src/delta_barth/analysis/forecast.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
+import copy
 import datetime
-# --- new: for calculating timedelta
-from dateutil.relativedelta import relativedelta
 import math
 from collections.abc import Mapping, Set
 from dataclasses import asdict
@@ -13,6 +12,9 @@ import numpy as np
 import pandas as pd
 import scipy.stats
 import sqlalchemy as sql
+
+# --- new: for calculating timedelta
+from dateutil.relativedelta import relativedelta
 from sklearn.metrics import mean_absolute_error, r2_score
 from sklearn.model_selection import KFold, RandomizedSearchCV
 from xgboost import XGBRegressor
@@ -185,16 +187,14 @@ def _process_sales(
     PipeResult
         _description_
     """
-    # cust_data: CustomerDataSalesForecast = CustomerDataSalesForecast()
-
     # filter data
     data = pipe.data
     assert data is not None, "processing not existing pipe result"
 
     DATE_FEAT: Final[str] = "buchungs_datum"
     SALES_FEAT: Final[str] = "betrag"
-    df_firma = data[(data["betrag"] > 0)]
-    df_cust = df_firma.copy()
+    df_filter = data[(data["betrag"] > 0)]
+    df_cust = df_filter.copy()
     df_cust = df_cust.sort_values(by=DATE_FEAT).reset_index()
     len_ds = len(df_cust)
 
@@ -218,9 +218,11 @@ def _process_sales(
     features = ["jahr", "monat"]
     target = SALES_FEAT
 
-    # --- 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")
+    # ?? --- new: dates and forecast
+    last_date = pd.to_datetime(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")
 
     # Randomized Search
@@ -236,30 +238,42 @@ def _process_sales(
         "early_stopping_rounds": [20, 50],
     }
 
-    # --- new: best_estimator (internal usage only)
+    # ?? --- new: best_estimator (internal usage only)
     best_estimator = None
-
     best_params: BestParametersXGBRegressor | None = None
     best_score_mae: float | None = float("inf")
     best_score_r2: float | None = None
     best_start_year: int | None = None
     too_few_month_points: bool = True
-    forecast: pd.DataFrame | None = None
-
+    # forecast: pd.DataFrame | None = None
+    # TODO: write routine to pad missing values in datetime row
+    # TODO problem: continuous timeline expected, but values can be empty for multiple months
+    # TODO: therefore, stepping with fixed value n does not result in timedelta of n episodes
+    # Option A: pad data frame with zero values --> could impede forecast algorithm
+    # Option B: calculate next index based on timedelta
     dates = monthly_sum.index
-    # --- new: use monthly basis for time windows
-    starting_date = datetime.now() - relativedelta(months=36)
-    #starting_date = dates.max() - relativedelta(months=36)
-    start_index = next((i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1)
+    # print("dates: ", dates)
+    # ?? --- new: use monthly basis for time windows
+    # baseline: 3 years - 36 months
+    starting_date = datetime.datetime.now() - relativedelta(months=12)
+    # starting_date = dates.max() - relativedelta(months=36)
+    start_index = next(
+        (i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1
+    )
+    print("start idx: ", start_index, "length dates: ", len(dates))
 
-    for index, i in enumerate(range(start_index, -1, -12)):
-        first_date = dates[i]
+    for add_year, date_idx in enumerate(range(start_index, -1, -12)):
+        print("date_idx: ", date_idx)
+        first_date = dates[date_idx]
+        print("first date: ", first_date)
         split_date = dates[-6]
 
         train = cast(
             pd.DataFrame,
             monthly_sum.loc[first_date:split_date].copy(),  # type: ignore
         )
+        print(train)
+        print("Length train: ", len(train))
         test = cast(
             pd.DataFrame,
             monthly_sum.loc[split_date:].copy(),  # type: ignore
@@ -267,8 +281,10 @@ 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:
+        # ?? --- new: adapted condition to fit new for-loop
+        # test set size fixed at 6 --> first iteration: baseline - 6 entries
+        # for each new year 10 new data points needed
+        if len(train) >= 30 + 10 * add_year:
             too_few_month_points = False
 
             rand = RandomizedSearchCV(
@@ -294,14 +310,16 @@ def _process_sales(
                     # --- new: use first_date for best_start_year
                     best_start_year = first_date.year
                     # --- new: store best_estimator
-                    best_estimator = rand.best_estimator_
+                    best_estimator = copy.copy(rand.best_estimator_)
 
-    # --- new: use best_estimator to calculate future values and store them in forecast
+    # ?? --- new: use best_estimator to calculate future values and store them in forecast
     if best_estimator is not None:
-        X_future = pd.DataFrame({"jahr": future_dates.year, "monat": future_dates.month}, index=future_dates)
-        y_future = best_estimator.predict(X_future)
-        forecast["vorhersage"] = y_future  
-        
+        X_future = pd.DataFrame(
+            {"jahr": future_dates.year, "monat": future_dates.month}, index=future_dates
+        )
+        y_future = best_estimator.predict(X_future)  # type: ignore
+        forecast["vorhersage"] = y_future
+
     best_score_mae = best_score_mae if not math.isinf(best_score_mae) else None
 
     if too_few_month_points:
@@ -317,7 +335,9 @@ def _process_sales(
         pipe.stats(stats)
         return pipe
 
-    assert forecast is not None, "forecast is None, but was attempted to be returned"
+    assert "vorhersage" in forecast.columns, (
+        "forecast does not contain prognosis values, but was attempted to be returned"
+    )
     status = STATUS_HANDLER.SUCCESS
     pipe.success(forecast, status)
     stats = SalesForecastStatistics(

src/delta_barth/constants.py

@@ -17,7 +17,7 @@ DUMMY_DATA_PATH: Final[Path] = dummy_data_pth
 # ** logging
 ENABLE_LOGGING: Final[bool] = True
 LOGGING_TO_FILE: Final[bool] = True
-LOGGING_TO_STDERR: Final[bool] = True
+LOGGING_TO_STDERR: Final[bool] = False
 LOG_FILENAME: Final[str] = "dopt-delbar.log"
 
 # ** databases
@@ -40,7 +40,7 @@ class KnownDelBarApiErrorCodes(enum.Enum):
 
 
 # ** API
-API_CON_TIMEOUT: Final[float] = 5.0  # secs to response
+API_CON_TIMEOUT: Final[float] = 10.0  # secs to response
 # ** API response parsing
 # ** column mapping [API-Response --> Target-Features]
 COL_MAP_SALES_PROGNOSIS: Final[DualDict[str, str]] = DualDict(