feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105)

* fix(FeatureExtractor): apply log to transform some series to normality

* feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features

* feat(FeatureExtractors): added standard scaling for exogenous data

* feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection

* fix(Config): sweep config

* feat(Models): output probability, store it

* feat(Core): added caching to select_features() and load_data()

* fix(Dependencies): added diskcache

* fix(Training): error when creating results DF

* feat(Models): added xgboost, fixed tests

* refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching

* fix(Tests): new syntax

* fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow

* fix(Model): XGBoost config

* feat(Cache): add run_clear_cache script

* fix(Pipeline) accidentally re-instatiating all_predictions for each asset

models/average.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 from models.base import Model
 import numpy as np
 
@@ -10,16 +11,20 @@ class StaticAverageModel(Model):
     only_column = 'model_'
     feature_selection = 'off'
     model_type = 'static'
+    predict_window_size = 'single_timestamp'
 
-    def fit(self, X, y, prev_model):
+    def fit(self, X: np.ndarray, y: np.ndarray) -> None:
         # This is a static model, it can' learn anything
         pass
 
-    def predict(self, X):
+    def predict(self, X) -> tuple[float, np.ndarray]:
         # Make sure there's data to average
         assert X.shape[1] > 0
         prediction = np.average(X[-1])
-        return np.array([prediction])
+        return (prediction, np.array([]))
 
-    def clone(self):
-        return self
+    def clone(self) -> StaticAverageModel:
+        return self
+
+    def get_name(self) -> str:
+        return 'static_average'