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m2 / m2.py

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Initial version of M2 implemented

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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
	from datasets import Features, Sequence, Value
	import pdb

	from m2scorer import get_m2score, get_m2score_from_raw, load_m2

	# 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 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.
	Returns:
	accuracy: description of the first score,
	another_score: description of the second score,
	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 M2(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=Features({
	'predictions': Value(dtype='string'),
	'references': {
	'source_sentence': Value(dtype='string'),
	'edits': Sequence({
	'from': Value(dtype='int32'),
	'to': Value(dtype='int32'),
	'text': [Value(dtype='string')],
	'aid': Value(dtype='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):
	"""Returns the scores"""
	gold_data = self._features_to_gold_data(references)
	# TODO: Compute the different scores of the module
	p, r, f = get_m2score(predictions, gold_data, tokenize=False, keep_gold=True)
	return {
	"f0.5": f,
	"precision": p,
	"recall": r,
	}

	def _features_to_gold_data(self, features):
	gold_data = []
	for entry in features:
	annotators = {}
	edits = entry['edits']
	for i in range(len(edits['from'])):
	edit = (edits['from'][i], edits['to'][i], edits['text'][i])
	if edits['aid'][i] not in annotators:
	annotators[edits['aid'][i]] = []
	annotators[edits['aid'][i]].append(edit)
	gold_data.append( (entry['source_sentence'], annotators) )
	return gold_data

	def load_m2_file(self, fpath):
	data = load_m2(fpath)
	result = []
	for src_sent, edits_ in data:
	edits = []
	for aid, annotator_edits in edits_.items():
	if len(annotator_edits) == 0:
	edits.append({'from': -1, 'to': -1, 'text': [''], 'aid': aid})
	for from_, to_, text_ in annotator_edits:
	edits.append({'from': from_, 'to': to_, 'text': text_, 'aid': aid})
	result.append({
	'source_sentence': src_sent,
	'edits': edits,
	})
	return result