results ae section
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@@ -75,7 +75,6 @@ PRETRAIN_SCHEMA = {
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"semi_anomalous": pl.Int32,
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"model": pl.Utf8, # always "ae"
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"fold": pl.Int32,
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"split": pl.Utf8, # "train" | "test"
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# timings and optimization
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"train_time": pl.Float64,
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"test_time": pl.Float64,
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@@ -577,7 +576,7 @@ def load_pretraining_results_dataframe(
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# Cast/optimize a bit (categoricals, ints, floats)
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df = df.with_columns(
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pl.col("network", "model", "split").cast(pl.Categorical),
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pl.col("network", "model").cast(pl.Categorical),
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pl.col(
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"latent_dim", "semi_normals", "semi_anomalous", "fold", "k_fold_num"
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).cast(pl.Int32),
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