72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
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from pathlib import Path
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import polars as pl
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from load_results import load_pretraining_results_dataframe, load_results_dataframe
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# ------------------------------------------------------------
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# Example “analysis-ready” queries (Polars idioms)
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# ------------------------------------------------------------
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def demo_queries(df: pl.DataFrame):
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# q1: lazy is fine, then collect
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q1 = (
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df.lazy()
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.filter(
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(pl.col("network") == "LeNet")
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& (pl.col("latent_dim") == 1024)
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& (pl.col("semi_normals") == 0)
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& (pl.col("semi_anomalous") == 0)
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& (pl.col("eval") == "exp_based")
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)
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.group_by(["model"])
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.agg(pl.col("auc").mean().alias("mean_auc"))
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.sort(["mean_auc"], descending=True)
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.collect()
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)
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# q2: do the filtering eagerly, then pivot (LazyFrame has no .pivot)
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base = df.filter(
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(pl.col("model") == "deepsad")
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& (pl.col("eval") == "exp_based")
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& (pl.col("network") == "LeNet")
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& (pl.col("semi_normals") == 0)
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& (pl.col("semi_anomalous") == 0)
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).select("fold", "latent_dim", "auc")
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q2 = base.pivot(
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values="auc",
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index="fold",
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columns="latent_dim",
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aggregate_function="first", # or "mean" if duplicates exist
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).sort("fold")
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# roc_subset: eager filter/select, then explode struct fields
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roc_subset = (
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df.filter(
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(pl.col("model") == "ocsvm")
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& (pl.col("eval") == "manual_based")
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& (pl.col("network") == "efficient")
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& (pl.col("latent_dim") == 1024)
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& (pl.col("semi_normals") == 0)
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& (pl.col("semi_anomalous") == 0)
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)
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.select("fold", "roc_curve")
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.with_columns(
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pl.col("roc_curve").struct.field("fpr").alias("fpr"),
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pl.col("roc_curve").struct.field("tpr").alias("tpr"),
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pl.col("roc_curve").struct.field("thr").alias("thr"),
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)
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)
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return q1, q2, roc_subset
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def main():
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root = Path("/home/fedex/mt/results/done")
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df = load_results_dataframe(root, allow_cache=True)
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demo_queries(df)
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if __name__ == "__main__":
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main()
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