Longitudinal preprocessor parallelization#246
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PhilipDeegan
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Jun 8, 2018
| # split the data with stratified KFold | ||
| kf = StratifiedKFold(n_folds, shuffle, self.random_state) | ||
| labels_interval = np.nonzero(p_labels)[1] | ||
| # labels_interval = np.nonzero(p_labels)[1] |
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Mbompr
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Jun 8, 2018
| ArrayULong &out_col, | ||
| ArrayDouble &out_data, | ||
| ulong censoring) const { | ||
| // TODO: add checks here ? Or do them in Python ? |
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Checks must be done in C++ to avoid segfault possibilities !
| """ | ||
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| def __init__(self, n_jobs=-1): | ||
| _attrinfos = {'n_jobs': {'writable': True}} |
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This _attrinfos is not necessary
| caution. | ||
| """ | ||
| global _cpp_preprocessor | ||
| _cpp_preprocessor = _LongitudinalFeaturesLagger(n_intervals, n_lags) |
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Here is a clever way (using __call__ in a class) to avoid global variable, did you give it a try?
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has this been obsoleted by #373 ? |
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Yep
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Make the longitudinal preprocessors serializable and make them parallel using python.multiprocessing.
To avoid sending the whole instance of each class to spawned or forked processes, I used an initializer in the process Pool to create a cpp object instance per process. I also used higher-order functions for the same purpose.
The drawback of doing so is having the pool initializers (
_inject_cpp_objectmethods) usingglobalvariables to store the cpp objects. This should not have dire consequences in the multiprocessing context, as each process possesses its own namespace. However, such methods could cause some trouble if called outside of this context (rogue/monkey user).