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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
parent 797d45d036
commit 31dc847be1
8 changed files with 119 additions and 77 deletions
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data_loader/process_data.py
+3 -1
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@@ -3,16 +3,18 @@ from operator import itemgetter
from utils.helpers import has_enough_samples_to_train
from feature_selection.dim_reduction import reduce_dimensionality
from models.model_map import default_feature_selector_regression, default_feature_selector_classification
from models.model_map import get_model_map
from feature_selection.feature_selection import select_features
import warnings
def process_data(X:pd.DataFrame, y:pd.Series, configs: dict) -> tuple[pd.DataFrame,pd.DataFrame]:
model_config, training_config, data_config = itemgetter('model_config', 'training_config', 'data_config')(configs)
_, _, _, default_feature_selector_regression, default_feature_selector_classification = get_model_map(model_config)
original_X = X.copy()