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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""TODO: Add a description here."""
import evaluate
from datasets import Features, Sequence, Value
import pdb
from m2scorer import get_m2score, get_m2score_from_raw, load_m2
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
accuracy: description of the first score,
another_score: description of the second score,
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = evaluate.load("my_new_module")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'accuracy': 1.0}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class M2(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=Features({
'predictions': Value(dtype='string'),
'references': {
'source_sentence': Value(dtype='string'),
'edits': Sequence({
'from': Value(dtype='int32'),
'to': Value(dtype='int32'),
'text': [Value(dtype='string')],
'aid': Value(dtype='int32'),
}),
},
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _compute(self, predictions, references):
"""Returns the scores"""
gold_data = self._features_to_gold_data(references)
# TODO: Compute the different scores of the module
p, r, f = get_m2score(predictions, gold_data, tokenize=False, keep_gold=True)
return {
"f0.5": f,
"precision": p,
"recall": r,
}
def _features_to_gold_data(self, features):
gold_data = []
for entry in features:
annotators = {}
edits = entry['edits']
for i in range(len(edits['from'])):
edit = (edits['from'][i], edits['to'][i], edits['text'][i])
if edits['aid'][i] not in annotators:
annotators[edits['aid'][i]] = []
annotators[edits['aid'][i]].append(edit)
gold_data.append( (entry['source_sentence'], annotators) )
return gold_data
def load_m2_file(self, fpath):
data = load_m2(fpath)
result = []
for src_sent, edits_ in data:
edits = []
for aid, annotator_edits in edits_.items():
if len(annotator_edits) == 0:
edits.append({'from': -1, 'to': -1, 'text': [''], 'aid': aid})
for from_, to_, text_ in annotator_edits:
edits.append({'from': from_, 'to': to_, 'text': text_, 'aid': aid})
result.append({
'source_sentence': src_sent,
'edits': edits,
})
return result
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