def cli_download(
from_file: click.File, url_list: Tuple[str, ...], verbose: bool = False
) -> None:
urls = [(x, None) for x in url_list]
if from_file:
urls += [(x.strip("\n"), None) for x in from_file.readlines()]
download(urls, verbose)
def cli_simple(
from_file: click.File,
author: str,
title: str,
url_list: Tuple[str, ...],
verbose: bool = False,
):
urls = [x for x in url_list]
if from_file:
urls += [x.strip("\n") for x in from_file.readlines()]
if not urls:
click.echo("You must provide at least one URL to download.")
return
story = HTMLStory(
chapters=urls,
author=author,
title=title,
url=furl("http://httpbin.org/status/200"),
verbose=verbose,
)
story.run()
def readlines(ctx, param, file: click.File) -> List[str]:
return [l.strip() for l in file.readlines()]
def compare(
source: click.File,
system_x: click.File,
system_y: click.File,
reference: click.File,
language: str,
metric: Union[Tuple[str], str],
filter: Union[Tuple[str], str],
length_min_val: float,
length_max_val: float,
seg_metric: str,
output_folder: str,
bootstrap: bool,
num_splits: int,
sample_ratio: float,
):
testset = PairwiseTestset(
src=[l.strip() for l in source.readlines()],
system_x=[l.strip() for l in system_x.readlines()],
system_y=[l.strip() for l in system_y.readlines()],
ref=[l.strip() for l in reference.readlines()],
language_pair="X-" + language,
filenames=[source.name, system_x.name, system_y.name, reference.name],
)
corpus_size = len(testset)
if filter:
filters = [
available_filters[f](testset) for f in filter if f != "length"
]
if "length" in filter:
filters.append(available_filters["length"](
testset, int(length_min_val * 100), int(length_max_val * 100)))
for filter in filters:
testset.apply_filter(filter)
if (1 - (len(testset) / corpus_size)) * 100 == 100:
click.secho("The current filters reduce the Corpus on 100%!",
fg="ref")
return
click.secho(
"Filters Successfully applied. Corpus reduced in {:.2f}%.".format(
(1 - (len(testset) / corpus_size)) * 100),
fg="green",
)
if seg_metric not in metric:
metric = tuple([
seg_metric,
] + list(metric))
else:
# Put COMET in first place
metric = list(metric)
metric.remove(seg_metric)
metric = tuple([
seg_metric,
] + metric)
results = {
m: available_metrics[m](
language=testset.target_language).pairwise_comparison(testset)
for m in metric
}
# results_dict = PairwiseResult.results_to_dict(list(results.values()))
results_df = PairwiseResult.results_to_dataframe(list(results.values()))
if bootstrap:
bootstrap_results = []
for m in metric:
bootstrap_result = available_metrics[m].bootstrap_resampling(
testset, num_splits, sample_ratio, results[m])
bootstrap_results.append(available_metrics[m].bootstrap_resampling(
testset, num_splits, sample_ratio, results[m]).stats)
bootstrap_results = {
k: [dic[k] for dic in bootstrap_results]
for k in bootstrap_results[0]
}
for k, v in bootstrap_results.items():
results_df[k] = v
click.secho(str(results_df), fg="yellow")
if output_folder != "":
if not output_folder.endswith("/"):
output_folder += "/"
results_df.to_json(output_folder + "results.json",
orient="index",
indent=4)
plot_segment_comparison(results[seg_metric], output_folder)
plot_pairwise_distributions(results[seg_metric], output_folder)
plot_bucket_comparison(results[seg_metric], output_folder)