updating imports for MlModel

.gitignore

@@ -252,6 +252,7 @@ backend/.idea
 open_uprn/.idea/
 conservation_areas/.idea/
 model_data/.idea/
+model_data/simulation_system/.idea/
 
 model_data/simulation_system/data*
 

model_data/simulation_system/MLModel/Models.py

@@ -13,15 +13,17 @@ from pathlib import Path
 import pandas as pd
 from autogluon.tabular import TabularDataset, TabularPredictor
 from sklearn.metrics import mean_absolute_percentage_error
-from core.Logger import logger
+from model_data.simulation_system.core.Logger import logger
 
 AUTOGLUON_HYPERPARAMETERS = ['problem_type', 'eval_metric', 'time_limit', 'presets', 'excluded_model_types']
 METRIC_FILENAME = "metrics.csv"
 
+
 class AutogluonModel:
     """
     Autogluon model that implements the MLModel Protocol
     """
+
     def __init__(self, output_filepath: Path = None) -> None:
         self.model = None
         self.output_filepath = output_filepath
@@ -40,10 +42,10 @@ class AutogluonModel:
         logger.info("Using AutoGluon Model - Model saving already occured")
 
     def train_model(
-            self, 
-            data: pd.DataFrame, 
-            target_column: str, 
-            hyperparameters: dict = None) -> None:
+        self,
+        data: pd.DataFrame,
+        target_column: str,
+        hyperparameters: dict = None) -> None:
         """
         For the given data and hyperparameters, a model is trained
         """
@@ -58,17 +60,16 @@ class AutogluonModel:
         AGdata = TabularDataset(data=data)
 
         self.model = TabularPredictor(
-            label=target_column, 
-            path=self.output_filepath, 
+            label=target_column,
+            path=self.output_filepath,
             problem_type=hyperparameters['problem_type'],
             eval_metric=hyperparameters['eval_metric']
-            ).fit(
-            AGdata, 
-            time_limit=hyperparameters['time_limit'], 
-            presets=hyperparameters['presets'], 
+        ).fit(
+            AGdata,
+            time_limit=hyperparameters['time_limit'],
+            presets=hyperparameters['presets'],
             excluded_model_types=hyperparameters['excluded_model_types']
-            )
-
+        )
 
     def generate_predictions(self, data: pd.DataFrame) -> pd.DataFrame:
         """
@@ -84,12 +85,12 @@ class AutogluonModel:
         return predictions
 
     def model_evaluation(
-            self, 
-            validation_data: pd.DataFrame, 
-            target_column: str, 
-            metrics_location: Path = None, 
-            metric_filename: str = METRIC_FILENAME
-            ) -> pd.DataFrame:
+        self,
+        validation_data: pd.DataFrame,
+        target_column: str,
+        metrics_location: Path = None,
+        metric_filename: str = METRIC_FILENAME
+    ) -> pd.DataFrame:
         """
         For any validation data, a set of predictions and metrics are return
         """
@@ -105,7 +106,7 @@ class AutogluonModel:
 
         logger.info("Prediction used for evaluations are saved in self.prediction")
         self.predictions = predictions
-        
+
         # TODO: Can have a custom metric class that defines all different metrics we want 
         metric_mape = mean_absolute_percentage_error(validation_data[target_column], predictions)
 
@@ -117,7 +118,7 @@ class AutogluonModel:
         metrics_df = pd.DataFrame([performance])
         metrics_df.to_csv(metrics_location / metric_filename)
         markdown_filename = metric_filename.split(".")[0] + ".md"
-        metrics_df.to_markdown(metrics_location/ markdown_filename)
+        metrics_df.to_markdown(metrics_location / markdown_filename)
 
         return metrics_df
 
@@ -135,8 +136,3 @@ class AutogluonModel:
 
         # This will return a string path of the location
         return self.model.clone_for_deployment(deployment_path)
-
-        
-
-
-        

model_data/simulation_system/training.py

@@ -7,7 +7,7 @@ from typing import List
 from model_data.simulation_system.core.Logger import logger
 from model_data.simulation_system.core.DataLoader import DataLoader
 from model_data.simulation_system.core.FeatureProcessor import FeatureProcessor
-from MLModel.Models import AutogluonModel
+from model_data.simulation_system.MLModel.Models import AutogluonModel
 import pandas as pd
 from model_data.simulation_system.core.Settings import (
     MODEL_DIRECTORY,