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| import pickle | |
| from renumics import spotlight | |
| import os | |
| import requests | |
| import pandas as pd | |
| from renumics import spotlight | |
| from renumics.spotlight.analysis import DataIssue | |
| 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: | |
| df = pickle.load(file) | |
| print("Dataset loaded from cache.") | |
| label_issue_rows = df[df["is_label_issue"]].sort_values("label_score").index.tolist() | |
| label_issue = DataIssue( | |
| severity="medium", | |
| title="label-issue", | |
| rows=label_issue_rows, | |
| description="Label issue found by cleanlab - Review and correct if necessary", | |
| ) | |
| outlier_issue_row = ( | |
| df[df["outlier_score"] < 0.6].sort_values("outlier_score").index.tolist() | |
| ) | |
| outlier_issue = DataIssue( | |
| severity="medium", | |
| title="outlier-issue", | |
| rows=outlier_issue_row, | |
| description="Outlier score < 0.6 - Review and remove or collect more data", | |
| ) | |
| near_duplicate_issue_row = ( | |
| df[df["is_near_duplicate_issue"]].sort_values("near_duplicate_score").index.tolist() | |
| ) | |
| near_duplicate_issue = DataIssue( | |
| severity="medium", | |
| title="near-duplicate-issue", | |
| rows=near_duplicate_issue_row, | |
| description="Near duplicate issue found by cleanlab - Review and remove if necessary", | |
| ) | |
| while True: | |
| dtypes = { | |
| "image": spotlight.Image, | |
| "image_full": spotlight.Image, | |
| "embedding": spotlight.Embedding, | |
| "embedding_reduced": spotlight.Embedding, | |
| "probabilities": spotlight.Embedding, | |
| } | |
| view = spotlight.show( | |
| df.rename(columns={"fine_label_str": "label", "fine_label_prediction_str":"pred"}), | |
| dtype=dtypes, | |
| issues=[label_issue,outlier_issue,near_duplicate_issue], | |
| layout="layout.json", | |
| port=7860, | |
| host="0.0.0.0", | |
| allow_filebrowsing=False, | |
| ) | |
| view.close() | |
| else: | |
| print(f"Dataset {cache_file} not found. Please run prepare.py first.") | |