Spaces:

Vlasta
/

pr_auc

Runtime error

App Files Files Community

pr_auc / pr_auc.py

Vlasta

Fix parameter doc

1d7fac4 almost 2 years ago

raw history blame contribute delete

No virus

3.07 kB

	# 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 sklearn.metrics import precision_recall_curve, auc


	_CITATION = """\
	@InProceedings{huggingface:module,
	title = {A great new module},
	authors={huggingface, Inc.},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	Computes the area under precision-recall curve. Implementation details taken from https://sinyi-chou.github.io/python-sklearn-precision-recall/
	"""


	# TODO: Add description of the arguments of the module here
	_KWARGS_DESCRIPTION = """
	Calculates how good are predictions given some references, using certain scores
	Args:
	prediction_scores: Model predictions
	references: list of reference for each prediction. Each
	reference should be a string with tokens separated by spaces.
	Returns:
	pr_auc: area under the precision-recall curve,
	Examples:
	No examples
	"""

	BAD_WORDS_URL = ""


	@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
	class PRAUC(evaluate.Metric):
	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({
	'prediction_scores': datasets.Value("float"),
	'references': 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, prediction_scores, references):
	"""Returns the scores"""
	precision, recall, thresholds = precision_recall_curve(references, prediction_scores)
	auc_precision_recall = auc(recall, precision)
	return {
	"pr_auc": auc_precision_recall,
	}