Closed apexjmr closed 4 months ago
In genetic.py, I have hard coded gplearn source to what I am hoping to have added as a parameter to gplearn:
fit:
X, y = self._validate_data(X, y, y_numeric=True, validate_separately=(
dict(force_all_finite=False),
dict(force_all_finite=True, ensure_2d=False),
))
transform:
X = check_array(X, force_all_finite=False)
tbh i dont see a lot of demand here. allowing NaN's in X would require all functions used in the evolution to handle them and currently all will propogate NA through to fitness evaluation.
I would like to enable nans in X. Is it possible to prevent this check?