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

config/config.py

@@ -15,7 +15,7 @@ def get_default_level_1_daily_config() -> tuple[dict, dict, dict]:
     )
 
     data_config = dict(
-        assets = ['hourly_crypto'],
+        assets = ['daily_crypto'],
         other_assets = [],
         exogenous_data = [],
         load_non_target_asset= True,
@@ -23,10 +23,11 @@ def get_default_level_1_daily_config() -> tuple[dict, dict, dict]:
         forecasting_horizon = 1,
         own_features = ['level_2', 'date_days'],
         other_features = ['single_mom'],
-        exogenous_features = ['fracdiff'],
+        exogenous_features = ['standard_scaling'],
         index_column= 'int',
         method= 'classification',
-        no_of_classes= 'three-balanced'
+        no_of_classes= 'three-balanced',
+        narrow_format = False,
     )
 
     regression_models = ["Lasso"]
@@ -64,10 +65,11 @@ def get_default_level_2_hourly_config() -> tuple[dict, dict, dict]:
         forecasting_horizon = 1,
         own_features = ['level_2', 'date_days', 'lags_up_to_5'],
         other_features = ['level_2'],
-        exogenous_features = ['fracdiff'],
+        exogenous_features = ['standard_scaling'],
         index_column= 'int',
         method= 'classification',
-        no_of_classes= 'three-balanced'
+        no_of_classes= 'three-balanced',
+        narrow_format = False,
     )
 
     regression_models = ["Lasso", "KNN", "RF"]
@@ -105,17 +107,18 @@ def get_default_level_2_daily_config() -> tuple[dict, dict, dict]:
         load_non_target_asset= True,
         log_returns= True,
         forecasting_horizon = 1,
-        own_features = ['level_2', 'date_days', 'fracdiff'],
-        other_features = ['level_2', 'fracdiff'],
-        exogenous_features = ['fracdiff'],
+        own_features = ['level_2', 'date_days', 'lags_up_to_5'],
+        other_features = ['level_2', 'lags_up_to_5'],
+        exogenous_features = ['standard_scaling'],
         index_column= 'int',
         method= 'classification',
-        no_of_classes= 'three-balanced'
+        no_of_classes= 'three-balanced',
+        narrow_format = False,
     )
 
     regression_models = ["Lasso", "KNN", "RF"]
     regression_ensemble_model = 'KNN'
-    classification_models = ["LDA", "KNN", "CART", "RF", "StaticMom"]
+    classification_models = ['LR', 'LDA', 'KNN', 'CART', 'NB', 'AB', 'RF', 'XGB', 'StaticMom']
     classification_ensemble_model = 'Ensemble_Average'
 
     model_config = dict(

config/hashing.py

@@ -0,0 +1,24 @@
+
+from utils.types import DataCollection
+
+def hash_data_config(data_config: dict) -> str:
+    
+    def hash_data_collection(data_collection: DataCollection) -> str: return ''.join([a[0] + a[1] for a in data_collection])
+    def hash_feature_extractors(feature_extractos) -> str: return ''.join([f[0] for f in feature_extractos])
+    def to_str(x): return ''.join([str(i) for i in x])
+    return '_'.join(to_str([
+        hash_data_collection(data_config['assets']),
+        hash_data_collection(data_config['other_assets']),
+        hash_data_collection(data_config['exogenous_data']),
+        data_config['target_asset'][0] + data_config['target_asset'][1],
+        data_config['load_non_target_asset'],
+        data_config['log_returns'],
+        data_config['forecasting_horizon'],
+        hash_feature_extractors(data_config['own_features']),
+        hash_feature_extractors(data_config['other_features']),
+        hash_feature_extractors(data_config['exogenous_features']),
+        data_config['index_column'],
+        data_config['method'],
+        data_config['no_of_classes'],
+        data_config['narrow_format']
+    ]))