# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# 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.
import itertools
import re
from logging import getLogger
from typing import List
import numpy as np
from deeppavlov.core.common.metrics_registry import register_metric
log = getLogger(__name__)
@register_metric('accuracy')
def accuracy(y_true: [list, np.ndarray], y_predicted: [list, np.ndarray]) -> float:
"""
Calculate accuracy in terms of absolute coincidence
Args:
y_true: array of true values
y_predicted: array of predicted values
Returns:
fraction of absolutely coincidental samples
"""
examples_len = len(y_true)
# if y1 and y2 are both arrays, == can be erroneously interpreted as element-wise equality
def _are_equal(y1, y2):
answer = (y1 == y2)
if isinstance(answer, np.ndarray):
answer = answer.all()
return answer
equalities = [_are_equal(y1, y2) for y1, y2 in zip(y_true, y_predicted)]
correct = sum(equalities)
return correct / examples_len if examples_len else 0
@register_metric('kbqa_accuracy')
def kbqa_accuracy(questions_batch, pred_answer_labels_batch, pred_answer_ids_batch, pred_query_batch,
gold_answer_labels_batch, gold_answer_ids_batch, gold_query_batch) -> float:
num_samples = len(pred_answer_ids_batch)
correct = 0
for question, pred_answer_label, pred_answer_ids, pred_query, gold_answer_labels, gold_answer_ids, gold_query in \
zip(questions_batch, pred_answer_labels_batch, pred_answer_ids_batch, pred_query_batch,
gold_answer_labels_batch, gold_answer_ids_batch, gold_query_batch):
found_date = False
if pred_answer_ids and gold_answer_ids and re.findall(r"[\d]{3,4}", pred_answer_ids[0]) and \
re.findall(r"[\d]{3,4}", pred_answer_ids[0]) == re.findall(r"[\d]{3,4}", gold_answer_ids[0]):
found_date = True
found_label = False
if len(gold_answer_labels) == 1 and len(pred_answer_label) > 1 and pred_answer_label == gold_answer_labels[0]:
found_label = True
no_answer = False
if pred_answer_label == "Not Found" and not gold_answer_ids:
no_answer = True
if set(pred_answer_ids) == set(gold_answer_ids) or gold_query in pred_query or found_date or found_label \
or no_answer:
correct += 1
log.debug(f"question: {question} -- gold_answer_ids: {gold_answer_ids} -- pred_answer_ids: {pred_answer_ids}")
return correct / num_samples if num_samples else 0
@register_metric('multitask_accuracy')
def multitask_accuracy(*args) -> float:
"""
Accuracy for multiple simultaneous tasks.
Args:
*args: a list of `2n` inputs. The first `n` inputs are the correct answers for `n` tasks,
and the last `n` are the predicted ones.
Returns:
The percentage of inputs where the answers for all `n` tasks are correct.
"""
n = len(args)
y_true_by_tasks, y_predicted_by_tasks = args[:n // 2], args[n // 2:]
answers = []
for true, pred in zip(y_true_by_tasks, y_predicted_by_tasks):
answers.append(accuracy(true, pred))
final_answer = sum(answers)/len(answers)
return final_answer
@register_metric('multitask_sequence_accuracy')
def multitask_sequence_accuracy(*args) -> float:
"""
Accuracy for multiple simultaneous sequence labeling (tagging) tasks.
For each sequence the model checks whether all its elements
are labeled correctly for all the individual taggers.
Args:
*args: a list of `2n` inputs. The first `n` inputs are the correct answers for `n` tasks,
and the last `n` are the predicted ones. For each task an
Returns:
The percentage of sequences where all the items has correct answers for all `n` tasks.
"""
n = len(args)
y_true_by_tasks, y_predicted_by_tasks = args[:n // 2], args[n // 2:]
y_true_by_sents = list(zip(*y_true_by_tasks))
y_predicted_by_sents = list(zip(*y_predicted_by_tasks))
y_true = list(list(zip(*elem)) for elem in y_true_by_sents)
y_predicted = list(list(zip(*elem)) for elem in y_predicted_by_sents)
return accuracy(y_true, y_predicted)
@register_metric('multitask_token_accuracy')
def multitask_token_accuracy(*args) -> float:
"""
Per-item accuracy for multiple simultaneous sequence labeling (tagging) tasks.
Args:
*args: a list of `2n` inputs. The first `n` inputs are the correct answers for `n` tasks
and the last `n` are the predicted ones. For each task an
Returns:
The percentage of sequence elements for which the answers for all `n` tasks are correct.
"""
n = len(args)
y_true_by_tasks, y_predicted_by_tasks = args[:n // 2], args[n // 2:]
y_true_by_sents = list(zip(*y_true_by_tasks))
y_predicted_by_sents = list(zip(*y_predicted_by_tasks))
y_true = list(list(zip(*elem)) for elem in y_true_by_sents)
y_predicted = list(list(zip(*elem)) for elem in y_predicted_by_sents)
return per_token_accuracy(y_true, y_predicted)
[docs]@register_metric('sets_accuracy')
def sets_accuracy(y_true: [list, np.ndarray], y_predicted: [list, np.ndarray]) -> float:
"""
Calculate accuracy in terms of sets coincidence
Args:
y_true: true values
y_predicted: predicted values
Returns:
portion of samples with absolutely coincidental sets of predicted values
Alias:
sets_accuracy
"""
examples_len = len(y_true)
correct = sum([set(y1) == set(y2) for y1, y2 in zip(y_true, y_predicted)])
return correct / examples_len if examples_len else 0
@register_metric('slots_accuracy')
def slots_accuracy(y_true, y_predicted):
y_true = [{tag.split('-')[-1] for tag in s if tag != 'O'} for s in y_true]
y_predicted = [set(s.keys()) for s in y_predicted]
return accuracy(y_true, y_predicted)
@register_metric('per_token_accuracy')
def per_token_accuracy(y_true, y_predicted):
y_true = list(itertools.chain(*y_true))
y_predicted = itertools.chain(*y_predicted)
examples_len = len(y_true)
correct = sum([y1 == y2 for y1, y2 in zip(y_true, y_predicted)])
return correct / examples_len if examples_len else 0
# region go-bot metrics
@register_metric('per_item_dialog_accuracy')
def per_item_dialog_accuracy(y_true, y_predicted: List[List[str]]):
# todo metric classes???
y_true = [y['text'] for dialog in y_true for y in dialog]
y_predicted = itertools.chain(*y_predicted)
examples_len = len(y_true)
correct = sum([y1.strip().lower() == y2.strip().lower() for y1, y2 in zip(y_true, y_predicted)])
return correct / examples_len if examples_len else 0
@register_metric('acc')
def round_accuracy(y_true, y_predicted):
"""
Rounds predictions and calculates accuracy in terms of absolute coincidence.
Args:
y_true: list of true values
y_predicted: list of predicted values
Returns:
portion of absolutely coincidental samples
"""
if isinstance(y_predicted[0], np.ndarray):
predictions = [np.round(x) for x in y_predicted]
else:
predictions = [round(x) for x in y_predicted]
examples_len = len(y_true)
correct = sum([y1 == y2 for y1, y2 in zip(y_true, predictions)])
return correct / examples_len if examples_len else 0