidates.indices
top_words = self.vocabs.target_vocab.lookup_word(top_indices)
original_words = input_tensors.target_string
top_scores = topk_candidates.values
if normalize_scores:
top_scores = tf.nn.softmax(top_scores)
return top_words, top_scores, original_words, attention_weights, input_tensors.path_source_token_strings, \
input_tensors.path_strings, input_tensors.path_target_token_strings, code_vectors
def predict(self, predict_data_lines: Iterable[str]) -> List[ModelPredictionResults]:
if self.predict_reader is None:
self.predict_reader = PathContextReader(vocabs=self.vocabs,
model_input_tensors_former=_TFEvaluateModelInputTensorsFormer(),
config=self.config, estimator_action=EstimatorAction.Predict)
self.predict_placeholder = tf.compat.v1.placeholder(tf.string)
reader_output = self.predict_reader.process_input_row(self.predict_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_tf_test_graph(reader_output, normalize_scores=True)
self._initialize_session_variables()
self.saver = tf.compat.v1.train.Saver()
self._load_inner_model(sess=self.sess)
prediction_results: List[ModelPredictionResults] = []
for line in predict_data_lines:
batch_top_words, batch_top_scores, batch_original_name, batch_attention_weights, batch_path_source_strings, \
batch_path_strings, batch_path_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: line})
# shapes:
# batch_top_words, top_scores: (batch, top_k)
# batch_original_name: (batch, )
# batch_attention_weights: (batch, max_context, 1)
# batch_path_source_strings, batch_path_strings, batch_path_target_strings: (batch, max_context)
# batch_code_vectors: (batch, code_vector_size)
# remove first axis: (batch=1, ...)
assert all(tensor.shape[0] == 1 for tensor in (batch_top_words, batch_top_scores, batch_original_name,
batch_attention_weights, batch_path_source_strings,
batch_path_strings, batch_path_target_strings,
batch_code_vectors))
top_words = np.squeeze(batch_top_words, axis=0)
top_scores = np.squeeze(batch_top_scores, axis=0)
original_name = batch_original_name[0]
attention_weights = np.squeeze(batch_attention_weights, axis=0)
path_source_strings = np.squeeze(batch_path_source_strings, axis=0)
path_strings = np.squeeze(batch_path_strings, axis=0)
path_target_strings = np.squeeze(batch_path_target_strings, axis=0)
code_vectors = np.squeeze(batch_code_vectors, axis=0)
top_words = common.binary_to_string_list(top_words)
original_name = common.binary_to_string(original_name)
attention_per_context = self._get_attention_weight_per_context(
path_source_strings, path_strings, path_target_strings, attention_weights)
prediction_results.append(ModelPredictionResults(
original_name=original_name,
topk_predicted_words=top_words,
topk_predicted_words_scores=top_scores,
attention_per_context=attention_per_context,
code_vector=(code_vectors if self.config.EXPORT_CODE_VECTORS else None)
))
return prediction_results
def _save_inner_model(self, path: str):
self.saver.save(self.sess, path)
def _load_inner_model(self, sess=None):
if sess is not None:
self.log('Loading model weights from: ' + self.config.MODEL_LOAD_PATH)
self.saver.restore(sess, self.config.MODEL_LOAD_PATH)
self.log('Done loading model weights')
def _get_vocab_embedding_as_np_array(self, vocab_type: VocabType) -> np.ndarray:
assert vocab_type in VocabType
vocab_tf_variable_name = self.vocab_type_to_tf_variable_name_mapping[vocab_type]
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._build_tf_test_graph(input_iterator.get_next())
if vocab_type is VocabType.Token:
shape = (self.vocabs.token_vocab.size, self.config.TOKEN_EMBEDDINGS_SIZE)
elif vocab_type is VocabType.Target:
shape = (self.vocabs.target_vocab.size, self.config.TARGET_EMBEDDINGS_SIZE)
elif vocab_type is VocabType.Path:
shape = (self.vocabs.path_vocab.size, self.config.PATH_EMBEDDINGS_SIZE)
with tf.compat.v1.variable_scope('model', reuse=True):
embeddings = tf.compat.v1.get_variable(vocab_tf_variable_name, shape=shape)
self.saver = tf.compat.v1.train.Saver()
self._initialize_session_variables()
self._load_inner_model(self.sess)
vocab_embedding_matrix = self.sess.run(embeddings)
return vocab_embedding_matrix
def get_should_reuse_variables(self):
if self.config.TRAIN_DATA_PATH_PREFIX:
return True
else:
return None
def _log_predictions_during_evaluation(self, results, output_file):
for original_name, top_predicted_words in results:
found_match = common.get_first_match_word_from_top_predictions(
self.vocabs.target_vocab.special_words, original_name, top_predicted_words)
if found_match is not None:
prediction_idx, predicted_word = found_match
if prediction_idx == 0:
output_file.write('Original: ' + original_name + ', predicted 1st: ' + predicted_word + '\n')
else:
output_file.write('\t\t predicted correctly at rank: ' + str(prediction_idx + 1) + '\n')
else:
output_file.write('No results for predicting: ' + original_name + '\n')
def _log_prediction_scores_during_evaluation(self, results, output_file):
for original_name, top_predicted_words, scores in results:
found_match = common.get_first_match_word_from_top_predictions_with_score(
self.vocabs.target_vocab.special_words, original_name, top_predicted_words, scores)
if found_match is not None:
prediction_idx, predicted_word, predicted_score = found_match
if prediction_idx == 0:
output_file.write('Original: ' + original_name + ', predicted 1st: ' + predicted_word + ', score: '
+ str(predicted_score) + '\n')
else:
output_file.write('\t\t predicted correctly at rank: ' + str(prediction_idx + 1) + ', score: '
+ str(predicted_score) + '\n')
else:
output_file.write('No results for predicting: ' + original_name + '\n')
def _log_predicted_words_during_evaluation(self, results, output_file):
for original_name, top_predicted_words in results:
output_file.write('Original: ' + original_name + ', predicted: ' + top_predicted_words[0] + '\n')
def _trace_training(self, sum_loss, batch_num, multi_batch_start_time):
multi_batch_elapsed = time.time() - multi_batch_start_time
avg_loss = sum_loss / (self.config.NUM_BATCHES_TO_LOG_PROGRESS * self.config.TRAIN_BATCH_SIZE)
throughput = self.config.TRAIN_BATCH_SIZE * self.config.NUM_BATCHES_TO_LOG_PROGRESS / \
(multi_batch_elapsed if multi_batch_elapsed > 0 else 1)
self.log('Average loss at batch %d: %f, \tthroughput: %d samples/sec' % (
batch_num, avg_loss, throughput))
def _trace_evaluation(self, total_predictions, elapsed):
state_message = 'Evaluated %d examples...' % total_predictions
throughput_message = "Prediction throughput: %d samples/sec" % int(
total_predictions / (elapsed if elapsed > 0 else 1))
self.log(state_message)
self.log(throughput_message)
def close_session(self):
self.sess.close()
def _initialize_session_variables(self):
self.sess.run(tf.group(
tf.compat.v1.global_variables_initializer(),
tf.compat.v1.local_variables_initializer(),
tf.compat.v1.tables_initializer()))
self.log('Initalized variables')
class SubtokensEvaluationMetric:
def __init__(self, filter_impossible_names_fn):
self.nr_true_positives: int = 0
self.nr_false_positives: int = 0
self.nr_false_negatives: int = 0
self.nr_predictions: int = 0
self.filter_impossible_names_fn = filter_impossible_names_fn
def update_batch(self, results):
for original_name, top_words in results:
prediction = self.filter_impossible_names_fn(top_words)[0]
original_subtokens = Counter(common.get_subtokens(original_name))
predicted_subtokens = Counter(common.get_subtokens(prediction))
self.nr_true_positives += sum(count for element, count in predicted_subtokens.items()
if element in original_subtokens)
self.nr_false_positives += sum(count for element, count in predicted_subtokens.items()
if element not in original_subtokens)
self.nr_false_negatives += sum(count for element, count in original_subtokens.items()
if element not in predicted_subtokens)
self.nr_predictions += 1
@property
def true_positive(self):
return self.nr_true_positives / self.nr_predictions
@property
def false_positive(self):
return self.nr_false_positives / self.nr_predictions
@property
def false_negative(self):
return self.nr_false_negatives / self.nr_predictions
@property
def precision(self):
return self.nr_true_positives / (self.nr_true_positives + self.nr_false_positives)
@property
def recall(self):
return self.nr_true_positives / (self.nr_true_positives + self.nr_false_negatives)
@property
def f1(self):
if self.precision + self.recall == 0:
return 0
else:
return 2 * self.precision * self.recall / (self.precision + self.recall)
class TopKAccuracyEvaluationMetric:
def __init__(self, top_k: int, get_first_match_word_from_top_predictions_fn):
self.top_k = top_k
self.nr_correct_predictions = np.zeros(self.top_k)
self.nr_predictions: int = 0
self.get_first_match_word_from_top_predictions_fn = get_first_match_word_from_top_predictions_fn
def update_batch(self, results):
for original_name, top_predicted_words in results:
self.nr_predictions += 1
found_match = self.get_first_match_word_from_top_predictions_fn(original_name, top_predicted_words)
if found_match is not None:
suggestion_idx, _ = found_match
self.nr_correct_predictions[suggestion_idx:self.top_k] += 1
@property
def topk_correct_predictions(self):
return self.nr_correct_predictions / self.nr_predictions
class _TFTrainModelInputTensorsFormer(ModelInputTensorsFormer):
def to_model_input_form(self, input_tensors: ReaderInputTensors):
return input_tensors.target_index, input_tensors.path_source_token_indices, input_tensors.path_indices, \
input_tensors.path_target_token_indices, input_tensors.context_valid_mask
def from_model_input_form(self, input_row) -> ReaderInputTensors:
return ReaderInputTensors(
target_index=input_row[0],
path_source_token_indices=input_row[1],
path_indices=input_row[2],
path_target_token_indices=input_row[3],
context_valid_mask=input_row[4]
)
class _TFEvaluateModelInputTensorsFormer(ModelInputTensorsFormer):
def to_model_input_form(self, input_tensors: ReaderInputTensors):
return (input_tensors.target_string, input_tensors.path_source_token_indices, input_tensors.path_indices,
input_tensors.path_target_token_indices, input_tensors.context_valid_mask,
input_tensors.path_source_token_strings, input_tensors.path_strings,
input_tensors.path_target_token_strings)
def from_model_input_form(self, input_row) -> ReaderInputTensors:
return ReaderInputTensors(
target_string=input_row[0],
path_source_token_indices=input_row[1],
path_indices=input_row[2],
path_target_token_indices=input_row[3],
context_valid_mask=input_row[4],
path_source_token_strings=input_row[5],
path_strings=input_row[6],
path_target_token_strings=input_row[7]
)