feat(Selection): added toggleable feature selection step into the pipeline (#83)

* feat(Selection): added prototype feature selection python script

* feat(Utils): added some helpers for the future from Advances in Financial ML book

* feat(Selection): added RFECV

* feat(Selection): added configurable feature selection step into pipeline

* feat(Config): added level_1 & level_2 default config, PCA before feature selection process starts

* feat(Selection): added backup feature selector models if current one can't output feature importance, removed unnecessary array for level-2 models

* fix(Training): deal with zero first value coming out of static models

* feat(Sweep): added feature selection sweep

* fix(Sweep): config problem

* fix(Sweep): config

* chore(Utils): removed unnecessary purged k-fold crossval class

* feat(Config): added dimensionality_reduction as a separate flag

* fix(Sweep): config updated

* fix(Sweep): sweep name

* chore(Config): updated level_2 config to the best performing configuation

utils/helpers.py

@@ -15,6 +15,8 @@ def flatten(list_of_lists: list) -> list:
     return [item for sublist in list_of_lists for item in sublist]
 
 def weighted_average(df: pd.DataFrame, weights_source: str) -> pd.DataFrame:
+    if df.shape[0] == 0:
+        return df
     mean_df = df.iloc[:,0]
     weights = df.loc[weights_source]
 

utils/rolling.py

@@ -1,6 +0,0 @@
-import numpy as np
-
-def rolling_window(a, window):
-    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
-    strides = a.strides + (a.strides[-1],)
-    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)