mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +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
53 lines
2.8 KiB
Python
53 lines
2.8 KiB
Python
from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, LogisticRegression, Ridge
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from sklearn.tree import DecisionTreeClassifier
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from sklearnex.neighbors import KNeighborsRegressor, KNeighborsClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearnex.svm import SVR
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from sklearn.naive_bayes import GaussianNB
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from sklearn.neural_network import MLPRegressor, MLPClassifier
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from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
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from sklearnex.ensemble import RandomForestClassifier
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from models.base import SKLearnModel
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from models.momentum import StaticMomentumModel
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from models.average import StaticAverageModel
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from models.naive import StaticNaiveModel
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model_map = {
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"regression_models": dict(
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LR = SKLearnModel(LinearRegression(n_jobs=-1)),
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Lasso = SKLearnModel(Lasso(alpha=100, random_state=1)),
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Ridge = SKLearnModel(Ridge(alpha=0.1)),
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BayesianRidge = SKLearnModel(BayesianRidge()),
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KNN = SKLearnModel(KNeighborsRegressor(n_neighbors=25)),
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AB = SKLearnModel(AdaBoostRegressor(random_state=1)),
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MLP = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
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RF = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1)),
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SVR = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1)),
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StaticNaive = StaticNaiveModel(),
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),
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"classification_models": dict(
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LR= SKLearnModel(LogisticRegression(C=10, random_state=1, max_iter=1000, n_jobs=-1)),
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LDA= SKLearnModel(LinearDiscriminantAnalysis()),
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KNN= SKLearnModel(KNeighborsClassifier()),
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CART= SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1)),
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NB= SKLearnModel(GaussianNB()),
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AB= SKLearnModel(AdaBoostClassifier(n_estimators=15)),
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RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)),
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StaticMom= StaticMomentumModel(allow_short=True),
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Ensemble_Average = StaticAverageModel(),
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),
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}
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model_names_classification = list(model_map["classification_models"].keys())
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model_names_regression = list(model_map["regression_models"].keys())
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default_feature_selector_regression = model_map['regression_models']['RF']
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default_feature_selector_classification = model_map['classification_models']['RF']
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def map_model_name_to_function(model_config:dict, method:str) -> dict:
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model_config['level_1_models'] = [(model_name, model_map[method + '_models'][model_name]) for model_name in model_config['level_1_models']]
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if model_config['level_2_model'] is not None:
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model_config['level_2_model'] = (model_config['level_2_model'], model_map[method + '_models'][model_config['level_2_model']])
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return model_config |