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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
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 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"
# This code was taken from https://gist.github.com/kylebgorman/1081951/bce3de986e4b05fc0b63d4d9e0cfa4bde6664365
def _dist(A, B, insertion, deletion, substitution):
D = np.zeros((len(A) + 1, len(B) + 1))
for i in range(len(A)):
D[i + 1][0] = D[i][0] + deletion
for j in range(len(B)):
D[0][j + 1] = D[0][j] + insertion
for i in range(len(A)): # fill out middle of matrix
for j in range(len(B)):
if A[i] == B[j]:
D[i + 1][j + 1] = D[i][j] # aka, it's free.
else:
D[i + 1][j + 1] = min(D[i + 1][j] + insertion,
D[i][j + 1] + deletion,
D[i][j] + substitution)
return D
def levenshtein_distance(l1, l2, normalize=False):
dist = _dist(l1, l2, 1, 1, 1)[-1][-1]
if normalize:
return dist / max(len(l1), len(l2))
else:
return dist
# @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class LevenshteinDistance(evaluate.Comparison):
"""TODO: Short description of my evaluation module."""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.ComparisonInfo(
# This is the description that will appear on the modules page.
module_type="comparison",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Value("string", id="sequence"),
}),
# 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, tokenizer=lambda x: x.split(), normalize=False):
"""Returns the scores"""
dists = []
for prediction, reference in zip(predictions, references):
tokenized_prediction = tokenizer(prediction)
tokenized_reference = tokenizer(reference)
dists.append(levenshtein_distance(tokenized_prediction, tokenized_reference, normalize=normalize))
avg_dist = np.mean(dists)
std_dist = np.std(dists)
return {
"levenshtein_distance": avg_dist,
"distance_std": std_dist,
"distances": dists,
} |