First attempt at ensemble classifier

distclassipy/classifier.py

@@ -476,7 +476,7 @@ def find_best_metrics(
     quantiles = pd.qcut(X_df[feature_name], q=n_quantiles)
 
     X_train, X_test, y_train, y_test = train_test_split(
-        X_df, y_df, test_size=0.33, stratify=quantiles
+        X_df, y_df, test_size=0.25, stratify=quantiles
     )
 
     clf.fit(X_train, y_train.to_numpy().ravel())
@@ -507,5 +507,61 @@ def find_best_metrics(
 
     # alt, but slower:
     # loop through each quantile, and append pred
+    group_bins = []
+    for bins, group in grouped_test_data:
+        group_bins.append(bins)
+    return quantile_scores_df, best_metrics_per_quantile, group_bins
 
-    return quantile_scores_df, best_metrics_per_quantile
+
+class EnsembleDistanceMetricClassifier(BaseEstimator, ClassifierMixin):
+    """An ensemble classifier that uses different metrics for each quantile."""
+
+    def __init__(
+        self,
+        feat_idx: int,
+        scale: bool = True,
+        central_stat: str = "median",
+        dispersion_stat: str = "std",
+    ) -> None:
+        """Initialize the classifier with specified parameters."""
+        self.feat_idx = feat_idx
+        self.scale = scale
+        self.central_stat = central_stat
+        self.dispersion_stat = dispersion_stat
+
+    def fit(
+        self, X: np.ndarray, y: np.ndarray, n_quantiles: int = 4
+    ) -> "EnsembleDistanceMetricClassifier":
+        """Fit the ensemble classifier using the best metrics for each quantile."""
+        self.clf_ = DistanceMetricClassifier(
+            scale=self.scale,
+            central_stat=self.central_stat,
+            dispersion_stat=self.dispersion_stat,
+        )
+        (
+            self.quantile_scores_df_,
+            self.best_metrics_per_quantile_,
+            self.group_bins,
+        ) = find_best_metrics(self.clf_, X, y, self.feat_idx, n_quantiles)
+        self.group_labels = [f"Quantile {i+1}" for i in range(n_quantiles)]
+        self.clf_.fit(X, y)
+        self.is_fitted_ = True
+        return self
+
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        """Predict class labels using the best metric for each quantile."""
+        check_is_fitted(self, "is_fitted_")
+        X = check_array(X)
+
+        quantiles = pd.cut(
+            X[:, self.feat_idx], bins=self.group_bins, labels=self.group_labels
+        )
+        self.grouped_data = pd.DataFrame(X).groupby(quantiles, observed=False)
+        return 0
+        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
+
+        return predictions