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feat(Model): added own Model class, SkLearnModel wrapper and StaticMomentumModel (#61)

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* feat(Model): added own `Model` class, SkLearnModel wrapper and StaticMomentumModel

* fix(Tests): added missing Model variable

* fix(Tests): added missing clone method()
This commit is contained in:
Mark Aron Szulyovszky
2021-12-21 10:30:09 +01:00
committed by GitHub
parent 85ad937078
commit 79d84cf0a3
8 changed files with 122 additions and 45 deletions
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run_pipeline.py
+25 -24
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@@ -13,21 +13,22 @@ from sklearn.svm import SVR
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier
from models.base import SKLearnModel
from models.momentum import StaticMomentumModel
import feature_extractors.feature_extractor_presets as feature_extractor_presets
from training.pipeline import run_single_asset_trainig_pipeline
from typing import Tuple
def get_config()->Tuple[dict, dict, dict]:
def get_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/',
@@ -41,30 +42,30 @@ def get_config()->Tuple[dict, dict, dict]:
method= 'classification',
)
classification_models = [
('LR', LogisticRegression(n_jobs=-1)),
# ('LDA', LinearDiscriminantAnalysis()),
('KNN', KNeighborsClassifier()),
# ('CART', DecisionTreeClassifier()),
# ('NB', GaussianNB()),
# ('AB', AdaBoostClassifier()),
# ('RF', RandomForestClassifier(n_jobs=-1))
]
regression_models = [
# ('Lasso', Lasso(alpha=0.1, max_iter=1000)),
('Ridge', Ridge(alpha=0.1)),
('BayesianRidge', BayesianRidge()),
# ('KNN', KNeighborsRegressor(n_neighbors=25)),
# ('AB', AdaBoostRegressor(random_state=1)),
# ('LR', LinearRegression(n_jobs=-1)),
# ('MLP', MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
# ('RF', RandomForestRegressor(n_jobs=-1)),
# ('SVR', SVR(kernel='rbf', C=1e3, gamma=0.1))
# ('Lasso', SKLearnModel(Lasso(alpha=0.1, max_iter=1000))),
('Ridge', SKLearnModel(Ridge(alpha=0.1))),
('BayesianRidge', SKLearnModel(BayesianRidge())),
# ('KNN', SKLearnModel(KNeighborsRegressor(n_neighbors=25))),
# ('AB', SKLearnModel(AdaBoostRegressor(random_state=1))),
# ('LR', SKLearnModel(LinearRegression(n_jobs=-1))),
# ('MLP', SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000))),
# ('RF', SKLearnModel(RandomForestRegressor(n_jobs=-1))),
# ('SVR', SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1)))
]
regression_ensemble_model = [('Ensemble - Ridge', SKLearnModel(Ridge(alpha=0.1)))]
regression_ensemble_model = [('Ensemble - Ridge', Ridge(alpha=0.1))]
classification_ensemble_model = [('Ensemble - CART', DecisionTreeClassifier())]
classification_models = [
('LR', SKLearnModel(LogisticRegression(n_jobs=-1))),
# ('LDA', SKLearnModel(LinearDiscriminantAnalysis())),
# ('KNN', SKLearnModel(KNeighborsClassifier())),
# ('CART', SKLearnModel(DecisionTreeClassifier())),
('StaticMomentum', StaticMomentumModel(allow_short=True))
# ('NB', SKLearnModel(GaussianNB())),
# ('AB', SKLearnModel(AdaBoostClassifier())),
# ('RF', SKLearnModel(RandomForestClassifier(n_jobs=-1)))
]
classification_ensemble_model = [('Ensemble - CART', SKLearnModel(DecisionTreeClassifier()))]
model_config = dict(
level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,