gavindiaz/drift
1
0
Fork 0
mirror of https://github.com/webclinic017/drift.git synced 2026-08-18 05:18:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat(Models): added StaticAverageModel for average ensembling & StaticNaiveModel (#64)

Browse Source 
* feat(Models): added StaticAverageModel for average ensembling

* feat(Models): made sure we only pipe in predictions to StaticAverageModel, added StaticNaiveModel as potential baseline

* chore(Models): removed unnecessary commented out code
This commit is contained in:
Mark Aron Szulyovszky
2021-12-21 15:57:08 +01:00
committed by GitHub
parent 79d84cf0a3
commit d3d7184ea4
6 changed files with 64 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files
run_pipeline.py
+11 -6
View File
@@ -15,6 +15,8 @@ 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
from models.average import StaticAverageModel
from models.naive import StaticNaiveModel
import feature_extractors.feature_extractor_presets as feature_extractor_presets
from training.pipeline import run_single_asset_trainig_pipeline
@@ -53,19 +55,22 @@ def get_config() -> tuple[dict, dict, dict]:
# ('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 - Average', StaticAverageModel())]
# regression_ensemble_model = [('Ensemble - Ridge', SKLearnModel(Ridge(alpha=0.1)))]
classification_models = [
('LR', SKLearnModel(LogisticRegression(n_jobs=-1))),
# ('LDA', SKLearnModel(LinearDiscriminantAnalysis())),
# ('KNN', SKLearnModel(KNeighborsClassifier())),
# ('CART', SKLearnModel(DecisionTreeClassifier())),
('StaticMomentum', StaticMomentumModel(allow_short=True))
('LDA', SKLearnModel(LinearDiscriminantAnalysis())),
('KNN', SKLearnModel(KNeighborsClassifier())),
('CART', SKLearnModel(DecisionTreeClassifier())),
('StaticMomentum', StaticMomentumModel(allow_short=True)),
# ('StaticNaive', StaticNaiveModel()),
# ('NB', SKLearnModel(GaussianNB())),
# ('AB', SKLearnModel(AdaBoostClassifier())),
# ('RF', SKLearnModel(RandomForestClassifier(n_jobs=-1)))
]
classification_ensemble_model = [('Ensemble - CART', SKLearnModel(DecisionTreeClassifier()))]
classification_ensemble_model = [('Ensemble - Average', StaticAverageModel())]
# classification_ensemble_model = [('Ensemble - CART', SKLearnModel(DecisionTreeClassifier()))]
model_config = dict(
level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,