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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."""
from operator import eq
from typing import Callable, Iterable, Union
import evaluate
import datasets
import numpy as np
import logging
logger = logging.getLogger(__name__)
# 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 = """\
Computes precision, recall, f1 scores for joint entity-relation extraction task.
"""
# 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.
eq_fn: function to compare two items. Defaults to the equality operator.
Returns:
recall:
precision:
f1:
Examples:
>>> jer = evaluate.load("jer")
>>> results = jer.compute(references=[["Baris | play | tennis", "Deniz | travel | London"]], predictions=[["Baris | play | tennis"]])
>>> print(results)
{'recall': 0.5, 'precision': 1.0, 'f1': 0.6666666666666666}
"""
Triplet = Union[str, tuple, int]
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class jer(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.features.Sequence(datasets.Value('string')),
'references': datasets.features.Sequence(datasets.Value('string')),
}),
# 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"""
pass
def _compute(self, predictions, references, eq_fn=eq):
"""Returns the scores"""
score_dicts = [
self._compute_single(prediction=prediction, reference=reference, eq_fn=eq_fn)
for prediction, reference in zip(predictions, references)
]
return {('mean_' + key): np.mean([scores[key] for scores in score_dicts]) for key in score_dicts[0].keys()}
def _compute_single(
self,
*,
prediction: Iterable[Triplet],
reference: Iterable[Triplet],
eq_fn: Callable[[Triplet, Triplet], bool],
):
reference_set = set(reference)
if len(reference) != len(reference_set):
logger.warn(f"Duplicates found in the reference list {reference}")
prediction_set = set(prediction)
tp = sum(int(is_in(item, prediction, eq_fn=eq_fn)) for item in reference)
fp = len(prediction_set) - tp
fn = len(reference_set) - tp
# Calculate metrics
precision = tp / (tp + fp) if tp + fp > 0 else 0
recall = tp / (tp + fn) if tp + fn > 0 else 0
f1_score = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
return {
'precision': precision,
'recall': recall,
'f1': f1_score
}
def is_in(target, collection: Iterable, eq_fn=eq) -> bool:
for item in collection:
if eq_fn(item, target):
return True
return False