inv & mppt id added to mlflow util

app.py

 if __name__ == "__main__":
     from dotenv import load_dotenv
     load_dotenv(dotenv_path='config.env')
-import pandas as pd
-import numpy as np
 import warnings
 from loguru import logger
-from scripts.utils.pycaret_util import PycaretUtil
 from scripts.core.engine.mppt_data import GetData
 from scripts.utils.reading_tags import GetTags
 from scripts.core.engine.tags_data import get_tags_data
@@ -49,6 +46,7 @@ def get_tag_details():
                                                                                         inv_id=inv_id,
                                                                                         mppt_id=mppt_id)
                     df_result = mppt_data.get_final_data(x_test=x_test, y_test=y_test, predictions=predictions)
+                    # function to push the data to kairos
                     logger.info(f'{df_result.shape}')
                 except Exception as e:
                     logger.exception(f'Exception - {e}')

config.env

@@ -9,8 +9,6 @@ KAFKA_PORT=9092
 KAFKA_TOPIC=ilens_dev
 START_RELATIVE=90
 END_RELATIVE=0
-SENDER_EMAIL=no-reply@unifytwin.com
-MONGO_URI=mongodb://ilens:ilens4321@192.168.0.220:2717/
 REQUIRED_TZ="Asia/Kolkata"
 
 MLFLOW_TRACKING_URI=https://qa.unifytwin.com/mlflow/
@@ -19,13 +17,11 @@ MLFLOW_TRACKING_PASSWORD=MlFlOwQA#4321
 AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=azrmlilensqa006382180551;AccountKey=tDGOKfiZ2svfoMvVmS0Fbpf0FTHfTq4wKYuDX7cAxlhve/3991QuzdvJHm9vWc+lo6mtC+x9yPSghWNR4+gacg==;EndpointSuffix=core.windows.net
 AZURE_STORAGE_ACCESS_KEY=tDGOKfiZ2svfoMvVmS0Fbpf0FTHfTq4wKYuDX7cAxlhve/3991QuzdvJHm9vWc+lo6mtC+x9yPSghWNR4+gacg==
 USER=Dalmia_degradation
-EXPERIMENT_NAME=Dalmia Solar Degradation1
+EXPERIMENT_NAME=Dalmia Solar Degradation2
 RUN_NAME=Degradation
 MODEL_NAME=versioning
 CHECK_PARAM=hours
-MODEL_CHECK_PARAM=144
-
-
+MODEL_CHECK_PARAM=480
 MODELS_LIST=lr,knn,gbr,rf,catboost,lightgbm,ada,et,xgboost,dt,en,par,huber
 SELECTED_METRIC=R2
 HYPERPARAMETER_TUNING_METHOD=scikit-learn
\ No newline at end of file

scripts/core/engine/inv_and_mppt_level.py

@@ -27,8 +27,9 @@ class TrainingInference:
             df_std = pd.concat([x_train_std, y_train_std], axis=1)
             df_std.dropna(axis=0, inplace=True)
             df_std.reset_index(drop=True, inplace=True)
-
-            model, pre_trained = ModelLoad().model_manager(df=df_std, target='current_mppt')
+            inv_mppt_id = f'{inv_id}_{mppt_id}'
+            model, pre_trained = ModelLoad().model_manager(df=df_std, target='current_mppt',
+                                                           inv_mppt_id=inv_mppt_id)
 
             return model, scaler_x, scaler_y
         except Exception as e:

scripts/core/engine/mppt_data.py

@@ -41,9 +41,9 @@ class GetData:
                                                        df_result['actual_current_mppt']
             df_result.loc[df_result['potential_current_mppt_loss'] < 0, 'potential_current_mppt_loss'] = 0
             df_result['hour'] = df_result['datetime'].dt.hour
-            df_result.loc[df_result['hour'].between(left=19, right=23, inclusive='both'),
+            df_result.loc[df_result['hour'].between(left=18, right=23, inclusive='both'),
                           'potential_current_mppt_loss'] = 0
-            df_result.loc[df_result['hour'].between(left=0, right=5, inclusive='both'),
+            df_result.loc[df_result['hour'].between(left=0, right=6, inclusive='both'),
                           'potential_current_mppt_loss'] = 0
             df_result.drop(['hour'], axis=1, inplace=True)
             df_result['total_potential_current_mppt_loss'] = df_result['potential_current_mppt_loss']. \

scripts/utils/mlflow_util.py

@@ -21,11 +21,11 @@ client = mlflow.tracking.MlflowClient()
 
 class ModelLoad(object):
 
-    def model_manager(self, df, target):
+    def model_manager(self, df, target, inv_mppt_id):
         try:
             experiment_id = self.create_experiment(experiment_name=MlFlow.experiment_name)
             days, latest_run_id = self.fetch_latest_model(experiment_id=experiment_id,
-                                                          run_name=MlFlow.run_name)
+                                                          run_name=MlFlow.run_name + '_' + inv_mppt_id)
             if days < int(MlFlow.model_check_param):
                 logger.debug(f'Using the pretrained model !')
                 energy_model = self.load_model_pyfunc(
@@ -33,7 +33,8 @@ class ModelLoad(object):
                 pre_trained = True
             else:
                 pre_trained = False
-                run_id = self.creating_run(experiment_id=experiment_id, run_name=MlFlow.run_name)
+                run_id = self.creating_run(experiment_id=experiment_id,
+                                           run_name=MlFlow.run_name + '_' + inv_mppt_id)
                 with mlflow.start_run(run_id=run_id):
                     nested_run_id = self.creating_new_nested_run(experiment_id=experiment_id,
                                                                  run_id=run_id,