cleanlab--cleanlab
Tata Ganesh
82 行
2.7 KiB
Python
82 行
2.7 KiB
Python
# Copyright (C) 2017-2023 Cleanlab Inc.
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# This file is part of cleanlab.
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#
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# cleanlab is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# cleanlab is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
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from __future__ import annotations
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from typing import TYPE_CHECKING, ClassVar
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import numpy as np
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import pandas as pd
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from cleanlab.datalab.internal.issue_manager import IssueManager
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if TYPE_CHECKING: # pragma: no cover
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from cleanlab.datalab.datalab import Datalab
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class ClassImbalanceIssueManager(IssueManager):
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"""Manages issues related to imbalance class examples.
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Parameters
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----------
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datalab:
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The Datalab instance that this issue manager searches for issues in.
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threshold:
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Minimum fraction of samples of each class that are present in a dataset without class imbalance.
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"""
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description: ClassVar[
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str
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] = """Examples belonging to the most under-represented class in the dataset."""
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issue_name: ClassVar[str] = "class_imbalance"
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verbosity_levels = {
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0: [],
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1: [],
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2: [],
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}
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def __init__(self, datalab: Datalab, threshold: float = 0.1):
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super().__init__(datalab)
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self.threshold = threshold
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def find_issues(
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self,
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**kwargs,
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) -> None:
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labels = self.datalab.labels
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K = len(self.datalab.class_names)
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class_probs = np.bincount(labels) / len(labels)
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rarest_class_idx = int(np.argmin(class_probs))
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imbalance_exists = class_probs[rarest_class_idx] < self.threshold * (1 / K)
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rarest_class = rarest_class_idx if imbalance_exists else -1
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is_issue_column = labels == rarest_class
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scores = np.where(is_issue_column, class_probs[rarest_class], 1)
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self.issues = pd.DataFrame(
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{
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f"is_{self.issue_name}_issue": is_issue_column,
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self.issue_score_key: scores,
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},
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)
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self.summary = self.make_summary(score=class_probs[rarest_class_idx])
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self.info = self.collect_info()
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def collect_info(self) -> dict:
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params_dict = {"threshold": self.threshold}
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info_dict = {**params_dict}
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return info_dict
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