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Feature(Speed): Python launches faster by conditionally importing models. (#169)

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* feat: Added optional import of models.

* fix: Models weren't wrapped into abstract class, fixed it.

* chore: Deleted leftover comments.

* fix: Same merge commit as on remote.

* fix: System wasn't putting in RF because there was no differentiation between RF as regressor and RF as classificator.

* fix(Models): use the XGBoostModel wrapper

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
This commit is contained in:
Daniel Szemerey
2022-01-14 14:29:24 +01:00
committed by GitHub
co-authored by Daniel Szemerey Mark Aron Szulyovszky
parent 797d45d036
commit 31dc847be1
8 changed files with 119 additions and 77 deletions
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sweep_ensemble.yaml
+3 -3
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@@ -47,9 +47,9 @@ parameters:
primary_models:
distribution: categorical
values:
- ["LDA", "LR_two_class", "KNN", "SVC", "CART", "NB", "AB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "NB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "LGBM", "RF", "XGB_two_class"]
- ["LDA", "LR_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
meta_labeling_models:
value: ["LGBM", "LR_two_class"]
own_features: