mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
f762ceed2a
* feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns * fix(Sweep): config * fix(Sweep): name * fix(Sweep): grid * feat(Config): separated sliding_window_size_level1 & sliding_window_size_level2 * feat(Dependencies): added ray, now using it to parallel process feature extraction * fix(Dependencies): added pip explicitly * fix(Dependencies): removed ray from root * fix(Models): average model was probably not taking the right timestamp to average * feat(Config): separated expanding_window_level1 & expanding_window_level2 * fix(Config): set n_features_to_select to the optimal 30
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
from collections import defaultdict
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from utils.load_data import get_crypto_assets
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from feature_extractors.feature_extractor_presets import presets
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from models.model_map import model_names_classification, model_names_regression
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def get_default_level_1_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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dimensionality_reduction = True,
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feature_selection = True,
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n_features_to_select = 30,
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expanding_window_level1 = False,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 380,
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sliding_window_size_level2 = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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include_original_data_in_ensemble = False,
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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= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days'],
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other_features = ['single_mom'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced'
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)
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regression_models = ["Lasso"]
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classification_models = ["KNN"]
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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_model = None
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)
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return model_config, training_config, data_config
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def get_default_level_2_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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dimensionality_reduction = True,
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feature_selection = True,
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n_features_to_select = 30,
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expanding_window_level1 = True,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 380,
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sliding_window_size_level2 = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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include_original_data_in_ensemble = False,
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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= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days', 'fracdiff'],
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other_features = ['level_2', 'fracdiff'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced'
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)
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regression_models = ["Lasso", "KNN", "RF"]
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regression_ensemble_model = 'KNN'
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classification_models = ["LR", "LDA", "KNN", "CART", "RF", "StaticMom"]
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classification_ensemble_model = '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_model = regression_ensemble_model if data_config['method'] == 'regression' else classification_ensemble_model
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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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# If level-2 model is there, we need more than one level-1 models to train
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if model_config["level_2_model"] is not None: 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 model_config["level_2_model"] is None: 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 model_config["level_2_model"] is not None:
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return model_config["level_2_model"][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") |