awg commit algorithm components

evaluation_processing/analysis_report.py

@@ -255,7 +255,7 @@ def put_analysis_report_to_html(args,df_clean,df_accuracy):
     """
     filename = f"{farmId}_{int(time.time() * 1000)}_{random.randint(1000, 9999)}.html"
     # 保存为 HTML
-    directory = '/data/html'
+    directory = '/usr/share/nginx/html'
     if not os.path.exists(directory):
         os.makedirs(directory)
     file_path = os.path.join(directory, filename)

models_processing/model_predict/model_prediction_lightgbm.py

@@ -7,10 +7,14 @@ import logging
 import traceback
 from common.database_dml import get_data_from_mongo,insert_data_into_mongo
 app = Flask('model_prediction_lightgbm——service')
-    
+def str_to_list(arg):
+    if arg == '':
+        return []
+    else:
+        return arg.split(',')
 
 def model_prediction(df,args):
-    mongodb_connection,mongodb_database,mongodb_model_table,model_name,model_key = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['mongodb_model_table']
+    mongodb_connection,mongodb_database,mongodb_model_table,model_name,col_reserve = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['mongodb_model_table'],args['model_name'],str_to_list(args['col_reserve'])
     client = MongoClient(mongodb_connection)
     db = client[mongodb_database]
     collection = db[mongodb_model_table]
@@ -22,7 +26,8 @@ def model_prediction(df,args):
         df['predict'] = model.predict(df[model.feature_name()])
         df['model'] = model_name
         print("model predict result  successfully!")
-    return df
+    features_reserve = col_reserve + ['model','predict']
+    return df[set(features_reserve)]
 
 
 @app.route('/model_prediction_lightgbm', methods=['POST'])

models_processing/model_predict/model_prediction_lstm.py

@@ -23,7 +23,7 @@ def create_sequences(data_features,data_target,time_steps):
 
 
 def model_prediction(df,args):
-    features, time_steps, col_time, model_name =  str_to_list(args['features']), int(args['time_steps']),args['col_time'],args['model_name']
+    features, time_steps, col_time, model_name,col_reserve =  str_to_list(args['features']), int(args['time_steps']),args['col_time'],args['model_name'],str_to_list(args['col_reserve'])
     feature_scaler,target_scaler = get_scaler_model_from_mongo(args)
     df = df.fillna(method='ffill').fillna(method='bfill').sort_values(by=col_time)
     scaled_features = feature_scaler.transform(df[features])
@@ -35,7 +35,8 @@ def model_prediction(df,args):
     result = df[-len(y_predict):]
     result['predict'] = y_predict
     result['model'] = model_name
-    return result
+    features_reserve = col_reserve + ['model', 'predict']
+    return result[set(features_reserve)]
 
 def str_to_list(arg):
     if arg == '':