wehub-resource-sync
136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
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
|
|
|
|
|
|
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}
|
|
|
|
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)
|
|
|
|
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
|