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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 collections import Counter
import numpy as np
# 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 module calculates the unigram precision, recall, and f1 score.
"""
# 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 list of int (token)
references: list of list of int (tokens)
Returns:
f1: the unigram f1 score.
precision: the unigram accuracy.
recall: the unigram recall.
Examples:
>>> my_new_module = evaluate.load("ckb/unigram")
>>> results = my_new_module.compute(references=[[0, 1]], predictions=[[0, 1]])
>>> print(results)
{'accuracy': 1.0}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class unigram(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('int64'),
'references': datasets.Value('int64'),
}),
# 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 _prec_recall_f1_score(pred_items, gold_items):
"""
Compute precision, recall and f1 given a set of gold and prediction items.
:param pred_items: iterable of predicted values
:param gold_items: iterable of gold values
:return: tuple (p, r, f1) for precision, recall, f1
"""
common = Counter(gold_items) & Counter(pred_items)
num_same = sum(common.values())
if num_same == 0:
return 0, 0, 0
precision = 1.0 * num_same / len(pred_items)
recall = 1.0 * num_same / len(gold_items)
f1 = (2 * precision * recall) / (precision + recall)
return np.array(precision, recall, f1)
def _compute(self, predictions, references):
"""Returns the scores"""
# TODO: Compute the different scores of the module
score = sum(self._prec_recall_f1_score(i,j) for i, j in zip(predictions, references)) / len(predictions)
return {
"precision": score[0],
"recall": score[1],
"f1": score[2],
}
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