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import pandas as pd |
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import numpy as np |
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from renumics import spotlight |
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from renumics.spotlight.analysis.typing import DataIssue |
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if __name__ == "__main__": |
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df = pd.read_json("df.json") |
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issues = pd.read_json("issues.json") |
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spotlight_issues = [] |
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for _, issue in issues.sort_values("metric").iterrows(): |
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issue_metric = issue["metric"] |
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issue_title = ( |
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f"Metric: {issue_metric:.2f} | Cause: {issue['predicates'][0]['column']}" |
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) |
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predicate_strings = [ |
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f"{x['minimum']:.1f} < {x['column']} < {x['maximum']:.1f}" |
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for x in issue["predicates"] |
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if ("minimum" in x and "maximum" in x) |
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] |
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issue_explanation = "Feature Importances:\n" + issue["explanation"] |
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if len(predicate_strings) > 0: |
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issue_explanation = ( |
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issue_explanation + "\n\nFeature Ranges:\n" + "; ".join(predicate_strings) |
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) |
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data_issue = DataIssue( |
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severity="high" if issue_metric < 0.5 else "medium", |
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title=issue_title, |
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description=issue_explanation, |
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rows=issue["rows"], |
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columns=[x["column"] for x in issue["predicates"]], |
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) |
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spotlight_issues.append(data_issue) |
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while True: |
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dtypes = { |
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"image": spotlight.Image, |
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"sg_emb_image": spotlight.Embedding, |
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"sg_projection": spotlight.Embedding, |
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} |
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view = spotlight.show( |
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df, |
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dtype=dtypes, |
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port=7860, |
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host="0.0.0.0", |
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allow_filebrowsing=False, |
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issues=spotlight_issues, |
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layout="spotlight-layout.json", |
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) |
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view.close() |
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