73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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"""Debug script to identify feature mismatch between training and inference."""
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from entropice.ml.dataset import DatasetEnsemble
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# Test with level 6 (the actual level used in production)
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ensemble = DatasetEnsemble(
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grid="healpix",
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level=10,
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target="darts_mllabels",
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members=[
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"AlphaEarth",
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"ArcticDEM",
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"ERA5-yearly",
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"ERA5-seasonal",
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"ERA5-shoulder",
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],
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add_lonlat=True,
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filter_target=False,
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)
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print("=" * 80)
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print("Creating training dataset...")
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print("=" * 80)
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training_data = ensemble.create_cat_training_dataset(task="binary", device="cpu")
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training_features = set(training_data.X.data.columns)
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print(f"\nTraining dataset created with {len(training_features)} features")
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print(f"Sample features: {sorted(list(training_features))[:10]}")
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print("\n" + "=" * 80)
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print("Creating inference batch...")
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print("=" * 80)
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batch_generator = ensemble.create_batches(batch_size=100, cache_mode="n")
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batch = next(batch_generator, None)
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# for batch in batch_generator:
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if batch is None:
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print("ERROR: No batch created!")
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else:
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print(f"\nBatch created with {len(batch.columns)} columns")
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print(f"Batch columns: {sorted(batch.columns)[:15]}")
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# Simulate the column dropping in predict_proba (inference.py)
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cols_to_drop = ["geometry"]
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if ensemble.target == "darts_mllabels":
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cols_to_drop += [col for col in batch.columns if col.startswith("dartsml_")]
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else:
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cols_to_drop += [col for col in batch.columns if col.startswith("darts_")]
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print(f"\nColumns to drop: {cols_to_drop}")
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inference_batch = batch.drop(columns=cols_to_drop)
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inference_features = set(inference_batch.columns)
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print(f"\nInference batch after dropping has {len(inference_features)} features")
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print(f"Sample features: {sorted(list(inference_features))[:10]}")
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print("\n" + "=" * 80)
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print("COMPARISON")
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print("=" * 80)
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print(f"Training features: {len(training_features)}")
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print(f"Inference features: {len(inference_features)}")
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if training_features == inference_features:
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print("\n✅ SUCCESS: Features match perfectly!")
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else:
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print("\n❌ MISMATCH DETECTED!")
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only_in_training = training_features - inference_features
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only_in_inference = inference_features - training_features
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if only_in_training:
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print(f"\n⚠️ Only in TRAINING ({len(only_in_training)}): {sorted(only_in_training)}")
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if only_in_inference:
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print(f"\n⚠️ Only in INFERENCE ({len(only_in_inference)}): {sorted(only_in_inference)}")
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