feat: Added working version of EnsembleDistanceMetricClassifier

distclassipy/classifier.py

@@ -537,7 +537,7 @@ def __init__(
         self.scale = scale
         self.central_stat = central_stat
         self.dispersion_stat = dispersion_stat
-        self.metrics_to_consider = metrics_to_consider or _ALL_METRICS
+        self.metrics_to_consider = metrics_to_consider
 
     def fit(
         self, X: np.ndarray, y: np.ndarray, n_quantiles: int = 4
@@ -604,12 +604,26 @@ def predict(self, X: np.ndarray) -> np.ndarray:
         quantiles = pd.cut(
             X[:, self.feat_idx], bins=self.group_bins, labels=self.group_labels
         )
-        grouped_data = pd.DataFrame(X).groupby(quantiles, observed=False)
+        # grouped_data = pd.DataFrame(X).groupby(quantiles, observed=False)
+        quantile_indices = quantiles.codes  # Get integer codes for quantiles
         predictions = np.empty(X.shape[0], dtype=int)
-        for i, (lim, subdf) in enumerate(grouped_data):
-            best_metric = self.best_metrics_per_quantile_.loc[self.group_labels[i]]
-            preds = self.clf_.predict(subdf.to_numpy(), metric=best_metric)
-            predictions[subdf.index] = preds
+        # for i, (lim, subdf) in enumerate(grouped_data):
+        #     best_metric = self.best_metrics_per_quantile_.loc[self.group_labels[i]]
+        #     preds = self.clf_.predict(subdf.to_numpy(), metric=best_metric)
+        #     predictions[subdf.index] = preds
+        # Precompute predictions for each quantile
+        quantile_predictions = {}
+        for i, label in enumerate(self.group_labels):
+            best_metric = self.best_metrics_per_quantile_.loc[label]
+            quantile_data = X[quantile_indices == i]
+            if quantile_data.size > 0:
+                quantile_predictions[i] = self.clf_.predict(
+                    quantile_data, metric=best_metric
+                )
+
+        # Assign predictions to the corresponding indices
+        for i, preds in quantile_predictions.items():
+            predictions[quantile_indices == i] = preds
 
         return predictions