fix metrics

quartz_solar_forecast/eval/metrics.py

@@ -1,4 +1,8 @@
-def metrics(results_df):
+import numpy as np
+import pandas as pd
+
+
+def metrics(results_df: pd.DataFrame):
     """
     Calculate and print metrics: MAE
 
@@ -11,7 +15,7 @@ def metrics(results_df):
 
     """
 
-    mae = (results_df["forecast_power"] - results_df['generation_power']).abs().mean().round(4)
+    mae = np.round((results_df["forecast_power"] - results_df["generation_power"]).abs().mean(), 4)
     print(f"MAE: {mae}")
 
     # calculate metrics over the different horizons hours
@@ -20,12 +24,22 @@ def metrics(results_df):
     for horizon_hour in horizon_hours:
         # filter results_df to only include the horizon_hour
         results_df_horizon = results_df[results_df["horizon_hour"] == horizon_hour]
-        mae = (results_df_horizon["forecast_power"] - results_df_horizon['generation_power']).abs().mean().round(3)
-        sem = ((results_df_horizon["forecast_power"] - results_df_horizon['generation_power']).abs().std() / len(results_df_horizon)**0.5).round(3)
+        mae = np.round(
+            (results_df_horizon["forecast_power"] - results_df_horizon["generation_power"])
+            .abs()
+            .mean(),
+            3,
+        )
+        sem = np.round(
+            (
+                (results_df_horizon["forecast_power"] - results_df_horizon["generation_power"])
+                .abs()
+                .std()
+                / len(results_df_horizon) ** 0.5
+            ),
+            3,
+        )
 
         print(f"MAE for horizon {horizon_hour}: {mae} +- {1.96*sem}")
 
     # TODO add more metrics using ocf_ml_metrics
-
-
-