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| import pickle | |
| import datasets | |
| import os | |
| import umap | |
| if __name__ == "__main__": | |
| cache_file = "dataset_cache.pkl" | |
| if os.path.exists(cache_file): | |
| # Load dataset from cache | |
| with open(cache_file, "rb") as file: | |
| dataset = pickle.load(file) | |
| print("Dataset loaded from cache.") | |
| else: | |
| # Load dataset using datasets.load_dataset() | |
| ds = datasets.load_dataset("renumics/cifar100-enriched", split="test") | |
| print("Dataset loaded using datasets.load_dataset().") | |
| df = ds.to_pandas() | |
| df = ds.rename_columns({"fine_label": "labels"}).to_pandas() | |
| from tabulate import tabulate | |
| from cleanlab import Datalab | |
| import pandas as pd | |
| import numpy as np | |
| lab = Datalab(data=ds, label_name="fine_label") | |
| features=np.array([x.tolist() for x in df["embedding"]]) | |
| pred_probs= np.array([x.tolist() for x in df["probabilities"]]) | |
| lab.find_issues(features=features,pred_probs=pred_probs) | |
| print(tabulate(lab.get_issues().iloc[[0,1,2,3,-3,-2,-1]], headers='keys', tablefmt='psql')) | |
| df_with_score = pd.concat([df, lab.get_issues()], axis=1) | |
| df = df_with_score | |
| # Save dataset to cache | |
| with open(cache_file, "wb") as file: | |
| pickle.dump(df, file) | |
| print("Dataset saved to cache.") | |