Open dkgaraujo opened 1 week ago
import numpy as np from gingado.utils import Lag from sklearn.pipeline import Pipeline X = np.random.rand(15, 2) y = np.random.rand(15) lags = 3 jump = 2 pipe = Pipeline([('lagger', Lag(lags=lags, jump=jump, keep_contemporaneous_X=False))]).fit_transform(X, y) # the above works well, but: pipe = Pipeline([('lagger', Lag(lags=lags, jump=jump, keep_contemporaneous_X=True))]).fit_transform(X, y) """ Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib64/python3.11/site-packages/sklearn/base.py", line 1473, in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib64/python3.11/site-packages/sklearn/pipeline.py", line 544, in fit_transform return last_step.fit_transform( ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib64/python3.11/site-packages/sklearn/utils/_set_output.py", line 313, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib64/python3.11/site-packages/sklearn/base.py", line 1101, in fit_transform return self.fit(X, y, **fit_params).transform(X) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib64/python3.11/site-packages/sklearn/utils/_set_output.py", line 313, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/douglas-araujo/.local/lib/python3.11/site-packages/gingado/utils.py", line 90, in transform X_colnames = list(self.feature_names_in_) if self.keep_contemporaneous_X else [] ^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'Lag' object has no attribute 'feature_names_in_' """