refactoring

src/delta_barth/analysis/forecast.py

@@ -72,14 +72,14 @@ def _parse_df_to_results(
 @wrap_result()
 def _parse_api_resp_to_df_wrapped(
     resp: SalesPrognosisResponse,
-) -> pd.DataFrame:  # pragma: no cover
+) -> pd.DataFrame:
     return _parse_api_resp_to_df(resp)
 
 
 @wrap_result()
 def _parse_df_to_api_resp_wrapped(
     data: pd.DataFrame,
-) -> SalesPrognosisResponse:  # pragma: no cover
+) -> SalesPrognosisResponse:
     return _parse_df_to_api_resp(data)
 
 
@@ -109,7 +109,7 @@ def _parse_df_to_results_wrapped(
 # TODO set min number of data points as constant, not parameter
 
 
-def _preprocess_sales_per_customer(
+def _preprocess_sales(
     resp: SalesPrognosisResponse,
     feature_map: Mapping[str, str],
     target_features: Set[str],
@@ -151,9 +151,8 @@ def _preprocess_sales_per_customer(
     return pipe
 
 
-def _process_sales_per_customer(
+def _process_sales(
     pipe: PipeResult[SalesPrognosisResultsExport],
-    # company_id: int,
     min_num_data_points: int = 100,
 ) -> PipeResult[SalesPrognosisResultsExport]:
     """n = 1
@@ -174,11 +173,9 @@ def _process_sales_per_customer(
     PipeResult
         _description_
     """
+    # cust_data: CustomerDataSalesForecast = CustomerDataSalesForecast()
 
-    cust_data: CustomerDataSalesForecast = CustomerDataSalesForecast()
     # filter data
-    # TODO change away from nested DataFrames: just use "f_umsatz_fakt"
-    # TODO with strong type checks
     data = pipe.data
     assert data is not None, "processing not existing pipe result"
     data = data.copy()
@@ -208,48 +205,46 @@ def _process_sales_per_customer(
     if len(df_cust) < min_num_data_points:
         pipe.fail(STATUS_HANDLER.pipe_states.TOO_FEW_POINTS)
         return pipe
-    else:
-        # Entwicklung der Umsätze: definierte Zeiträume Monat
-        df_cust["jahr"] = df_cust[DATE_FEAT].dt.year
-        df_cust["monat"] = df_cust[DATE_FEAT].dt.month
 
-        monthly_sum = df_cust.groupby(["jahr", "monat"])[SALES_FEAT].sum().reset_index()
-        monthly_sum[DATE_FEAT] = (
-            monthly_sum["monat"].astype(str) + "." + monthly_sum["jahr"].astype(str)
-        )
-        monthly_sum[DATE_FEAT] = pd.to_datetime(monthly_sum[DATE_FEAT], format="%m.%Y")
-        monthly_sum = monthly_sum.set_index(DATE_FEAT)
+    # Entwicklung der Umsätze: definierte Zeiträume Monat
+    df_cust["jahr"] = df_cust[DATE_FEAT].dt.year
+    df_cust["monat"] = df_cust[DATE_FEAT].dt.month
 
-        train = monthly_sum.iloc[:-5].copy()
-        test = monthly_sum.iloc[-5:].copy()
+    monthly_sum = df_cust.groupby(["jahr", "monat"])[SALES_FEAT].sum().reset_index()
+    monthly_sum[DATE_FEAT] = (
+        monthly_sum["monat"].astype(str) + "." + monthly_sum["jahr"].astype(str)
+    )
+    monthly_sum[DATE_FEAT] = pd.to_datetime(monthly_sum[DATE_FEAT], format="%m.%Y")
+    monthly_sum = monthly_sum.set_index(DATE_FEAT)
 
-        features = ["jahr", "monat"]
-        target = SALES_FEAT
+    train = monthly_sum.iloc[:-5].copy()
+    test = monthly_sum.iloc[-5:].copy()
 
-        X_train, y_train = train[features], train[target]
-        X_test, y_test = test[features], test[target]
+    features = ["jahr", "monat"]
+    target = SALES_FEAT
 
-        reg = XGBRegressor(
-            base_score=0.5,
-            booster="gbtree",
-            n_estimators=1000,
-            early_stopping_rounds=50,
-            objective="reg:squarederror",
-            max_depth=3,
-            learning_rate=0.01,
-        )
-        reg.fit(
-            X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=100
-        )
+    X_train, y_train = train[features], train[target]
+    X_test, y_test = test[features], test[target]
 
-        test.loc[:, "vorhersage"] = reg.predict(X_test)
-        test = test.reset_index(drop=True)
+    reg = XGBRegressor(
+        base_score=0.5,
+        booster="gbtree",
+        n_estimators=1000,
+        early_stopping_rounds=50,
+        objective="reg:squarederror",
+        max_depth=3,
+        learning_rate=0.01,
+    )
+    reg.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=100)
 
-        pipe.success(test, STATUS_HANDLER.SUCCESS)
-        return pipe
+    test.loc[:, "vorhersage"] = reg.predict(X_test)
+    test = test.reset_index(drop=True)
+
+    pipe.success(test, STATUS_HANDLER.SUCCESS)
+    return pipe
 
 
-def _postprocess_sales_per_customer(
+def _postprocess_sales(
     pipe: PipeResult[SalesPrognosisResultsExport],
     feature_map: Mapping[str, str],
 ) -> PipeResult[SalesPrognosisResultsExport]:
@@ -287,7 +282,7 @@ def _export_on_fail(
     return SalesPrognosisResultsExport(response=response, status=status)
 
 
-def pipeline(
+def pipeline_sales(
     session: Session,
     company_id: int | None = None,
     start_date: Datetime | None = None,
@@ -300,7 +295,7 @@ def pipeline(
     if status != STATUS_HANDLER.SUCCESS:
         return _export_on_fail(status)
 
-    pipe = _preprocess_sales_per_customer(
+    pipe = _preprocess_sales(
         response,
         feature_map=COL_MAP_SALES_PROGNOSIS,
         target_features=FEATURES_SALES_PROGNOSIS,
@@ -308,14 +303,14 @@ def pipeline(
     if pipe.status != STATUS_HANDLER.SUCCESS:
         return _export_on_fail(pipe.status)
 
-    pipe = _process_sales_per_customer(
+    pipe = _process_sales(
         pipe,
         min_num_data_points=MIN_NUMBER_DATAPOINTS,
     )
     if pipe.status != STATUS_HANDLER.SUCCESS:
         return _export_on_fail(pipe.status)
 
-    pipe = _postprocess_sales_per_customer(
+    pipe = _postprocess_sales(
         pipe,
         feature_map=DualDict(),
     )

src/delta_barth/pipelines.py

@@ -11,7 +11,7 @@ def pipeline_sales_forecast(
     company_id: int | None,
     start_date: Datetime | None,
 ) -> tuple[JsonResponse, JsonStatus]:
-    result = forecast.pipeline(SESSION, company_id=company_id, start_date=start_date)
+    result = forecast.pipeline_sales(SESSION, company_id=company_id, start_date=start_date)
     response = JsonResponse(result.response.model_dump_json())
     status = JsonStatus(result.status.model_dump_json())