fixed r model metric calculation

examples/foundation-model-examples/chronos/01_chronos_load_inference.py

@@ -435,7 +435,3 @@ def forecast(input_data, url=endpoint_url, databricks_token=token):
 
 # Delete the serving endpoint
 func_delete_model_serving_endpoint(model_serving_endpoint_name)
-
-# COMMAND ----------
-
-

examples/foundation_daily.py

@@ -195,7 +195,3 @@ def transform_group(df):
 # COMMAND ----------
 
 display(spark.sql(f"delete from {catalog}.{db}.daily_scoring_output"))
-
-# COMMAND ----------
-
-

examples/local_univariate_daily.py

@@ -234,7 +234,3 @@ def transform_group(df):
 # COMMAND ----------
 
 display(spark.sql(f"delete from {catalog}.{db}.daily_scoring_output"))
-
-# COMMAND ----------
-
-

examples/local_univariate_external_regressors_daily.py

@@ -40,7 +40,7 @@
 
 catalog = "mmf" # Name of the catalog we use to manage our assets
 db = "rossmann" # Name of the schema we use to manage our assets (e.g. datasets)
-volume = "csv" # Name of the volume where you have your rossmann dataset csv sotred
+volume = "csv" # Name of the volume where you have your rossmann dataset csv stored
 user = spark.sql('select current_user() as user').collect()[0]['user'] # User email address
 
 # COMMAND ----------

mmf_sa/Forecaster.py

@@ -222,11 +222,12 @@ def evaluate_local_model(self, model_conf):
             evaluate_one_local_model_fn = functools.partial(
                 Forecaster.evaluate_one_local_model, model=model
             )
+
             res_sdf = (
                 src_df.groupby(self.conf["group_id"])
                 .applyInPandas(evaluate_one_local_model_fn, schema=output_schema)
             )
-
+        
             # Write evaluation result to a delta table
             if self.conf.get("evaluation_output", None) is not None:
                 (

mmf_sa/models/abstract_model.py

@@ -137,37 +137,25 @@ def calculate_metrics(
         Returns: metrics (Dict[str, Union[str, float, bytes]]): A dictionary specifying the metrics.
         """
         pred_df, model_fitted = self.predict(hist_df, val_df)
+
+        actual = val_df[self.params["target"]].to_numpy()
+        forecast = pred_df[self.params["target"]].to_numpy()
 
         if self.params["metric"] == "smape":
             smape = MeanAbsolutePercentageError(symmetric=True)
-            metric_value = smape(
-                val_df[self.params["target"]],
-                pred_df[self.params["target"]],
-            )
+            metric_value = smape(actual, forecast)
         elif self.params["metric"] == "mape":
             mape = MeanAbsolutePercentageError(symmetric=False)
-            metric_value = mape(
-                val_df[self.params["target"]],
-                pred_df[self.params["target"]],
-            )
+            metric_value = mape(actual, forecast)
         elif self.params["metric"] == "mae":
             mae = MeanAbsoluteError()
-            metric_value = mae(
-                val_df[self.params["target"]],
-                pred_df[self.params["target"]],
-            )
+            metric_value = mae(actual, forecast)
         elif self.params["metric"] == "mse":
             mse = MeanSquaredError(square_root=False)
-            metric_value = mse(
-                val_df[self.params["target"]],
-                pred_df[self.params["target"]],
-            )
+            metric_value = mse(actual, forecast)
         elif self.params["metric"] == "rmse":
             rmse = MeanSquaredError(square_root=True)
-            metric_value = rmse(
-                val_df[self.params["target"]],
-                pred_df[self.params["target"]],
-            )
+            metric_value = rmse(actual, forecast)
         else:
             raise Exception(f"Metric {self.params['metric']} not supported!")