ai 通用性改动

ai/EstimateMarketTrendV20.py

@@ -151,7 +151,7 @@ if __name__ == '__main__':
         if len(LABEL_RANGE) > 2:
             from ai.reporter import do_reporter2
 
-            do_reporter2()
+            do_reporter2(excel_name="Forcast_Report_32_old.xlsx")
         else:
             from ai.reporter import do_reporter
 

ai/model_trainer.py

@@ -44,7 +44,7 @@ class ModelTrainer(ABC):
                 for predict, date in zip(y_pred, date_index):
                     datas.append(
                         {'predict': predict, 'date': date, 'rbd_id': self._pid, 'create_time': datetime.now()})
-                reporter.do_reporter2(records=datas, excel_name='Backtest_Report_chu.xlsx')
+                reporter.do_reporter2(records=datas, excel_name='Backtest_Report_32_old.xlsx')
 
         # cm_display = ConfusionMatrixDisplay(confusion_matrix=result0, display_labels=labels)
         # cm_display.plot()

ai/training_data_builder.py

@@ -185,23 +185,22 @@ class TrainingDataBuilder(ABC):
         # scale data
         labels = list(LABEL_RANGE.keys())
         scaler = MinMaxScaler(feature_range=(labels[-1], labels[0]))
-        # scaledX = scaler.fit_transform(X)
         DataScaler = scaler.fit(X)
         scaledX = DataScaler.transform(X)
         scaledX_forecast = None
-        if (self._toForecast):
+        if self._toForecast:
             scaledX_forecast = DataScaler.transform(X_forecast)
 
             X_train = scaledX
             y_train = y
             X_test = []
             y_test = []
-
+            date_index = []
         else:
             # Step 2: Split data into train set and test set
             X_train, X_test, y_train, y_test = train_test_split(scaledX, y, test_size=0.02, shuffle=False)
-
+            date_index = DataAll['date'][-len(X_test):-self._numForecastDays].to_numpy()
             # To avoid data leak, test set should start from numForecastDays later
-            X_test = X_test[self._numForecastDays:]
-            y_test = y_test[self._numForecastDays:]
-        return X_train, X_test, y_train, y_test, scaledX_forecast, forecastDay
+            X_test = X_test[:-self._numForecastDays]
+            y_test = y_test[:-self._numForecastDays]
+        return X_train, X_test, y_train, y_test, scaledX_forecast, forecastDay, date_index