feature(Inference): Created the inference process, added model saving. (#153)

* feat: Basic scaffolding up for inference process after training.

* feat: Saving and loading models works. Inference works nearly.

* feat: Added inference pipeline.

* feat: Saving model now accoring to date and time; loading models now selects from latest file. Fixed the creation of dictionary of models.

* feat: Added lightweight asset config, but full pipeline.

* feat: Added new naming for dictionary.

* fix: Fixed dictionary naming convention.

* fix: Fixed naming again, now the model structure is good

* fix: Changed the output path and the return values from run_pipeline.

* feat: Added function to make sure folder exists for output models.

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>

training/inference.py

@@ -0,0 +1,79 @@
+
+import pandas as pd
+from data_loader.load_data import load_data
+from typing import Optional, Union
+import warnings
+
+
+def run_inference_pipeline(data_config:dict, training_config:dict, all_models_all_assets:dict):
+    data_params = data_config.copy()
+    data_params['target_asset'] = data_params['assets'][0]
+    
+    X, y, _ = load_data(**data_params)
+    input_features = __select_data(X, training_config)
+    
+    primary_models, secondary_models = __select_models(data_params, all_models_all_assets) 
+    
+    result = __inference(input_features, primary_models, secondary_models)
+    
+    return result
+
+
+def __inference(data:pd.DataFrame, primary_models:Union[dict,None], secondary_models:Union[dict,None]) -> pd.DataFrame:
+    assert primary_models is not None, "No primary models found. Cancelling Inference."
+    
+    data = __primary_models(data, primary_models)
+    
+    if secondary_models is not None:
+        warnings.warn("Secondary models are not specified.")
+        data = __secondary_models(data, secondary_models)
+    
+    return data
+
+
+def __select_models( data_params:dict, all_models_all_assets:dict)-> tuple[Optional[dict],Optional[dict]]:
+    target_asset_name = data_params['target_asset'][1]
+    primary_models, secondary_models = None, None
+
+    if 'primary_models' in all_models_all_assets[target_asset_name]:
+        primary_models = all_models_all_assets[target_asset_name]['primary_models']
+    else: 
+        assert("No primary models found for asset: " + target_asset_name)
+    
+    if 'secondary_model' in all_models_all_assets[target_asset_name]:
+        secondary_models = all_models_all_assets[target_asset_name]['secondary_model']
+    
+    return primary_models, secondary_models
+
+
+def __select_data(X:pd.DataFrame, training_config:dict)-> pd.DataFrame:
+    window_size = training_config['sliding_window_size_primary']
+    num_rows = X.shape[0]
+    
+    if num_rows <= window_size: 
+        return X.copy() 
+    else: 
+        return X.truncate(before=int(num_rows-window_size), after=num_rows, copy=True)
+
+
+def __primary_models(data:pd.DataFrame, models:dict)-> pd.DataFrame:
+    for k, model in models:
+        last_model = model[-1]
+        prediction = last_model.predict(data.to_numpy())
+        
+        # result = evaluate_predictions(
+        #     model_name = model_name,
+        #     target_returns = target_returns,
+        #     y_pred = preds,
+        #     y_true = y,
+        #     method = method,
+        #     no_of_classes=no_of_classes,
+        #     print_results = print_results,
+        #     discretize=True
+        # )
+        
+    return data
+
+def __secondary_models(data:pd.DataFrame, model:dict)-> pd.DataFrame:
+    
+    return data