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/opt/anaconda3/lib/python3.6/site-packages/autosklearn/automl.py in subsample_if_too_large(X, y, logger, seed, memory_limit, task)
820 elif X.dtype in (np.float64, np.float):
821 multiplier = 8
--> 822 elif X.dtype == np.float128:
823 multiplier = 16
824 else:
AttributeError: module 'numpy' has no attribute 'float128'
To Reproduce
On a Debian Linux 32-bit, with Python 3.6.7 (Anaconda 32-bit)
Linux 3.13.0-170-generic Error on Refit with ProjLogit #220-Ubuntu SMP Thu May 9 12:41:17 UTC 2019 i686 athlon i686 GNU/Linux
The following code:
import autosklearn.classification
automl = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=2060,
per_run_time_limit=260,
n_jobs=2,
memory_limit=1024,
seed=42,
include_preprocessors=["no_preprocessing"]
)
automl.fit(X_train, y_train)
Expected behavior
The code should work regardless of 32 or 64-bit architectures, as Scikit-Learn does.
Environment and installation:
Please give details about your installation:
OS: Ubuntu 32-bit
Is your installation in a virtual environment or conda environment? Anaconda
Python version: 3.6.7
Auto-sklearn version: 0.12.5
The text was updated successfully, but these errors were encountered:
Thanks for reporting this issue @hjort. Sorry for the trouble, I was not aware of the fact that np.float128 is not available on 32 bit systems. We would appreciate a PR fixing this issue.
Describe the bug
Got the following issue:
/opt/anaconda3/lib/python3.6/site-packages/autosklearn/automl.py in subsample_if_too_large(X, y, logger, seed, memory_limit, task)
820 elif X.dtype in (np.float64, np.float):
821 multiplier = 8
--> 822 elif X.dtype == np.float128:
823 multiplier = 16
824 else:
AttributeError: module 'numpy' has no attribute 'float128'
To Reproduce
Linux 3.13.0-170-generic Error on Refit with ProjLogit #220-Ubuntu SMP Thu May 9 12:41:17 UTC 2019 i686 athlon i686 GNU/Linux
import autosklearn.classification
automl = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=2060,
per_run_time_limit=260,
n_jobs=2,
memory_limit=1024,
seed=42,
include_preprocessors=["no_preprocessing"]
)
automl.fit(X_train, y_train)
Expected behavior
The code should work regardless of 32 or 64-bit architectures, as Scikit-Learn does.
Environment and installation:
Please give details about your installation:
The text was updated successfully, but these errors were encountered: