def generate_feature_lists(root_dir, features_dir, features, lists_dir,
list_suffix, feature_extension, replace_extension,
splits, verbose):
"""
:param root_dir:
:param features_dir:
:param features:
:param lists_dir:
:param list_suffix:
:param feature_extension:
:param splits:
:param verbose:
:return:
"""
create_dir_if_not_exists(root_dir + '/' + lists_dir + '/' + features)
path_features = features_dir + '/' + features
print("Storing features in:", root_dir + '/' + lists_dir + '/' + features)
for split in splits:
print('Processing split', split)
ids = file2list(root_dir + '/' + lists_dir + '/' + split + list_suffix)
new_ids = [
path_features + '/' + split + '/' +
sample_id[:-replace_extension] + feature_extension
for sample_id in ids
]
list2file(
root_dir + '/' + lists_dir + '/' + features + '/' + split +
'_list_features.txt', new_ids)
print('Done!')
def score_corpus(args, params):
print "Using an ensemble of %d models" % len(args.models)
models = [loadModel(m, -1, full_path=True) for m in args.models]
dataset = loadDataset(args.dataset)
if args.source is not None:
dataset = update_dataset_from_file(dataset, args.source, params, splits=args.splits,
output_text_filename=args.target, compute_state_below=True)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
# Apply scoring
extra_vars = dict()
extra_vars['tokenize_f'] = eval('dataset.' + params['TOKENIZATION_METHOD'])
for s in args.splits:
# Apply model predictions
params_prediction = {'max_batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s]}
if params['BEAM_SEARCH']:
params_prediction['beam_size'] = params['BEAM_SIZE']
params_prediction['maxlen'] = params['MAX_OUTPUT_TEXT_LEN_TEST']
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
params_prediction['normalize_probs'] = params.get('NORMALIZE_SAMPLING', False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get('COVERAGE_PENALTY', False)
params_prediction['length_penalty'] = params.get('LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get('LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get('COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get('MAXLEN_GIVEN_X', True)
params_prediction['output_max_length_depending_on_x_factor'] = params.get('MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get('MINLEN_GIVEN_X', True)
params_prediction['output_min_length_depending_on_x_factor'] = params.get('MINLEN_GIVEN_X_FACTOR', 2)
beam_searcher = BeamSearchEnsemble(models, dataset, params_prediction, verbose=args.verbose)
scores = beam_searcher.scoreNet()[s]
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(filepath, scores)
elif params['SAMPLING_SAVE_MODE'] == 'numpy':
numpy2file(filepath, scores)
else:
raise Exception('The sampling mode ' + params['SAMPLING_SAVE_MODE'] + ' is not currently supported.')
else:
print scores
def generate_feature_lists(root_dir, features_dir, features, lists_dir,
list_suffix, feature_extension, replace_extension,
splits, verbose):
"""
:param root_dir: Base working directory.
:param features_dir: Directory for storing the features.
:param features: Features name.
:param lists_dir: Directory (under --root-dir) containing the list splitting the dataset.
:param list_suffix: Suffix for the lists splitting the features. Will be preceded with each of the options given in splits.
:param feature_extension: MIME of the features.
:param replace_extension: Remove this number of characters from the features names. Set to 4 for removing MIME extensions such as '.png', '.jpg' and replacing it by feature_extension.
:param splits: Splits to create.
:return:
"""
create_dir_if_not_exists(root_dir + '/' + lists_dir + '/' + features)
path_features = features_dir + '/' + features
print("Storing features in:", root_dir + '/' + lists_dir + '/' + features)
for split in splits:
print('Processing split', split)
ids = file2list(root_dir + '/' + lists_dir + '/' + split + list_suffix)
if replace_extension > 0:
new_ids = [
path_features + '/' + split + '/' +
sample_id[:-replace_extension] + feature_extension
for sample_id in ids
]
else:
new_ids = [
path_features + '/' + split + '/' + sample_id +
feature_extension for sample_id in ids
]
list2file(
root_dir + '/' + lists_dir + '/' + features + '/' + split +
'_list_features.txt', new_ids)
print('Done!')
def sample_ensemble(args, params):
from data_engine.prepare_data import update_dataset_from_file
from keras_wrapper.model_ensemble import BeamSearchEnsemble
from keras_wrapper.cnn_model import loadModel
from keras_wrapper.dataset import loadDataset
from keras_wrapper.utils import decode_predictions_beam_search
logging.info("Using an ensemble of %d models" % len(args.models))
models = [loadModel(m, -1, full_path=True) for m in args.models]
dataset = loadDataset(args.dataset)
dataset = update_dataset_from_file(dataset,
args.text,
params,
splits=args.splits,
remove_outputs=True)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
# For converting predictions into sentences
index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
if params.get('APPLY_DETOKENIZATION', False):
detokenize_function = eval('dataset.' +
params['DETOKENIZATION_METHOD'])
params_prediction = dict()
params_prediction['max_batch_size'] = params.get('BATCH_SIZE', 20)
params_prediction['n_parallel_loaders'] = params.get('PARALLEL_LOADERS', 1)
params_prediction['beam_size'] = params.get('BEAM_SIZE', 6)
params_prediction['maxlen'] = params.get('MAX_OUTPUT_TEXT_LEN_TEST', 100)
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
params_prediction['search_pruning'] = params.get('SEARCH_PRUNING', False)
params_prediction['normalize_probs'] = params.get('NORMALIZE_SAMPLING',
False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get('COVERAGE_PENALTY',
False)
params_prediction['length_penalty'] = params.get('LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get(
'LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get(
'COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get(
'MAXLEN_GIVEN_X', True)
params_prediction['output_max_length_depending_on_x_factor'] = params.get(
'MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get(
'MINLEN_GIVEN_X', True)
params_prediction['output_min_length_depending_on_x_factor'] = params.get(
'MINLEN_GIVEN_X_FACTOR', 2)
params_prediction['attend_on_output'] = params.get(
'ATTEND_ON_OUTPUT', 'transformer' in params['MODEL_TYPE'].lower())
heuristic = params.get('HEURISTIC', 0)
mapping = None if dataset.mapping == dict() else dataset.mapping
model_weights = args.weights
if model_weights is not None and model_weights != []:
assert len(model_weights) == len(
models
), 'You should give a weight to each model. You gave %d models and %d weights.' % (
len(models), len(model_weights))
model_weights = map(lambda x: float(x), model_weights)
if len(model_weights) > 1:
logger.info('Giving the following weights to each model: %s' %
str(model_weights))
for s in args.splits:
# Apply model predictions
params_prediction['predict_on_sets'] = [s]
beam_searcher = BeamSearchEnsemble(models,
dataset,
params_prediction,
model_weights=model_weights,
n_best=args.n_best,
verbose=args.verbose)
if args.n_best:
predictions, n_best = beam_searcher.predictBeamSearchNet()[s]
else:
predictions = beam_searcher.predictBeamSearchNet()[s]
n_best = None
if params_prediction['pos_unk']:
samples = predictions[0]
alphas = predictions[1]
sources = [
x.strip() for x in open(args.text, 'r').read().split('\n')
]
sources = sources[:-1] if len(sources[-1]) == 0 else sources
else:
samples = predictions
alphas = None
heuristic = None
sources = None
predictions = decode_predictions_beam_search(samples,
index2word_y,
alphas=alphas,
x_text=sources,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
predictions = map(detokenize_function, predictions)
if args.n_best:
n_best_predictions = []
for i, (n_best_preds, n_best_scores,
n_best_alphas) in enumerate(n_best):
n_best_sample_score = []
for n_best_pred, n_best_score, n_best_alpha in zip(
n_best_preds, n_best_scores, n_best_alphas):
pred = decode_predictions_beam_search(
[n_best_pred],
index2word_y,
alphas=[n_best_alpha]
if params_prediction['pos_unk'] else None,
x_text=[sources[i]]
if params_prediction['pos_unk'] else None,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
pred = map(detokenize_function, pred)
n_best_sample_score.append([i, pred, n_best_score])
n_best_predictions.append(n_best_sample_score)
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params.get('SAMPLING_SAVE_MODE', 'list'):
list2file(filepath, predictions)
if args.n_best:
nbest2file(filepath + '.nbest', n_best_predictions)
else:
raise Exception(
'Only "list" is allowed in "SAMPLING_SAVE_MODE"')
else:
list2stdout(predictions)
if args.n_best:
logging.info('Storing n-best sentences in ./' + s + '.nbest')
nbest2file('./' + s + '.nbest', n_best_predictions)
logging.info('Sampling finished')
def evaluate(self, epoch, counter_name='epoch', logs=None):
"""
Evaluation function. Works for evaluators external to Keras.
Computes the predictions according to the configuration and evaluates them, storing the results.
:param epoch: Current epoch or update.
:param counter_name: 'epoch' or 'update', string used for logging.
:param logs:
:return:
"""
if logs is None:
logs = {}
# Change inputs and outputs mappings for evaluation
self.changeInOutMappings()
# Evaluate on each set separately
all_metrics = []
for s in self.set_name:
# Apply model predictions
if self.beam_search:
params_prediction = {
'max_batch_size':
self.batch_size,
'n_parallel_loaders':
self.extra_vars.get('n_parallel_loaders', 1),
'predict_on_sets': [s],
'beam_batch_size':
self.beam_batch_size
if self.beam_batch_size is not None else self.batch_size,
'pos_unk':
False,
'normalize':
self.normalize,
'normalization_type':
self.normalization_type,
'max_eval_samples':
self.max_eval_samples
}
params_prediction.update(
checkDefaultParamsBeamSearch(self.extra_vars))
predictions_all = self.model_to_eval.predictBeamSearchNet(
self.ds, params_prediction)[s]
else:
orig_size = self.extra_vars.get('eval_orig_size', False)
params_prediction = {
'batch_size':
self.batch_size,
'n_parallel_loaders':
self.extra_vars.get('n_parallel_loaders', 1),
'predict_on_sets': [s],
'normalize':
self.normalize,
'normalization_type':
self.normalization_type,
'max_eval_samples':
self.max_eval_samples,
'model_name':
self.model_name,
}
# Convert predictions
postprocess_fun = None
if self.is_3DLabel:
postprocess_fun = [
self.ds.convert_3DLabels_to_bboxes,
self.extra_vars[s]['references_orig_sizes']
]
elif orig_size:
postprocess_fun = [
self.ds.resize_semantic_output,
self.extra_vars[s]['eval_orig_size_id']
]
predictions_all = \
self.model_to_eval.predictNet(self.ds,
params_prediction,
postprocess_fun=postprocess_fun)[s]
# Single-output model
if not self.gt_pos or self.gt_pos == 0 or len(self.gt_pos) == 1:
if len(predictions_all) != 2:
predictions_all = [predictions_all]
gt_positions = [0]
# Multi-output model
else:
gt_positions = self.gt_pos
# Select each output to evaluate separately
for gt_pos, type_out, these_metrics, gt_id, write_type, index2word_y, index2word_x in zip(
gt_positions, self.output_types, self.metric_name,
self.gt_id, self.write_type, self.index2word_y,
self.index2word_x):
predictions = predictions_all[gt_pos]
prediction_costs = None
if self.verbose > 0:
print('')
logger.info('Prediction output ' + str(gt_pos) + ': ' +
str(gt_id) + ' (' + str(type_out) + ')')
# Postprocess outputs of type text
if type_out == 'text':
samples = predictions['samples']
prediction_costs = predictions['costs']
alphas = None
sources = None
if params_prediction.get('pos_unk', False):
alphas = predictions['alphas']
if eval('self.ds.loaded_raw_' + s + '[0]'):
sources = predictions['sources']
else:
sources = []
for preds in predictions['sources']:
for src in preds[self.input_text_id]:
sources.append(src)
sources = decode_predictions_beam_search(
sources,
index2word_x,
pad_sequences=True,
verbose=self.verbose)
if self.out_pred_idx is not None:
samples = samples[self.out_pred_idx]
# Convert predictions into sentences
if self.beam_search:
decoded_predictions = decode_predictions_beam_search(
samples,
index2word_y,
glossary=self.extra_vars.get('glossary', None),
alphas=alphas,
x_text=sources,
heuristic=self.extra_vars.get('heuristic', 0),
mapping=self.extra_vars.get('mapping', None),
verbose=self.verbose)
else:
probs = predictions
decoded_predictions = decode_predictions(
predictions,
1,
# always set temperature to 1
index2word_y,
self.sampling_type,
verbose=self.verbose)
# Apply detokenization function if needed
if self.extra_vars.get('apply_detokenization', False):
decoded_predictions = list(
map(self.extra_vars['detokenize_f'],
decoded_predictions))
# Postprocess outputs of type binary
elif type_out == 'binary':
decoded_predictions = decode_multilabel(
predictions,
index2word_y,
min_val=self.min_pred_multilabel[gt_pos],
verbose=self.verbose)
# Prepare references
y_split = getattr(self.ds, 'Y_' + s)
y_raw = y_split[gt_id]
self.extra_vars[gt_pos][s][
'references'] = self.ds.loadBinary(y_raw, gt_id)
# Postprocess outputs of type 3DLabel
elif type_out == '3DLabel':
self.extra_vars[gt_pos][s] = dict()
y_split = getattr(self.ds, 'Y_' + s)
ref = y_split[gt_id]
[ref, original_sizes
] = self.ds.convert_GT_3DLabels_to_bboxes(ref)
self.extra_vars[gt_pos][s]['references'] = ref
self.extra_vars[gt_pos][s][
'references_orig_sizes'] = original_sizes
# Postprocess outputs of type 3DSemanticLabel
elif type_out == '3DSemanticLabel':
self.extra_vars[gt_pos][
'eval_orig_size'] = self.eval_orig_size
self.extra_vars[gt_pos][s] = dict()
y_split = getattr(self.ds, 'Y_' + s)
ref = y_split[gt_id]
if self.eval_orig_size:
old_crop = copy.deepcopy(self.ds.img_size_crop)
self.ds.img_size_crop = copy.deepcopy(self.ds.img_size)
self.extra_vars[gt_pos][s][
'eval_orig_size_id'] = np.array([gt_id] * len(ref))
ref = self.ds.load_GT_3DSemanticLabels(ref, gt_id)
if self.eval_orig_size:
self.ds.img_size_crop = copy.deepcopy(old_crop)
self.extra_vars[gt_pos][s]['references'] = ref
# Other output data types
else:
y_split = getattr(self.ds, 'Y_' + s)
self.extra_vars[gt_pos][s]['references'] = y_split[gt_id]
# Store predictions
if self.write_samples:
# Store result
filepath = os.path.join(
self.save_path,
s + '_' + counter_name + '_' + str(epoch) +
'_output_' + str(gt_pos) + '.pred') # results file
if write_type == 'list':
list2file(filepath, decoded_predictions)
elif write_type == 'vqa':
try:
y_split = getattr(self.ds, 'Y_' + s)
refs = y_split[gt_id]
except Exception:
refs = ['N/A' for _ in range(probs.shape[0])]
extra_data_plot = {
'reference': refs,
'probs': probs,
'vocab': index2word_y
}
list2vqa(filepath,
decoded_predictions,
self.extra_vars[gt_pos][s]['question_ids'],
extra=extra_data_plot)
elif write_type == 'listoflists':
listoflists2file(filepath, decoded_predictions)
elif write_type == 'numpy':
numpy2file(filepath, decoded_predictions)
elif write_type == '3DLabels':
raise NotImplementedError(
'Write 3DLabels function is not implemented')
elif write_type == '3DSemanticLabel':
folder_path = os.path.join(
self.save_path,
s + '_' + counter_name + '_' + str(epoch))
numpy2imgs(
folder_path, decoded_predictions,
eval('self.ds.X_' + s + '["' + self.input_id +
'"]'), self.ds)
else:
raise NotImplementedError('The store type "' +
self.write_type +
'" is not implemented.')
# Store current epoch/iteration in model log
self.model_to_eval.log(s, counter_name, epoch)
# Evaluate on each metric
for metric in these_metrics:
if self.verbose > 0:
logger.info('Evaluating on metric ' + metric)
filepath = os.path.join(self.save_path, s + '.' + metric)
if s == 'train':
logger.info(
"WARNING: evaluation results on 'train' split might be incorrect when"
"applying random image shuffling.")
# Evaluate on the chosen metric
metrics = evaluation.select[metric](
pred_list=decoded_predictions,
verbose=self.verbose,
extra_vars=self.extra_vars[gt_pos],
split=s,
costs=prediction_costs)
self.model_to_eval.log_tensorboard(metrics, epoch, split=s)
# Print results to file and store in model log
with open(filepath, 'a') as f:
header = counter_name + ','
line = str(epoch) + ','
for metric_ in sorted(metrics):
value = metrics[metric_]
# Multiple-output model
if self.gt_pos and self.gt_pos != 0:
metric_ += '_output_' + str(gt_pos)
all_metrics.append(metric_)
header += metric_ + ','
line += str(value) + ','
# Store in model log
self.model_to_eval.log(s, metric_, value)
if not self.written_header:
f.write(header + '\n')
self.written_header = True
f.write(line + '\n')
if self.verbose > 0:
logger.info('Done evaluating on metric ' + metric)
# Store losses
if logs.get('loss') is not None:
self.model_to_eval.log('train', 'train_loss', logs['loss'])
if logs.get('valid_loss') is not None:
self.model_to_eval.log('val', 'val_loss', logs['valid_loss'])
# Plot results so far
if self.do_plot:
if self.metric_name:
self.model_to_eval.plot(counter_name,
set(all_metrics),
self.set_name,
upperbound=self.max_plot)
# Save the model
if self.save_each_evaluation:
from keras_wrapper.cnn_model import saveModel
saveModel(self.model_to_eval,
epoch,
store_iter=not self.eval_on_epochs)
# Recover inputs and outputs mappings for resume training
self.recoverInOutMappings()
Ross_predictions = Ross_model.predictBeamSearchNet(dataset,
params_prediction)['test']
vocab = dataset.vocabulary['target_text']['idx2words']
Ross_predictions = decode_predictions_beam_search(Ross_predictions,
vocab,
verbose=params['VERBOSE'])
## see how they compare to ground truth
#
from keras_wrapper.extra.read_write import list2file
from keras_wrapper.extra import evaluation
Ross_path = 'Ross_M7.pred'
list2file(Ross_path, Ross_predictions)
dataset.setOutput('data/Ross_test.reply',
'test',
type='text',
id='target_text',
pad_on_batch=True,
tokenization='tokenize_basic',
sample_weights=True,
max_text_len=30,
max_words=0)
print(dataset)
keep_n_captions(dataset, repeat=1, n=1, set_names=['test'])
def apply_Video_model(params):
"""
Function for using a previously trained model for sampling.
"""
########### Load data
dataset = build_dataset(params)
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
###########
########### Load model
video_model = loadModel(params['STORE_PATH'], params['RELOAD'])
video_model.setOptimizer()
###########
########### Apply sampling
extra_vars = dict()
extra_vars['tokenize_f'] = eval('dataset.' + params['TOKENIZATION_METHOD'])
extra_vars['language'] = params.get('TRG_LAN', 'en')
for s in params["EVAL_ON_SETS"]:
# Apply model predictions
params_prediction = {
'max_batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s]
}
# Convert predictions into sentences
vocab = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
if params['BEAM_SEARCH']:
params_prediction['beam_size'] = params['BEAM_SIZE']
params_prediction['maxlen'] = params['MAX_OUTPUT_TEXT_LEN_TEST']
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params[
'OUTPUTS_IDS_DATASET']
params_prediction['normalize_probs'] = params['NORMALIZE_SAMPLING']
params_prediction['alpha_factor'] = params['ALPHA_FACTOR']
predictions = video_model.predictBeamSearchNet(
dataset, params_prediction)[s]
predictions = video_model.decode_predictions_beam_search(
predictions, vocab, verbose=params['VERBOSE'])
else:
predictions = video_model.predictNet(dataset, params_prediction)[s]
predictions = video_model.decode_predictions(
predictions,
1, # always set temperature to 1
vocab,
params['SAMPLING'],
verbose=params['VERBOSE'])
# Store result
filepath = video_model.model_path + '/' + s + '_sampling.pred' # results file
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(filepath, predictions)
else:
raise Exception, 'Only "list" is allowed in "SAMPLING_SAVE_MODE"'
# Evaluate if any metric in params['METRICS']
for metric in params['METRICS']:
logging.info('Evaluating on metric ' + metric)
filepath = video_model.model_path + '/' + s + '_sampling.' + metric # results file
# Evaluate on the chosen metric
extra_vars[s] = dict()
extra_vars[s]['references'] = dataset.extra_variables[s][
params['OUTPUTS_IDS_DATASET'][0]]
metrics = evaluation.select[metric](pred_list=predictions,
verbose=1,
extra_vars=extra_vars,
split=s)
# Print results to file
with open(filepath, 'w') as f:
header = ''
line = ''
for metric_ in sorted(metrics):
value = metrics[metric_]
header += metric_ + ','
line += str(value) + ','
f.write(header + '\n')
f.write(line + '\n')
logging.info('Done evaluating on metric ' + metric)
def score_corpus(args, params):
"""
Use one or several translation models for scoring source--target pairs-
:param argparse.Namespace args: Arguments given to the method:
* dataset: Dataset instance with data.
* source: Text file with source sentences.
* target: Text file with target sentences.
* splits: Splits to sample. Should be already included in the dataset object.
* dest: Output file to save scores.
* weights: Weight given to each model in the ensemble. You should provide the same number of weights than models. By default, it applies the same weight to each model (1/N).
* verbose: Be verbose or not.
* config: Config .pkl for loading the model configuration. If not specified, hyperparameters are read from config.py.
* models: Path to the models.
:param dict params: parameters of the translation model.
"""
from data_engine.prepare_data import update_dataset_from_file
from keras_wrapper.dataset import loadDataset
from keras_wrapper.cnn_model import loadModel
from keras_wrapper.model_ensemble import BeamSearchEnsemble
logging.info("Using an ensemble of %d models" % len(args.models))
models = [loadModel(m, -1, full_path=True) for m in args.models]
dataset = loadDataset(args.dataset)
dataset = update_dataset_from_file(dataset,
args.source,
params,
splits=args.splits,
output_text_filename=args.target,
compute_state_below=True)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
# Apply scoring
extra_vars = dict()
extra_vars['tokenize_f'] = eval('dataset.' + params['TOKENIZATION_METHOD'])
model_weights = args.weights
if model_weights is not None and model_weights != []:
assert len(model_weights) == len(
models
), 'You should give a weight to each model. You gave %d models and %d weights.' % (
len(models), len(model_weights))
model_weights = map(float, model_weights)
if len(model_weights) > 1:
logger.info('Giving the following weights to each model: %s' %
str(model_weights))
for s in args.splits:
# Apply model predictions
params_prediction = {
'max_batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s]
}
if params['BEAM_SEARCH']:
params_prediction['beam_size'] = params['BEAM_SIZE']
params_prediction['maxlen'] = params['MAX_OUTPUT_TEXT_LEN_TEST']
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params[
'OUTPUTS_IDS_DATASET']
params_prediction['normalize_probs'] = params.get(
'NORMALIZE_SAMPLING', False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get(
'COVERAGE_PENALTY', False)
params_prediction['length_penalty'] = params.get(
'LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get(
'LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get(
'COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get(
'MAXLEN_GIVEN_X', True)
params_prediction[
'output_max_length_depending_on_x_factor'] = params.get(
'MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get(
'MINLEN_GIVEN_X', True)
params_prediction[
'output_min_length_depending_on_x_factor'] = params.get(
'MINLEN_GIVEN_X_FACTOR', 2)
params_prediction['attend_on_output'] = params.get(
'ATTEND_ON_OUTPUT', 'transformer'
in params['MODEL_TYPE'].lower())
beam_searcher = BeamSearchEnsemble(models,
dataset,
params_prediction,
model_weights=model_weights,
verbose=args.verbose)
scores = beam_searcher.scoreNet()[s]
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(filepath, scores)
elif params['SAMPLING_SAVE_MODE'] == 'numpy':
numpy2file(filepath, scores)
else:
raise Exception('The sampling mode ' +
params['SAMPLING_SAVE_MODE'] +
' is not currently supported.')
else:
print(scores)
def apply_Feature_Extractor_model(params, dataset=None, extractor_model=None):
"""
Function for using a previously trained model for sampling.
"""
########### Load data
if dataset is None:
dataset = build_dataset(params)
########### Load model
if extractor_model is None and params['RELOAD'] > 0:
extractor_model = loadModel(params['STORE_PATH'], params['RELOAD'])
else:
extractor_model = Feature_Extractor(params,
type=params['MODEL_TYPE'],
verbose=params['VERBOSE'],
model_name=params['MODEL_NAME'],
store_path=params['STORE_PATH'])
# Define the inputs and outputs mapping from our Dataset instance to our model
inputMapping = dict()
for i, id_in in enumerate(params['INPUTS_IDS_DATASET']):
if len(extractor_model.ids_inputs) > i:
pos_source = dataset.ids_inputs.index(id_in)
id_dest = extractor_model.ids_inputs[i]
inputMapping[id_dest] = pos_source
extractor_model.setInputsMapping(inputMapping)
########### Apply sampling
extra_vars = dict()
for s in params["EVAL_ON_SETS"]:
# Apply model predictions
params_prediction = {'batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s],
'verbose': 0}
logging.info("<<< Predicting outputs of " + s + " set >>>")
if params['SAMPLING_SAVE_MODE'] == 'list':
filepath = extractor_model.model_path + '/' + s + '_sampling.pred' # results file
list2file(filepath, [], permission='w')
start_time = time.time()
eta = -1
mode = 'w'
for n_sample in range(0, eval('dataset.len_' + s), params.get('PREDICTION_STEP', 100)):
params_prediction['init_sample'] = n_sample
params_prediction['final_sample'] = min(n_sample + params.get('PREDICTION_STEP', 100), eval('dataset.len_' + s))
predictions = extractor_model.predictNet(dataset, params_prediction)[s]
# Store result
if params['SAMPLING_SAVE_MODE'] == 'list':
filepath = extractor_model.model_path + '/' + s + '_sampling.pred' # results file
list2file(filepath, predictions, permission='a')
elif params['SAMPLING_SAVE_MODE'] == 'npy':
filepath = extractor_model.model_path + '/' + s + '_' + params.get('MODEL_TYPE', '') + '_features.npy'
numpy2file(filepath, predictions, permission=mode)
elif params['SAMPLING_SAVE_MODE'] == 'hdf5':
filepath = extractor_model.model_path + '/' + s + '_' + params.get('MODEL_TYPE', '') + '_features.hdf5'
numpy2hdf5(filepath, predictions, permission=mode)
else:
raise Exception, 'Only "list" or "hdf5" are allowed in "SAMPLING_SAVE_MODE"'
mode = 'a'
sys.stdout.write('\r')
sys.stdout.write("\t Processed %d/%d - ETA: %ds " % (n_sample, eval('dataset.len_' + s), int(eta)))
sys.stdout.flush()
eta = (eval('dataset.len_' + s) - n_sample) * (time.time() - start_time) / max(n_sample, 1)
def interactive_translation(
src_seq,
src_line,
trg_line,
params_prediction,
args,
tokenize_f,
index2word_y,
word2index_y,
index2word_x,
word2index_x,
unk_id,
total_errors,
total_mouse_actions,
n_line=-1,
):
errors_sentence = 0
mouse_actions_sentence = 0
hypothesis_number = 0
# Get (tokenized) input
tokenized_reference = tokenize_f(
trg_line) if args.tokenize_references else trg_line
# Get reference as desired by the user, i.e. detokenized if necessary
reference = params_prediction['detokenize_f'](tokenized_reference) if \
args.detokenize_bpe else tokenized_reference
# Detokenize line for nicer logging :)
if args.detokenize_bpe:
src_line = params_prediction['detokenize_f'](src_line)
logger.debug(u'\n\nProcessing sentence %d' % n_line)
logger.debug(u'Source: %s' % src_line)
logger.debug(u'Target: %s' % reference)
# 1. Get a first hypothesis
trans_indices, costs, alphas = interactive_beam_searcher.sample_beam_search_interactive(
src_seq)
# 1.1 Set unk replacemet strategy
if params_prediction['pos_unk']:
alphas = [alphas]
sources = [tokenized_input]
heuristic = params_prediction['heuristic']
else:
alphas = None
heuristic = None
sources = None
# 1.2 Decode hypothesis
hypothesis = decode_predictions_beam_search([trans_indices],
index2word_y,
alphas=alphas,
x_text=sources,
heuristic=heuristic,
mapping=mapping,
pad_sequences=True,
verbose=0)[0]
# 1.3 Store result (optional)
hypothesis = params_prediction['detokenize_f'](hypothesis) \
if params_prediction.get('apply_detokenization', False) else hypothesis
if args.original_dest is not None:
filepath = args.original_dest # results file
if params_prediction['SAMPLING_SAVE_MODE'] == 'list':
list2file(filepath, [hypothesis + '\n'], permission='a')
else:
raise Exception('Only "list" is allowed in "SAMPLING_SAVE_MODE"')
logger.debug(u'Hypo_%d: %s' % (hypothesis_number, hypothesis))
# 2.0 Interactive translation
if hypothesis == reference:
# 2.1 If the sentence is correct, we validate it
pass
else:
# 2.2 Wrong hypothesis -> Interactively translate the sentence
correct_hypothesis = False
last_correct_pos = 0
while not correct_hypothesis:
# 2.2.1 Empty data structures for the next sentence
fixed_words_user = OrderedDict()
unk_words_dict = OrderedDict()
if not args.prefix:
Exception(
NotImplementedError, 'Segment-based interaction at'
' character level is still unimplemented')
else:
isle_indices = []
unks_in_isles = []
# 2.2.2 Compute longest common character prefix (LCCP)
next_correction_pos, validated_prefix = common_prefix(
hypothesis, reference)
if next_correction_pos == len(reference):
correct_hypothesis = True
break
# 2.2.3 Get next correction by checking against the reference
next_correction = reference[next_correction_pos]
# 2.2.4 Tokenize the prefix properly (possibly applying BPE)
tokenized_validated_prefix = tokenize_f(validated_prefix +
next_correction)
# 2.2.5 Validate words
for pos, word in enumerate(tokenized_validated_prefix.split()):
fixed_words_user[pos] = word2index_y.get(word, unk_id)
if word2index_y.get(word) is None:
unk_words_dict[pos] = word
# 2.2.6 Constrain search for the last word
last_user_word_pos = fixed_words_user.keys()[-1]
if next_correction != u' ':
last_user_word = tokenized_validated_prefix.split()[-1]
filtered_idx2word = dict(
(word2index_y[candidate_word], candidate_word)
for candidate_word in word2index_y
if candidate_word.decode(
'utf-8')[:len(last_user_word)] == last_user_word)
if filtered_idx2word != dict():
del fixed_words_user[last_user_word_pos]
if last_user_word_pos in unk_words_dict.keys():
del unk_words_dict[last_user_word_pos]
else:
filtered_idx2word = dict()
logger.debug(u'"%s" to character %d.' %
(next_correction, next_correction_pos))
# 2.2.7 Generate a hypothesis compatible with the feedback provided by the user
hypothesis = generate_constrained_hypothesis(
interactive_beam_searcher, src_seq, fixed_words_user,
params_prediction, args, isle_indices,
filtered_idx2word, index2word_y, sources, heuristic, mapping,
unk_words_dict.keys(), unk_words_dict.values(), unks_in_isles)
hypothesis_number += 1
hypothesis = u' '.join(hypothesis) # Hypothesis is unicode
hypothesis = params_prediction['detokenize_f'](hypothesis) \
if args.detokenize_bpe else hypothesis
logger.debug(u'Target: %s' % reference)
logger.debug(u"Hypo_%d: %s" % (hypothesis_number, hypothesis))
# 2.2.8 Add a keystroke
errors_sentence += 1
# 2.2.9 Add a mouse action if we moved the pointer
if next_correction_pos - last_correct_pos > 1:
mouse_actions_sentence += 1
last_correct_pos = next_correction_pos
# 2.3 Final check: The reference is a subset of the hypothesis: Cut the hypothesis
if len(reference) < len(hypothesis):
hypothesis = hypothesis[:len(reference)]
errors_sentence += 1
logger.debug("Cutting hypothesis")
# 2.4 Security assertion
assert hypothesis == reference, "Error: The final hypothesis does not match with the reference! \n" \
"\t Split: %s \n" \
"\t Sentence: %d \n" \
"\t Hypothesis: %s\n" \
"\t Reference: %s" % (str(s.encode('utf-8')), n_line,
hypothesis.encode('utf-8'),
reference.encode('utf-8'))
# 3. Update user effort counters
mouse_actions_sentence += 1 # This +1 is the validation action
chars_sentence = len(hypothesis)
total_errors += errors_sentence
total_mouse_actions += mouse_actions_sentence
# 3.1 Log some info
logger.debug(u"Final hypotesis: %s" % hypothesis)
logger.debug(
"%d errors. "
"Sentence WSR: %4f. "
"Sentence mouse strokes: %d "
"Sentence MAR: %4f. "
"Sentence MAR_c: %4f. "
"Sentence KSMR: %4f. " %
(errors_sentence, float(errors_sentence) / len(hypothesis),
mouse_actions_sentence, float(mouse_actions_sentence) /
len(hypothesis), float(mouse_actions_sentence) / chars_sentence,
float(errors_sentence + mouse_actions_sentence) / chars_sentence))
# 5 Write correct sentences into a file
return hypothesis, total_errors, total_mouse_actions
'MINLEN_GIVEN_X_FACTOR', 2)
mapping = None if dataset.mapping == dict() else dataset.mapping
if params['POS_UNK']:
params_prediction['heuristic'] = params['HEURISTIC']
input_text_id = params['INPUTS_IDS_DATASET'][0]
vocab_src = dataset.vocabulary[input_text_id]['idx2words']
else:
input_text_id = None
vocab_src = None
mapping = None
beam_searcher = BeamSearchEnsemble(models,
dataset,
params_prediction,
verbose=args.verbose)
scores = beam_searcher.scoreNet()[s]
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(filepath, scores)
elif params['SAMPLING_SAVE_MODE'] == 'numpy':
numpy2file(filepath, scores)
else:
raise Exception('The sampling mode ' +
params['SAMPLING_SAVE_MODE'] +
' is not currently supported.')
else:
print scores
def score_corpus(args, params):
from data_engine.prepare_data import update_dataset_from_file
from keras_wrapper.dataset import loadDataset
from keras_wrapper.cnn_model import loadModel
from keras_wrapper.model_ensemble import BeamSearchEnsemble
logging.info("Using an ensemble of %d models" % len(args.models))
models = [loadModel(m, -1, full_path=True) for m in args.models]
dataset = loadDataset(args.dataset)
dataset = update_dataset_from_file(dataset,
args.source,
params,
splits=args.splits,
output_text_filename=args.target,
compute_state_below=True)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
# Apply scoring
extra_vars = dict()
extra_vars['tokenize_f'] = eval('dataset.' + params['TOKENIZATION_METHOD'])
model_weights = args.weights
if model_weights is not None and model_weights != []:
assert len(model_weights) == len(
models
), 'You should give a weight to each model. You gave %d models and %d weights.' % (
len(models), len(model_weights))
model_weights = map(lambda x: float(x), model_weights)
if len(model_weights) > 1:
logger.info('Giving the following weights to each model: %s' %
str(model_weights))
for s in args.splits:
# Apply model predictions
params_prediction = {
'max_batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s]
}
if params['BEAM_SEARCH']:
params_prediction['beam_size'] = params['BEAM_SIZE']
params_prediction['maxlen'] = params['MAX_OUTPUT_TEXT_LEN_TEST']
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params[
'OUTPUTS_IDS_DATASET']
params_prediction['normalize_probs'] = params.get(
'NORMALIZE_SAMPLING', False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get(
'COVERAGE_PENALTY', False)
params_prediction['length_penalty'] = params.get(
'LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get(
'LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get(
'COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get(
'MAXLEN_GIVEN_X', True)
params_prediction[
'output_max_length_depending_on_x_factor'] = params.get(
'MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get(
'MINLEN_GIVEN_X', True)
params_prediction[
'output_min_length_depending_on_x_factor'] = params.get(
'MINLEN_GIVEN_X_FACTOR', 2)
params_prediction['attend_on_output'] = params.get(
'ATTEND_ON_OUTPUT', 'transformer'
in params['MODEL_TYPE'].lower())
beam_searcher = BeamSearchEnsemble(models,
dataset,
params_prediction,
model_weights=model_weights,
verbose=args.verbose)
scores = beam_searcher.scoreNet()[s]
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(filepath, scores)
elif params['SAMPLING_SAVE_MODE'] == 'numpy':
numpy2file(filepath, scores)
else:
raise Exception('The sampling mode ' +
params['SAMPLING_SAVE_MODE'] +
' is not currently supported.')
else:
print(scores)
def interactive_simulation():
args = parse_args()
# Update parameters
if args.config is not None:
logger.info('Reading parameters from %s.' % args.config)
params = update_parameters({}, pkl2dict(args.config))
else:
logger.info('Reading parameters from config.py.')
params = load_parameters()
if args.online:
from config_online import load_parameters as load_parameters_online
online_parameters = load_parameters_online(params)
params = update_parameters(params, online_parameters)
try:
for arg in args.changes:
try:
k, v = arg.split('=')
except ValueError:
print(
'Overwritten arguments must have the form key=Value. \n Currently are: %s'
% str(args.changes))
exit(1)
try:
params[k] = ast.literal_eval(v)
except ValueError:
params[k] = v
except ValueError:
print('Error processing arguments: (', k, ",", v, ")")
exit(2)
check_params(params)
if args.verbose:
logging.info("params = " + str(params))
dataset = loadDataset(args.dataset)
# dataset = update_dataset_from_file(dataset, args.source, params, splits=args.splits, remove_outputs=True)
# Dataset backwards compatibility
bpe_separator = dataset.BPE_separator if hasattr(
dataset,
"BPE_separator") and dataset.BPE_separator is not None else u'@@'
# Set tokenization method
params[
'TOKENIZATION_METHOD'] = 'tokenize_bpe' if args.tokenize_bpe else params.get(
'TOKENIZATION_METHOD', 'tokenize_none')
# Build BPE tokenizer if necessary
if 'bpe' in params['TOKENIZATION_METHOD'].lower():
logger.info('Building BPE')
if not dataset.BPE_built:
dataset.build_bpe(params.get(
'BPE_CODES_PATH',
params['DATA_ROOT_PATH'] + '/training_codes.joint'),
separator=bpe_separator)
# Build tokenization function
tokenize_f = eval('dataset.' +
params.get('TOKENIZATION_METHOD', 'tokenize_none'))
if args.online:
# Traning params
params_training = { # Traning params
'n_epochs': params['MAX_EPOCH'],
'shuffle': False,
'loss': params.get('LOSS', 'categorical_crossentropy'),
'batch_size': params.get('BATCH_SIZE', 1),
'homogeneous_batches': False,
'optimizer': params.get('OPTIMIZER', 'SGD'),
'lr': params.get('LR', 0.1),
'lr_decay': params.get('LR_DECAY', None),
'lr_gamma': params.get('LR_GAMMA', 1.),
'epochs_for_save': -1,
'verbose': args.verbose,
'eval_on_sets': params['EVAL_ON_SETS_KERAS'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'extra_callbacks': [], # callbacks,
'reload_epoch': 0,
'epoch_offset': 0,
'data_augmentation': params['DATA_AUGMENTATION'],
'patience': params.get('PATIENCE', 0),
'metric_check': params.get('STOP_METRIC', None),
'eval_on_epochs': params.get('EVAL_EACH_EPOCHS', True),
'each_n_epochs': params.get('EVAL_EACH', 1),
'start_eval_on_epoch': params.get('START_EVAL_ON_EPOCH', 0),
'additional_training_settings': {
'k': params.get('K', 1),
'tau': params.get('TAU', 1),
'lambda': params.get('LAMBDA', 0.5),
'c': params.get('C', 0.5),
'd': params.get('D', 0.5)
}
}
else:
params_training = dict()
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
logger.info("<<< Using an ensemble of %d models >>>" % len(args.models))
if args.online:
# Load trainable model(s)
logging.info('Loading models from %s' % str(args.models))
model_instances = [
Captioning_Model(params,
model_type=params['MODEL_TYPE'],
verbose=params['VERBOSE'],
model_name=params['MODEL_NAME'] + '_' + str(i),
vocabularies=dataset.vocabulary,
store_path=params['STORE_PATH'],
clear_dirs=False,
set_optimizer=False)
for i in range(len(args.models))
]
models = [
updateModel(model, path, -1, full_path=True)
for (model, path) in zip(model_instances, args.models)
]
# Set additional inputs to models if using a custom loss function
params['USE_CUSTOM_LOSS'] = True if 'PAS' in params[
'OPTIMIZER'] else False
if params['N_BEST_OPTIMIZER']:
logging.info('Using N-best optimizer')
models = build_online_models(models, params)
online_trainer = OnlineTrainer(models,
dataset,
None,
None,
params_training,
verbose=args.verbose)
else:
# Otherwise, load regular model(s)
models = [loadModel(m, -1, full_path=True) for m in args.models]
# Load text files
logger.info("<<< Storing corrected hypotheses into: %s >>>" %
str(args.dest))
ftrans = open(args.dest, 'w')
ftrans.close()
# Do we want to save the original sentences?
if args.original_dest is not None:
logger.info("<<< Storing original hypotheses into: %s >>>" %
str(args.original_dest))
ftrans_ori = open(args.original_dest, 'w')
ftrans_ori.close()
if args.references is not None:
ftrg = codecs.open(args.references, 'r', encoding='utf-8'
) # File with post-edited (or reference) sentences.
all_references = ftrg.read().split('\n')
if all_references[-1] == u'':
all_references = all_references[:-1]
# Get word2index and index2word dictionaries
index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['idx2words']
word2index_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET']
[0]]['words2idx']
unk_id = dataset.extra_words['<unk>']
# Initialize counters
total_errors = 0
total_words = 0
total_chars = 0
total_mouse_actions = 0
try:
for s in args.splits:
# Apply model predictions
params_prediction = {
'max_batch_size':
params['BATCH_SIZE'],
'n_parallel_loaders':
params['PARALLEL_LOADERS'],
'predict_on_sets': [s],
'beam_size':
params['BEAM_SIZE'],
'maxlen':
params['MAX_OUTPUT_TEXT_LEN_TEST'],
'optimized_search':
params['OPTIMIZED_SEARCH'],
'model_inputs':
params['INPUTS_IDS_MODEL'],
'model_outputs':
params['OUTPUTS_IDS_MODEL'],
'dataset_inputs':
params['INPUTS_IDS_DATASET'],
'dataset_outputs':
params['OUTPUTS_IDS_DATASET'],
'normalize_probs':
params.get('NORMALIZE_SAMPLING', False),
'alpha_factor':
params.get('ALPHA_FACTOR', 1.0),
'normalize':
params.get('NORMALIZATION', False),
'normalization_type':
params.get('NORMALIZATION_TYPE', None),
'data_augmentation':
params.get('DATA_AUGMENTATION', False),
'mean_substraction':
params.get('MEAN_SUBTRACTION', False),
'wo_da_patch_type':
params.get('WO_DA_PATCH_TYPE', 'whole'),
'da_patch_type':
params.get('DA_PATCH_TYPE', 'resize_and_rndcrop'),
'da_enhance_list':
params.get('DA_ENHANCE_LIST', None),
'heuristic':
params.get('HEURISTIC', None),
'search_pruning':
params.get('SEARCH_PRUNING', False),
'state_below_index':
-1,
'output_text_index':
0,
'apply_tokenization':
params.get('APPLY_TOKENIZATION', False),
'tokenize_f':
eval('dataset.' +
params.get('TOKENIZATION_METHOD', 'tokenize_none')),
'apply_detokenization':
params.get('APPLY_DETOKENIZATION', True),
'detokenize_f':
eval('dataset.' +
params.get('DETOKENIZATION_METHOD', 'detokenize_none')),
'coverage_penalty':
params.get('COVERAGE_PENALTY', False),
'length_penalty':
params.get('LENGTH_PENALTY', False),
'length_norm_factor':
params.get('LENGTH_NORM_FACTOR', 0.0),
'coverage_norm_factor':
params.get('COVERAGE_NORM_FACTOR', 0.0),
'pos_unk':
False,
'state_below_maxlen':
-1 if params.get('PAD_ON_BATCH', True) else params.get(
'MAX_OUTPUT_TEXT_LEN_TEST', 50),
'output_max_length_depending_on_x':
params.get('MAXLEN_GIVEN_X', False),
'output_max_length_depending_on_x_factor':
params.get('MAXLEN_GIVEN_X_FACTOR', 3),
'output_min_length_depending_on_x':
params.get('MINLEN_GIVEN_X', False),
'output_min_length_depending_on_x_factor':
params.get('MINLEN_GIVEN_X_FACTOR', 2),
'attend_on_output':
params.get('ATTEND_ON_OUTPUT', 'transformer'
in params['MODEL_TYPE'].lower()),
'n_best_optimizer':
params.get('N_BEST_OPTIMIZER', False)
}
# Build interactive sampler
interactive_beam_searcher = InteractiveBeamSearchSampler(
models,
dataset,
params_prediction,
excluded_words=None,
verbose=args.verbose)
start_time = time.time()
if args.verbose:
logging.info("Params prediction = " + str(params_prediction))
if args.online:
logging.info("Params training = " + str(params_training))
n_samples = getattr(dataset, 'len_' + s)
if args.references is None:
all_references = dataset.extra_variables[s][
params['OUTPUTS_IDS_DATASET'][0]]
# Start to translate the source file interactively
for n_sample in range(n_samples):
errors_sentence = 0
mouse_actions_sentence = 0
hypothesis_number = 0
# Load data from dataset
current_input = dataset.getX_FromIndices(
s, [n_sample],
normalization_type=params_prediction.get(
'normalization_type'),
normalization=params_prediction.get('normalize', False),
dataAugmentation=params_prediction.get(
'data_augmentation', False),
wo_da_patch_type=params_prediction.get(
'wo_da_patch_type', 'whole'),
da_patch_type=params_prediction.get(
'da_patch_type', 'resize_and_rndcrop'),
da_enhance_list=params_prediction.get(
'da_enhance_list', None))[0][0]
# Load references
references = all_references[n_sample]
tokenized_references = list(map(
tokenize_f,
references)) if args.tokenize_references else references
# Get reference as desired by the user, i.e. detokenized if necessary
reference = list(map(params_prediction['detokenize_f'], tokenized_references)) if \
args.detokenize_bpe else tokenized_references
# Detokenize line for nicer logging :)
logger.debug(u'\n\nProcessing sample %d' % (n_sample + 1))
logger.debug(u'Target: %s' % reference)
# 1. Get a first hypothesis
trans_indices, costs, alphas = interactive_beam_searcher.sample_beam_search_interactive(
current_input)
# 1.2 Decode hypothesis
hypothesis = decode_predictions_beam_search([trans_indices],
index2word_y,
pad_sequences=True,
verbose=0)[0]
# 1.3 Store result (optional)
hypothesis = params_prediction['detokenize_f'](hypothesis) \
if params_prediction.get('apply_detokenization', False) else hypothesis
if args.original_dest is not None:
if params['SAMPLING_SAVE_MODE'] == 'list':
list2file(args.original_dest, [hypothesis],
permission='a')
else:
raise Exception(
'Only "list" is allowed in "SAMPLING_SAVE_MODE"')
logger.debug(u'Hypo_%d: %s' % (hypothesis_number, hypothesis))
# 2.0 Interactive translation
if hypothesis in tokenized_references:
# 2.1 If the sentence is correct, we validate it
pass
else:
# 2.2 Wrong hypothesis -> Interactively translate the sentence
correct_hypothesis = False
last_correct_pos = 0
while not correct_hypothesis:
# 2.2.1 Empty data structures for the next sentence
fixed_words_user = OrderedDict()
unk_words_dict = OrderedDict()
isle_indices = []
unks_in_isles = []
if args.prefix:
# 2.2.2 Compute longest common character prefix (LCCP)
reference_idx, next_correction_pos, validated_prefix = common_prefixes(
hypothesis, tokenized_references)
else:
# 2.2.2 Compute common character segments
#TODO
next_correction_pos, validated_prefix, validated_segments = common_segments(
hypothesis, reference)
reference = tokenized_references[reference_idx]
if next_correction_pos == len(reference):
correct_hypothesis = True
break
# 2.2.3 Get next correction by checking against the reference
next_correction = reference[next_correction_pos]
# 2.2.4 Tokenize the prefix properly (possibly applying BPE)
tokenized_validated_prefix = tokenize_f(
validated_prefix + next_correction)
# 2.2.5 Validate words
for pos, word in enumerate(
tokenized_validated_prefix.split()):
fixed_words_user[pos] = word2index_y.get(
word, unk_id)
if word2index_y.get(word) is None:
unk_words_dict[pos] = word
# 2.2.6 Constrain search for the last word
last_user_word_pos = list(fixed_words_user.keys())[-1]
if next_correction != u' ':
last_user_word = tokenized_validated_prefix.split(
)[-1]
filtered_idx2word = dict(
(word2index_y[candidate_word], candidate_word)
for candidate_word in word2index_y
if candidate_word[:len(last_user_word)] ==
last_user_word)
if filtered_idx2word != dict():
del fixed_words_user[last_user_word_pos]
if last_user_word_pos in unk_words_dict.keys():
del unk_words_dict[last_user_word_pos]
else:
filtered_idx2word = dict()
logger.debug(u'"%s" to character %d.' %
(next_correction, next_correction_pos))
# 2.2.7 Generate a hypothesis compatible with the feedback provided by the user
hypothesis = generate_constrained_hypothesis(
interactive_beam_searcher, current_input,
fixed_words_user, params_prediction, args,
isle_indices, filtered_idx2word, index2word_y,
None, None, None, unk_words_dict.keys(),
unk_words_dict.values(), unks_in_isles)
hypothesis_number += 1
hypothesis = u' '.join(
hypothesis) # Hypothesis is unicode
hypothesis = params_prediction['detokenize_f'](hypothesis) \
if args.detokenize_bpe else hypothesis
logger.debug(u'Target: %s' % reference)
logger.debug(u"Hypo_%d: %s" %
(hypothesis_number, hypothesis))
# 2.2.8 Add a keystroke
errors_sentence += 1
# 2.2.9 Add a mouse action if we moved the pointer
if next_correction_pos - last_correct_pos > 1:
mouse_actions_sentence += 1
last_correct_pos = next_correction_pos
# 2.3 Final check: The reference is a subset of the hypothesis: Cut the hypothesis
if len(reference) < len(hypothesis):
hypothesis = hypothesis[:len(reference)]
errors_sentence += 1
logger.debug(u"Cutting hypothesis")
# 2.4 Security assertion
assert hypothesis in references, "Error: The final hypothesis does not match with the reference! \n" \
"\t Split: %s \n" \
"\t Sentence: %d \n" \
"\t Hypothesis: %s\n" \
"\t Reference: %s" % (s, n_sample + 1,
hypothesis,
reference)
# 3. Update user effort counters
mouse_actions_sentence += 1 # This +1 is the validation action
chars_sentence = len(hypothesis)
total_errors += errors_sentence
total_words += len(hypothesis.split())
total_chars += chars_sentence
total_mouse_actions += mouse_actions_sentence
# 3.1 Log some info
logger.debug(u"Final hypotesis: %s" % hypothesis)
logger.debug(
u"%d errors. "
u"Sentence WSR: %4f. "
u"Sentence mouse strokes: %d "
u"Sentence MAR: %4f. "
u"Sentence MAR_c: %4f. "
u"Sentence KSMR: %4f. "
u"Accumulated (should only be considered for debugging purposes!) "
u"WSR: %4f. "
u"MAR: %4f. "
u"MAR_c: %4f. "
u"KSMR: %4f.\n\n\n\n" %
(errors_sentence, float(errors_sentence) / len(hypothesis),
mouse_actions_sentence,
float(mouse_actions_sentence) / len(hypothesis),
float(mouse_actions_sentence) / chars_sentence,
float(errors_sentence + mouse_actions_sentence) /
chars_sentence, float(total_errors) / total_words,
float(total_mouse_actions) / total_words,
float(total_mouse_actions) / total_chars,
float(total_errors + total_mouse_actions) / total_chars))
# 4. If we are performing OL after each correct sample:
if args.online:
# 4.1 Compute model inputs
# 4.1.1 Source text -> Already computed (used for the INMT process)
# 4.1.2 State below
state_below = dataset.loadText(
[reference],
vocabularies=dataset.vocabulary[
params['OUTPUTS_IDS_DATASET'][0]],
max_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
offset=1,
fill=dataset.fill_text[params['INPUTS_IDS_DATASET']
[-1]],
pad_on_batch=dataset.pad_on_batch[
params['INPUTS_IDS_DATASET'][-1]],
words_so_far=False,
loading_X=True)[0]
# 4.1.3 Ground truth sample -> Interactively translated sentence
trg_seq = dataset.loadTextOneHot(
[reference],
vocabularies=dataset.vocabulary[
params['OUTPUTS_IDS_DATASET'][0]],
vocabulary_len=dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]],
max_len=params['MAX_OUTPUT_TEXT_LEN_TEST'],
offset=0,
fill=dataset.fill_text[params['OUTPUTS_IDS_DATASET']
[0]],
pad_on_batch=dataset.pad_on_batch[
params['OUTPUTS_IDS_DATASET'][0]],
words_so_far=False,
sample_weights=params['SAMPLE_WEIGHTS'],
loading_X=False)
# 4.2 Train online!
online_trainer.train_online(
[np.asarray([current_input]), state_below],
trg_seq,
trg_words=[reference])
# 5 Write correct sentences into a file
list2file(args.dest, [hypothesis], permission='a')
if (n_sample + 1) % 50 == 0:
logger.info(u"%d sentences processed" % (n_sample + 1))
logger.info(u"Current speed is {} per sentence".format(
(time.time() - start_time) / (n_sample + 1)))
logger.info(u"Current WSR is: %f" %
(float(total_errors) / total_words))
logger.info(u"Current MAR is: %f" %
(float(total_mouse_actions) / total_words))
logger.info(u"Current MAR_c is: %f" %
(float(total_mouse_actions) / total_chars))
logger.info(u"Current KSMR is: %f" %
(float(total_errors + total_mouse_actions) /
total_chars))
# 6. Final!
# 6.1 Log some information
print(u"Total number of errors:", total_errors)
print(u"Total number selections", total_mouse_actions)
print(u"WSR: %f" % (float(total_errors) / total_words))
print(u"MAR: %f" % (float(total_mouse_actions) / total_words))
print(u"MAR_c: %f" % (float(total_mouse_actions) / total_chars))
print(u"KSMR: %f" %
(float(total_errors + total_mouse_actions) / total_chars))
except KeyboardInterrupt:
print(u'Interrupted!')
print(u"Total number of corrections (up to now):", total_errors)
print(u"WSR: %f" % (float(total_errors) / total_words))
print(u"MAR: %f" % (float(total_mouse_actions) / total_words))
print(u"MAR_c: %f" % (float(total_mouse_actions) / total_chars))
print(u"KSMR: %f" %
(float(total_errors + total_mouse_actions) / total_chars))
for n_best_pred, n_best_score, n_best_alpha in zip(
n_best_preds, n_best_scores, n_best_alphas):
pred = decode_predictions_beam_search([n_best_pred],
index2word_y,
alphas=n_best_alpha,
x_text=sources,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
pred = map(detokenize_function, pred)
n_best_sample_score.append([i, pred, n_best_score])
n_best_predictions.append(n_best_sample_score)
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params.get('SAMPLING_SAVE_MODE', 'list'):
list2file(filepath, predictions)
if args.n_best:
nbest2file(filepath + '.nbest', n_best_predictions)
else:
raise Exception(
'Only "list" is allowed in "SAMPLING_SAVE_MODE"')
else:
list2stdout(predictions)
if args.n_best:
logging.info('Storing n-best sentences in ./' + s + '.nbest')
nbest2file('./' + s + '.nbest', n_best_predictions)
def sample_ensemble(args, params):
"""
Use several translation models for obtaining predictions from a source text file.
:param argparse.Namespace args: Arguments given to the method:
* dataset: Dataset instance with data.
* text: Text file with source sentences.
* splits: Splits to sample. Should be already included in the dataset object.
* dest: Output file to save scores.
* weights: Weight given to each model in the ensemble. You should provide the same number of weights than models. By default, it applies the same weight to each model (1/N).
* n_best: Write n-best list (n = beam size).
* config: Config .pkl for loading the model configuration. If not specified, hyperparameters are read from config.py.
* models: Path to the models.
* verbose: Be verbose or not.
:param params: parameters of the translation model.
"""
from data_engine.prepare_data import update_dataset_from_file
from keras_wrapper.model_ensemble import BeamSearchEnsemble
from keras_wrapper.cnn_model import loadModel
from keras_wrapper.dataset import loadDataset
from keras_wrapper.utils import decode_predictions_beam_search
logger.info("Using an ensemble of %d models" % len(args.models))
models = [loadModel(m, -1, full_path=True) for m in args.models]
dataset = loadDataset(args.dataset)
dataset = update_dataset_from_file(dataset, args.text, params, splits=args.splits, remove_outputs=True)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
# For converting predictions into sentences
index2word_y = dataset.vocabulary[params['OUTPUTS_IDS_DATASET'][0]]['idx2words']
if params.get('APPLY_DETOKENIZATION', False):
detokenize_function = eval('dataset.' + params['DETOKENIZATION_METHOD'])
params_prediction = dict()
params_prediction['max_batch_size'] = params.get('BATCH_SIZE', 20)
params_prediction['n_parallel_loaders'] = params.get('PARALLEL_LOADERS', 1)
params_prediction['beam_size'] = params.get('BEAM_SIZE', 6)
params_prediction['maxlen'] = params.get('MAX_OUTPUT_TEXT_LEN_TEST', 100)
params_prediction['optimized_search'] = params['OPTIMIZED_SEARCH']
params_prediction['model_inputs'] = params['INPUTS_IDS_MODEL']
params_prediction['model_outputs'] = params['OUTPUTS_IDS_MODEL']
params_prediction['dataset_inputs'] = params['INPUTS_IDS_DATASET']
params_prediction['dataset_outputs'] = params['OUTPUTS_IDS_DATASET']
params_prediction['search_pruning'] = params.get('SEARCH_PRUNING', False)
params_prediction['normalize_probs'] = params.get('NORMALIZE_SAMPLING', False)
params_prediction['alpha_factor'] = params.get('ALPHA_FACTOR', 1.0)
params_prediction['coverage_penalty'] = params.get('COVERAGE_PENALTY', False)
params_prediction['length_penalty'] = params.get('LENGTH_PENALTY', False)
params_prediction['length_norm_factor'] = params.get('LENGTH_NORM_FACTOR', 0.0)
params_prediction['coverage_norm_factor'] = params.get('COVERAGE_NORM_FACTOR', 0.0)
params_prediction['pos_unk'] = params.get('POS_UNK', False)
params_prediction['state_below_maxlen'] = -1 if params.get('PAD_ON_BATCH', True) \
else params.get('MAX_OUTPUT_TEXT_LEN', 50)
params_prediction['output_max_length_depending_on_x'] = params.get('MAXLEN_GIVEN_X', True)
params_prediction['output_max_length_depending_on_x_factor'] = params.get('MAXLEN_GIVEN_X_FACTOR', 3)
params_prediction['output_min_length_depending_on_x'] = params.get('MINLEN_GIVEN_X', True)
params_prediction['output_min_length_depending_on_x_factor'] = params.get('MINLEN_GIVEN_X_FACTOR', 2)
params_prediction['attend_on_output'] = params.get('ATTEND_ON_OUTPUT',
'transformer' in params['MODEL_TYPE'].lower())
params_prediction['glossary'] = params.get('GLOSSARY', None)
heuristic = params.get('HEURISTIC', 0)
mapping = None if dataset.mapping == dict() else dataset.mapping
model_weights = args.weights
if args.glossary is not None:
glossary = pkl2dict(args.glossary)
elif params_prediction['glossary'] is not None:
glossary = pkl2dict(params_prediction['glossary'])
else:
glossary = None
if model_weights:
assert len(model_weights) == len(
models), 'You should give a weight to each model. You gave %d models and %d weights.' % (
len(models), len(model_weights))
model_weights = list(map(float, model_weights))
if len(model_weights) > 1:
logger.info('Giving the following weights to each model: %s' % str(model_weights))
for s in args.splits:
# Apply model predictions
params_prediction['predict_on_sets'] = [s]
beam_searcher = BeamSearchEnsemble(models,
dataset,
params_prediction,
model_weights=model_weights,
n_best=args.n_best,
verbose=args.verbose)
predictions = beam_searcher.predictBeamSearchNet()[s]
samples = predictions['samples']
alphas = predictions['alphas'] if params_prediction['pos_unk'] else None
if params_prediction['pos_unk']:
sources = [x.strip() for x in open(args.text, 'r').read().split('\n')]
sources = sources[:-1] if len(sources[-1]) == 0 else sources
else:
sources = None
decoded_predictions = decode_predictions_beam_search(samples,
index2word_y,
glossary=glossary,
alphas=alphas,
x_text=sources,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
decoded_predictions = list(map(detokenize_function, decoded_predictions))
if args.n_best:
n_best_predictions = []
for i, (n_best_preds, n_best_scores, n_best_alphas) in enumerate(predictions['n_best']):
n_best_sample_score = []
for n_best_pred, n_best_score, n_best_alpha in zip(n_best_preds, n_best_scores, n_best_alphas):
pred = decode_predictions_beam_search([n_best_pred],
index2word_y,
glossary=glossary,
alphas=[n_best_alpha] if params_prediction[
'pos_unk'] else None,
x_text=[sources[i]] if params_prediction['pos_unk'] else None,
heuristic=heuristic,
mapping=mapping,
verbose=args.verbose)
# Apply detokenization function if needed
if params.get('APPLY_DETOKENIZATION', False):
pred = list(map(detokenize_function, pred))
n_best_sample_score.append([i, pred, n_best_score])
n_best_predictions.append(n_best_sample_score)
# Store result
if args.dest is not None:
filepath = args.dest # results file
if params.get('SAMPLING_SAVE_MODE', 'list'):
list2file(filepath, decoded_predictions)
if args.n_best:
nbest2file(filepath + '.nbest', n_best_predictions)
else:
raise Exception('Only "list" is allowed in "SAMPLING_SAVE_MODE"')
else:
list2stdout(decoded_predictions)
if args.n_best:
logger.info('Storing n-best sentences in ./' + s + '.nbest')
nbest2file('./' + s + '.nbest', n_best_predictions)
logger.info('Sampling finished')
Control_predictions = Control_model.predictBeamSearchNet(
dataset, params_prediction)['test']
vocab = dataset.vocabulary['target_text']['idx2words']
Control_predictions = decode_predictions_beam_search(Control_predictions,
vocab,
verbose=params['VERBOSE'])
## see how they compare to ground truth
#
from keras_wrapper.extra.read_write import list2file
from keras_wrapper.extra import evaluation
Control_path = 'Control_M7.pred'
list2file(Control_path, Control_predictions)
dataset.setOutput('data/Ross_test.reply',
'test',
type='text',
id='target_text',
pad_on_batch=True,
tokenization='tokenize_basic',
sample_weights=True,
max_text_len=30,
max_words=0)
keep_n_captions(dataset, repeat=1, n=1, set_names=['test'])
metric = 'coco'
#Apply sampling
def apply_Clas_model(params):
"""
Function for using a previously trained model for sampling.
"""
########### Load data
dataset = build_dataset(params)
params['INPUT_SCR_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
###########
########### Load model
text_class_model = loadModel(params['STORE_PATH'], params['RELOAD'])
text_class_model.setOptimizer()
###########
########### Apply sampling
extra_vars = dict()
extra_vars['tokenize_f'] = eval('dataset.' + params['TOKENIZATION_METHOD'])
for s in params["EVAL_ON_SETS"]:
# Apply model predictions
params_prediction = {
'batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s]
}
predictions = text_class_model.predictNet(dataset,
params_prediction)[s]
# Store result
filepath = text_class_model.model_path + '/' + s + '.pred' # results file
if params['SAMPLING_SAVE_MODE'] == 'list':
read_write.list2file(filepath, predictions)
else:
raise Exception, 'Only "list" is allowed in "SAMPLING_SAVE_MODE"'
# Evaluate if any metric in params['METRICS']
for metric in params['METRICS']:
logging.info('Evaluating on metric ' + metric)
filepath = text_class_model.model_path + '/' + s + '_sampling.' + metric # results file
# Evaluate on the chosen metric
extra_vars[s] = dict()
extra_vars[s]['references'] = dataset.extra_variables[s][
params['OUTPUTS_IDS_DATASET'][0]]
metrics = evaluation.select[metric](pred_list=predictions,
verbose=1,
extra_vars=extra_vars,
split=s)
# Print results to file
with open(filepath, 'w') as f:
header = ''
line = ''
for metric_ in sorted(metrics):
value = metrics[metric_]
header += metric_ + ','
line += str(value) + ','
f.write(header + '\n')
f.write(line + '\n')
logging.info('Done evaluating on metric ' + metric)
