wehub-resource-sync
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
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[str] = (
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"""Examples belonging to the most under-represented class in the dataset."""
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)
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issue_name: ClassVar[str] = "class_imbalance"
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verbosity_levels = {
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0: ["Rarest Class"],
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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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if not isinstance(labels, np.ndarray):
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error_msg = (
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f"Expected labels to be a numpy array of shape (n_samples,) to use with ClassImbalanceIssueManager, "
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f"but got {type(labels)} instead."
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)
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raise TypeError(error_msg)
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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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# solely one class is identified as rarest, ties go to class w smaller integer index
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scores = np.where(labels == rarest_class_idx, class_probs[rarest_class_idx], 1)
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imbalance_exists = class_probs[rarest_class_idx] < self.threshold * (1 / K)
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rarest_class_issue = rarest_class_idx if imbalance_exists else -1
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is_issue_column = labels == rarest_class_issue
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rarest_class_name = self.datalab._label_map.get(rarest_class_issue, "NA")
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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(class_name=rarest_class_name, labels=labels)
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def collect_info(self, class_name: str, labels: np.ndarray) -> dict:
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params_dict = {
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"threshold": self.threshold,
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"Rarest Class": class_name,
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"given_label": labels,
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}
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info_dict = {**params_dict}
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return info_dict
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