afreg = AutoFeatRegressor(verbose=1, feateng_steps=3, n_jobs=24)
afreg.fit(x_train, y_train)
ValueError Traceback (most recent call last)
Input In [15], in <cell line: 2>()
1 afreg = AutoFeatRegressor(verbose=1, feateng_steps=1, n_jobs=24)
----> 2 afreg.fit(x_train, y_train)
File /app/conda/b2b-latest/lib/python3.8/site-packages/autofeat/autofeat.py:422, in AutoFeatModel.fit(self, X, y)
420 print("[AutoFeat] Warning: This just calls fit_transform() but does not return the transformed dataframe.")
421 print("[AutoFeat] It is much more efficient to call fit_transform() instead of fit() and transform()!")
--> 422 _ = self.fit_transform(X, y) # noqa
423 return self
File /app/conda/b2b-latest/lib/python3.8/site-packages/autofeat/autofeat.py:353, in AutoFeatModel.fit_transform(self, X, y)
351 else:
352 if self.problem_type in ("regression", "classification"):
--> 353 good_cols = select_features(
354 df_subs, target_sub, self.featsel_runs, None, self.problem_type, self.n_jobs, self.verbose
355 )
356 # if no features were selected, take the original features
357 if not good_cols:
File /app/conda/b2b-latest/lib/python3.8/site-packages/autofeat/featsel.py:245, in select_features(df, target, featsel_runs, keep, problem_type, n_jobs, verbose)
241 def flatten_lists(l: list):
242 return [item for sublist in l for item in sublist]
244 selected_columns = flatten_lists(
--> 245 Parallel(n_jobs=n_jobs, verbose=100 * verbose)(delayed(run_select_features)(i) for i in range(featsel_runs))
246 )
248 if selected_columns:
249 selected_columns = Counter(selected_columns)
File /app/conda/b2b-latest/lib/python3.8/site-packages/joblib/parallel.py:1944, in Parallel.call(self, iterable)
1938 # The first item from the output is blank, but it makes the interpreter
1939 # progress until it enters the Try/Except block of the generator and
1940 # reach the first yield
statement. This starts the aynchronous
1941 # dispatch of the tasks to the workers.
1942 next(output)
-> 1944 return output if self.return_generator else list(output)
File /app/conda/b2b-latest/lib/python3.8/site-packages/joblib/parallel.py:1587, in Parallel._get_outputs(self, iterator, pre_dispatch)
1584 yield
1586 with self._backend.retrieval_context():
-> 1587 yield from self._retrieve()
1589 except GeneratorExit:
1590 # The generator has been garbage collected before being fully
1591 # consumed. This aborts the remaining tasks if possible and warn
1592 # the user if necessary.
1593 self._exception = True
File /app/conda/b2b-latest/lib/python3.8/site-packages/joblib/parallel.py:1691, in Parallel._retrieve(self)
1684 while self._wait_retrieval():
1685
1686 # If the callback thread of a worker has signaled that its task
1687 # triggered an exception, or if the retrieval loop has raised an
1688 # exception (e.g. GeneratorExit
), exit the loop and surface the
1689 # worker traceback.
1690 if self._aborting:
-> 1691 self._raise_error_fast()
1692 break
1694 # If the next job is not ready for retrieval yet, we just wait for
1695 # async callbacks to progress.
File /app/conda/b2b-latest/lib/python3.8/site-packages/joblib/parallel.py:1726, in Parallel._raise_error_fast(self)
1722 # If this error job exists, immediatly raise the error by
1723 # calling get_result. This job might not exists if abort has been
1724 # called directly or if the generator is gc'ed.
1725 if error_job is not None:
-> 1726 error_job.get_result(self.timeout)
File /app/conda/b2b-latest/lib/python3.8/site-packages/joblib/parallel.py:735, in BatchCompletionCallBack.get_result(self, timeout)
729 backend = self.parallel._backend
731 if backend.supports_retrieve_callback:
732 # We assume that the result has already been retrieved by the
733 # callback thread, and is stored internally. It's just waiting to
734 # be returned.
--> 735 return self._return_or_raise()
737 # For other backends, the main thread needs to run the retrieval step.
738 try:
File /app/conda/b2b-latest/lib/python3.8/site-packages/joblib/parallel.py:753, in BatchCompletionCallBack._return_or_raise(self)
751 try:
752 if self.status == TASK_ERROR:
--> 753 raise self._result
754 return self._result
755 finally:
ValueError: Input X contains NaN.
LassoLarsCV does not accept missing values encoded as NaN natively.