The feature extraction as well as the feature selection offer the possibility of parallelization. Out of the box both tasks are parallelized by tsfresh. However, the overhead introduced with the parallelization should not be underestimated. Here we discuss the different settings to control the parallelization. To achieve best results for your use-case you should experiment with the parameters.
Please let us know about your results tuning the below mentioned parameters! It will help improve this document as well as the default settings.
Parallelization of Feature Selection¶
We use a
multiprocessing.Pool to parallelize the calculation of the p-values for each feature. On
instantiation we set the Pool’s number of worker processes to
n_jobs. This field defaults to
the number of processors on the current system. We recommend setting it to the maximum number of available (and
otherwise idle) processors.
The chunksize of the Pool’s map function is another important parameter to consider. It can be set via the
chunksize field. By default it is up to
multiprocessing.Pool is parallelisation parameter. One data chunk is
defined as a singular time series for one id and one kind. The chunksize is the
number of chunks that are submitted as one task to one worker process. If you
set the chunksize to 10, then it means that one worker task corresponds to
calculate all features for 10 id/kind time series combinations. If it is set it
to None, depending on distributor, heuristics are used to find the optimal
chunksize. The chunksize can have an crucial influence on the optimal cluster
performance and should be optimised in benchmarks for the problem at hand.
Parallelization of Feature Extraction¶
For the feature extraction tsfresh exposes the parameters n_jobs and chunksize. Both behave analogue to the parameters for the feature selection.
To do performance studies and profiling, it sometimes quite useful to turn off parallelization at all. This can be setting the parameter n_jobs to 0.