converted most print statements to logging outputs
moved tests and make pytest compatible
more qa and style fixes (using ruff)
2.1.1 (2023-06-25)
fixed annotations for backwards compatibility
2.1.0 (2023-05-14)
added predict_proba functionality for classifier (by @mglowacki100)
formatting fixes (using black)
added type hints
2.0.10 (2021-10-28)
fixed issue #29 (by @stephanos-stephani)
2.0.9 (2021-06-12)
speed up correlation computation; fixes issue #28
2.0.8 (2021-06-03)
use numba jit for feature generation (by @jeethu)
2.0.7 (2021-06-02)
use numba for standardization (by @jeethu)
2.0.5 (2021-01-16)
fixed TypeError while running tests with scikit-learn 0.24.0 (by @jeethu)
minor efficiency improvements in apply_transformations (by @jeethu)
use numba to accelerate feateng (by @jeethu)
2.0.4 (2020-11-30)
update sympy call to work with new version
2.0.3 (2020-11-11)
turn scaling off by default
remove more correlated cols by starting with the features that has the most correlated columns
2.0.2 (2020-11-11)
fixed typo
2.0.1 (2020-11-11)
use correlation threshold in autofeat light as parameter
2.0.0 (2020-11-07)
added AutoFeatLight model for simple feature selection (removing zero variance and redundant features), engineering (product and ratio of original features) and power transform to make features more normally distributed
1.1.3 (2020-07-21)
categorical columns can contain strings now
1.1.2 (2020-02-28)
don't generate addition/subtr features at the highest level, i.e., if they would just be removed anyways
1.1.1 (2020-02-25)
use LassoLarsCV instead of RidgeCV as final regression model
minor tweaks to feature selection to avoid longer formulas
1.1.0 (2020-02-24)
include categorical columns for feateng by default
add correlation filtering back into feat selection
1.0.0 (2020-02-24)
changed autofeat model to differentiate between regression and classification tasks, adding the AutoFeatRegressor and AutoFeatClassifier classes
simplified feature selection process
0.2.5 (2019-05-12)
more robust featsel with noise filtering
0.2.2 (2019-05-09)
change default value for feateng_steps to 2, in line with results on realworld datasets
0.2.1 (2019-05-09)
make feature selection less prone to overfitting
0.2.0 (2019-05-02)
add FeatureSelector class to use feature selection separately
make feature selection more robust and move into featsel
make the models more sklearn like and test with sklearn estimator tests