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smellycloud
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Jan 12, 2026
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| def __init__( | ||
| self, | ||
| index_cols: List[str] = ["time", "entity_id"], |
Collaborator
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Standardizes index names to ["time", "entity_id"] by default, but the CMDataset/PGMDataset handlers use month_id/country_id or month_id/priogrid_id.
| first_df = next(iter(df_to_aggregate.values())) | ||
| index_cols = list(first_df.index.names) | ||
| #target_cols = [c for c in first_df.columns if c not in index_cols] ### This is not right, but how to chose target cols? | ||
| target_cols = [c for c in first_df.columns if c not in index_cols] |
Collaborator
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For predictions, only columns like pred_* or specific target columns should be aggregated, not all non-index columns. This could accidentally aggregate metadata columns. Use self.configs["targets"] for actual target columns.
| for i in range(len(dfs[0])): | ||
| df_to_aggregate = [df[i] for df in dfs] | ||
| for i in range(len(n_outputs)): | ||
| model_dfs_i = {model_name: dfs[i] for model_name, dfs in eval_results.items()} |
Collaborator
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If any model returns fewer DataFrames than expected, this will raise an IndexError without a helpful error message.
Contributor
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Added explicit error message
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