# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from collections import defaultdict
import numpy as np
import paddle
from paddlenlp.utils.log import logger
from seqeval.metrics.sequence_labeling import get_entities
def extract_tp_actual_correct(y_true, y_pred, suffix, *args):
entities_true = defaultdict(set)
entities_pred = defaultdict(set)
for type_name, start, end in get_entities(y_true, suffix):
entities_true[type_name].add((start, end))
for type_name, start, end in get_entities(y_pred, suffix):
entities_pred[type_name].add((start, end))
target_names = sorted(set(entities_true.keys()) | set(entities_pred.keys()))
tp_sum = np.array([], dtype=np.int32)
pred_sum = np.array([], dtype=np.int32)
true_sum = np.array([], dtype=np.int32)
for type_name in target_names:
entities_true_type = entities_true.get(type_name, set())
entities_pred_type = entities_pred.get(type_name, set())
tp_sum = np.append(tp_sum, len(entities_true_type & entities_pred_type))
pred_sum = np.append(pred_sum, len(entities_pred_type))
true_sum = np.append(true_sum, len(entities_true_type))
return pred_sum, tp_sum, true_sum
[docs]class ChunkEvaluator(paddle.metric.Metric):
"""
ChunkEvaluator computes the precision, recall and F1-score for chunk detection.
It is often used in sequence tagging tasks, such as Named Entity Recognition(NER).
Args:
label_list (list):
The label list.
suffix (bool):
If set True, the label ends with '-B', '-I', '-E' or '-S', else the label starts with them.
Defaults to `False`.
Example:
.. code-block::
from paddlenlp.metrics import ChunkEvaluator
num_infer_chunks = 10
num_label_chunks = 9
num_correct_chunks = 8
label_list = [1,1,0,0,1,0,1]
evaluator = ChunkEvaluator(label_list)
evaluator.update(num_infer_chunks, num_label_chunks, num_correct_chunks)
precision, recall, f1 = evaluator.accumulate()
print(precision, recall, f1)
# 0.8 0.8888888888888888 0.8421052631578948
"""
def __init__(self, label_list, suffix=False):
super(ChunkEvaluator, self).__init__()
self.id2label_dict = dict(enumerate(label_list))
self.suffix = suffix
self.num_infer_chunks = 0
self.num_label_chunks = 0
self.num_correct_chunks = 0
[docs] def compute(self, lengths, predictions, labels, dummy=None):
"""
Computes the precision, recall and F1-score for chunk detection.
Args:
lengths (Tensor): The valid length of every sequence, a tensor with shape `[batch_size]`
predictions (Tensor): The predictions index, a tensor with shape `[batch_size, sequence_length]`.
labels (Tensor): The labels index, a tensor with shape `[batch_size, sequence_length]`.
dummy (Tensor, optional): Unnecessary parameter for compatibility with older versions with parameters list `inputs`, `lengths`, `predictions`, `labels`. Defaults to None.
Returns:
tuple: Returns tuple (`num_infer_chunks, num_label_chunks, num_correct_chunks`).
With the fields:
- `num_infer_chunks` (Tensor):
The number of the inference chunks.
- `num_label_chunks` (Tensor):
The number of the label chunks.
- `num_correct_chunks` (Tensor):
The number of the correct chunks.
"""
if dummy is not None:
# TODO(qiujinxuan): rm compatibility support after lic.
dummy, lengths, predictions, labels = lengths, predictions, labels, dummy
if not getattr(self, "has_warn", False):
logger.warning(
"Compatibility Warning: The params of ChunkEvaluator.compute has been modified. The old version is `inputs`, `lengths`, `predictions`, `labels` while the current version is `lengths`, `predictions`, `labels`. Please update the usage."
)
self.has_warn = True
labels = labels.numpy()
predictions = predictions.numpy()
unpad_labels = [
[self.id2label_dict[index] for index in labels[sent_index][: lengths[sent_index]]]
for sent_index in range(len(lengths))
]
unpad_predictions = [
[self.id2label_dict.get(index, "O") for index in predictions[sent_index][: lengths[sent_index]]]
for sent_index in range(len(lengths))
]
pred_sum, tp_sum, true_sum = extract_tp_actual_correct(unpad_labels, unpad_predictions, self.suffix)
num_correct_chunks = paddle.to_tensor([tp_sum.sum()])
num_infer_chunks = paddle.to_tensor([pred_sum.sum()])
num_label_chunks = paddle.to_tensor([true_sum.sum()])
return num_infer_chunks, num_label_chunks, num_correct_chunks
def _is_number_or_matrix(self, var):
def _is_number_(var):
return (
isinstance(var, int)
or isinstance(var, np.int64)
or isinstance(var, float)
or (isinstance(var, np.ndarray) and var.shape == (1,))
)
return _is_number_(var) or isinstance(var, np.ndarray)
[docs] def update(self, num_infer_chunks, num_label_chunks, num_correct_chunks):
"""
This function takes (num_infer_chunks, num_label_chunks, num_correct_chunks) as input,
to accumulate and update the corresponding status of the ChunkEvaluator object. The update method is as follows:
.. math::
\\\\ \\begin{array}{l}{\\text { self. num_infer_chunks }+=\\text { num_infer_chunks }} \\\\ {\\text { self. num_Label_chunks }+=\\text { num_label_chunks }} \\\\ {\\text { self. num_correct_chunks }+=\\text { num_correct_chunks }}\\end{array} \\\\
Args:
num_infer_chunks(int|numpy.array):
The number of chunks in Inference on the given minibatch.
num_label_chunks(int|numpy.array):
The number of chunks in Label on the given mini-batch.
num_correct_chunks(int|float|numpy.array):
The number of chunks both in Inference and Label on the given mini-batch.
"""
if not self._is_number_or_matrix(num_infer_chunks):
raise ValueError("The 'num_infer_chunks' must be a number(int) or a numpy ndarray.")
if not self._is_number_or_matrix(num_label_chunks):
raise ValueError("The 'num_label_chunks' must be a number(int, float) or a numpy ndarray.")
if not self._is_number_or_matrix(num_correct_chunks):
raise ValueError("The 'num_correct_chunks' must be a number(int, float) or a numpy ndarray.")
self.num_infer_chunks += num_infer_chunks
self.num_label_chunks += num_label_chunks
self.num_correct_chunks += num_correct_chunks
[docs] def accumulate(self):
"""
This function returns the mean precision, recall and f1 score for all accumulated minibatches.
Returns:
tuple: Returns tuple (`precision, recall, f1 score`).
"""
precision = float(self.num_correct_chunks / self.num_infer_chunks) if self.num_infer_chunks else 0.0
recall = float(self.num_correct_chunks / self.num_label_chunks) if self.num_label_chunks else 0.0
f1_score = float(2 * precision * recall / (precision + recall)) if self.num_correct_chunks else 0.0
return precision, recall, f1_score
[docs] def reset(self):
"""
Reset function empties the evaluation memory for previous mini-batches.
"""
self.num_infer_chunks = 0
self.num_label_chunks = 0
self.num_correct_chunks = 0
[docs] def name(self):
"""
Return name of metric instance.
"""
return "precision", "recall", "f1"
