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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
import datasets
from itertools import repeat
# 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 prediction
should be an input id.
references: list of reference for each prediction. Each
reference should be an input id.
actions_seen: number of actions token seen before generating the predicted action token.
max_actions_seen: the number of scores to calculate. For example, with max_actions_seen = 5,
it will calculate score for prediction with actions_seen = 0, 1, 2, 3, 4, 5.
Returns:
score_k: accuracy score calculated on predictions with n = k. The number of scores
calculated in this way depends on the value of max_actions_seen. For example,
with max_actions_seen = 5, we will have score_0, score_1, ..., score_5.
support_k: the number of predictions that support the corresponding score_k.
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 MetricaTesi(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=datasets.Features(
{
"predictions": datasets.Value("int32"),
"references": datasets.Value("int32"),
"actions_seen": datasets.Value("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, actions_seen, max_actions_seen=20):
"""Returns the scores"""
results = dict()
for i in range(max_actions_seen + 1):
score = 0.0
support = sum(n == i for n in actions_seen)
if support != 0:
for prediction, reference, n in zip(predictions, references, actions_seen):
if n == i:
if prediction == reference:
score += 1
score /= support
if support != 0:
results[f"support_{i}"] = support
results[f"score_{i}"] = score
return results
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