def text_eval(encoder,
features_iter,
model_dir,
global_step,
eval_tag,
enable_logging,
inputs_pattern="^inputs[0-9]*$",
targets_key="targets",
predictions_key="outputs",
additional_keys=(),
num_reserved=None):
"""Evaluates a set of text targets/predictions."""
decode_fn = lambda x: ids2str(encoder, x, num_reserved)
scorers_dict = {}
scorers_dict[_ROUGE_METRIC] = rouge_scorer.RougeScorer(
["rouge1", "rouge2", "rougeL", "rougeLsum"], use_stemmer=True)
scorers_dict[_BLEU_METRIC] = bleu_scorer.BleuScorer()
scorers_dict[_REPETITION_METRIC] = repetition_scorer.RepetitionScorer(
["regs1", "regs2", "regs3", "regsTCR"])
scorers_dict[_LENGTH_METRIC] = length_scorer.LengthScorer(["word", "char"])
aggregators_dict = {k: scoring.BootstrapAggregator() for k in scorers_dict}
with LogWriter(additional_keys, model_dir, global_step, eval_tag,
enable_logging) as log_writer:
for i, features in enumerate(features_iter):
inputs_list = []
for k in sorted(features):
if re.match(inputs_pattern, k):
single_inputs = decode_matrix(decode_fn, features[k])
if isinstance(single_inputs, list):
inputs_list.extend(single_inputs)
else:
inputs_list.append(single_inputs)
inputs = "\n".join(inputs_list)
targets = decode_fn(features[targets_key])
preds = decode_fn(features[predictions_key])
text_dict = {
"inputs": inputs_list,
"targets": targets,
"predictions": preds
}
for key in additional_keys:
if key == "selected_ids":
text_dict[key] = decode_selected_indices(decode_fn, features)
else:
text_dict[key] = decode_matrix(decode_fn, features[key])
log_writer.write(text_dict, i)
for key, scorer in scorers_dict.items():
scores_i = scorer.score(targets, preds)
aggregators_dict[key].add_scores(scores_i)
aggregates_dict = {k: v.aggregate() for k, v in aggregators_dict.items()}
length_histograms = scorers_dict[_LENGTH_METRIC].histograms(as_string=True)
_write_aggregates(model_dir, global_step, eval_tag, aggregates_dict,
length_histograms)
_write_aggregate_summaries(model_dir, global_step, eval_tag, aggregates_dict)
def text_eval(preds_file,
model_dir,
global_step: int = 0,
eval_tag: str = "",
enable_logging: bool = True):
"""Evaluates a set of text targets/predictions."""
scorers_dict = {
_ROUGE_METRIC:
rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL", "rougeLsum"],
use_stemmer=True),
_BLEU_METRIC:
bleu_scorer.BleuScorer(),
_REPETITION_METRIC:
repetition_scorer.RepetitionScorer(
["regs1", "regs2", "regs3", "regsTCR"]),
_LENGTH_METRIC:
length_scorer.LengthScorer(["word", "char"])
}
aggregators_dict = {k: scoring.BootstrapAggregator() for k in scorers_dict}
with LogWriter((), model_dir, 0, "", enable_logging) as log_writer:
with open(preds_file) as csv_file:
reader = csv.DictReader(csv_file)
for i, row in enumerate(reader):
text_dict = {
"inputs": row['prompt'],
"targets": row['targets'],
"predictions": row['predictions']
}
log_writer.write(text_dict, i)
for key, scorer in scorers_dict.items():
scores_i = scorer.score(row['targets'], row['predictions'])
aggregators_dict[key].add_scores(scores_i)
aggregates_dict = {k: v.aggregate() for k, v in aggregators_dict.items()}
length_histograms = scorers_dict[_LENGTH_METRIC].histograms(as_string=True)
_write_aggregates(model_dir, global_step, eval_tag, aggregates_dict,
length_histograms)
_write_aggregate_summaries(model_dir, global_step, eval_tag,
aggregates_dict)