修改python包版本、list(set())、howlongago

.idea/algorithm_platform.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="py312" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.12 (python3.12_env)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">

models_processing/model_predict/model_prediction_bp.py

@@ -21,13 +21,14 @@ def model_prediction(df,args):
     # model = load_model(f'{farmId}_model.h5', custom_objects={'rmse': rmse})
     model = get_h5_model_from_mongo(args)
     y_predict = list(chain.from_iterable(target_scaler.inverse_transform([model.predict(scaled_features).flatten()])))
-    result['howlongago'] = howlongago
+
     result = df[-len(y_predict):]
     result['predict'] = y_predict
     result.loc[result['predict'] < 0, 'predict'] = 0
     result['model'] = model_name
+    result['howlongago'] = howlongago
     features_reserve = col_reserve + ['model', 'predict', 'howlongago']
-    return result[set(features_reserve)]
+    return result[list(set(features_reserve))]
 
 
 @app.route('/model_prediction_bp', methods=['POST'])

models_processing/model_predict/model_prediction_lightgbm.py

@@ -91,7 +91,7 @@ def model_prediction(df,args):
         df['howlongago'] = howlongago
         print("model predict result  successfully!")
     features_reserve = col_reserve + ['model', 'predict', 'howlongago']
-    return df[set(features_reserve)]
+    return df[list(set(features_reserve))]
 
 
 @app.route('/model_prediction_lightgbm', methods=['POST'])

models_processing/model_predict/model_prediction_lstm.py

@@ -72,13 +72,14 @@ def model_prediction(df,args):
     # model = load_model(f'{farmId}_model.h5', custom_objects={'rmse': rmse})
     model = get_h5_model_from_mongo(args)
     y_predict = list(chain.from_iterable(target_scaler.inverse_transform([model.predict(X_predict).flatten()])))
-    result['howlongago'] = howlongago
+
     result = df[-len(y_predict):]
     result['predict'] = y_predict
     result.loc[result['predict'] < 0, 'predict'] = 0
     result['model'] = model_name
+    result['howlongago'] = howlongago
     features_reserve = col_reserve + ['model', 'predict', 'howlongago']
-    return result[set(features_reserve)]
+    return result[list(set(features_reserve))]
 
 
 @app.route('/model_prediction_lstm', methods=['POST'])

models_processing/model_predict/res_prediction.py

@@ -35,7 +35,7 @@ def model_prediction_lightgbm():
         power_df['model'] = args['model']
         power_df['predict'] = power_df[args['col_pre']]
         features_reserve = col_reserve + ['model', 'predict']
-        insert_data_into_mongo(power_df[set(features_reserve)], args)
+        insert_data_into_mongo(power_df[list(set(features_reserve))], args)
         success = 1
     except Exception as e:
         my_exception = traceback.format_exc()

requirements.txt

@@ -1,18 +1,19 @@
-pymongo==4.7.3
+pymongo==4.11.2
 pandas==2.2.3
-SQLAlchemy==1.4.46
+SQLAlchemy==2.0.39
 PyMySQL==1.1.1
-Flask==3.0.2
-waitress==2.1.2
-requests==2.31.0
-numpy== 1.26.0
+Flask==3.1.0
+waitress==3.0.2
+requests==2.32.3
+numpy== 2.1.3
 scikit-learn== 1.6.1
 plotly==5.18.0
 lightgbm==4.5.0
-joblib==1.3.2
-tensorflow==2.16.1
+joblib==1.4.2
+tensorflow==2.19.0
 matplotlib==3.10.0
-protobuf==3.20.3
+protobuf==5.29.3
 APScheduler==3.10.4
 paramiko==3.5.0
-PyYAML==6.0.1
+PyYAML==6.0.1
+h5py==3.13.0