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# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Library for reading/writing input and score files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import glob
from absl import logging
import six
from six.moves import zip
from six.moves import zip_longest
def compute_scores_and_write_to_csv(target_filepattern,
prediction_filepattern,
output_filename,
scorer,
aggregator,
delimiter="\n"):
"""Runs aggregate score calculations and outputs results to a CSV file.
Args:
target_filepattern: Pattern for files containing target text.
prediction_filepattern: Pattern for files containing prediction text.
output_filename: Name of file to write results to.
scorer: A BaseScorer object to compute scores.
aggregator: An aggregator to aggregate scores. If None, outputs are
per-example scores.
delimiter: Record delimiter.
"""
target_filenames = _glob(target_filepattern)
prediction_filenames = _glob(prediction_filepattern)
if (len(target_filenames) < 1 or
len(target_filenames) != len(prediction_filenames)):
raise ValueError("Must have equal and positive number of target and "
"prediction files. Found: %d target files (%s),"
" %d prediction files (%s)." %
(len(target_filenames), target_filepattern,
len(prediction_filenames), prediction_filepattern))
scores = _compute_scores(target_filenames, prediction_filenames, scorer,
delimiter)
if aggregator:
for score in scores:
aggregator.add_scores(score)
_write_aggregates_to_csv(output_filename, aggregator.aggregate())
else:
_write_scores_to_csv(output_filename, scores)
def _glob(filepattern):
return glob.glob(filepattern) # pylint: disable=unreachable
def _open(filepattern, mode="r"):
return open(filepattern, mode) # pylint: disable=unreachable
def _record_gen(filename, delimiter):
"""Opens file and yields records separated by delimiter."""
with _open(filename) as f:
records = f.read().split(six.ensure_str(delimiter))
if records[-1]:
# Need a final delimiter at end of file to be able to detect an empty last
# record.
logging.warn("Expected delimiter at end of file")
else:
records = records[:-1]
for record in records:
yield record
def _compute_scores(target_filenames, prediction_filenames, scorer, delimiter):
"""Computes aggregates scores across the given target and prediction files.
Args:
target_filenames: List of filenames from which to read target lines.
prediction_filenames: List of filenames from which to read prediction lines.
scorer: A BaseScorer object to compute scores.
delimiter: string delimiter between each record in input files
Returns:
A list of dicts mapping score_type to Score objects.
Raises:
ValueError: If invalid targets or predictions are provided.
"""
scores = []
for target_filename, prediction_filename in zip(
sorted(target_filenames), sorted(prediction_filenames)):
logging.info("Reading targets from %s.", target_filename)
logging.info("Reading predictions from %s.", prediction_filename)
targets = _record_gen(target_filename, delimiter)
preds = _record_gen(prediction_filename, delimiter)
for target_rec, prediction_rec in zip_longest(targets, preds):
if target_rec is None or prediction_rec is None:
raise ValueError("Must have equal number of lines across target and "
"prediction files. Mismatch between files: %s, %s." %
(target_filename, prediction_filename))
scores.append(scorer.score(target_rec, prediction_rec))
return scores
def _write_aggregates_to_csv(output_filename, aggregates):
"""Writes aggregate scores to an output CSV file.
Output file is a comma separated where each line has the format:
score_type-(P|R|F),low_ci,mean,high_ci
P/R/F indicates whether the score is a precision, recall or f-measure.
Args:
output_filename: Name of file to write results to.
aggregates: A dict mapping each score_type to a AggregateScore object.
"""
logging.info("Writing results to %s.", output_filename)
with _open(output_filename, "w") as output_file:
output_file.write("score_type,low,mid,high\n")
for score_type, aggregate in sorted(aggregates.items()):
output_file.write("%s-R,%f,%f,%f\n" %
(score_type, aggregate.low.recall, aggregate.mid.recall,
aggregate.high.recall))
output_file.write("%s-P,%f,%f,%f\n" %
(score_type, aggregate.low.precision,
aggregate.mid.precision, aggregate.high.precision))
output_file.write("%s-F,%f,%f,%f\n" %
(score_type, aggregate.low.fmeasure,
aggregate.mid.fmeasure, aggregate.high.fmeasure))
logging.info("Finished writing results.")
def _write_scores_to_csv(output_filename, scores):
"""Writes scores for each individual example to an output CSV file.
Output file is a comma separated where each line has the format:
id,score1,score2,score3,...
The header row indicates the type of each score column.
Args:
output_filename: Name of file to write results to.
scores: A list of dicts mapping each score_type to a Score object.
"""
if len(scores) < 1:
logging.warn("No scores to write")
return
rouge_types = sorted(scores[0].keys())
logging.info("Writing results to %s.", output_filename)
with _open(output_filename, "w") as out_file:
out_file.write("id")
for rouge_type in rouge_types:
out_file.write(",{t}-P,{t}-R,{t}-F".format(t=rouge_type))
out_file.write("\n")
for i, result in enumerate(scores):
out_file.write("%d" % i)
for rouge_type in rouge_types:
out_file.write(",%f,%f,%f" %
(result[rouge_type].precision, result[rouge_type].recall,
result[rouge_type].fmeasure))
out_file.write("\n")
logging.info("Finished writing results.")
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