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unigram / unigram.py

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e1f597a about 1 year ago

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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.Sequence(datasets.Value('int64')),
	'references': datasets.Sequence(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(self, 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)]) / float(len(predictions))

	return {
	"precision": score[0],
	"recall": score[1],
	"f1": score[2],
	}