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email = "f.foerster@d-opt.com"}, ] -dependencies = [] -requires-python = ">=3.12" +dependencies = ["scikit-learn>=1.6.1", "pandas>=2.2.3", "xgboost>=2.1.4", "joblib>=1.4.2"] +requires-python = ">=3.11" readme = "README.md" license = {text = "LicenseRef-Proprietary"} diff --git a/src/delta_barth/prognose.py b/src/delta_barth/prognose.py new file mode 100644 index 0000000..79ed39d --- /dev/null +++ b/src/delta_barth/prognose.py @@ -0,0 +1,65 @@ +import pandas as pd +from xgboost import XGBRegressor +from sklearn.metrics import mean_squared_error + + + +# ----------------------------------------------------------------------------------------------------------------------------- +# Input: +# DataFrame df mit Columns f_umsatz_fakt, firmen, art, v_warengrp +# kunde (muss enthalten sein in df['firmen']['firma_refid']) + +# Output: +# Integer umsetzung (Prognose möglich): 0 ja, 1 nein (zu wenig Daten verfügbar), 2 nein (Daten nicht für Prognose geeignet) +# DataFrame test: Jahr, Monat, Vorhersage +# ----------------------------------------------------------------------------------------------------------------------------- + + + +# Prognose Umsatz je Firma + +def prognose(df, kunde): + daten = {'Auftrag': [], 'Datum': [], 'Umsatz': []} + df_firma = df['f_umsatz_fakt'][(df['f_umsatz_fakt']['firma_refid'] == kunde) & (df['f_umsatz_fakt']['beleg_typ'] == 1) & (df['f_umsatz_fakt']['betrag'] > 0)] + for auftrag in df_firma['vorgang_refid'].unique(): + daten['Auftrag'].append(auftrag) + daten['Datum'].append(df_firma[df_firma['vorgang_refid'] == auftrag]['buchungs_datum'].iloc[0]) + daten['Umsatz'].append(df_firma[df_firma['vorgang_refid'] == auftrag]['betrag'].sum()) + + daten = pd.DataFrame(daten) + daten = daten.sort_values(by='Datum') + daten = daten.reset_index() + + # Datenverfügbarkeit prüfen + if len(daten) >= 100: + # Entwicklung der Umsätze: definierte Zeiträume Monat + daten['Jahr'] = daten['Datum'].dt.year + daten['Monat'] = daten['Datum'].dt.month + + monthly_sum = daten.groupby(['Jahr', 'Monat'])['Umsatz'].sum().reset_index() + monthly_sum['Datum'] = monthly_sum['Monat'].astype(str) + '.' + monthly_sum['Jahr'].astype(str) + monthly_sum['Datum'] = pd.to_datetime(monthly_sum['Datum'], format='%m.%Y') + monthly_sum = monthly_sum.set_index('Datum') + + train = monthly_sum.iloc[:-5].copy() + test = monthly_sum.iloc[-5:].copy() + + features = ['Jahr', 'Monat'] + target = 'Umsatz' + + X_train, y_train = train[features], train[target] + X_test, y_test = test[features], test[target] + + 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) + + test.loc[:, 'Vorhersage'] = reg.predict(X_test) + test = test.reset_index(drop=True) + + # umsetzung, prognose + return 0, test + + # zu wenig Daten verfügbar + else: + # umsetzung, prognose + return 1, None diff --git a/src/delta_barth/workflow.py b/src/delta_barth/workflow.py new file mode 100644 index 0000000..3e1f8d3 --- /dev/null +++ b/src/delta_barth/workflow.py @@ -0,0 +1,81 @@ +import pandas as pd +from sklearn.metrics import mean_squared_error +from xgboost import XGBRegressor + +# ----------------------------------------------------------------------------------------------------------------------------- +# Input: +# DataFrame df mit Columns f_umsatz_fakt, firmen, art, v_warengrp +# kunde (muss enthalten sein in df['firmen']['firma_refid']) + +# Output: +# Integer umsetzung (Prognose möglich): 0 ja, 1 nein (zu wenig Daten verfügbar), 2 nein (Daten nicht für Prognose geeignet) +# DataFrame test: Jahr, Monat, Vorhersage +# ----------------------------------------------------------------------------------------------------------------------------- + + +# Prognose Umsatz je Firma + + +def prognose(df, kunde): + daten = {'Auftrag': [], 'Datum': [], 'Umsatz': []} + df_firma = df['f_umsatz_fakt'][ + (df['f_umsatz_fakt']['firma_refid'] == kunde) + & (df['f_umsatz_fakt']['beleg_typ'] == 1) + & (df['f_umsatz_fakt']['betrag'] > 0) + ] + for auftrag in df_firma['vorgang_refid'].unique(): + daten['Auftrag'].append(auftrag) + daten['Datum'].append( + df_firma[df_firma['vorgang_refid'] == auftrag]['buchungs_datum'].iloc[0] + ) + daten['Umsatz'].append(df_firma[df_firma['vorgang_refid'] == auftrag]['betrag'].sum()) + + daten = pd.DataFrame(daten) + daten = daten.sort_values(by='Datum') + daten = daten.reset_index() + + # Datenverfügbarkeit prüfen + if len(daten) >= 100: + # Entwicklung der Umsätze: definierte Zeiträume Monat + daten['Jahr'] = daten['Datum'].dt.year + daten['Monat'] = daten['Datum'].dt.month + + monthly_sum = daten.groupby(['Jahr', 'Monat'])['Umsatz'].sum().reset_index() + monthly_sum['Datum'] = ( + monthly_sum['Monat'].astype(str) + '.' + monthly_sum['Jahr'].astype(str) + ) + monthly_sum['Datum'] = pd.to_datetime(monthly_sum['Datum'], format='%m.%Y') + monthly_sum = monthly_sum.set_index('Datum') + + train = monthly_sum.iloc[:-5].copy() + test = monthly_sum.iloc[-5:].copy() + + features = ['Jahr', 'Monat'] + target = 'Umsatz' + + X_train, y_train = train[features], train[target] + X_test, y_test = test[features], test[target] + + 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 + ) + + test.loc[:, 'Vorhersage'] = reg.predict(X_test) + test = test.reset_index(drop=True) + + # umsetzung, prognose + return 0, test + + # zu wenig Daten verfügbar + else: + # umsetzung, prognose + return 1, None