This PR fixes seed initialization which is a prerequisite for test reproducibility.
Even after using the new RNG (see #50) some tests were not producing consistent results. The failure seemed to only affect the classification kernel, and after several tests I was able to fix the issue by initializing the global np.random seed using np.random.seed. Apparently this affects sklearn, and after setting it to the given seed I was able to have consistent results in the tests (see also this SO thread).
Grant also suggested to pass the RNG to sklearn.model_selection.train_test_split. This is probably a better solution, but I still need to run more tests before creating a new PR.
Note that in this PR I also set the TensorFlow seed -- it doesn't appear to be used, but it's good to have them all set to the same value for consistency.
This PR fixes seed initialization which is a prerequisite for test reproducibility.
Even after using the new RNG (see #50) some tests were not producing consistent results. The failure seemed to only affect the classification kernel, and after several tests I was able to fix the issue by initializing the global
np.randomseed usingnp.random.seed. Apparently this affectssklearn, and after setting it to the given seed I was able to have consistent results in the tests (see also this SO thread).Grant also suggested to pass the RNG to sklearn.model_selection.train_test_split. This is probably a better solution, but I still need to run more tests before creating a new PR.
Note that in this PR I also set the TensorFlow seed -- it doesn't appear to be used, but it's good to have them all set to the same value for consistency.