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planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
author bgruening
date Wed, 28 May 2025 11:30:39 +0000
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ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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1 import argparse
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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3 import numpy as np
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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4 import pandas as pd
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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5 from cleanlab.datalab.datalab import Datalab
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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6 from cleanlab.regression.rank import get_label_quality_scores
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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7 from sklearn.linear_model import LinearRegression
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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8 from sklearn.model_selection import cross_val_predict, KFold, StratifiedKFold
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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9 from xgboost import XGBClassifier
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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11 # -------------------
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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12 # Issue Handler
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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13 # -------------------
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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16 class IssueHandler:
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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17 def __init__(self, dataset, task, target_column, n_splits=3, quality_threshold=0.2):
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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18 self.dataset = dataset
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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19 self.task = task
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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20 self.target_column = target_column
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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21 self.n_splits = n_splits
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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22 self.quality_threshold = quality_threshold
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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23 self.issues = None
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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24 self.features = self.dataset.drop(target_column, axis=1).columns.tolist()
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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25 self.issue_summary = None
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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26 self.pred_probs = None
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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28 def report_issues(self):
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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29 X = self.dataset.drop(self.target_column, axis=1)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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30 y = self.dataset[self.target_column]
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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31
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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32 # Ensure compatibility with Galaxy
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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33 X = X.to_numpy() if hasattr(X, 'to_numpy') else np.asarray(X)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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34 y = y.to_numpy() if hasattr(y, 'to_numpy') else np.asarray(y)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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35
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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36 if self.task == 'classification':
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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37 model = XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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38 cv = StratifiedKFold(n_splits=self.n_splits, shuffle=True, random_state=42)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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39 self.pred_probs = cross_val_predict(model, X, y, cv=cv, method='predict_proba')
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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40
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41 lab = Datalab(self.dataset, label_name=self.target_column)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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42 lab.find_issues(pred_probs=self.pred_probs)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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43 self.issues = lab.get_issues()
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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44 self.issue_summary = lab.get_issue_summary()
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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45 print(self.issue_summary)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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46
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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47 elif self.task == 'regression':
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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48 model = LinearRegression()
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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49 cv = KFold(n_splits=self.n_splits, shuffle=True, random_state=42)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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50 pred_y = cross_val_predict(model, X, y, cv=cv, method='predict')
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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51 scores = get_label_quality_scores(y, pred_y, method='residual')
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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52 is_low_quality = scores < self.quality_threshold
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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53 self.issues = pd.DataFrame({
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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54 'label_quality': scores,
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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55 'is_low_quality': is_low_quality
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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56 })
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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57 self.issue_summary = {
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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58 'quality_threshold': self.quality_threshold,
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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59 'num_low_quality': int(is_low_quality.sum()),
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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60 'mean_label_quality': float(np.mean(scores)),
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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61 'median_label_quality': float(np.median(scores)),
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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62 'min_label_quality': float(np.min(scores)),
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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63 'max_label_quality': float(np.max(scores)),
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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64 }
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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65 print("Regression Issue Summary:")
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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66 for k, v in self.issue_summary.items():
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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67 print(f"{k.replace('_', ' ').capitalize()}: {v:.4f}" if isinstance(v, float) else f"{k.replace('_', ' ').capitalize()}: {v}")
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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68
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69 return self.dataset.copy(), self.issues.copy(), self.issue_summary
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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70
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71 def clean_selected_issues(self, method='remove', label_issues=True, outliers=True, near_duplicates=True, non_iid=True):
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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72 if self.issues is None:
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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73 raise RuntimeError("Must run report_issues() before cleaning.")
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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74
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75 if self.task == 'regression':
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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76 clean_mask = self.issues['is_low_quality'].fillna(False)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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77 else:
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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78 clean_mask = pd.Series([False] * len(self.dataset))
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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79 for issue_type, use_flag in [
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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80 ('is_label_issue', label_issues),
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81 ('is_outlier_issue', outliers),
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82 ('is_near_duplicate_issue', near_duplicates),
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83 ('is_non_iid_issue', non_iid)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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84 ]:
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85 if use_flag and issue_type in self.issues.columns:
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86 clean_mask |= self.issues[issue_type].fillna(False)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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87
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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88 if method == 'remove':
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89 return self.dataset[~clean_mask].copy()
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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90
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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91 elif method == 'replace' and self.task == 'classification':
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92 most_likely = np.argmax(self.pred_probs, axis=1)
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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93 fixed = self.dataset.copy()
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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94 to_fix = self.issues['is_label_issue'] & label_issues
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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95 fixed.loc[to_fix, self.target_column] = most_likely[to_fix]
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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96 return fixed
ecc18228c32e planemo upload for repository https://github.com/cleanlab/cleanlab commit ac4753a61ee908bc2a5953b6c6d38d2bbbacc6c0
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97
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98 elif method == 'replace' and self.task == 'regression':
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99 raise NotImplementedError("Replace method not implemented for regression label correction.")
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100
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101 else:
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102 raise ValueError("Invalid method or unsupported combination.")
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103
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104 # -------------------
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105 # Main CLI Entry
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106 # -------------------
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107
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108
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109 def main():
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110 parser = argparse.ArgumentParser(description="Cleanlab Issue Handler CLI")
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111 parser.add_argument("--input_file", nargs=2, required=True, metavar=('FILE', 'EXT'), help="Input file path and its extension")
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112 parser.add_argument("--task", required=True, choices=["classification", "regression"], help="Type of ML task")
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113 parser.add_argument("--target_column", default="target", help="Name of the target column")
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114 parser.add_argument("--method", default="remove", choices=["remove", "replace"], help="Cleaning method")
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115 parser.add_argument("--summary", action="store_true", help="Print and save issue summary only, no cleaning")
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116 parser.add_argument("--no-label-issues", action="store_true", help="Exclude label issues from cleaning")
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117 parser.add_argument("--no-outliers", action="store_true", help="Exclude outlier issues from cleaning")
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118 parser.add_argument("--no-near-duplicates", action="store_true", help="Exclude near-duplicate issues from cleaning")
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119 parser.add_argument("--no-non-iid", action="store_true", help="Exclude non-i.i.d. issues from cleaning")
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120 parser.add_argument('--quality-threshold', type=float, default=0.2, help='Threshold for low-quality labels (regression only)')
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121
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122 args = parser.parse_args()
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123
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124 # Load dataset based on file extension
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125 file_path, file_ext = args.input_file
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126 file_ext = file_ext.lower()
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127
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128 print(f"Loading dataset from: {file_path} with extension: {file_ext}")
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129
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130 if file_ext == "csv":
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131 df = pd.read_csv(file_path)
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132 elif file_ext in ["tsv", "tabular"]:
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133 df = pd.read_csv(file_path, sep="\t")
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134 else:
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135 raise ValueError(f"Unsupported file format: {file_ext}")
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136
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137 # Run IssueHandler
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138 handler = IssueHandler(dataset=df,
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139 task=args.task,
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140 target_column=args.target_column,
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141 quality_threshold=args.quality_threshold)
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142 _, issues, summary = handler.report_issues()
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143
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144 # Save summary
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145 if summary is not None:
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146 with open("summary.txt", "w") as f:
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147 if args.task == "regression":
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148 f.write("Regression Issue Summary:\n")
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149 for k, v in summary.items():
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150 text = f"{k.replace('_', ' ').capitalize()}: {v:.4f}" if isinstance(v, float) else f"{k.replace('_', ' ').capitalize()}: {v}"
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151 f.write(text + "\n")
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152 else:
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153 f.write(str(summary))
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154 print("Issue summary saved to: summary.txt")
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155
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156 if args.summary:
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157 return
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158
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159 # Clean selected issues
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160 cleaned_df = handler.clean_selected_issues(
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161 method=args.method,
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162 label_issues=not args.no_label_issues,
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163 outliers=not args.no_outliers,
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164 near_duplicates=not args.no_near_duplicates,
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165 non_iid=not args.no_non_iid
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166 )
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167
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168 print(f"Cleaned dataset shape: {cleaned_df.shape}")
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169 print(f"Original dataset shape: {df.shape}")
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170
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171 output_filename = "cleaned_data"
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172 if file_ext == "csv":
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173 cleaned_df.to_csv(output_filename, index=False)
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174 elif file_ext in ["tsv", "tabular"]:
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175 cleaned_df.to_csv(output_filename, sep="\t", index=False)
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176 else:
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177 raise ValueError(f"Unsupported output format: {file_ext}")
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178
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179 print(f"Cleaned dataset saved to: {output_filename}")
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180
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181 # -------------------
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182 # Entry point
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183 # -------------------
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184
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185
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186 if __name__ == "__main__":
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187 main()