# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
""""Class for Metric EmScore"""
from mindnlp.abc import Metric
from mindnlp.common.metrics import _compute_exact, _metric_max_over_ground_truths, _check_value_type
[文档]class EmScore(Metric):
r"""
Calculates the exact match (EM) score. This metric measures the percentage of
predictions that match any one of the ground truth answers exactly.
Args:
name (str): Name of the metric.
Example:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindnlp.engine.metrics import EmScore
>>> preds = "this is the best span"
>>> examples = ["this is a good span", "something irrelevant"]
>>> metric = EmScore()
>>> metric.update(preds, examples)
>>> em_score = metric.eval()
>>> print(em_score)
0.0
"""
def __init__(self, name='EmScore'):
super().__init__()
self._name = name
self.count = 0
self.exact_match = 0
[文档] def clear(self):
"""Clears the internal evaluation results."""
self.count = 0
self.exact_match = 0
[文档] def update(self, *inputs):
"""
Updates local variables.
Args:
inputs: Input `preds` and `examples`.
- preds (Union[str, list]): Predicted value.
- examples (list): Ground truth.
Raises:
ValueError: If the number of inputs is not 2.
RuntimeError: If `preds` and `examples` have different lengths.
"""
if len(inputs) != 2:
raise ValueError(f'For `EmScore.update`, it needs 2 inputs (`preds` and `examples`), '
f'but got {len(inputs)}.')
preds = inputs[0]
examples = inputs[1]
_check_value_type("preds", preds, [str, list])
_check_value_type("examples", examples, [list])
if not isinstance(preds, list):
preds = [preds]
examples = [examples]
if len(preds) != len(examples):
raise RuntimeError(f'For `EmScore.update`, `preds` and `examples` should have the same '
f'length, but got `examples` length {len(preds)}, `labels` length '
f'{len(examples)})')
self.count += len(preds)
for pred, example in zip(preds, examples):
self.exact_match += _metric_max_over_ground_truths(
_compute_exact, pred, example
)
[文档] def eval(self):
"""
Computes and returns the EM score.
Returns:
- **exact_match** (float) - The computed result.
"""
total_em = int(self.exact_match)
exact_match = total_em / self.count if self.count > 0 else 0
return exact_match
[文档] def get_metric_name(self):
"""
Returns the name of the metric.
"""
return self._name