wehub-resource-sync
91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
"""
|
|
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])
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
|
|
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]
|