From c7b6f38fa9ae29d3c48d314a2519ae5bed88b2d0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Florian=20F=C3=B6rster?= Date: Wed, 5 Mar 2025 13:10:17 +0100 Subject: [PATCH] result objects in forecast --- src/delta_barth/analysis/forecast.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/delta_barth/analysis/forecast.py b/src/delta_barth/analysis/forecast.py index 155d352..71ea51a 100644 --- a/src/delta_barth/analysis/forecast.py +++ b/src/delta_barth/analysis/forecast.py @@ -8,9 +8,10 @@ import pandas as pd from sklearn.metrics import mean_squared_error from xgboost import XGBRegressor +from delta_barth._management import ERROR_HANDLER from delta_barth.analysis import parse from delta_barth.constants import COL_MAP_SALES_PROGNOSIS, FEATURES_SALES_PROGNOSIS -from delta_barth.types import CustomerDataSalesForecast, DataPipelineErrors +from delta_barth.types import CustomerDataSalesForecast, DataPipelineErrors, doptResult if TYPE_CHECKING: from delta_barth.api.common import SalesPrognosisResponse @@ -65,7 +66,7 @@ def sales_per_customer( data: pd.DataFrame, customer_id: int, min_num_data_points: int = 100, -) -> FcResult: +) -> doptResult: """_summary_ Parameters @@ -105,7 +106,7 @@ def sales_per_customer( # check data availability if len(df_cust) < min_num_data_points: - return DataPipelineErrors.DATA_TOO_FEW_POINTS, None + return doptResult(resp=ERROR_HANDLER.data_pipelines.TOO_FEW_POINTS, res=None) else: # Entwicklung der Umsätze: definierte Zeiträume Monat df_cust["year"] = df_cust["date"].dt.year @@ -144,4 +145,4 @@ def sales_per_customer( test = test.reset_index(drop=True) # umsetzung, prognose - return DataPipelineErrors.SUCCESS, test + return doptResult(resp=ERROR_HANDLER.data_pipelines.SUCCESS, res=test)