mirror of
https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
6ae8acf70e
* feat(Models): added `debug_future_lookahead`, sped up LogisticRegression & DecisionTreeClassifier * feat(Training): added ability to train on expanding_window * feat(Models): tuned some hyperparameters, added expanding_window to sweep config, fixed tests * feat(Models): tune parameters of ensemble models * fix(Config): use window size that works with ensembling
57 lines
2.5 KiB
Python
57 lines
2.5 KiB
Python
from utils.load_data import get_crypto_assets
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import feature_extractors.feature_extractor_presets as feature_extractor_presets
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from models.model_map import model_names_classification, model_names_regression
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def get_default_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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expanding_window = True,
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sliding_window_size = 200,
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retrain_every = 100,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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include_original_data_in_ensemble = True,
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)
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data_config = dict(
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path='data/',
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all_assets = get_crypto_assets('data/'),
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load_other_assets= False,
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log_returns= True,
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forecasting_horizon = 1,
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own_features= feature_extractor_presets.date + feature_extractor_presets.level1,
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other_features= [],
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index_column= 'int',
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method= 'classification',
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)
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# regression_models = ["Lasso", "Ridge", "BayesianRidge", "KNN", "AB", "LR", "MLP", "RF", "SVR"]
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regression_models = model_names_regression
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regression_ensemble_models = ['Ensemble_Average']
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# classification_models = ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
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classification_models = model_names_classification
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classification_ensemble_models = ['Ensemble_Average']
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_models = regression_ensemble_models if data_config['method'] == 'regression' else classification_ensemble_models
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)
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return model_config, training_config, data_config
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def validate_config(model_config:dict, training_config:dict, data_config:dict):
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# We need to make sure there's only one output from the pipeline
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# We're not prepared for more than 1 level-2 models at the moment
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assert len(model_config["level_2_models"]) <= 1
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# If level-2 model is there, we need more than one level-1 models to train
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if len(model_config["level_2_models"]) == 1: assert len(model_config["level_1_models"]) > 0
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# If there's no level-2 model, we need to have only one level-1 model
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if len(model_config["level_2_models"]) == 0: assert len(model_config["level_1_models"]) == 1
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def get_model_name(model_config:dict) -> str:
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if len(model_config["level_2_models"]) == 1:
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return model_config["level_2_models"][0][0]
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elif len(model_config["level_1_models"]) == 1:
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return model_config["level_1_models"][0][0]
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else:
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raise Exception("No model name found") |