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outliercapper.py

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+class OutlierCapper(BaseEstimator, TransformerMixin):
+
+    def fit(self, X, y=None):
+
+        self.bounds = []
+
+        # If X is a DataFrame, convert to numpy array for percentile calculation to avoid FutureWarning
+        X_np = X.values if isinstance(X, pd.DataFrame) else X
+
+        for i in range(X_np.shape[1]):
+            Q1 = np.percentile(X_np[:, i], 25)
+            Q3 = np.percentile(X_np[:, i], 75)
+            IQR = Q3 - Q1
+            self.bounds.append((Q1-1.5*IQR, Q3+1.5*IQR))
+
+        return self
+
+    def transform(self, X):
+
+        # If X is a DataFrame, convert to numpy array for manipulation, then back to DataFrame if needed
+        X_transformed = X.copy()
+        if isinstance(X_transformed, pd.DataFrame):
+            column_names = X_transformed.columns
+            X_np = X_transformed.values
+        else:
+            column_names = None # Column names are lost if X is already numpy
+            X_np = X_transformed
+
+        for i, (low, high) in enumerate(self.bounds):
+            X_np[:, i] = np.clip(X_np[:, i], low, high)
+
+        if column_names is not None:
+            return pd.DataFrame(X_np, columns=column_names) # Return DataFrame to preserve column names
+        else:
+            return X_np # Return numpy array if no original column names