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feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit() (#77)

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* feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit()

* fix(Sweep): removed unused `other_features` parameter that fails sweep

* feat(Config): using preset names for defining feature extractors again

* fix(Tests): fixed model stub classes
This commit is contained in:
Mark Aron Szulyovszky
2021-12-23 10:35:20 +01:00
committed by GitHub
parent 6ae8acf70e
commit b6cd6b14fe
14 changed files with 78 additions and 44 deletions
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run_pipeline.py
+4 -2
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@@ -1,8 +1,9 @@
from utils.load_data import load_data
import pandas as pd
from training.training import run_single_asset_trainig
from reporting.wandb import launch_wandb, send_report_to_wandb, seperate_configs
from reporting.wandb import launch_wandb, send_report_to_wandb, register_config_with_wandb
from models.model_map import map_model_name_to_function
from feature_extractors.feature_extractor_presets import preprocess_feature_extractors_config
from config import get_default_config, validate_config, get_model_name
def setup_pipeline(project_name:str, with_wandb: bool, sweep: bool):
@@ -11,9 +12,10 @@ def setup_pipeline(project_name:str, with_wandb: bool, sweep: bool):
wandb = None
if with_wandb:
wandb = launch_wandb(project_name=project_name, default_config=dict(**model_config, **training_config, **data_config), sweep=sweep)
model_config, training_config, data_config = seperate_configs(wandb, model_config, training_config, data_config)
register_config_with_wandb(wandb, model_config, training_config, data_config)
model_config = map_model_name_to_function(model_config, data_config['method'])
data_config = preprocess_feature_extractors_config(data_config)
pipeline(project_name, wandb, sweep, model_config, training_config, data_config)