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

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* 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
This commit is contained in:
Mark Aron Szulyovszky
2021-12-27 21:59:22 +01:00
committed by GitHub
parent a9b05dbd42
commit cc70d3f907
19 changed files with 442 additions and 59 deletions
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reporting/wandb.py
+9 -8
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@@ -17,15 +17,16 @@ def launch_wandb(project_name:str, default_config:dict, sweep:bool=False):
def register_config_with_wandb(wandb: Optional[object], model_config:dict, training_config:dict, data_config:dict):
config: dict = wandb.config
if wandb is None: return
if type(wandb) is not type(None):
for k in training_config:
training_config[k] = config[k]
for k in model_config:
model_config[k] = config[k]
for k in data_config:
data_config[k] = config[k]
config: dict = wandb.config
for k in training_config:
training_config[k] = config[k]
for k in model_config:
model_config[k] = config[k]
for k in data_config:
data_config[k] = config[k]
def send_report_to_wandb(results: pd.DataFrame, wandb:Optional[object], project_name: str, model_name: str):