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# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""Triplet Margin Loss metric."""
import datasets
import evaluate
import numpy as np
_DESCRIPTION = """
Triplet margin loss is a loss function that measures a relative similarity between the samples.
A triplet is comprised of reference input 'anchor (a)', matching input 'positive examples (p)' and non-matching input 'negative examples (n)'.
The loss function for each triplet is given by:\n
L(a, p, n) = max{d(a,p) - d(a,n) + margin, 0}\n
where d(x, y) is the 2nd order (Euclidean) pairwise distance between x and y.
"""
_KWARGS_DESCRIPTION = """
Args:
anchor (`list` of `float`): Reference inputs.
positive (`list` of `float`): Matching inputs.
negative (`list` of `float`): Non-matching inputs.
margin (`float`): Margin, default:`1.0`
Returns:
triplet_margin_loss (`float`): Total loss.
Examples:
Example 1-A simple example
>>> triplet_margin_loss = evaluate.load("theAIguy/triplet_margin_loss")
>>> loss = triplet_margin_loss.compute(
anchor=[-0.4765, 1.7133, 1.3971, -1.0121, 0.0732],
positive=[0.9218, 0.6305, 0.3381, 0.1412, 0.2607],
negative=[0.1971, 0.7246, 0.6729, 0.0941, 0.1011])
>>> print(loss)
{'triplet_margin_loss': 1.59}
Example 2-The same as Example 1, except `margin` set to `2.0`.
>>> triplet_margin_loss = evaluate.load("theAIguy/triplet_margin_loss")
>>> results = triplet_margin_loss.compute(
anchor=[-0.4765, 1.7133, 1.3971, -1.0121, 0.0732],
positive=[0.9218, 0.6305, 0.3381, 0.1412, 0.2607],
negative=[0.1971, 0.7246, 0.6729, 0.0941, 0.1011]),
margin=2.0)
>>> print(results)
{'triplet_margin_loss': 2.59}
"""
_CITATION = """
@article{schultz2003learning,
title={Learning a distance metric from relative comparisons},
author={Schultz, Matthew and Joachims, Thorsten},
journal={Advances in neural information processing systems},
volume={16},
year={2003}
}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class TripletMarginLoss(evaluate.EvaluationModule):
def _info(self):
return evaluate.EvaluationModuleInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"anchor": datasets.Sequence(datasets.Value("float")),
"positive": datasets.Sequence(datasets.Value("float")),
"negative": datasets.Sequence(datasets.Value("float"))
}
),
reference_urls=["https://proceedings.neurips.cc/paper/2003/hash/d3b1fb02964aa64e257f9f26a31f72cf-Abstract.html"],
)
def _compute(self, anchor, positive, negative, margin=1.0):
if not (len(anchor) == len(positive) == len(negative)):
raise ValueError("Anchor, Positive and Negative examples must be of same length.")
d_a_p_sum = 0.0
d_a_n_sum = 0.0
for a, p, n in zip(anchor, positive, negative):
d_a_p_sum += (a - p)**2
d_a_n_sum += (a - n)**2
loss = max(np.sqrt(d_a_p_sum) - np.sqrt(d_a_n_sum) + margin, 0)
return {
"triplet_margin_loss": float(
loss
)
} |