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@@ -1,121 +1,609 @@
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# pylint: disable=wrong-import-position,wrong-import-order,import-error
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"""Dask extensions for distributed training. See xgboost/demo/dask for examples."""
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import os
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import math
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import platform
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import logging
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from threading import Thread
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from . import rabit
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from .core import DMatrix
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from .compat import (DaskDataFrame, DaskSeries, DaskArray,
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distributed_get_worker)
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from .tracker import RabitTracker
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def _start_tracker(n_workers):
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""" Start Rabit tracker """
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host = distributed_get_worker().address
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if '://' in host:
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host = host.rsplit('://', 1)[1]
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host, port = host.split(':')
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port = int(port)
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env = {'DMLC_NUM_WORKER': n_workers}
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rabit_context = RabitTracker(hostIP=host, nslave=n_workers)
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env.update(rabit_context.slave_envs())
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rabit_context.start(n_workers)
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thread = Thread(target=rabit_context.join)
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thread.daemon = True
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thread.start()
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return env
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def get_local_data(data):
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"""
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Unpacks a distributed data object to get the rows local to this worker
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:param data: A distributed dask data object
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:return: Local data partition e.g. numpy or pandas
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"""
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if isinstance(data, DaskArray):
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total_partitions = len(data.chunks[0])
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else:
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total_partitions = data.npartitions
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partition_size = int(math.ceil(total_partitions / rabit.get_world_size()))
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begin_partition = partition_size * rabit.get_rank()
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end_partition = min(begin_partition + partition_size, total_partitions)
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if isinstance(data, DaskArray):
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return data.blocks[begin_partition:end_partition].compute()
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return data.partitions[begin_partition:end_partition].compute()
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def create_worker_dmatrix(*args, **kwargs):
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"""
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Creates a DMatrix object local to a given worker. Simply forwards arguments onto the standard
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DMatrix constructor, if one of the arguments is a dask dataframe, unpack the data frame to
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get the local components.
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All dask dataframe arguments must use the same partitioning.
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:param args: DMatrix constructor args.
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:return: DMatrix object containing data local to current dask worker
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"""
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dmatrix_args = []
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dmatrix_kwargs = {}
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# Convert positional args
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for arg in args:
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if isinstance(arg, (DaskDataFrame, DaskSeries, DaskArray)):
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dmatrix_args.append(get_local_data(arg))
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else:
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dmatrix_args.append(arg)
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# Convert keyword args
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for k, v in kwargs.items():
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if isinstance(v, (DaskDataFrame, DaskSeries, DaskArray)):
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dmatrix_kwargs[k] = get_local_data(v)
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else:
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dmatrix_kwargs[k] = v
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return DMatrix(*dmatrix_args, **dmatrix_kwargs)
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def _run_with_rabit(rabit_args, func, *args):
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worker = distributed_get_worker()
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try:
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os.environ["OMP_NUM_THREADS"] = str(worker.ncores)
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except AttributeError:
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os.environ["OMP_NUM_THREADS"] = str(worker.nthreads)
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try:
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rabit.init(rabit_args)
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result = func(*args)
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finally:
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rabit.finalize()
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return result
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def run(client, func, *args):
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"""Launch arbitrary function on dask workers. Workers are connected by rabit,
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allowing distributed training. The environment variable OMP_NUM_THREADS is
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defined on each worker according to dask - this means that calls to
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xgb.train() will use the threads allocated by dask by default, unless the
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user overrides the nthread parameter.
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Note: Windows platforms are not officially
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supported. Contributions are welcome here.
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:param client: Dask client representing the cluster
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:param func: Python function to be executed by each worker. Typically
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contains xgboost training code.
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:param args: Arguments to be forwarded to func
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:return: Dict containing the function return value for each worker
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"""
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if platform.system() == 'Windows':
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logging.warning('Windows is not officially supported for dask/xgboost'
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'integration. Contributions welcome.')
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workers = list(client.scheduler_info()['workers'].keys())
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env = client.run(_start_tracker, len(workers), workers=[workers[0]])
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rabit_args = [('%s=%s' % item).encode() for item in env[workers[0]].items()]
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return client.run(_run_with_rabit, rabit_args, func, *args)
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# pylint: disable=too-many-arguments, too-many-locals
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"""Dask extensions for distributed training. See
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https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for simple
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tutorial. Also xgboost/demo/dask for some examples.
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There are two sets of APIs in this module, one is the functional API including
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``train`` and ``predict`` methods. Another is stateful Scikit-Learner wrapper
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inherited from single-node Scikit-Learn interface.
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The implementation is heavily influenced by dask_xgboost:
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https://github.com/dask/dask-xgboost
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"""
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import platform
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import logging
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from collections import defaultdict
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from threading import Thread
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import numpy
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from . import rabit
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from .compat import DASK_INSTALLED
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from .compat import distributed_get_worker, distributed_wait, distributed_comm
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from .compat import da, dd, delayed, get_client
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from .compat import sparse, scipy_sparse
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from .compat import PANDAS_INSTALLED, DataFrame, Series, pandas_concat
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from .core import DMatrix, Booster, _expect
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from .training import train as worker_train
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from .tracker import RabitTracker
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from .sklearn import XGBModel, XGBClassifierBase
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# Current status is considered as initial support, many features are
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# not properly supported yet.
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#
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# TODOs:
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# - Callback.
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# - Label encoding.
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# - CV
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# - Ranking
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def _start_tracker(host, n_workers):
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"""Start Rabit tracker """
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env = {'DMLC_NUM_WORKER': n_workers}
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rabit_context = RabitTracker(hostIP=host, nslave=n_workers)
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env.update(rabit_context.slave_envs())
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rabit_context.start(n_workers)
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thread = Thread(target=rabit_context.join)
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thread.daemon = True
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thread.start()
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return env
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def _assert_dask_installed():
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if not DASK_INSTALLED:
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raise ImportError(
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'Dask needs to be installed in order to use this module')
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class RabitContext:
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'''A context controling rabit initialization and finalization.'''
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def __init__(self, args):
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self.args = args
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def __enter__(self):
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rabit.init(self.args)
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logging.debug('-------------- rabit say hello ------------------')
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def __exit__(self, *args):
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rabit.finalize()
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logging.debug('--------------- rabit say bye ------------------')
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def concat(value):
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'''To be replaced with dask builtin.'''
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if isinstance(value[0], numpy.ndarray):
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return numpy.concatenate(value, axis=0)
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if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
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return scipy_sparse.vstack(value, format='csr')
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if sparse and isinstance(value[0], sparse.SparseArray):
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return sparse.concatenate(value, axis=0)
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if PANDAS_INSTALLED and isinstance(value[0], (DataFrame, Series)):
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return pandas_concat(value, axis=0)
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return dd.multi.concat(list(value), axis=0)
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def _xgb_get_client(client):
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'''Simple wrapper around testing None.'''
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ret = get_client() if client is None else client
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return ret
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class DaskDMatrix:
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# pylint: disable=missing-docstring, too-many-instance-attributes
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'''DMatrix holding on references to Dask DataFrame or Dask Array.
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Parameters
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----------
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client: dask.distributed.Client
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Specify the dask client used for training. Use default client
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returned from dask if it's set to None.
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data : dask.array.Array/dask.dataframe.DataFrame
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data source of DMatrix.
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label: dask.array.Array/dask.dataframe.DataFrame
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label used for trainin.
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missing : float, optional
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Value in the input data (e.g. `numpy.ndarray`) which needs
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to be present as a missing value. If None, defaults to np.nan.
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weight : dask.array.Array/dask.dataframe.DataFrame
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Weight for each instance.
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feature_names : list, optional
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Set names for features.
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feature_types : list, optional
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Set types for features
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'''
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_feature_names = None # for previous version's pickle
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_feature_types = None
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def __init__(self,
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client,
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data,
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label=None,
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missing=None,
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weight=None,
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feature_names=None,
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feature_types=None):
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_assert_dask_installed()
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self._feature_names = feature_names
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self._feature_types = feature_types
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self._missing = missing
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if len(data.shape) != 2:
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_expect('2 dimensions input', data.shape)
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self.n_rows = data.shape[0]
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self.n_cols = data.shape[1]
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if not any(isinstance(data, t) for t in (dd.DataFrame, da.Array)):
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raise TypeError(_expect((dd.DataFrame, da.Array), type(data)))
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if not any(
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isinstance(label, t)
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for t in (dd.DataFrame, da.Array, dd.Series, type(None))):
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raise TypeError(
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_expect((dd.DataFrame, da.Array, dd.Series), type(label)))
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self.worker_map = None
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self.has_label = label is not None
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self.has_weights = weight is not None
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client = _xgb_get_client(client)
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client.sync(self.map_local_data, client, data, label, weight)
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async def map_local_data(self, client, data, label=None, weights=None):
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'''Obtain references to local data.'''
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data = data.persist()
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if label is not None:
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label = label.persist()
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if weights is not None:
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weights = weights.persist()
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# Breaking data into partitions, a trick borrowed from dask_xgboost.
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# `to_delayed` downgrades high-level objects into numpy or pandas
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# equivalents.
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X_parts = data.to_delayed()
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if isinstance(X_parts, numpy.ndarray):
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assert X_parts.shape[1] == 1
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X_parts = X_parts.flatten().tolist()
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if label is not None:
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y_parts = label.to_delayed()
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if isinstance(y_parts, numpy.ndarray):
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assert y_parts.ndim == 1 or y_parts.shape[1] == 1
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y_parts = y_parts.flatten().tolist()
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if weights is not None:
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w_parts = weights.to_delayed()
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if isinstance(w_parts, numpy.ndarray):
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assert w_parts.ndim == 1 or w_parts.shape[1] == 1
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w_parts = w_parts.flatten().tolist()
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parts = [X_parts]
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if label is not None:
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assert len(X_parts) == len(
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y_parts), 'Partitions between X and y are not consistent'
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parts.append(y_parts)
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if weights is not None:
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assert len(X_parts) == len(
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w_parts), 'Partitions between X and weight are not consistent.'
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parts.append(w_parts)
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parts = list(map(delayed, zip(*parts)))
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parts = client.compute(parts)
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await distributed_wait(parts) # async wait for parts to be computed
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for part in parts:
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assert part.status == 'finished'
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key_to_partition = {part.key: part for part in parts}
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who_has = await client.scheduler.who_has(
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keys=[part.key for part in parts])
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worker_map = defaultdict(list)
|
|
|
|
|
for key, workers in who_has.items():
|
|
|
|
|
worker_map[next(iter(workers))].append(key_to_partition[key])
|
|
|
|
|
|
|
|
|
|
self.worker_map = worker_map
|
|
|
|
|
|
|
|
|
|
def get_worker_parts(self, worker):
|
|
|
|
|
'''Get mapped parts of data in each worker.'''
|
|
|
|
|
list_of_parts = self.worker_map[worker.address]
|
|
|
|
|
assert list_of_parts, 'data in ' + worker.address + ' was moved.'
|
|
|
|
|
assert isinstance(list_of_parts, list)
|
|
|
|
|
|
|
|
|
|
# `get_worker_parts` is launched inside worker. In dask side
|
|
|
|
|
# this should be equal to `worker._get_client`.
|
|
|
|
|
client = get_client()
|
|
|
|
|
list_of_parts = client.gather(list_of_parts)
|
|
|
|
|
|
|
|
|
|
if self.has_label:
|
|
|
|
|
if self.has_weights:
|
|
|
|
|
data, labels, weights = zip(*list_of_parts)
|
|
|
|
|
else:
|
|
|
|
|
data, labels = zip(*list_of_parts)
|
|
|
|
|
weights = None
|
|
|
|
|
else:
|
|
|
|
|
data = [d[0] for d in list_of_parts]
|
|
|
|
|
labels = None
|
|
|
|
|
weights = None
|
|
|
|
|
return data, labels, weights
|
|
|
|
|
|
|
|
|
|
def get_worker_data(self, worker):
|
|
|
|
|
'''Get data that local to worker.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
worker: The worker used as key to data.
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
A DMatrix object.
|
|
|
|
|
|
|
|
|
|
'''
|
|
|
|
|
data, labels, weights = self.get_worker_parts(worker)
|
|
|
|
|
|
|
|
|
|
data = concat(data)
|
|
|
|
|
|
|
|
|
|
if self.has_label:
|
|
|
|
|
labels = concat(labels)
|
|
|
|
|
else:
|
|
|
|
|
labels = None
|
|
|
|
|
if self.has_weights:
|
|
|
|
|
weights = concat(weights)
|
|
|
|
|
else:
|
|
|
|
|
weights = None
|
|
|
|
|
|
|
|
|
|
dmatrix = DMatrix(data,
|
|
|
|
|
labels,
|
|
|
|
|
weight=weights,
|
|
|
|
|
missing=self._missing,
|
|
|
|
|
feature_names=self._feature_names,
|
|
|
|
|
feature_types=self._feature_types)
|
|
|
|
|
return dmatrix
|
|
|
|
|
|
|
|
|
|
def get_worker_data_shape(self, worker):
|
|
|
|
|
'''Get the shape of data X in each worker.'''
|
|
|
|
|
data, _, _ = self.get_worker_parts(worker)
|
|
|
|
|
|
|
|
|
|
shapes = [d.shape for d in data]
|
|
|
|
|
rows = 0
|
|
|
|
|
cols = 0
|
|
|
|
|
for shape in shapes:
|
|
|
|
|
rows += shape[0]
|
|
|
|
|
cols += shape[1]
|
|
|
|
|
return (rows, cols)
|
|
|
|
|
|
|
|
|
|
def num_row(self):
|
|
|
|
|
return self.n_rows
|
|
|
|
|
|
|
|
|
|
def num_col(self):
|
|
|
|
|
return self.n_cols
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _get_rabit_args(worker_map, client):
|
|
|
|
|
'''Get rabit context arguments from data distribution in DaskDMatrix.'''
|
|
|
|
|
host = distributed_comm.get_address_host(client.scheduler.address)
|
|
|
|
|
|
|
|
|
|
env = client.run_on_scheduler(_start_tracker, host.strip('/:'),
|
|
|
|
|
len(worker_map))
|
|
|
|
|
rabit_args = [('%s=%s' % item).encode() for item in env.items()]
|
|
|
|
|
return rabit_args
|
|
|
|
|
|
|
|
|
|
# train and predict methods are supposed to be "functional", which meets the
|
|
|
|
|
# dask paradigm. But as a side effect, the `evals_result` in single-node API
|
|
|
|
|
# is no longer supported since it mutates the input parameter, and it's not
|
|
|
|
|
# intuitive to sync the mutation result. Therefore, a dictionary containing
|
|
|
|
|
# evaluation history is instead returned.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def train(client, params, dtrain, *args, evals=(), **kwargs):
|
|
|
|
|
'''Train XGBoost model.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
client: dask.distributed.Client
|
|
|
|
|
Specify the dask client used for training. Use default client
|
|
|
|
|
returned from dask if it's set to None.
|
|
|
|
|
|
|
|
|
|
Other parameters are the same as `xgboost.train` except for `evals_result`,
|
|
|
|
|
which is returned as part of function return value instead of argument.
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
results: dict
|
|
|
|
|
A dictionary containing trained booster and evaluation history.
|
|
|
|
|
`history` field is the same as `eval_result` from `xgboost.train`.
|
|
|
|
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
|
|
|
|
|
|
{'booster': xgboost.Booster,
|
|
|
|
|
'history': {'train': {'logloss': ['0.48253', '0.35953']},
|
|
|
|
|
'eval': {'logloss': ['0.480385', '0.357756']}}}
|
|
|
|
|
|
|
|
|
|
'''
|
|
|
|
|
_assert_dask_installed()
|
|
|
|
|
if platform.system() == 'Windows':
|
|
|
|
|
msg = 'Windows is not officially supported for dask/xgboost,'
|
|
|
|
|
msg += ' contribution are welcomed.'
|
|
|
|
|
logging.warning(msg)
|
|
|
|
|
|
|
|
|
|
if 'evals_result' in kwargs.keys():
|
|
|
|
|
raise ValueError(
|
|
|
|
|
'evals_result is not supported in dask interface.',
|
|
|
|
|
'The evaluation history is returned as result of training.')
|
|
|
|
|
|
|
|
|
|
client = _xgb_get_client(client)
|
|
|
|
|
|
|
|
|
|
worker_map = dtrain.worker_map
|
|
|
|
|
rabit_args = _get_rabit_args(worker_map, client)
|
|
|
|
|
|
|
|
|
|
def dispatched_train(worker_id):
|
|
|
|
|
'''Perform training on worker.'''
|
|
|
|
|
logging.info('Training on %d', worker_id)
|
|
|
|
|
worker = distributed_get_worker()
|
|
|
|
|
local_dtrain = dtrain.get_worker_data(worker)
|
|
|
|
|
|
|
|
|
|
local_evals = []
|
|
|
|
|
if evals:
|
|
|
|
|
for mat, name in evals:
|
|
|
|
|
local_mat = mat.get_worker_data(worker)
|
|
|
|
|
local_evals.append((local_mat, name))
|
|
|
|
|
|
|
|
|
|
with RabitContext(rabit_args):
|
|
|
|
|
local_history = {}
|
|
|
|
|
local_param = params.copy() # just to be consistent
|
|
|
|
|
bst = worker_train(params=local_param,
|
|
|
|
|
dtrain=local_dtrain,
|
|
|
|
|
*args,
|
|
|
|
|
evals_result=local_history,
|
|
|
|
|
evals=local_evals,
|
|
|
|
|
**kwargs)
|
|
|
|
|
ret = {'booster': bst, 'history': local_history}
|
|
|
|
|
if rabit.get_rank() != 0:
|
|
|
|
|
ret = None
|
|
|
|
|
return ret
|
|
|
|
|
|
|
|
|
|
futures = client.map(dispatched_train,
|
|
|
|
|
range(len(worker_map)),
|
|
|
|
|
workers=list(worker_map.keys()))
|
|
|
|
|
results = client.gather(futures)
|
|
|
|
|
return list(filter(lambda ret: ret is not None, results))[0]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def predict(client, model, data, *args):
|
|
|
|
|
'''Run prediction with a trained booster.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
client: dask.distributed.Client
|
|
|
|
|
Specify the dask client used for training. Use default client
|
|
|
|
|
returned from dask if it's set to None.
|
|
|
|
|
model: A Booster or a dictionary returned by `xgboost.dask.train`.
|
|
|
|
|
The trained model.
|
|
|
|
|
data: DaskDMatrix
|
|
|
|
|
Input data used for prediction.
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
prediction: dask.array.Array
|
|
|
|
|
|
|
|
|
|
'''
|
|
|
|
|
_assert_dask_installed()
|
|
|
|
|
if isinstance(model, Booster):
|
|
|
|
|
booster = model
|
|
|
|
|
elif isinstance(model, dict):
|
|
|
|
|
booster = model['booster']
|
|
|
|
|
else:
|
|
|
|
|
raise TypeError(_expect([Booster, dict], type(model)))
|
|
|
|
|
|
|
|
|
|
if not isinstance(data, DaskDMatrix):
|
|
|
|
|
raise TypeError(_expect([DaskDMatrix], type(data)))
|
|
|
|
|
|
|
|
|
|
worker_map = data.worker_map
|
|
|
|
|
client = _xgb_get_client(client)
|
|
|
|
|
|
|
|
|
|
rabit_args = _get_rabit_args(worker_map, client)
|
|
|
|
|
|
|
|
|
|
def dispatched_predict(worker_id):
|
|
|
|
|
'''Perform prediction on each worker.'''
|
|
|
|
|
logging.info('Predicting on %d', worker_id)
|
|
|
|
|
worker = distributed_get_worker()
|
|
|
|
|
local_x = data.get_worker_data(worker)
|
|
|
|
|
|
|
|
|
|
with RabitContext(rabit_args):
|
|
|
|
|
local_predictions = booster.predict(data=local_x, *args)
|
|
|
|
|
return local_predictions
|
|
|
|
|
|
|
|
|
|
futures = client.map(dispatched_predict,
|
|
|
|
|
range(len(worker_map)),
|
|
|
|
|
workers=list(worker_map.keys()))
|
|
|
|
|
|
|
|
|
|
def dispatched_get_shape(worker_id):
|
|
|
|
|
'''Get shape of data in each worker.'''
|
|
|
|
|
logging.info('Trying to get data shape on %d', worker_id)
|
|
|
|
|
worker = distributed_get_worker()
|
|
|
|
|
rows, cols = data.get_worker_data_shape(worker)
|
|
|
|
|
return rows, cols
|
|
|
|
|
|
|
|
|
|
# Constructing a dask array from list of numpy arrays
|
|
|
|
|
# See https://docs.dask.org/en/latest/array-creation.html
|
|
|
|
|
futures_shape = client.map(dispatched_get_shape,
|
|
|
|
|
range(len(worker_map)),
|
|
|
|
|
workers=list(worker_map.keys()))
|
|
|
|
|
shapes = client.gather(futures_shape)
|
|
|
|
|
arrays = []
|
|
|
|
|
for i in range(len(futures_shape)):
|
|
|
|
|
arrays.append(da.from_delayed(futures[i], shape=shapes[i],
|
|
|
|
|
dtype=numpy.float32))
|
|
|
|
|
predictions = da.concatenate(arrays, axis=0)
|
|
|
|
|
return predictions
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _evaluation_matrices(client, validation_set, sample_weights):
|
|
|
|
|
'''
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
validation_set: list of tuples
|
|
|
|
|
Each tuple contains a validation dataset including input X and label y.
|
|
|
|
|
E.g.:
|
|
|
|
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
|
|
|
|
|
|
[(X_0, y_0), (X_1, y_1), ... ]
|
|
|
|
|
|
|
|
|
|
sample_weights: list of arrays
|
|
|
|
|
The weight vector for validation data.
|
|
|
|
|
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
evals: list of validation DMatrix
|
|
|
|
|
'''
|
|
|
|
|
evals = []
|
|
|
|
|
if validation_set is not None:
|
|
|
|
|
assert isinstance(validation_set, list)
|
|
|
|
|
for i, e in enumerate(validation_set):
|
|
|
|
|
w = (sample_weights[i]
|
|
|
|
|
if sample_weights is not None else None)
|
|
|
|
|
dmat = DaskDMatrix(client=client, data=e[0], label=e[1], weight=w)
|
|
|
|
|
evals.append((dmat, 'validation_{}'.format(i)))
|
|
|
|
|
else:
|
|
|
|
|
evals = None
|
|
|
|
|
return evals
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class DaskScikitLearnBase(XGBModel):
|
|
|
|
|
'''Base class for implementing scikit-learn interface with Dask'''
|
|
|
|
|
|
|
|
|
|
_client = None
|
|
|
|
|
|
|
|
|
|
# pylint: disable=arguments-differ
|
|
|
|
|
def fit(self,
|
|
|
|
|
X,
|
|
|
|
|
y,
|
|
|
|
|
sample_weights=None,
|
|
|
|
|
eval_set=None,
|
|
|
|
|
sample_weight_eval_set=None):
|
|
|
|
|
'''Fit the regressor.
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
X : array_like
|
|
|
|
|
Feature matrix
|
|
|
|
|
y : array_like
|
|
|
|
|
Labels
|
|
|
|
|
sample_weight : array_like
|
|
|
|
|
instance weights
|
|
|
|
|
eval_set : list, optional
|
|
|
|
|
A list of (X, y) tuple pairs to use as validation sets, for which
|
|
|
|
|
metrics will be computed.
|
|
|
|
|
Validation metrics will help us track the performance of the model.
|
|
|
|
|
sample_weight_eval_set : list, optional
|
|
|
|
|
A list of the form [L_1, L_2, ..., L_n], where each L_i is a list
|
|
|
|
|
of group weights on the i-th validation set.'''
|
|
|
|
|
raise NotImplementedError
|
|
|
|
|
|
|
|
|
|
def predict(self, data): # pylint: disable=arguments-differ
|
|
|
|
|
'''Predict with `data`.
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
data: data that can be used to construct a DaskDMatrix
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
prediction : dask.array.Array'''
|
|
|
|
|
raise NotImplementedError
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
|
def client(self):
|
|
|
|
|
'''The dask client used in this model.'''
|
|
|
|
|
client = _xgb_get_client(self._client)
|
|
|
|
|
return client
|
|
|
|
|
|
|
|
|
|
@client.setter
|
|
|
|
|
def client(self, clt):
|
|
|
|
|
self._client = clt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class DaskXGBRegressor(DaskScikitLearnBase):
|
|
|
|
|
# pylint: disable=missing-docstring
|
|
|
|
|
__doc__ = ('Implementation of the scikit-learn API for XGBoost ' +
|
|
|
|
|
'regression. \n\n') + '\n'.join(
|
|
|
|
|
XGBModel.__doc__.split('\n')[2:])
|
|
|
|
|
|
|
|
|
|
def fit(self,
|
|
|
|
|
X,
|
|
|
|
|
y,
|
|
|
|
|
sample_weights=None,
|
|
|
|
|
eval_set=None,
|
|
|
|
|
sample_weight_eval_set=None):
|
|
|
|
|
_assert_dask_installed()
|
|
|
|
|
dtrain = DaskDMatrix(client=self.client,
|
|
|
|
|
data=X, label=y, weight=sample_weights)
|
|
|
|
|
params = self.get_xgb_params()
|
|
|
|
|
evals = _evaluation_matrices(self.client,
|
|
|
|
|
eval_set, sample_weight_eval_set)
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results = train(self.client, params, dtrain,
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num_boost_round=self.get_num_boosting_rounds(),
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evals=evals)
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self._Booster = results['booster']
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# pylint: disable=attribute-defined-outside-init
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self.evals_result_ = results['history']
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return self
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def predict(self, data): # pylint: disable=arguments-differ
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_assert_dask_installed()
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test_dmatrix = DaskDMatrix(client=self.client, data=data)
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pred_probs = predict(client=self.client,
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model=self.get_booster(), data=test_dmatrix)
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return pred_probs
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class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
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# pylint: disable=missing-docstring
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_client = None
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__doc__ = ('Implementation of the scikit-learn API for XGBoost ' +
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'classification.\n\n') + '\n'.join(
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XGBModel.__doc__.split('\n')[2:])
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def fit(self,
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X,
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y,
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sample_weights=None,
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eval_set=None,
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sample_weight_eval_set=None):
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_assert_dask_installed()
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dtrain = DaskDMatrix(client=self.client,
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data=X, label=y, weight=sample_weights)
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params = self.get_xgb_params()
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# pylint: disable=attribute-defined-outside-init
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self.classes_ = da.unique(y).compute()
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self.n_classes_ = len(self.classes_)
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if self.n_classes_ > 2:
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params["objective"] = "multi:softprob"
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params['num_class'] = self.n_classes_
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else:
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params["objective"] = "binary:logistic"
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params.setdefault('num_class', self.n_classes_)
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evals = _evaluation_matrices(self.client,
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eval_set, sample_weight_eval_set)
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results = train(self.client, params, dtrain,
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num_boost_round=self.get_num_boosting_rounds(),
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evals=evals)
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self._Booster = results['booster']
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# pylint: disable=attribute-defined-outside-init
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self.evals_result_ = results['history']
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return self
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def predict(self, data): # pylint: disable=arguments-differ
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_assert_dask_installed()
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test_dmatrix = DaskDMatrix(client=self.client, data=data)
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pred_probs = predict(client=self.client,
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model=self.get_booster(), data=test_dmatrix)
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return pred_probs
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Jiaming Yuan
GitHub