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feature(MetaLabeling): replaced previous non-functional Ensembling method with Meta-labeling method available for both lvl1 and lvl2 models (#110)

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* feature(MetaLabeling): added hacky prototype

* fix(MetaLabeling): drop index until first valid X & y

* fix(MetaLabeling): transform both X & y before feature selection

* fix(MetaLabeling): got feature selection to work

* fix(MetaLabeling): correct values for meta_y

* feat(MetaLabeling): created predictions multiplied by bet sizes

* feat(Pipeline): print out averaged result

* fix(Evaluation): correctly deal with non-discretized data

* fix(Pipeline): use the right column names

* refactor(Pipeline): move out meta-labeling

* refactor(Pipeline): complete refactoring

* feat(CI): post results to PR

* fix(Pipeline): use the correct filename

* chore(Config): removed now redundant feature_selection flag

* feat(Models): added SVC

* fix(Pipeline): accidentally switched two return values

* feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file

* fix(Pipeline): wrong function name

* fix(Sweep): yaml + run_sweep

* fix(Sweep): typo in name

* fix(Reporting): only save averaged results

* feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging

* feat(Reporting): print out sharpe improvement in meta-labeling step

* fix(Sweep): adjusted config, defaulted to good defaults

* fix(Sweep): adjusted sweep
This commit is contained in:
Mark Aron Szulyovszky
2022-01-06 16:36:45 +01:00
committed by GitHub
parent b1dcdfc09d
commit 9488e92597
20 changed files with 335 additions and 132 deletions
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models/model_map.py
+4 -2
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@@ -2,7 +2,7 @@ from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, Logisti
from sklearn.tree import DecisionTreeClassifier
from sklearnex.neighbors import KNeighborsRegressor, KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearnex.svm import SVR
from sklearnex.svm import SVR, SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
@@ -44,7 +44,9 @@ model_map = {
NB= SKLearnModel(GaussianNB()),
AB= SKLearnModel(AdaBoostClassifier(n_estimators=15)),
RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)),
XGB= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, use_label_encoder=True, objective='multi:softprob', eval_metric='mlogloss')),
SVC = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True)),
XGB_three_class= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, use_label_encoder=True, objective='multi:softprob', eval_metric='mlogloss')),
XGB_two_class= SKLearnModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', eval_metric='mlogloss')),
StaticMom= StaticMomentumModel(allow_short=True),
Ensemble_Average= StaticAverageModel(),
),