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[REVIEW] Dask RF Classifier #56
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cjnolet
suggested changes
Jul 16, 2019
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I know this PR was just meant for a quick overview, but I notice this does not include any automated pytests.
Also, it's going to be important that the dataframe passed in contains only 1 partition per worker. Originally I was thiking of writing some custom code to do this, but I believe it's better that we use Dask's repartition() function for this first iteration.
| import math | ||
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| @gen.coroutine | ||
| def _extract_ddf_partitions(ddf): |
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This is actually a utility function inside cuml.dask.dask_df_utils in the new comms PR.
| """ | ||
| c = default_client() | ||
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| X_futures = c.sync(_extract_ddf_partitions, X) |
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Call version from cuml.dask.common.dask_df_utils
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This PR includes the dask RF classifier. To run the code, cuml from the following link should be installed;
https://github.com/oyilmaz-nvidia/cuml/tree/fea-update-predict
Once this PR is approved, another PR for the the updates made in the fea-update-predict branch of cuml will be created.