feat(Inference): Inference now runs on the entire pipeline, only train/predict one asset, adjust trading costs (#173)

* fix, feat: Fixed inference processing data. Add transformation attribute.

* feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop).

* feat: Truncated models over time and transformations over time. Fixed some typing aswell.

* fix: Fixed a number of out of array problems.

* feat: Inference now works!

* fix(Steps): runtime error not checking for None

* fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every

* fix(CI): disable ray memory monitoring

* refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start

* feat(Inference): added index_from parameter

* fix(Tests): walk_forward test

* refactor(Pipeline): only predict one asset

* refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py)

* fix(Evaluation): adjust transaction costs

* fix(Config): adjusted retrain_every

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>

reporting/saving.py

@@ -0,0 +1,43 @@
+import pickle
+import datetime
+from typing import Optional, Union
+import os
+import warnings
+from reporting.types import Reporting
+
+
+
+def save_models(all_models: Reporting.Asset, data_config:dict, training_config:dict, model_config:dict) -> None:
+    dict_for_pickle = dict()
+    dict_for_pickle['training_config'] = training_config
+    dict_for_pickle['data_config'] = data_config
+    dict_for_pickle['model_config'] = model_config
+    dict_for_pickle['all_models'] = all_models
+    
+    date_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
+    
+    if not os.path.exists('output/models'):
+        warnings.warn("No folder exists, creating one.")
+        os.makedirs('output/models')
+        
+    pickle.dump( dict_for_pickle, open( "output/models/{}.p".format(date_string), "wb" ) )
+
+
+def load_models(file_name:Union[str, None]) -> tuple[Reporting.Asset, dict, dict, dict]:
+
+    if file_name is None: 
+        warnings.warn("No file name provided, will load latest models and configurations.")
+        files_in_directory:list = os.listdir('output/models')
+        
+        assert len(files_in_directory) > 0, "No models found in output/models."
+        file_name = sorted(files_in_directory)[-1]
+    
+    packacked_dict = pickle.load( open( "output/models/{}".format(file_name), "rb" ) )
+    
+    data_config = packacked_dict.pop("data_config", None)
+    training_config = packacked_dict.pop("training_config", None)
+    model_config = packacked_dict.pop("model_config", None)    
+    all_models = packacked_dict.pop("all_models", None)    
+    
+    return all_models, data_config, training_config, model_config
+