Source code for deeppavlov.metrics.accuracy

# 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
from typing import List, Tuple
import numpy as np

from deeppavlov.core.common.metrics_registry import register_metric


@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:
        portion of absolutely coincidental samples
    """
    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


[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 """ 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_item_accuracy') def per_item_accuracy(y_true, y_predicted): if isinstance(y_true[0], (tuple, list)): y_true = (y[0] for y in y_true) 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 @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 @register_metric('per_item_dialog_accuracy') def per_item_dialog_accuracy(y_true, y_predicted): 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 """ 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