@@ -230,7 +230,7 @@ def _train(client, data, label, params, model_factory, sample_weight=None, group
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return part # trigger error locally
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# Find locations of all parts and map them to particular Dask workers
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- key_to_part_dict = dict ([( part .key , part ) for part in parts ])
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+ key_to_part_dict = { part .key : part for part in parts }
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who_has = client .who_has (parts )
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worker_map = defaultdict (list )
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for key , workers in who_has .items ():
@@ -280,6 +280,18 @@ def _train(client, data, label, params, model_factory, sample_weight=None, group
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for num_thread_alias in _ConfigAliases .get ('num_threads' ):
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params .pop (num_thread_alias , None )
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+ # machines is constructed manually, so remove it and all aliases of it from params
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+ for machine_alias in _ConfigAliases .get ('machines' ):
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+ params .pop (machine_alias , None )
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+
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+ # machines is constructed manually, so remove machine_list_filename and all aliases of it from params
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+ for machine_list_filename_alias in _ConfigAliases .get ('machine_list_filename' ):
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+ params .pop (machine_list_filename_alias , None )
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+
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+ # machines is constructed manually, so remove num_machines and all aliases of it from params
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+ for num_machine_alias in _ConfigAliases .get ('num_machines' ):
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+ params .pop (num_machine_alias , None )
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+
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# Tell each worker to train on the parts that it has locally
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futures_classifiers = [
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client .submit (
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