From f49744ca45de600123123db581e12c48c1f60283 Mon Sep 17 00:00:00 2001
From: frasu <frasu@noreply.localhost>
Date: Thu, 10 Apr 2025 17:33:00 +0000
Subject: [PATCH] src/delta_barth/analysis/forecast.py aktualisiert

---
 src/delta_barth/analysis/forecast.py | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

diff --git a/src/delta_barth/analysis/forecast.py b/src/delta_barth/analysis/forecast.py
index c32e078..bc402bc 100644
--- a/src/delta_barth/analysis/forecast.py
+++ b/src/delta_barth/analysis/forecast.py
@@ -218,12 +218,7 @@ def _process_sales(
     features = ["jahr", "monat"]
     target = SALES_FEAT
 
-    # --- new: not necessary anymore
-    #current_year = datetime.datetime.now().year
-    #first_year = cast(int, df_cust["jahr"].min())
-
-    # --- new: dates und forecast
-    #last_date = pd.to_datetime(monthly_sum.index[-1], format="%m.%Y")
+    # --- new: dates and forecast
     last_date = pd.to_datetime(datetime.now().strftime("%m.%Y"), format="%m.%Y")
     future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=6, freq="MS")
     forecast = pd.DataFrame({"datum": future_dates.strftime("%m.%Y")}).set_index("datum")
@@ -241,7 +236,7 @@ def _process_sales(
         "early_stopping_rounds": [20, 50],
     }
 
-    # --- new: best_estimator (internal usage)
+    # --- new: best_estimator (internal usage only)
     best_estimator = None
 
     best_params: BestParametersXGBRegressor | None = None
@@ -258,12 +253,12 @@ def _process_sales(
     start_index = next((i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1)
 
     for index, i in enumerate(range(start_index, -1, -12)):
-        start_date = dates[i]
+        first_date = dates[i]
         split_date = dates[-6]
 
         train = cast(
             pd.DataFrame,
-            monthly_sum.loc[start_date:split_date].copy(),  # type: ignore
+            monthly_sum.loc[first_date:split_date].copy(),  # type: ignore
         )
         test = cast(
             pd.DataFrame,
@@ -272,6 +267,7 @@ def _process_sales(
         X_train, X_test = train[features], test[features]
         y_train, y_test = train[target], test[target]
 
+        # --- new: adapted condition to fit new for-loop
         if len(train) >= 30 + 10 * index:
             too_few_month_points = False
 
@@ -292,13 +288,13 @@ def _process_sales(
             if len(np.unique(y_pred)) != 1:
                 error = cast(float, mean_absolute_error(y_test, y_pred))
                 if error < best_score_mae:
-                    # --- new: store best_estimator
-                    best_estimator = cast(BestEstimatorXGBRegressor, rand.best_estimator_)
                     best_params = cast(BestParametersXGBRegressor, rand.best_params_)
                     best_score_mae = error
                     best_score_r2 = cast(float, r2_score(y_test, y_pred))
-                    # --- new: use store start_date in best_start_year
-                    best_start_year = start_date
+                    # --- new: use first_date for best_start_year
+                    best_start_year = first_date.year
+                    # --- new: store best_estimator
+                    best_estimator = rand.best_estimator_
 
     # --- new: use best_estimator to calculate future values and store them in forecast
     if best_estimator is not None: