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License: GNU Affero General Public License v3.0
Bayesian neural networks using Numpy and Scipy
License: GNU Affero General Public License v3.0
In a few of the bn functions (e.g. get_data
, npBNN.init
) the global seed is being set --- np.random.seed(seed)
. This is causing issues for me with other processes that use randoms numbers. In my case, this is affecting the generation of train/test indices in cross validation. If running the following example, the train/test indices are simply repeated where they shouldn't be. I believe the fix to this is to use a Random object and set its seed instead of the global seed.
import np_bnn as bn
import pandas as pd
from sklearn.model_selection import RepeatedKFold
# Example data
from import_trait_data import NUMERIC_TRAITS, IMPORTED_TRAIT_CSV
train_data = pd.read_csv(IMPORTED_TRAIT_CSV)
X = train_data[NUMERIC_TRAITS]
y = train_data['Activity_Antimalarial']
rskf = RepeatedKFold(n_splits=2, n_repeats=5)
for i, (train_index, test_index) in enumerate(rskf.split(X, y)):
print(f'Fold: {i}')
print(train_index)
print(test_index)
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
train_dat = bn.get_data(X_train, y_train,
seed=1234,
testsize=0,
randomize_order=False,
instance_id=None, from_file=False)
Is pickle5 really required? I'm having trouble installing it on a server but npBNN appears to run as expected without it
Line 96 in 9439b02
In the example, feature_blocks
are given as a list which, for me, raises the error: "AttributeError: 'list' object has no attribute 'keys'", as the function expects feature_blocks
to be a dict
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