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

tests/test_evaluation.py

@@ -42,13 +42,13 @@ class EvenOddStubModel(Model):
         super().__init__()
         self.window_length = window_length
 
-    def fit(self, X, y, prev_model):
+    def fit(self, X, y):
         assert len(X) == self.window_length
         for i in range(len(X)):
             assert y[i] == -1 if X[i][0] == 1 else 1
 
     def predict(self, X):
-        return np.array([-1 if X[0][0] == 1 else 1])
+        return (-1 if X[0][0] == 1 else 1, np.array([]))
 
     def clone(self):
         return self
@@ -62,7 +62,7 @@ def test_evaluation():
     model = EvenOddStubModel(window_length = window_length)
     scaler = None
     
-    models, predictions = walk_forward_train_test(
+    models, predictions, probs = walk_forward_train_test(
         model_name='test',
         model=model,
         X=X,