mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-31 20:57:51 +00:00
Daniel Szemerey
1c1b8b2e54
* feat: Parametricized model selection works now. * feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute. * feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file. * fix: Took out prints and fixed the mismatch of ensemble models when classifing. * fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem * fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models` * feat(Sweep): added `run_sweep.py` shortcut * feat(Pipeline): skip training a meta model if array is empty Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
from utils.load_data import get_crypto_assets
|
|
import feature_extractors.feature_extractor_presets as feature_extractor_presets
|
|
from models.model_map import model_names_classification, model_names_regression
|
|
|
|
def get_default_config() -> tuple[dict, dict, dict]:
|
|
|
|
training_config = dict(
|
|
sliding_window_size = 150,
|
|
retrain_every = 20,
|
|
scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
|
|
include_original_data_in_ensemble = True,
|
|
)
|
|
|
|
data_config = dict(
|
|
path='data/',
|
|
all_assets = get_crypto_assets('data/'),
|
|
load_other_assets= False,
|
|
log_returns= True,
|
|
forecasting_horizon = 1,
|
|
own_features= feature_extractor_presets.date + feature_extractor_presets.level1,
|
|
other_features= [],
|
|
index_column= 'int',
|
|
method= 'classification',
|
|
)
|
|
|
|
# regression_models = ["Lasso", "Ridge", "BayesianRidge", "KNN", "AB", "LR", "MLP", "RF", "SVR"]
|
|
regression_models = model_names_regression
|
|
regression_ensemble_models = ['Ensemble_Average']
|
|
# classification_models = ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
|
|
classification_models = model_names_classification
|
|
classification_ensemble_models = ['Ensemble_Average']
|
|
|
|
model_config = dict(
|
|
level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
|
|
level_2_models = regression_ensemble_models if data_config['method'] == 'regression' else classification_ensemble_models
|
|
)
|
|
|
|
return model_config, training_config, data_config |