fcfeb4959c
Deprecate positional arguments in following functions: - `__init__` for all classes in sklearn module. - `fit` method for all classes in sklearn module. - dask interface. - `set_info` for `DMatrix` class. Refactor the evaluation matrices handling.
1274 lines
47 KiB
Python
1274 lines
47 KiB
Python
# pylint: disable=too-many-arguments, too-many-locals
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# pylint: disable=missing-class-docstring, invalid-name
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# pylint: disable=too-many-lines
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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 collections.abc import Sequence
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from threading import Thread
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from typing import List
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import numpy
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from . import rabit
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from .compat import LazyLoader
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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 .compat import CUDF_concat
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from .compat import lazy_isinstance
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from .core import DMatrix, DeviceQuantileDMatrix, Booster, _expect, DataIter
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from .core import _deprecate_positional_args
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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, XGBRegressorBase, XGBClassifierBase
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from .sklearn import xgboost_model_doc
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dd = LazyLoader('dd', globals(), 'dask.dataframe')
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da = LazyLoader('da', globals(), 'dask.array')
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dask = LazyLoader('dask', globals(), 'dask')
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distributed = LazyLoader('distributed', globals(), 'dask.distributed')
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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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#
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# Note for developers:
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# As of writing asyncio is still a new feature of Python and in depth
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# documentation is rare. Best examples of various asyncio tricks are in dask
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# (luckily). Classes like Client, Worker are awaitable. Some general rules
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# for the implementation here:
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# - Synchronous world is different from asynchronous one, and they don't
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# mix well.
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# - Write everything with async, then use distributed Client sync function
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# to do the switch.
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LOGGER = logging.getLogger('[xgboost.dask]')
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def _start_tracker(n_workers):
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"""Start Rabit tracker """
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env = {'DMLC_NUM_WORKER': n_workers}
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import socket
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host = socket.gethostbyname(socket.gethostname())
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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_support():
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try:
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import dask # pylint: disable=W0621,W0611
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except ImportError as e:
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raise ImportError(
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'Dask needs to be installed in order to use this module') from e
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if platform.system() == 'Windows':
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msg = 'Windows is not officially supported for dask/xgboost,'
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msg += ' contribution are welcomed.'
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LOGGER.warning(msg)
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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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worker = distributed.get_worker()
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self.args.append(
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('DMLC_TASK_ID=[xgboost.dask]:' + str(worker.address)).encode())
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def __enter__(self):
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rabit.init(self.args)
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LOGGER.debug('-------------- rabit say hello ------------------')
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def __exit__(self, *args):
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rabit.finalize()
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LOGGER.debug('--------------- rabit say bye ------------------')
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def concat(value): # pylint: disable=too-many-return-statements
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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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if lazy_isinstance(value[0], 'cudf.core.dataframe', 'DataFrame') or \
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lazy_isinstance(value[0], 'cudf.core.series', 'Series'):
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return CUDF_concat(value, axis=0)
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if lazy_isinstance(value[0], 'cupy.core.core', 'ndarray'):
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import cupy # pylint: disable=import-error
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# pylint: disable=c-extension-no-member,no-member
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d = cupy.cuda.runtime.getDevice()
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for v in value:
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d_v = v.device.id
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assert d_v == d, 'Concatenating arrays on different devices.'
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return cupy.concatenate(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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if not isinstance(client, (type(distributed.get_client()), type(None))):
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raise TypeError(
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_expect([type(distributed.get_client()), type(None)], type(client)))
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ret = distributed.get_client() if client is None else client
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return ret
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# From the implementation point of view, DaskDMatrix complicates a lots of
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# things. A large portion of the code base is about syncing and extracting
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# stuffs from DaskDMatrix. But having an independent data structure gives us a
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# chance to perform some specialized optimizations, like building histogram
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# index directly.
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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. Constructing
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a `DaskDMatrix` forces all lazy computation to be carried out. Wait for
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the input data explicitly if you want to see actual computation of
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constructing `DaskDMatrix`.
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.. note::
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DaskDMatrix does not repartition or move data between workers. It's
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the caller's responsibility to balance the data.
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.. versionadded:: 1.0.0
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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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base_margin : dask.array.Array/dask.dataframe.DataFrame
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Global bias for each instance.
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label_lower_bound : dask.array.Array/dask.dataframe.DataFrame
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Upper bound for survival training.
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label_upper_bound : dask.array.Array/dask.dataframe.DataFrame
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Lower bound for survival training.
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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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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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base_margin=None,
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label_lower_bound=None,
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label_upper_bound=None,
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feature_names=None,
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feature_types=None):
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_assert_dask_support()
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client: distributed.Client = _xgb_get_client(client)
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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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raise ValueError(
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'Expecting 2 dimensional input, got: {shape}'.format(
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shape=data.shape))
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if not isinstance(data, (dd.DataFrame, da.Array)):
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raise TypeError(_expect((dd.DataFrame, da.Array), type(data)))
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if not isinstance(label, (dd.DataFrame, da.Array, dd.Series,
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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.is_quantile = False
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self._init = client.sync(self.map_local_data,
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client, data, label=label, weights=weight,
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base_margin=base_margin,
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label_lower_bound=label_lower_bound,
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label_upper_bound=label_upper_bound)
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def __await__(self):
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return self._init.__await__()
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async def map_local_data(self, client, data, label=None, weights=None,
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base_margin=None,
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label_lower_bound=None, label_upper_bound=None):
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'''Obtain references to local data.'''
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def inconsistent(left, left_name, right, right_name):
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msg = 'Partitions between {a_name} and {b_name} are not ' \
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'consistent: {a_len} != {b_len}. ' \
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'Please try to repartition/rechunk your data.'.format(
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a_name=left_name, b_name=right_name, a_len=len(left),
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b_len=len(right)
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)
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return msg
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def check_columns(parts):
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# x is required to be 2 dim in __init__
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assert parts.ndim == 1 or parts.shape[1], 'Data should be' \
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' partitioned by row. To avoid this specify the number' \
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' of columns for your dask Array explicitly. e.g.' \
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' chunks=(partition_size, X.shape[1])'
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data = data.persist()
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for meta in [label, weights, base_margin, label_lower_bound,
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label_upper_bound]:
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if meta is not None:
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meta = meta.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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check_columns(X_parts)
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X_parts = X_parts.flatten().tolist()
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def flatten_meta(meta):
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if meta is not None:
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meta_parts = meta.to_delayed()
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if isinstance(meta_parts, numpy.ndarray):
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check_columns(meta_parts)
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meta_parts = meta_parts.flatten().tolist()
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return meta_parts
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return None
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y_parts = flatten_meta(label)
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w_parts = flatten_meta(weights)
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margin_parts = flatten_meta(base_margin)
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ll_parts = flatten_meta(label_lower_bound)
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lu_parts = flatten_meta(label_upper_bound)
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parts = [X_parts]
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meta_names = []
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def append_meta(m_parts, name: str):
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if m_parts is not None:
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assert len(X_parts) == len(
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m_parts), inconsistent(X_parts, 'X', m_parts, name)
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parts.append(m_parts)
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meta_names.append(name)
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append_meta(y_parts, 'labels')
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append_meta(w_parts, 'weights')
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append_meta(margin_parts, 'base_margin')
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append_meta(ll_parts, 'label_lower_bound')
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append_meta(lu_parts, 'label_upper_bound')
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# At this point, `parts` looks like:
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# [(x0, x1, ..), (y0, y1, ..), ..] in delayed form
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# delay the zipped result
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parts = list(map(dask.delayed, zip(*parts)))
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# At this point, the mental model should look like:
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# [(x0, y0, ..), (x1, y1, ..), ..] in delayed form
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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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# Preserving the partition order for prediction.
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self.partition_order = {}
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for i, part in enumerate(parts):
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self.partition_order[part.key] = i
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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(keys=[part.key for part in parts])
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worker_map = defaultdict(list)
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for key, workers in who_has.items():
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worker_map[next(iter(workers))].append(key_to_partition[key])
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self.worker_map = worker_map
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self.meta_names = meta_names
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return self
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def create_fn_args(self, worker_addr: str):
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'''Create a dictionary of objects that can be pickled for function
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arguments.
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'''
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return {'feature_names': self.feature_names,
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'feature_types': self.feature_types,
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'meta_names': self.meta_names,
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'missing': self.missing,
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'parts': self.worker_map.get(worker_addr, None),
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'is_quantile': self.is_quantile}
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def _get_worker_parts_ordered(meta_names, list_of_keys, list_of_parts, partition_order):
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# List of partitions like: [(x3, y3, w3, m3, ..), ..], order is not preserved.
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assert isinstance(list_of_parts, list)
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list_of_parts_value = list_of_parts
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result = []
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for i, _ in enumerate(list_of_parts):
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data = list_of_parts_value[i][0]
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labels = None
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weights = None
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base_margin = None
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label_lower_bound = None
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label_upper_bound = None
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# Iterate through all possible meta info, brings small overhead as in xgboost
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# there are constant number of meta info available.
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for j, blob in enumerate(list_of_parts_value[i][1:]):
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if meta_names[j] == 'labels':
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labels = blob
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elif meta_names[j] == 'weights':
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weights = blob
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elif meta_names[j] == 'base_margin':
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base_margin = blob
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elif meta_names[j] == 'label_lower_bound':
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label_lower_bound = blob
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elif meta_names[j] == 'label_upper_bound':
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label_upper_bound = blob
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else:
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raise ValueError('Unknown metainfo:', meta_names[j])
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if partition_order:
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result.append((data, labels, weights, base_margin, label_lower_bound,
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label_upper_bound, partition_order[list_of_keys[i]]))
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else:
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result.append((data, labels, weights, base_margin, label_lower_bound,
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label_upper_bound))
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return result
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def _unzip(list_of_parts):
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return list(zip(*list_of_parts))
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def _get_worker_parts(list_of_parts: List[tuple], meta_names):
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partitions = _get_worker_parts_ordered(meta_names, None, list_of_parts, None)
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partitions = _unzip(partitions)
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return partitions
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class DaskPartitionIter(DataIter): # pylint: disable=R0902
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'''A data iterator for `DaskDeviceQuantileDMatrix`.
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'''
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def __init__(self, data, label=None, weight=None, base_margin=None,
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label_lower_bound=None, label_upper_bound=None,
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feature_names=None, feature_types=None):
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self._data = data
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self._labels = label
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self._weights = weight
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self._base_margin = base_margin
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self._label_lower_bound = label_lower_bound
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self._label_upper_bound = label_upper_bound
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self._feature_names = feature_names
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self._feature_types = feature_types
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assert isinstance(self._data, Sequence)
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types = (Sequence, type(None))
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assert isinstance(self._labels, types)
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assert isinstance(self._weights, types)
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assert isinstance(self._base_margin, types)
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assert isinstance(self._label_lower_bound, types)
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assert isinstance(self._label_upper_bound, types)
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self._iter = 0 # set iterator to 0
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super().__init__()
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def data(self):
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'''Utility function for obtaining current batch of data.'''
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return self._data[self._iter]
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def labels(self):
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'''Utility function for obtaining current batch of label.'''
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if self._labels is not None:
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return self._labels[self._iter]
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return None
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def weights(self):
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'''Utility function for obtaining current batch of label.'''
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if self._weights is not None:
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return self._weights[self._iter]
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return None
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def base_margins(self):
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'''Utility function for obtaining current batch of base_margin.'''
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if self._base_margin is not None:
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return self._base_margin[self._iter]
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return None
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def label_lower_bounds(self):
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'''Utility function for obtaining current batch of label_lower_bound.
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'''
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if self._label_lower_bound is not None:
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return self._label_lower_bound[self._iter]
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return None
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def label_upper_bounds(self):
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'''Utility function for obtaining current batch of label_upper_bound.
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'''
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if self._label_upper_bound is not None:
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return self._label_upper_bound[self._iter]
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return None
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def reset(self):
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'''Reset the iterator'''
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self._iter = 0
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def next(self, input_data):
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'''Yield next batch of data'''
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if self._iter == len(self._data):
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# Return 0 when there's no more batch.
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return 0
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if self._feature_names:
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feature_names = self._feature_names
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else:
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if hasattr(self.data(), 'columns'):
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feature_names = self.data().columns.format()
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else:
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feature_names = None
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input_data(data=self.data(), label=self.labels(),
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weight=self.weights(), group=None,
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label_lower_bound=self.label_lower_bounds(),
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label_upper_bound=self.label_upper_bounds(),
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feature_names=feature_names,
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feature_types=self._feature_types)
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self._iter += 1
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return 1
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class DaskDeviceQuantileDMatrix(DaskDMatrix):
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'''Specialized data type for `gpu_hist` tree method. This class is
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used to reduce the memory usage by eliminating data copies.
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|
Internally the data is merged by weighted GK sketching. So the
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number of partitions from dask may affect training accuracy as GK
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|
generates error for each merge.
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|
|
|
.. versionadded:: 1.2.0
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Parameters
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|
----------
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|
max_bin: Number of bins for histogram construction.
|
|
|
|
|
|
'''
|
|
def __init__(self, client,
|
|
data,
|
|
label=None,
|
|
missing=None,
|
|
weight=None,
|
|
base_margin=None,
|
|
label_lower_bound=None,
|
|
label_upper_bound=None,
|
|
feature_names=None,
|
|
feature_types=None,
|
|
max_bin=256):
|
|
super().__init__(client=client, data=data, label=label,
|
|
missing=missing,
|
|
weight=weight, base_margin=base_margin,
|
|
label_lower_bound=label_lower_bound,
|
|
label_upper_bound=label_upper_bound,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types)
|
|
self.max_bin = max_bin
|
|
self.is_quantile = True
|
|
|
|
def create_fn_args(self, worker_addr: str):
|
|
args = super().create_fn_args(worker_addr)
|
|
args['max_bin'] = self.max_bin
|
|
return args
|
|
|
|
|
|
def _create_device_quantile_dmatrix(feature_names, feature_types,
|
|
meta_names, missing, parts,
|
|
max_bin):
|
|
worker = distributed.get_worker()
|
|
if parts is None:
|
|
msg = 'worker {address} has an empty DMatrix. '.format(
|
|
address=worker.address)
|
|
LOGGER.warning(msg)
|
|
import cupy # pylint: disable=import-error
|
|
d = DeviceQuantileDMatrix(cupy.zeros((0, 0)),
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
max_bin=max_bin)
|
|
return d
|
|
|
|
(data, labels, weights, base_margin,
|
|
label_lower_bound, label_upper_bound) = _get_worker_parts(
|
|
parts, meta_names)
|
|
it = DaskPartitionIter(data=data, label=labels, weight=weights,
|
|
base_margin=base_margin,
|
|
label_lower_bound=label_lower_bound,
|
|
label_upper_bound=label_upper_bound)
|
|
|
|
dmatrix = DeviceQuantileDMatrix(it,
|
|
missing=missing,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
nthread=worker.nthreads,
|
|
max_bin=max_bin)
|
|
return dmatrix
|
|
|
|
|
|
def _create_dmatrix(feature_names, feature_types, meta_names, missing, parts):
|
|
'''Get data that local to worker from DaskDMatrix.
|
|
|
|
Returns
|
|
-------
|
|
A DMatrix object.
|
|
|
|
'''
|
|
worker = distributed.get_worker()
|
|
list_of_parts = parts
|
|
if list_of_parts is None:
|
|
msg = 'worker {address} has an empty DMatrix. '.format(address=worker.address)
|
|
LOGGER.warning(msg)
|
|
d = DMatrix(numpy.empty((0, 0)),
|
|
feature_names=feature_names,
|
|
feature_types=feature_types)
|
|
return d
|
|
|
|
def concat_or_none(data):
|
|
if any([part is None for part in data]):
|
|
return None
|
|
return concat(data)
|
|
|
|
(data, labels, weights, base_margin,
|
|
label_lower_bound, label_upper_bound) = _get_worker_parts(list_of_parts, meta_names)
|
|
|
|
labels = concat_or_none(labels)
|
|
weights = concat_or_none(weights)
|
|
base_margin = concat_or_none(base_margin)
|
|
label_lower_bound = concat_or_none(label_lower_bound)
|
|
label_upper_bound = concat_or_none(label_upper_bound)
|
|
|
|
data = concat(data)
|
|
dmatrix = DMatrix(data,
|
|
labels,
|
|
missing=missing,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
nthread=worker.nthreads)
|
|
dmatrix.set_info(base_margin=base_margin, weight=weights,
|
|
label_lower_bound=label_lower_bound,
|
|
label_upper_bound=label_upper_bound)
|
|
return dmatrix
|
|
|
|
|
|
def _dmatrix_from_list_of_parts(is_quantile, **kwargs):
|
|
if is_quantile:
|
|
return _create_device_quantile_dmatrix(**kwargs)
|
|
return _create_dmatrix(**kwargs)
|
|
|
|
|
|
async def _get_rabit_args(n_workers: int, client):
|
|
'''Get rabit context arguments from data distribution in DaskDMatrix.'''
|
|
env = await client.run_on_scheduler(_start_tracker, n_workers)
|
|
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 _get_workers_from_data(dtrain: DaskDMatrix, evals=()):
|
|
X_worker_map = set(dtrain.worker_map.keys())
|
|
if evals:
|
|
for e in evals:
|
|
assert len(e) == 2
|
|
assert isinstance(e[0], DaskDMatrix) and isinstance(e[1], str)
|
|
worker_map = set(e[0].worker_map.keys())
|
|
X_worker_map.union(worker_map)
|
|
return X_worker_map
|
|
|
|
|
|
async def _train_async(client,
|
|
params,
|
|
dtrain: DaskDMatrix,
|
|
*args,
|
|
evals=(),
|
|
early_stopping_rounds=None,
|
|
**kwargs):
|
|
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.')
|
|
|
|
workers = list(_get_workers_from_data(dtrain, evals))
|
|
_rabit_args = await _get_rabit_args(len(workers), client)
|
|
|
|
def dispatched_train(worker_addr, rabit_args, dtrain_ref, dtrain_idt, evals_ref):
|
|
'''Perform training on a single worker. A local function prevents pickling.
|
|
|
|
'''
|
|
LOGGER.info('Training on %s', str(worker_addr))
|
|
worker = distributed.get_worker()
|
|
with RabitContext(rabit_args):
|
|
local_dtrain = _dmatrix_from_list_of_parts(**dtrain_ref)
|
|
local_evals = []
|
|
if evals_ref:
|
|
for ref, name, idt in evals_ref:
|
|
if idt == dtrain_idt:
|
|
local_evals.append((local_dtrain, name))
|
|
continue
|
|
local_evals.append((_dmatrix_from_list_of_parts(**ref), name))
|
|
|
|
local_history = {}
|
|
local_param = params.copy() # just to be consistent
|
|
msg = 'Overriding `nthreads` defined in dask worker.'
|
|
override = ['nthread', 'n_jobs']
|
|
for p in override:
|
|
val = local_param.get(p, None)
|
|
if val is not None and val != worker.nthreads:
|
|
LOGGER.info(msg)
|
|
else:
|
|
local_param[p] = worker.nthreads
|
|
bst = worker_train(params=local_param,
|
|
dtrain=local_dtrain,
|
|
*args,
|
|
evals_result=local_history,
|
|
evals=local_evals,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
**kwargs)
|
|
ret = {'booster': bst, 'history': local_history}
|
|
if local_dtrain.num_row() == 0:
|
|
ret = None
|
|
return ret
|
|
|
|
# Note for function purity:
|
|
# XGBoost is deterministic in most of the cases, which means train function is
|
|
# supposed to be idempotent. One known exception is gblinear with shotgun updater.
|
|
# We haven't been able to do a full verification so here we keep pure to be False.
|
|
futures = []
|
|
for i, worker_addr in enumerate(workers):
|
|
if evals:
|
|
evals_per_worker = [(e.create_fn_args(worker_addr), name, id(e))
|
|
for e, name in evals]
|
|
else:
|
|
evals_per_worker = []
|
|
f = client.submit(dispatched_train,
|
|
worker_addr,
|
|
_rabit_args,
|
|
dtrain.create_fn_args(workers[i]),
|
|
id(dtrain),
|
|
evals_per_worker,
|
|
pure=False)
|
|
futures.append(f)
|
|
|
|
results = await client.gather(futures)
|
|
return list(filter(lambda ret: ret is not None, results))[0]
|
|
|
|
|
|
def train(client, params, dtrain, *args, evals=(), early_stopping_rounds=None,
|
|
**kwargs):
|
|
'''Train XGBoost model.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
Parameters
|
|
----------
|
|
client: dask.distributed.Client
|
|
Specify the dask client used for training. Use default client
|
|
returned from dask if it's set to None.
|
|
\\*\\*kwargs:
|
|
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_support()
|
|
client = _xgb_get_client(client)
|
|
return client.sync(
|
|
_train_async, client, params, dtrain=dtrain, *args, evals=evals,
|
|
early_stopping_rounds=early_stopping_rounds, **kwargs)
|
|
|
|
|
|
async def _direct_predict_impl(client, data, predict_fn):
|
|
if isinstance(data, da.Array):
|
|
predictions = await client.submit(
|
|
da.map_blocks,
|
|
predict_fn, data, False, drop_axis=1,
|
|
dtype=numpy.float32
|
|
).result()
|
|
return predictions
|
|
if isinstance(data, dd.DataFrame):
|
|
predictions = await client.submit(
|
|
dd.map_partitions,
|
|
predict_fn, data, True,
|
|
meta=dd.utils.make_meta({'prediction': 'f4'})
|
|
).result()
|
|
return predictions.iloc[:, 0]
|
|
raise TypeError('data of type: ' + str(type(data)) +
|
|
' is not supported by direct prediction')
|
|
|
|
|
|
# pylint: disable=too-many-statements
|
|
async def _predict_async(client, model, data, missing=numpy.nan, **kwargs):
|
|
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, da.Array, dd.DataFrame)):
|
|
raise TypeError(_expect([DaskDMatrix, da.Array, dd.DataFrame],
|
|
type(data)))
|
|
|
|
def mapped_predict(partition, is_df):
|
|
worker = distributed.get_worker()
|
|
booster.set_param({'nthread': worker.nthreads})
|
|
m = DMatrix(partition, missing=missing, nthread=worker.nthreads)
|
|
predt = booster.predict(m, validate_features=False, **kwargs)
|
|
if is_df:
|
|
if lazy_isinstance(partition, 'cudf', 'core.dataframe.DataFrame'):
|
|
import cudf # pylint: disable=import-error
|
|
predt = cudf.DataFrame(predt, columns=['prediction'])
|
|
else:
|
|
predt = DataFrame(predt, columns=['prediction'])
|
|
return predt
|
|
# Predict on dask collection directly.
|
|
if isinstance(data, (da.Array, dd.DataFrame)):
|
|
return await _direct_predict_impl(client, data, mapped_predict)
|
|
|
|
# Prediction on dask DMatrix.
|
|
worker_map = data.worker_map
|
|
partition_order = data.partition_order
|
|
feature_names = data.feature_names
|
|
feature_types = data.feature_types
|
|
missing = data.missing
|
|
meta_names = data.meta_names
|
|
|
|
def dispatched_predict(worker_id, list_of_keys, list_of_parts):
|
|
'''Perform prediction on each worker.'''
|
|
LOGGER.info('Predicting on %d', worker_id)
|
|
c = distributed.get_client()
|
|
list_of_keys = c.compute(list_of_keys).result()
|
|
worker = distributed.get_worker()
|
|
list_of_parts = _get_worker_parts_ordered(
|
|
meta_names, list_of_keys, list_of_parts, partition_order)
|
|
predictions = []
|
|
|
|
booster.set_param({'nthread': worker.nthreads})
|
|
for parts in list_of_parts:
|
|
(data, _, _, base_margin, _, _, order) = parts
|
|
local_part = DMatrix(
|
|
data,
|
|
base_margin=base_margin,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
missing=missing,
|
|
nthread=worker.nthreads
|
|
)
|
|
predt = booster.predict(
|
|
data=local_part,
|
|
validate_features=local_part.num_row() != 0,
|
|
**kwargs)
|
|
columns = 1 if len(predt.shape) == 1 else predt.shape[1]
|
|
ret = ((dask.delayed(predt), columns), order)
|
|
predictions.append(ret)
|
|
|
|
return predictions
|
|
|
|
def dispatched_get_shape(worker_id, list_of_keys, list_of_parts):
|
|
'''Get shape of data in each worker.'''
|
|
LOGGER.info('Get shape on %d', worker_id)
|
|
c = distributed.get_client()
|
|
list_of_keys = c.compute(list_of_keys).result()
|
|
list_of_parts = _get_worker_parts_ordered(
|
|
meta_names,
|
|
list_of_keys,
|
|
list_of_parts,
|
|
partition_order,
|
|
)
|
|
shapes = []
|
|
for parts in list_of_parts:
|
|
(data, _, _, _, _, _, order) = parts
|
|
shapes.append((data.shape, order))
|
|
return shapes
|
|
|
|
async def map_function(func):
|
|
'''Run function for each part of the data.'''
|
|
futures = []
|
|
workers_address = list(worker_map.keys())
|
|
for wid, worker_addr in enumerate(workers_address):
|
|
worker_addr = workers_address[wid]
|
|
list_of_parts = worker_map[worker_addr]
|
|
list_of_keys = [part.key for part in list_of_parts]
|
|
f = await client.submit(func, worker_id=wid,
|
|
list_of_keys=dask.delayed(list_of_keys),
|
|
list_of_parts=list_of_parts,
|
|
pure=False, workers=[worker_addr])
|
|
futures.append(f)
|
|
# Get delayed objects
|
|
results = await client.gather(futures)
|
|
# flatten into 1 dim list
|
|
results = [t for list_per_worker in results for t in list_per_worker]
|
|
# sort by order, l[0] is the delayed object, l[1] is its order
|
|
results = sorted(results, key=lambda l: l[1])
|
|
results = [predt for predt, order in results] # remove order
|
|
return results
|
|
|
|
results = await map_function(dispatched_predict)
|
|
shapes = await map_function(dispatched_get_shape)
|
|
|
|
# Constructing a dask array from list of numpy arrays
|
|
# See https://docs.dask.org/en/latest/array-creation.html
|
|
arrays = []
|
|
for i, shape in enumerate(shapes):
|
|
arrays.append(da.from_delayed(
|
|
results[i][0], shape=(shape[0],)
|
|
if results[i][1] == 1 else (shape[0], results[i][1]),
|
|
dtype=numpy.float32))
|
|
predictions = await da.concatenate(arrays, axis=0)
|
|
return predictions
|
|
|
|
|
|
def predict(client, model, data, missing=numpy.nan, **kwargs):
|
|
'''Run prediction with a trained booster.
|
|
|
|
.. note::
|
|
|
|
Only default prediction mode is supported right now.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
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/dask.dataframe.DataFrame/dask.array.Array
|
|
Input data used for prediction. When input is a dataframe object,
|
|
prediction output is a series.
|
|
missing: float
|
|
Used when input data is not DaskDMatrix. Specify the value
|
|
considered as missing.
|
|
|
|
Returns
|
|
-------
|
|
prediction: dask.array.Array/dask.dataframe.Series
|
|
|
|
'''
|
|
_assert_dask_support()
|
|
client = _xgb_get_client(client)
|
|
return client.sync(_predict_async, client, model, data,
|
|
missing=missing, **kwargs)
|
|
|
|
|
|
async def _inplace_predict_async(client, model, data,
|
|
iteration_range=(0, 0),
|
|
predict_type='value',
|
|
missing=numpy.nan):
|
|
client = _xgb_get_client(client)
|
|
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, (da.Array, dd.DataFrame)):
|
|
raise TypeError(_expect([da.Array, dd.DataFrame], type(data)))
|
|
|
|
def mapped_predict(data, is_df):
|
|
worker = distributed.get_worker()
|
|
booster.set_param({'nthread': worker.nthreads})
|
|
prediction = booster.inplace_predict(
|
|
data,
|
|
iteration_range=iteration_range,
|
|
predict_type=predict_type,
|
|
missing=missing)
|
|
if is_df:
|
|
if lazy_isinstance(data, 'cudf.core.dataframe', 'DataFrame'):
|
|
import cudf # pylint: disable=import-error
|
|
prediction = cudf.DataFrame({'prediction': prediction},
|
|
dtype=numpy.float32)
|
|
else:
|
|
# If it's from pandas, the partition is a numpy array
|
|
prediction = DataFrame(prediction, columns=['prediction'],
|
|
dtype=numpy.float32)
|
|
return prediction
|
|
|
|
return await _direct_predict_impl(client, data, mapped_predict)
|
|
|
|
|
|
def inplace_predict(client, model, data,
|
|
iteration_range=(0, 0),
|
|
predict_type='value',
|
|
missing=numpy.nan):
|
|
'''Inplace prediction.
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
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: Booster/dict
|
|
The trained model.
|
|
iteration_range: tuple
|
|
Specify the range of trees used for prediction.
|
|
predict_type: str
|
|
* 'value': Normal prediction result.
|
|
* 'margin': Output the raw untransformed margin value.
|
|
missing: float
|
|
Value in the input data which needs to be present as a missing
|
|
value. If None, defaults to np.nan.
|
|
Returns
|
|
-------
|
|
prediction: dask.array.Array
|
|
'''
|
|
_assert_dask_support()
|
|
client = _xgb_get_client(client)
|
|
return client.sync(_inplace_predict_async, client, model=model, data=data,
|
|
iteration_range=iteration_range,
|
|
predict_type=predict_type,
|
|
missing=missing)
|
|
|
|
|
|
async def _evaluation_matrices(client, validation_set, sample_weight, missing):
|
|
'''
|
|
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_weight[i] if sample_weight is not None else None)
|
|
dmat = await DaskDMatrix(client=client, data=e[0], label=e[1],
|
|
weight=w, missing=missing)
|
|
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
|
|
@_deprecate_positional_args
|
|
def fit(self, X, y, *,
|
|
sample_weight=None,
|
|
base_margin=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
early_stopping_rounds=None,
|
|
verbose=True):
|
|
'''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.
|
|
early_stopping_rounds : int
|
|
Activates early stopping.
|
|
verbose : bool
|
|
If `verbose` and an evaluation set is used, writes the evaluation
|
|
metric measured on the validation set to stderr.'''
|
|
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
|
|
|
|
def __await__(self):
|
|
# Generate a coroutine wrapper to make this class awaitable.
|
|
async def _():
|
|
return self
|
|
return self.client.sync(_).__await__()
|
|
|
|
@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
|
|
|
|
|
|
@xgboost_model_doc("""Implementation of the Scikit-Learn API for XGBoost.""",
|
|
['estimators', 'model'])
|
|
class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
|
|
# pylint: disable=missing-class-docstring
|
|
async def _fit_async(self, X, y, sample_weight, base_margin, eval_set,
|
|
sample_weight_eval_set, early_stopping_rounds,
|
|
verbose):
|
|
dtrain = await DaskDMatrix(client=self.client,
|
|
data=X,
|
|
label=y,
|
|
weight=sample_weight,
|
|
base_margin=base_margin,
|
|
missing=self.missing)
|
|
params = self.get_xgb_params()
|
|
evals = await _evaluation_matrices(self.client, eval_set,
|
|
sample_weight_eval_set,
|
|
self.missing)
|
|
results = await train(client=self.client,
|
|
params=params,
|
|
dtrain=dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
verbose_eval=verbose,
|
|
early_stopping_rounds=early_stopping_rounds)
|
|
self._Booster = results['booster']
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.evals_result_ = results['history']
|
|
return self
|
|
|
|
# pylint: disable=missing-docstring
|
|
@_deprecate_positional_args
|
|
def fit(self,
|
|
X,
|
|
y,
|
|
*,
|
|
sample_weight=None,
|
|
base_margin=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
early_stopping_rounds=None,
|
|
verbose=True):
|
|
_assert_dask_support()
|
|
return self.client.sync(self._fit_async,
|
|
X=X,
|
|
y=y,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
verbose=verbose)
|
|
|
|
async def _predict_async(
|
|
self, data, output_margin=False, base_margin=None):
|
|
test_dmatrix = await DaskDMatrix(
|
|
client=self.client, data=data, base_margin=base_margin,
|
|
missing=self.missing
|
|
)
|
|
pred_probs = await predict(client=self.client,
|
|
model=self.get_booster(), data=test_dmatrix,
|
|
output_margin=output_margin)
|
|
return pred_probs
|
|
|
|
# pylint: disable=arguments-differ
|
|
def predict(self, data, output_margin=False, base_margin=None):
|
|
_assert_dask_support()
|
|
return self.client.sync(self._predict_async, data,
|
|
output_margin=output_margin,
|
|
base_margin=base_margin)
|
|
|
|
|
|
@xgboost_model_doc(
|
|
'Implementation of the scikit-learn API for XGBoost classification.',
|
|
['estimators', 'model'])
|
|
class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
|
|
# pylint: disable=missing-class-docstring
|
|
async def _fit_async(self, X, y, sample_weight, base_margin, eval_set,
|
|
sample_weight_eval_set, early_stopping_rounds,
|
|
verbose):
|
|
dtrain = await DaskDMatrix(client=self.client,
|
|
data=X,
|
|
label=y,
|
|
weight=sample_weight,
|
|
base_margin=base_margin,
|
|
missing=self.missing)
|
|
params = self.get_xgb_params()
|
|
|
|
# pylint: disable=attribute-defined-outside-init
|
|
if isinstance(y, (da.Array)):
|
|
self.classes_ = await self.client.compute(da.unique(y))
|
|
else:
|
|
self.classes_ = await self.client.compute(y.drop_duplicates())
|
|
self.n_classes_ = len(self.classes_)
|
|
|
|
if self.n_classes_ > 2:
|
|
params["objective"] = "multi:softprob"
|
|
params['num_class'] = self.n_classes_
|
|
else:
|
|
params["objective"] = "binary:logistic"
|
|
|
|
evals = await _evaluation_matrices(self.client, eval_set,
|
|
sample_weight_eval_set,
|
|
self.missing)
|
|
results = await train(client=self.client,
|
|
params=params,
|
|
dtrain=dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
verbose_eval=verbose)
|
|
self._Booster = results['booster']
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.evals_result_ = results['history']
|
|
return self
|
|
|
|
@_deprecate_positional_args
|
|
def fit(self,
|
|
X,
|
|
y,
|
|
*,
|
|
sample_weight=None,
|
|
base_margin=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
early_stopping_rounds=None,
|
|
verbose=True):
|
|
_assert_dask_support()
|
|
return self.client.sync(self._fit_async,
|
|
X=X,
|
|
y=y,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
verbose=verbose)
|
|
|
|
async def _predict_proba_async(self, data, output_margin=False,
|
|
base_margin=None):
|
|
test_dmatrix = await DaskDMatrix(
|
|
client=self.client, data=data, base_margin=base_margin,
|
|
missing=self.missing
|
|
)
|
|
pred_probs = await predict(client=self.client,
|
|
model=self.get_booster(),
|
|
data=test_dmatrix,
|
|
output_margin=output_margin)
|
|
return pred_probs
|
|
|
|
# pylint: disable=arguments-differ,missing-docstring
|
|
def predict_proba(self, data, output_margin=False, base_margin=None):
|
|
_assert_dask_support()
|
|
return self.client.sync(
|
|
self._predict_proba_async,
|
|
data,
|
|
output_margin=output_margin,
|
|
base_margin=base_margin
|
|
)
|
|
|
|
async def _predict_async(self, data, output_margin=False, base_margin=None):
|
|
test_dmatrix = await DaskDMatrix(
|
|
client=self.client, data=data, base_margin=base_margin,
|
|
missing=self.missing
|
|
)
|
|
pred_probs = await predict(client=self.client,
|
|
model=self.get_booster(),
|
|
data=test_dmatrix,
|
|
output_margin=output_margin)
|
|
|
|
if self.n_classes_ == 2:
|
|
preds = (pred_probs > 0.5).astype(int)
|
|
else:
|
|
preds = da.argmax(pred_probs, axis=1)
|
|
|
|
return preds
|
|
|
|
# pylint: disable=arguments-differ
|
|
def predict(self, data, output_margin=False, base_margin=None):
|
|
_assert_dask_support()
|
|
return self.client.sync(
|
|
self._predict_async,
|
|
data,
|
|
output_margin=output_margin,
|
|
base_margin=base_margin
|
|
)
|