add smape

modules/ml-pipeline/src/pipeline/core/MLMetrics.py

@@ -4,6 +4,7 @@ Implementation of MLMetrics, all of which will have two methods:
 - Generate Plot Suite
 """
 
+import numpy as np
 import pandas as pd
 from typing import Union
 from sklearn.metrics import (
@@ -14,6 +15,18 @@ from sklearn.metrics import (
 )
 from core.interface.InterfaceMetrics import MLMetrics
 
+# Define the function to return the SMAPE value
+def symmetric_mape(actual, predicted) -> float:
+
+    # Convert actual and predicted to numpy
+    # array data type if not already
+    if not all([isinstance(actual, np.ndarray), isinstance(predicted, np.ndarray)]):
+        actual, predicted = np.array(actual), np.array(predicted)
+
+    return np.mean(
+        np.abs(predicted - actual) / ((np.abs(predicted) + np.abs(actual)) / 2)
+    )
+
 
 def metrics_factory(metrics_type: str) -> MLMetrics:
     metrics = {
@@ -34,7 +47,7 @@ class RegressionMetrics:
         median_absolute_error,
         mean_squared_error,
         mean_absolute_percentage_error,
-        # max_error
+        symmetric_mape,
     ]
 
     def generate_metrics(