Update app.py

app.py

@@ -160,14 +160,6 @@ ax.grid()
 # ax.show()
 st.pyplot(fig)
 
- # данные по среднему чеку также спрогнозируем помесячно
-# Обучаем модель SARIMA
-# seasonal_order = (1, 1, 1, 12)  # Настройте параметры сезонности (P, D, Q, s)
-# model = SARIMAX(data['Средний чек'].dropna(), seasonal_order=seasonal_order)
-# model_fit = model.fit()
-# # Прогнозируем
-# forecast_chek = model_fit.forecast(steps=count_month)
-
 forecast_chek = fit_model(data['Средний чек'], count_month)
 
 # Визуализируем результаты
@@ -180,11 +172,11 @@ ax.legend()
 ax.grid()
 st.pyplot(fig)
 
-st.subheader('Прогнозные значения без влияния фичи')
+st.subheader('Прогнозные значения без влияния фичи с поправочным коэффициентом 0,5')
 df_no_feature = pd.DataFrame()
 df_no_feature['Дата'] = forecast_new.index
-df_no_feature['Оборот'] = list(forecast_new)
-df_no_feature['Средний чек'] = list(forecast_chek)
+df_no_feature['Оборот поогноз'] = [i*0.95 for i in list(forecast_new)]
+df_no_feature['Средний чек'] = [i*0.95 for i in list(forecast_chek)]
 st.dataframe(df_no_feature)
 
 list_retention = [0.48, 0.58, 0.52, 0.47, 0.44, 0.42, 0.4, 0.38, 0.37, 0.35, 0.34, 0.33, 0.32, 0.31, 0.30, 0.29, 0.28, 0.27, 0.26, 0.25, 0.24]
@@ -196,7 +188,7 @@ month_start = date_start
 # summ_from_feature = 0
 
 # random_colors = ['red', 'blue', 'lightgreen', 'gold', 'slategreay', 'indigo', 'coral', 'plum', 'blue', 'tomato']
-new_summ_with_features = ['red']
+# new_summ_with_features = ['red']
 
 if money == 'Выставлю сам':
     forecast_chek = [money_self for i in range(len(forecast_new))]
@@ -206,7 +198,7 @@ if money == 'Выставлю сам':
 
 list_with_colour = ['red', 'blue', 'lightgreen', 'gold', 'plum', 'indigo', 'coral', 'plum', 'blue', 'tomato']
 r = 1
-oborot = df_no_feature['Оборот']
+oborot = df_no_feature['Оборот поогноз']
 
 for j in list_with_data_by_features:
     # st.write(j)
@@ -214,6 +206,7 @@ for j in list_with_data_by_features:
         forecast_chek = [j[3] for l in range(len(forecast_new))]
     new_summ_with_features = []
     list_retention = list_retention[:duration+1] + [0 for g in range(25)]
+    summ_from_feature = 0
     for i in range(len(forecast_new)):
         # st.write(i)
         summ_from_feature = 0
@@ -224,7 +217,7 @@ for j in list_with_data_by_features:
             # st.write((forecast_new.index[i].date() >= j[5]) & (
             #     forecast_new.index[i].date() <= j[5] + timedelta(days=180)))
             # oborot = df_no_feature['Оборот']
-            summ_from_feature += j[0] * j[1] * j[2] * forecast_chek[i] * list_retention[i]
+            summ_from_feature = j[0] * j[1] * j[2] * forecast_chek[i] * list_retention[i]
             # print("Доп сумма от фичи:", summ_from_feature)
             # st.write(summ_from_feature)
 
@@ -246,7 +239,7 @@ for j in list_with_data_by_features:
 fig, ax = plt.subplots(figsize=(10, 5))
 # plt.plot(train['Оборот по всем копилкам за месяц'], label='Train')
 ax.plot(data['Оборот'], label='Исторические данные', color='orange')
-ax.plot(forecast_new.index, forecast_new, label='Прогноз', color='green', linestyle='--')
+ax.plot(forecast_new.index, df_no_feature['Оборот поогноз'], label='Прогноз', color='green', linestyle='--')
 for k in range(len(list_with_data_by_features)):
     ax.plot(forecast_new.index, df_no_feature[f'Прогноз с учетом фичи # {k+1}'], label=f'Прогноз с добавлением фичи # {k+1}', color=list_with_colour[k], linestyle='--')