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Fix documentation. #663

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merged 1 commit into from
Apr 11, 2020
Merged

Fix documentation. #663

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8 changes: 4 additions & 4 deletions tsfresh/feature_selection/relevance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,10 +113,10 @@ def calculate_relevance_table(X, y, ml_task='auto', n_jobs=defaults.N_PROCESSES,
:type show_warnings: bool

:param chunksize: The size of one chunk that is submitted to the worker
process for the parallelisation. Where one chunk is defined as a
singular time series for one id and one kind. If you set the chunksize
to 10, then it means that one task is to calculate all features for 10
time series. If it is set it to None, depending on distributor,
process for the parallelisation. Where one chunk is defined as
the data for one feature. If you set the chunksize
to 10, then it means that one task is to filter 10 features.
If it is set it to None, depending on distributor,
heuristics are used to find the optimal chunksize. If you get out of
memory exceptions, you can try it with the dask distributor and a
smaller chunksize.
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8 changes: 4 additions & 4 deletions tsfresh/feature_selection/selection.py
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Expand Up @@ -105,10 +105,10 @@ def select_features(X, y, test_for_binary_target_binary_feature=defaults.TEST_FO
:type show_warnings: bool

:param chunksize: The size of one chunk that is submitted to the worker
process for the parallelisation. Where one chunk is defined as a
singular time series for one id and one kind. If you set the chunksize
to 10, then it means that one task is to calculate all features for 10
time series. If it is set it to None, depending on distributor,
process for the parallelisation. Where one chunk is defined as
the data for one feature. If you set the chunksize
to 10, then it means that one task is to filter 10 features.
If it is set it to None, depending on distributor,
heuristics are used to find the optimal chunksize. If you get out of
memory exceptions, you can try it with the dask distributor and a
smaller chunksize.
Expand Down