Make the Model State Page great again

scripts/01darts.sh

@@ -1,11 +1,22 @@
 #! /bin/bash
 
-pixi run darts extract_darts_mllabels --grid hex --level 3
-pixi run darts extract_darts_mllabels --grid hex --level 4
-pixi run darts extract_darts_mllabels --grid hex --level 5
-pixi run darts extract_darts_mllabels --grid hex --level 6
-pixi run darts extract_darts_mllabels --grid healpix --level 6
-pixi run darts extract_darts_mllabels --grid healpix --level 7
-pixi run darts extract_darts_mllabels --grid healpix --level 8
-pixi run darts extract_darts_mllabels --grid healpix --level 9
-pixi run darts extract_darts_mllabels --grid healpix --level 10
+# pixi shell
+darts extract-darts-rts --grid hex --level 3
+darts extract-darts-rts --grid hex --level 4
+darts extract-darts-rts --grid hex --level 5
+darts extract-darts-rts --grid hex --level 6
+darts extract-darts-rts --grid healpix --level 6
+darts extract-darts-rts --grid healpix --level 7
+darts extract-darts-rts --grid healpix --level 8
+darts extract-darts-rts --grid healpix --level 9
+darts extract-darts-rts --grid healpix --level 10
+
+darts extract-darts-mllabels --grid hex --level 3
+darts extract-darts-mllabels --grid hex --level 4
+darts extract-darts-mllabels --grid hex --level 5
+darts extract-darts-mllabels --grid hex --level 6
+darts extract-darts-mllabels --grid healpix --level 6
+darts extract-darts-mllabels --grid healpix --level 7
+darts extract-darts-mllabels --grid healpix --level 8
+darts extract-darts-mllabels --grid healpix --level 9
+darts extract-darts-mllabels --grid healpix --level 10

scripts/05train.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+pixi run train --grid hex --level 5 --target darts_mllabels --n-iter 1000 --task density --model espa
+pixi run train --grid hex --level 5 --target darts_mllabels --n-iter 1000 --task density --model xgboost

scripts/fix_xgboost_importance.py

@@ -0,0 +1,191 @@
+"""Fix XGBoost feature importance in existing model state files.
+
+This script repairs XGBoost model state files that have all-zero feature importance
+values due to incorrect feature name lookup. It reloads the pickled models and
+regenerates the feature importance arrays with the correct feature index mapping.
+"""
+
+import pickle
+from pathlib import Path
+
+import toml
+import xarray as xr
+from rich import print
+
+from entropice.paths import RESULTS_DIR
+
+
+def fix_xgboost_model_state(results_dir: Path) -> bool:
+    """Fix a single XGBoost model state file.
+
+    Args:
+        results_dir: Directory containing the model files.
+
+    Returns:
+        True if fixed successfully, False otherwise.
+
+    """
+    # Check if this is an XGBoost model
+    settings_file = results_dir / "search_settings.toml"
+    if not settings_file.exists():
+        return False
+
+    settings = toml.load(settings_file)
+    model_type = settings.get("settings", {}).get("model", "")
+
+    if model_type != "xgboost":
+        print(f"Skipping {results_dir.name} - not an XGBoost model (model={model_type})")
+        return False
+
+    # Check if required files exist
+    model_file = results_dir / "best_estimator_model.pkl"
+    state_file = results_dir / "best_estimator_state.nc"
+
+    if not model_file.exists():
+        print(f"⚠️  Missing model file in {results_dir.name}")
+        return False
+
+    if not state_file.exists():
+        print(f"⚠️  Missing state file in {results_dir.name}")
+        return False
+
+    # Load the pickled model
+    print(f"Loading model from {results_dir.name}...")
+    with open(model_file, "rb") as f:
+        best_estimator = pickle.load(f)
+
+    # Load the old state to get feature names
+    old_state = xr.open_dataset(state_file, engine="h5netcdf")
+    features = old_state.coords["feature"].values.tolist()
+    old_state.close()
+
+    # Get the booster and extract feature importance with correct mapping
+    booster = best_estimator.get_booster()
+
+    importance_weight = booster.get_score(importance_type="weight")
+    importance_gain = booster.get_score(importance_type="gain")
+    importance_cover = booster.get_score(importance_type="cover")
+    importance_total_gain = booster.get_score(importance_type="total_gain")
+    importance_total_cover = booster.get_score(importance_type="total_cover")
+
+    # Align importance using feature indices (f0, f1, ...)
+    def align_importance(importance_dict, features):
+        """Align importance dict to feature list using feature indices."""
+        return [importance_dict.get(f"f{i}", 0.0) for i in range(len(features))]
+
+    # Create new DataArrays
+    feature_importance_weight = xr.DataArray(
+        align_importance(importance_weight, features),
+        dims=["feature"],
+        coords={"feature": features},
+        name="feature_importance_weight",
+        attrs={"description": "Number of times a feature is used to split the data across all trees."},
+    )
+    feature_importance_gain = xr.DataArray(
+        align_importance(importance_gain, features),
+        dims=["feature"],
+        coords={"feature": features},
+        name="feature_importance_gain",
+        attrs={"description": "Average gain across all splits the feature is used in."},
+    )
+    feature_importance_cover = xr.DataArray(
+        align_importance(importance_cover, features),
+        dims=["feature"],
+        coords={"feature": features},
+        name="feature_importance_cover",
+        attrs={"description": "Average coverage across all splits the feature is used in."},
+    )
+    feature_importance_total_gain = xr.DataArray(
+        align_importance(importance_total_gain, features),
+        dims=["feature"],
+        coords={"feature": features},
+        name="feature_importance_total_gain",
+        attrs={"description": "Total gain across all splits the feature is used in."},
+    )
+    feature_importance_total_cover = xr.DataArray(
+        align_importance(importance_total_cover, features),
+        dims=["feature"],
+        coords={"feature": features},
+        name="feature_importance_total_cover",
+        attrs={"description": "Total coverage across all splits the feature is used in."},
+    )
+
+    # Create new state dataset
+    n_trees = booster.num_boosted_rounds()
+    state = xr.Dataset(
+        {
+            "feature_importance_weight": feature_importance_weight,
+            "feature_importance_gain": feature_importance_gain,
+            "feature_importance_cover": feature_importance_cover,
+            "feature_importance_total_gain": feature_importance_total_gain,
+            "feature_importance_total_cover": feature_importance_total_cover,
+        },
+        attrs={
+            "description": "Inner state of the best XGBClassifier from RandomizedSearchCV.",
+            "n_trees": n_trees,
+            "objective": str(best_estimator.objective),
+        },
+    )
+
+    # Backup the old file
+    backup_file = state_file.with_suffix(".nc.backup")
+    if not backup_file.exists():
+        print(f"  Creating backup: {backup_file.name}")
+        state_file.rename(backup_file)
+    else:
+        print("  Backup already exists, removing old state file")
+        state_file.unlink()
+
+    # Save the fixed state
+    print(f"  Saving fixed state to {state_file.name}")
+    state.to_netcdf(state_file, engine="h5netcdf")
+
+    # Verify the fix
+    total_importance = (
+        feature_importance_weight.sum().item()
+        + feature_importance_gain.sum().item()
+        + feature_importance_cover.sum().item()
+    )
+
+    if total_importance > 0:
+        print(f"  ✓ Success! Total importance: {total_importance:.2f}")
+        return True
+    else:
+        print("  ✗ Warning: Total importance is still 0!")
+        return False
+
+
+def main():
+    """Find and fix all XGBoost model state files."""
+    print("Scanning for XGBoost model results...")
+
+    # Find all result directories
+    result_dirs = [d for d in RESULTS_DIR.iterdir() if d.is_dir()]
+    print(f"Found {len(result_dirs)} result directories")
+
+    fixed_count = 0
+    skipped_count = 0
+    failed_count = 0
+
+    for result_dir in sorted(result_dirs):
+        try:
+            success = fix_xgboost_model_state(result_dir)
+            if success:
+                fixed_count += 1
+            elif success is False:
+                # Explicitly skipped (not XGBoost)
+                skipped_count += 1
+        except Exception as e:
+            print(f"❌ Error processing {result_dir.name}: {e}")
+            failed_count += 1
+
+    print("\n" + "=" * 60)
+    print("Summary:")
+    print(f"  ✓ Fixed: {fixed_count}")
+    print(f"  ⊘ Skipped: {skipped_count}")
+    print(f"  ✗ Failed: {failed_count}")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()