# Copyright (C) 2017-2023 Cleanlab Inc.
# This file is part of cleanlab.
#
# cleanlab is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cleanlab is distributed in the hope that it will be useful,
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
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# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
"""
Helper functions used internally for multi-label classification tasks.
"""
from typing import List, Optional, Tuple
import numpy as np
from cleanlab.internal.util import get_num_classes
def _is_multilabel(y: np.ndarray) -> bool:
"""Checks whether `y` is in a multi-label indicator matrix format.
Sparse matrices are not supported.
"""
if not (isinstance(y, np.ndarray) and y.ndim == 2 and y.shape[1] > 1):
return False
return np.array_equal(np.unique(y), [0, 1])
[docs]def stack_complement(pred_prob_slice: np.ndarray) -> np.ndarray:
"""
Extends predicted probabilities of a single class to two columns.
Parameters
----------
pred_prob_slice:
A 1D array with predicted probabilities for a single class.
Example
-------
>>> pred_prob_slice = np.array([0.1, 0.9, 0.3, 0.8])
>>> stack_complement(pred_prob_slice)
array([[0.9, 0.1],
[0.1, 0.9],
[0.7, 0.3],
[0.2, 0.8]])
"""
return np.vstack((1 - pred_prob_slice, pred_prob_slice)).T
[docs]def get_onehot_num_classes(
labels: list, pred_probs: Optional[np.ndarray] = None
) -> Tuple[np.ndarray, int]:
"""Returns OneHot encoding of MultiLabel Data, and number of classes"""
num_classes = get_num_classes(labels=labels, pred_probs=pred_probs)
try:
y_one = int2onehot(labels, K=num_classes)
except TypeError:
raise ValueError(
"wrong format for labels, should be a list of list[indices], please check the documentation in find_label_issues for further information"
)
return y_one, num_classes
[docs]def int2onehot(labels: list, K: int) -> np.ndarray:
"""Convert multi-label classification `labels` from a ``List[List[int]]`` format to a onehot matrix.
This returns a binarized format of the labels as a multi-hot vector for each example, where the entries in this vector are 1 for each class that applies to this example and 0 otherwise.
Parameters
----------
labels: list of lists of integers
e.g. [[0,1], [3], [1,2,3], [1], [2]]
All integers from 0,1,...,K-1 must be represented.
K: int
The number of classes."""
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer(classes=range(K))
return mlb.fit_transform(labels)
[docs]def onehot2int(onehot_matrix: np.ndarray) -> List[List[int]]:
"""Convert multi-label classification `labels` from a onehot matrix format to a ``List[List[int]]`` format that can be used with other cleanlab functions.
Parameters
----------
onehot_matrix: 2D np.ndarray of 0s and 1s
A matrix representation of multi-label classification labels in a binarized format as a multi-hot vector for each example.
The entries in this vector are 1 for each class that applies to this example and 0 otherwise.
Returns
-------
labels: list of lists of integers
e.g. [[0,1], [3], [1,2,3], [1], [2]]
All integers from 0,1,...,K-1 must be represented."""
return [np.where(row)[0].tolist() for row in onehot_matrix]