Spaces:
Runtime error
Runtime error
File size: 5,403 Bytes
636a855 34af34b 636a855 34af34b 636a855 34af34b 636a855 34af34b 636a855 34af34b 636a855 34af34b 636a855 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
# Copyright 2022 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.
"""Brier Score Metric"""
from dataclasses import dataclass
from typing import List, Optional, Union
import datasets
from sklearn.metrics import brier_score_loss
import evaluate
_CITATION = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_DESCRIPTION = """\
Brier score is a type of evaluation metric for classification tasks, where you predict outcomes such as win/lose, spam/ham, click/no-click etc.
`BrierScore = 1/N * sum( (p_i - o_i)^2 )`
"""
_KWARGS_DESCRIPTION = """
Args:
y_true : array of shape (n_samples,)
True targets.
y_prob : array of shape (n_samples,)
Probabilities of the positive class.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
pos_label : int or str, default=None
Label of the positive class. `pos_label` will be inferred in the
following manner:
* if `y_true` in {-1, 1} or {0, 1}, `pos_label` defaults to 1;
* else if `y_true` contains string, an error will be raised and
`pos_label` should be explicitly specified;
* otherwise, `pos_label` defaults to the greater label,
i.e. `np.unique(y_true)[-1]`.
Returns
score : float
Brier score loss.
Examples:
Example-1: if y_true in {-1, 1} or {0, 1}, pos_label defaults to 1.
>>> import numpy as np
>>> brier_score = evaluate.load("brier_score")
>>> references = np.array([0, 0, 1, 1])
>>> predictions = np.array([0.1, 0.9, 0.8, 0.3])
>>> results = brier_score.compute(references=references, predictions=predictions)
>>> print(round(results["brier_score"], 4))
0.3375
Example-2: if y_true contains string, an error will be raised and pos_label should be explicitly specified.
>>> import numpy as np
>>> brier_score = evaluate.load("brier_score")
>>> references = np.array(["spam", "ham", "ham", "spam"])
>>> predictions = np.array([0.1, 0.9, 0.8, 0.3])
>>> results = brier_score.compute(references=references, predictions=predictions, pos_label="ham")
>>> print(round(results["brier_score"], 4))
0.0375
"""
@dataclass
class BrierScoreConfig(evaluate.info.Config):
name: str = "default"
pos_label: Union[str, int] = 1
sample_weight: Optional[List[float]] = None
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class BrierScore(evaluate.Metric):
CONFIG_CLASS = BrierScoreConfig
ALLOWED_CONFIG_NAMES = ["default", "multilist"]
def _info(self, config):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
config=config,
features=self._get_feature_types(),
reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html"],
)
def _get_feature_types(self):
if self.config_name == "multilist":
return [
datasets.Features(
{
"references": datasets.Sequence(datasets.Value("float")),
"predictions": datasets.Sequence(datasets.Value("float")),
}
),
datasets.Features(
{
"references": datasets.Sequence(datasets.Value("string")),
"predictions": datasets.Sequence(datasets.Value("float")),
}
),
]
else:
return [
datasets.Features(
{
"references": datasets.Value("float"),
"predictions": datasets.Value("float"),
}
),
datasets.Features(
{
"references": datasets.Value("string"),
"predictions": datasets.Value("float"),
}
),
]
def _compute(self, references, predictions):
brier_score = brier_score_loss(
references, predictions, sample_weight=self.config.sample_weight, pos_label=self.config.pos_label
)
return {"brier_score": brier_score}
|