mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
cc70d3f907
* 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
95 lines
3.4 KiB
Python
95 lines
3.4 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 = False,
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feature_selection = True,
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expanding_window = False,
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sliding_window_size = 380,
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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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expanding_window = True,
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sliding_window_size = 380,
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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', 'lags_up_to_5'],
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other_features = ['level_2', 'lags_up_to_5'],
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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") |