# 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,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
from __future__ import annotations
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List
import pandas as pd
from cleanlab.datalab.internal.issue_manager import IssueManager
from cleanlab.internal.multilabel_utils import onehot2int
from cleanlab.multilabel_classification.filter import find_label_issues
from cleanlab.multilabel_classification.rank import get_label_quality_scores
if TYPE_CHECKING: # pragma: no cover
import numpy.typing as npt
import pandas as pd
from cleanlab.datalab.datalab import Datalab
[docs]class MultilabelIssueManager(IssueManager):
"""Manages label issues in Datalab for multilabel tasks.
Parameters
----------
datalab :
A Datalab instance.
"""
description: ClassVar[
str
] = """Examples whose given label(s) are estimated to be potentially incorrect
(e.g. due to annotation error) are flagged as having label issues.
"""
_PREDICTED_LABEL_THRESH = 0.5
"""Internal variable specifying threshold for predicted label."""
issue_name: ClassVar[str] = "label"
verbosity_levels = {
0: [],
1: [],
2: [],
3: [],
}
def __init__(
self,
datalab: Datalab,
**_,
):
super().__init__(datalab)
@staticmethod
def _process_find_label_issues_kwargs(**kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""Searches for keyword arguments that are meant for the
multilabel_classification.filter.find_label_issues method call.
Examples
--------
>>> from cleanlab.datalab.internal.issue_manager.multilabel.label import MultilabelIssueManager
>>> MultilabelIssueManager._process_find_label_issues_kwargs(frac_noise=0.9)
{'frac_noise': 0.9}
"""
accepted_kwargs = [
"filter_by",
"frac_noise",
"num_to_remove_per_class",
"min_examples_per_class",
"confident_joint",
"n_jobs",
"verbose",
"low_memory",
]
return {k: v for k, v in kwargs.items() if k in accepted_kwargs and v is not None}
@staticmethod
def _process_get_label_quality_scores_kwargs(**kwargs: Dict[str, Any]) -> Dict[str, Any]:
"""Searches for keyword arguments that are meant for the
multilabel_classification.rank.get_label_quality_scores method call.
Examples
--------
>>> from cleanlab.datalab.internal.issue_manager.multilabel.label import MultilabelIssueManager
>>> MultilabelIssueManager._process_get_label_quality_scores_kwargs(method="self_confidence")
{'method': 'self_confidence'}
"""
accepted_kwargs = ["method", "adjust_pred_probs", "aggregator_kwargs"]
return {k: v for k, v in kwargs.items() if k in accepted_kwargs and v is not None}
[docs] def find_issues(
self,
pred_probs: npt.NDArray,
**kwargs,
) -> None:
"""Find label issues in a multilabel dataset.
Parameters
----------
pred_probs :
The predicted probabilities for each example.
"""
predicted_labels = onehot2int(pred_probs > self._PREDICTED_LABEL_THRESH)
# Find examples with label issues
assert isinstance(self.datalab.labels, List) # Type Narrowing
is_issue_column = find_label_issues(
labels=self.datalab.labels,
pred_probs=pred_probs,
**self._process_find_label_issues_kwargs(**kwargs),
)
scores = get_label_quality_scores(
labels=self.datalab.labels,
pred_probs=pred_probs,
**self._process_get_label_quality_scores_kwargs(**kwargs),
)
self.issues = pd.DataFrame(
{
f"is_{self.issue_name}_issue": is_issue_column,
self.issue_score_key: scores,
},
)
# Get a summarized dataframe of the label issues
self.summary = self.make_summary(score=scores.mean())
# Collect info about the label issues
self.info = self.collect_info(self.datalab.labels, predicted_labels)
[docs] def collect_info(
self, given_labels: List[List[int]], predicted_labels: List[List[int]]
) -> Dict[str, Any]:
issues_info = {
"given_label": given_labels,
"predicted_label": predicted_labels,
}
return issues_info