1113 lines
40 KiB
Python
1113 lines
40 KiB
Python
# pylint: disable=too-many-arguments, too-many-locals
|
|
# pylint: disable=missing-class-docstring, invalid-name
|
|
# pylint: disable=too-many-lines
|
|
"""Dask extensions for distributed training. See
|
|
https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for simple
|
|
tutorial. Also xgboost/demo/dask for some examples.
|
|
|
|
There are two sets of APIs in this module, one is the functional API including
|
|
``train`` and ``predict`` methods. Another is stateful Scikit-Learner wrapper
|
|
inherited from single-node Scikit-Learn interface.
|
|
|
|
The implementation is heavily influenced by dask_xgboost:
|
|
https://github.com/dask/dask-xgboost
|
|
|
|
"""
|
|
import platform
|
|
import logging
|
|
from collections import defaultdict
|
|
from collections.abc import Sequence
|
|
from threading import Thread
|
|
|
|
import numpy
|
|
|
|
from . import rabit
|
|
|
|
from .compat import DASK_INSTALLED
|
|
from .compat import distributed_get_worker, distributed_wait, distributed_comm
|
|
from .compat import da, dd, delayed, get_client
|
|
from .compat import sparse, scipy_sparse
|
|
from .compat import PANDAS_INSTALLED, DataFrame, Series, pandas_concat
|
|
from .compat import CUDF_concat
|
|
from .compat import lazy_isinstance
|
|
|
|
from .core import DMatrix, DeviceQuantileDMatrix, Booster, _expect, DataIter
|
|
from .training import train as worker_train
|
|
from .tracker import RabitTracker
|
|
from .sklearn import XGBModel, XGBRegressorBase, XGBClassifierBase
|
|
from .sklearn import xgboost_model_doc
|
|
|
|
try:
|
|
from distributed import Client
|
|
except ImportError:
|
|
Client = None
|
|
|
|
# Current status is considered as initial support, many features are
|
|
# not properly supported yet.
|
|
#
|
|
# TODOs:
|
|
# - Callback.
|
|
# - Label encoding.
|
|
# - CV
|
|
# - Ranking
|
|
#
|
|
# Note for developers:
|
|
|
|
# As of writing asyncio is still a new feature of Python and in depth
|
|
# documentation is rare. Best examples of various asyncio tricks are in dask
|
|
# (luckily). Classes like Client, Worker are awaitable. Some general rules
|
|
# for the implementation here:
|
|
# - Synchronous world is different from asynchronous one, and they don't
|
|
# mix well.
|
|
# - Write everything with async, then use distributed Client sync function
|
|
# to do the switch.
|
|
|
|
|
|
LOGGER = logging.getLogger('[xgboost.dask]')
|
|
|
|
|
|
def _start_tracker(host, n_workers):
|
|
"""Start Rabit tracker """
|
|
env = {'DMLC_NUM_WORKER': n_workers}
|
|
rabit_context = RabitTracker(hostIP=host, nslave=n_workers)
|
|
env.update(rabit_context.slave_envs())
|
|
|
|
rabit_context.start(n_workers)
|
|
thread = Thread(target=rabit_context.join)
|
|
thread.daemon = True
|
|
thread.start()
|
|
return env
|
|
|
|
|
|
def _assert_dask_support():
|
|
if not DASK_INSTALLED:
|
|
raise ImportError(
|
|
'Dask needs to be installed in order to use this module')
|
|
if platform.system() == 'Windows':
|
|
msg = 'Windows is not officially supported for dask/xgboost,'
|
|
msg += ' contribution are welcomed.'
|
|
LOGGER.warning(msg)
|
|
|
|
|
|
class RabitContext:
|
|
'''A context controling rabit initialization and finalization.'''
|
|
def __init__(self, args):
|
|
self.args = args
|
|
worker = distributed_get_worker()
|
|
self.args.append(
|
|
('DMLC_TASK_ID=[xgboost.dask]:' + str(worker.address)).encode())
|
|
|
|
def __enter__(self):
|
|
rabit.init(self.args)
|
|
LOGGER.debug('-------------- rabit say hello ------------------')
|
|
|
|
def __exit__(self, *args):
|
|
rabit.finalize()
|
|
LOGGER.debug('--------------- rabit say bye ------------------')
|
|
|
|
|
|
def concat(value): # pylint: disable=too-many-return-statements
|
|
'''To be replaced with dask builtin.'''
|
|
if isinstance(value[0], numpy.ndarray):
|
|
return numpy.concatenate(value, axis=0)
|
|
if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
|
|
return scipy_sparse.vstack(value, format='csr')
|
|
if sparse and isinstance(value[0], sparse.SparseArray):
|
|
return sparse.concatenate(value, axis=0)
|
|
if PANDAS_INSTALLED and isinstance(value[0], (DataFrame, Series)):
|
|
return pandas_concat(value, axis=0)
|
|
if lazy_isinstance(value[0], 'cudf.core.dataframe', 'DataFrame') or \
|
|
lazy_isinstance(value[0], 'cudf.core.series', 'Series'):
|
|
return CUDF_concat(value, axis=0)
|
|
if lazy_isinstance(value[0], 'cupy.core.core', 'ndarray'):
|
|
import cupy # pylint: disable=import-error
|
|
# pylint: disable=c-extension-no-member,no-member
|
|
d = cupy.cuda.runtime.getDevice()
|
|
for v in value:
|
|
d_v = v.device.id
|
|
assert d_v == d, 'Concatenating arrays on different devices.'
|
|
return cupy.concatenate(value, axis=0)
|
|
return dd.multi.concat(list(value), axis=0)
|
|
|
|
|
|
def _xgb_get_client(client):
|
|
'''Simple wrapper around testing None.'''
|
|
if not isinstance(client, (type(get_client()), type(None))):
|
|
raise TypeError(
|
|
_expect([type(get_client()), type(None)], type(client)))
|
|
ret = get_client() if client is None else client
|
|
return ret
|
|
|
|
|
|
def _get_client_workers(client):
|
|
workers = client.scheduler_info()['workers']
|
|
return workers
|
|
|
|
# From the implementation point of view, DaskDMatrix complicates a lots of
|
|
# things. A large portion of the code base is about syncing and extracting
|
|
# stuffs from DaskDMatrix. But having an independent data structure gives us a
|
|
# chance to perform some specialized optimizations, like building histogram
|
|
# index directly.
|
|
|
|
|
|
class DaskDMatrix:
|
|
# pylint: disable=missing-docstring, too-many-instance-attributes
|
|
'''DMatrix holding on references to Dask DataFrame or Dask Array. Constructing
|
|
a `DaskDMatrix` forces all lazy computation to be carried out. Wait for
|
|
the input data explicitly if you want to see actual computation of
|
|
constructing `DaskDMatrix`.
|
|
|
|
.. note::
|
|
|
|
DaskDMatrix does not repartition or move data between workers. It's
|
|
the caller's responsibility to balance the data.
|
|
|
|
.. 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.
|
|
data : dask.array.Array/dask.dataframe.DataFrame
|
|
data source of DMatrix.
|
|
label: dask.array.Array/dask.dataframe.DataFrame
|
|
label used for trainin.
|
|
missing : float, optional
|
|
Value in the input data (e.g. `numpy.ndarray`) which needs
|
|
to be present as a missing value. If None, defaults to np.nan.
|
|
weight : dask.array.Array/dask.dataframe.DataFrame
|
|
Weight for each instance.
|
|
feature_names : list, optional
|
|
Set names for features.
|
|
feature_types : list, optional
|
|
Set types for features
|
|
|
|
'''
|
|
|
|
def __init__(self,
|
|
client,
|
|
data,
|
|
label=None,
|
|
missing=None,
|
|
weight=None,
|
|
feature_names=None,
|
|
feature_types=None):
|
|
_assert_dask_support()
|
|
client: Client = _xgb_get_client(client)
|
|
|
|
self.feature_names = feature_names
|
|
self.feature_types = feature_types
|
|
self.missing = missing
|
|
|
|
if len(data.shape) != 2:
|
|
raise ValueError(
|
|
'Expecting 2 dimensional input, got: {shape}'.format(
|
|
shape=data.shape))
|
|
|
|
if not isinstance(data, (dd.DataFrame, da.Array)):
|
|
raise TypeError(_expect((dd.DataFrame, da.Array), type(data)))
|
|
if not isinstance(label, (dd.DataFrame, da.Array, dd.Series,
|
|
type(None))):
|
|
raise TypeError(
|
|
_expect((dd.DataFrame, da.Array, dd.Series), type(label)))
|
|
|
|
self.worker_map = None
|
|
self.has_label = label is not None
|
|
self.has_weights = weight is not None
|
|
|
|
self.is_quantile = False
|
|
|
|
self._init = client.sync(self.map_local_data,
|
|
client, data, label, weight)
|
|
|
|
def __await__(self):
|
|
return self._init.__await__()
|
|
|
|
async def map_local_data(self, client, data, label=None, weights=None):
|
|
'''Obtain references to local data.'''
|
|
|
|
def inconsistent(left, left_name, right, right_name):
|
|
msg = 'Partitions between {a_name} and {b_name} are not ' \
|
|
'consistent: {a_len} != {b_len}. ' \
|
|
'Please try to repartition/rechunk your data.'.format(
|
|
a_name=left_name, b_name=right_name, a_len=len(left),
|
|
b_len=len(right)
|
|
)
|
|
return msg
|
|
|
|
def check_columns(parts):
|
|
# x is required to be 2 dim in __init__
|
|
assert parts.ndim == 1 or parts.shape[1], 'Data should be' \
|
|
' partitioned by row. To avoid this specify the number' \
|
|
' of columns for your dask Array explicitly. e.g.' \
|
|
' chunks=(partition_size, X.shape[1])'
|
|
|
|
data = data.persist()
|
|
if label is not None:
|
|
label = label.persist()
|
|
if weights is not None:
|
|
weights = weights.persist()
|
|
# Breaking data into partitions, a trick borrowed from dask_xgboost.
|
|
|
|
# `to_delayed` downgrades high-level objects into numpy or pandas
|
|
# equivalents.
|
|
X_parts = data.to_delayed()
|
|
if isinstance(X_parts, numpy.ndarray):
|
|
check_columns(X_parts)
|
|
X_parts = X_parts.flatten().tolist()
|
|
|
|
if label is not None:
|
|
y_parts = label.to_delayed()
|
|
if isinstance(y_parts, numpy.ndarray):
|
|
check_columns(y_parts)
|
|
y_parts = y_parts.flatten().tolist()
|
|
if weights is not None:
|
|
w_parts = weights.to_delayed()
|
|
if isinstance(w_parts, numpy.ndarray):
|
|
check_columns(w_parts)
|
|
w_parts = w_parts.flatten().tolist()
|
|
|
|
parts = [X_parts]
|
|
if label is not None:
|
|
assert len(X_parts) == len(
|
|
y_parts), inconsistent(X_parts, 'X', y_parts, 'labels')
|
|
parts.append(y_parts)
|
|
if weights is not None:
|
|
assert len(X_parts) == len(
|
|
w_parts), inconsistent(X_parts, 'X', w_parts, 'weights')
|
|
parts.append(w_parts)
|
|
parts = list(map(delayed, zip(*parts)))
|
|
|
|
parts = client.compute(parts)
|
|
await distributed_wait(parts) # async wait for parts to be computed
|
|
|
|
for part in parts:
|
|
assert part.status == 'finished'
|
|
|
|
self.partition_order = {}
|
|
for i, part in enumerate(parts):
|
|
self.partition_order[part.key] = i
|
|
|
|
key_to_partition = {part.key: part for part in parts}
|
|
who_has = await client.scheduler.who_has(
|
|
keys=[part.key for part in parts])
|
|
|
|
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
|
|
|
|
return self
|
|
|
|
def create_fn_args(self):
|
|
'''Create a dictionary of objects that can be pickled for function
|
|
arguments.
|
|
|
|
'''
|
|
return {'feature_names': self.feature_names,
|
|
'feature_types': self.feature_types,
|
|
'has_label': self.has_label,
|
|
'has_weights': self.has_weights,
|
|
'missing': self.missing,
|
|
'worker_map': self.worker_map,
|
|
'is_quantile': self.is_quantile}
|
|
|
|
|
|
def _get_worker_x_ordered(worker_map, partition_order, worker):
|
|
list_of_parts = worker_map[worker.address]
|
|
client = get_client()
|
|
list_of_parts_value = client.gather(list_of_parts)
|
|
result = []
|
|
for i, part in enumerate(list_of_parts):
|
|
result.append((list_of_parts_value[i][0],
|
|
partition_order[part.key]))
|
|
return result
|
|
|
|
|
|
def _get_worker_parts(has_label, has_weights, worker_map, worker):
|
|
'''Get mapped parts of data in each worker from DaskDMatrix.'''
|
|
list_of_parts = 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 has_label:
|
|
if 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
|
|
|
|
|
|
class DaskPartitionIter(DataIter): # pylint: disable=R0902
|
|
'''A data iterator for `DaskDeviceQuantileDMatrix`.
|
|
'''
|
|
def __init__(self, data, label=None, weight=None, base_margin=None,
|
|
label_lower_bound=None, label_upper_bound=None,
|
|
feature_names=None, feature_types=None):
|
|
self._data = data
|
|
self._labels = label
|
|
self._weights = weight
|
|
self._base_margin = base_margin
|
|
self._label_lower_bound = label_lower_bound
|
|
self._label_upper_bound = label_upper_bound
|
|
self._feature_names = feature_names
|
|
self._feature_types = feature_types
|
|
|
|
assert isinstance(self._data, Sequence)
|
|
|
|
types = (Sequence, type(None))
|
|
assert isinstance(self._labels, types)
|
|
assert isinstance(self._weights, types)
|
|
assert isinstance(self._base_margin, types)
|
|
assert isinstance(self._label_lower_bound, types)
|
|
assert isinstance(self._label_upper_bound, types)
|
|
|
|
self._iter = 0 # set iterator to 0
|
|
super().__init__()
|
|
|
|
def data(self):
|
|
'''Utility function for obtaining current batch of data.'''
|
|
return self._data[self._iter]
|
|
|
|
def labels(self):
|
|
'''Utility function for obtaining current batch of label.'''
|
|
if self._labels is not None:
|
|
return self._labels[self._iter]
|
|
return None
|
|
|
|
def weights(self):
|
|
'''Utility function for obtaining current batch of label.'''
|
|
if self._weights is not None:
|
|
return self._weights[self._iter]
|
|
return None
|
|
|
|
def base_margins(self):
|
|
'''Utility function for obtaining current batch of base_margin.'''
|
|
if self._base_margin is not None:
|
|
return self._base_margin[self._iter]
|
|
return None
|
|
|
|
def label_lower_bounds(self):
|
|
'''Utility function for obtaining current batch of label_lower_bound.
|
|
'''
|
|
if self._label_lower_bound is not None:
|
|
return self._label_lower_bound[self._iter]
|
|
return None
|
|
|
|
def label_upper_bounds(self):
|
|
'''Utility function for obtaining current batch of label_upper_bound.
|
|
'''
|
|
if self._label_upper_bound is not None:
|
|
return self._label_upper_bound[self._iter]
|
|
return None
|
|
|
|
def reset(self):
|
|
'''Reset the iterator'''
|
|
self._iter = 0
|
|
|
|
def next(self, input_data):
|
|
'''Yield next batch of data'''
|
|
if self._iter == len(self._data):
|
|
# Return 0 when there's no more batch.
|
|
return 0
|
|
if self._feature_names:
|
|
feature_names = self._feature_names
|
|
else:
|
|
if hasattr(self.data(), 'columns'):
|
|
feature_names = self.data().columns.format()
|
|
else:
|
|
feature_names = None
|
|
input_data(data=self.data(), label=self.labels(),
|
|
weight=self.weights(), group=None,
|
|
label_lower_bound=self.label_lower_bounds(),
|
|
label_upper_bound=self.label_upper_bounds(),
|
|
feature_names=feature_names,
|
|
feature_types=self._feature_types)
|
|
self._iter += 1
|
|
return 1
|
|
|
|
|
|
class DaskDeviceQuantileDMatrix(DaskDMatrix):
|
|
'''Specialized data type for `gpu_hist` tree method. This class is
|
|
used to reduce the memory usage by eliminating data copies.
|
|
Internally the data is merged by weighted GK sketching. So the
|
|
number of partitions from dask may affect training accuracy as GK
|
|
generates error for each merge.
|
|
|
|
.. versionadded:: 1.2.0
|
|
|
|
Parameters
|
|
----------
|
|
max_bin: Number of bins for histogram construction.
|
|
|
|
|
|
'''
|
|
def __init__(self, client, data, label=None, weight=None,
|
|
missing=None,
|
|
feature_names=None,
|
|
feature_types=None,
|
|
max_bin=256):
|
|
super().__init__(client=client, data=data, label=label, weight=weight,
|
|
missing=missing,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types)
|
|
self.max_bin = max_bin
|
|
self.is_quantile = True
|
|
|
|
def create_fn_args(self):
|
|
args = super().create_fn_args()
|
|
args['max_bin'] = self.max_bin
|
|
return args
|
|
|
|
|
|
def _create_device_quantile_dmatrix(feature_names, feature_types,
|
|
has_label,
|
|
has_weights, missing, worker_map,
|
|
max_bin):
|
|
worker = distributed_get_worker()
|
|
if worker.address not in set(worker_map.keys()):
|
|
msg = 'worker {address} has an empty DMatrix. ' \
|
|
'All workers associated with this DMatrix: {workers}'.format(
|
|
address=worker.address,
|
|
workers=set(worker_map.keys()))
|
|
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 = _get_worker_parts(has_label, has_weights,
|
|
worker_map, worker)
|
|
it = DaskPartitionIter(data=data, label=labels, weight=weights)
|
|
|
|
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, has_label,
|
|
has_weights, missing, worker_map):
|
|
'''Get data that local to worker from DaskDMatrix.
|
|
|
|
Returns
|
|
-------
|
|
A DMatrix object.
|
|
|
|
'''
|
|
worker = distributed_get_worker()
|
|
if worker.address not in set(worker_map.keys()):
|
|
msg = 'worker {address} has an empty DMatrix. ' \
|
|
'All workers associated with this DMatrix: {workers}'.format(
|
|
address=worker.address,
|
|
workers=set(worker_map.keys()))
|
|
LOGGER.warning(msg)
|
|
d = DMatrix(numpy.empty((0, 0)),
|
|
feature_names=feature_names,
|
|
feature_types=feature_types)
|
|
return d
|
|
|
|
data, labels, weights = _get_worker_parts(has_label, has_weights,
|
|
worker_map, worker)
|
|
data = concat(data)
|
|
|
|
if has_label:
|
|
labels = concat(labels)
|
|
else:
|
|
labels = None
|
|
if has_weights:
|
|
weights = concat(weights)
|
|
else:
|
|
weights = None
|
|
dmatrix = DMatrix(data,
|
|
labels,
|
|
weight=weights,
|
|
missing=missing,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
nthread=worker.nthreads)
|
|
return dmatrix
|
|
|
|
|
|
def _dmatrix_from_worker_map(is_quantile, **kwargs):
|
|
if is_quantile:
|
|
return _create_device_quantile_dmatrix(**kwargs)
|
|
return _create_dmatrix(**kwargs)
|
|
|
|
|
|
async def _get_rabit_args(worker_map, client: Client):
|
|
'''Get rabit context arguments from data distribution in DaskDMatrix.'''
|
|
host = distributed_comm.get_address_host(client.scheduler.address)
|
|
env = await 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.
|
|
|
|
|
|
async def _train_async(client, params, dtrain: DaskDMatrix,
|
|
*args, evals=(), **kwargs):
|
|
_assert_dask_support()
|
|
client: Client = _xgb_get_client(client)
|
|
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_client_workers(client).keys())
|
|
rabit_args = await _get_rabit_args(workers, client)
|
|
|
|
def dispatched_train(worker_addr, dtrain_ref, 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_worker_map(**dtrain_ref)
|
|
local_evals = []
|
|
if evals_ref:
|
|
for ref, name in evals_ref:
|
|
if ref['worker_map'] == dtrain_ref['worker_map']:
|
|
local_evals.append((local_dtrain, name))
|
|
continue
|
|
local_evals.append((_dmatrix_from_worker_map(**ref), name))
|
|
|
|
local_history = {}
|
|
local_param = params.copy() # just to be consistent
|
|
msg = 'Overriding `nthreads` defined in dask worker.'
|
|
if 'nthread' in local_param.keys() and \
|
|
local_param['nthread'] is not None and \
|
|
local_param['nthread'] != worker.nthreads:
|
|
msg += '`nthread` is specified. ' + msg
|
|
LOGGER.warning(msg)
|
|
elif 'n_jobs' in local_param.keys() and \
|
|
local_param['n_jobs'] is not None and \
|
|
local_param['n_jobs'] != worker.nthreads:
|
|
msg = '`n_jobs` is specified. ' + msg
|
|
LOGGER.warning(msg)
|
|
else:
|
|
local_param['nthread'] = worker.nthreads
|
|
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 local_dtrain.num_row() == 0:
|
|
ret = None
|
|
return ret
|
|
|
|
if evals:
|
|
evals = [(e.create_fn_args(), name) for e, name in evals]
|
|
|
|
futures = client.map(dispatched_train,
|
|
workers,
|
|
[dtrain.create_fn_args()] * len(workers),
|
|
[evals] * len(workers),
|
|
pure=False,
|
|
workers=workers)
|
|
results = await client.gather(futures)
|
|
return list(filter(lambda ret: ret is not None, results))[0]
|
|
|
|
|
|
def train(client, params, dtrain, *args, evals=(), **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, **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: Client, model, data, *args,
|
|
missing=numpy.nan):
|
|
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, *args, validate_features=False)
|
|
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
|
|
|
|
def dispatched_predict(worker_id):
|
|
'''Perform prediction on each worker.'''
|
|
LOGGER.info('Predicting on %d', worker_id)
|
|
worker = distributed_get_worker()
|
|
list_of_parts = _get_worker_x_ordered(worker_map, partition_order,
|
|
worker)
|
|
predictions = []
|
|
booster.set_param({'nthread': worker.nthreads})
|
|
for part, order in list_of_parts:
|
|
local_x = DMatrix(part, feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
missing=missing, nthread=worker.nthreads)
|
|
predt = booster.predict(data=local_x,
|
|
validate_features=local_x.num_row() != 0,
|
|
*args)
|
|
columns = 1 if len(predt.shape) == 1 else predt.shape[1]
|
|
ret = ((delayed(predt), columns), order)
|
|
predictions.append(ret)
|
|
return predictions
|
|
|
|
def dispatched_get_shape(worker_id):
|
|
'''Get shape of data in each worker.'''
|
|
LOGGER.info('Get shape on %d', worker_id)
|
|
worker = distributed_get_worker()
|
|
list_of_parts = _get_worker_x_ordered(worker_map,
|
|
partition_order, worker)
|
|
shapes = [(part.shape, order) for part, order in list_of_parts]
|
|
return shapes
|
|
|
|
async def map_function(func):
|
|
'''Run function for each part of the data.'''
|
|
futures = []
|
|
for wid in range(len(worker_map)):
|
|
list_of_workers = [list(worker_map.keys())[wid]]
|
|
f = await client.submit(func, wid,
|
|
pure=False,
|
|
workers=list_of_workers)
|
|
futures.append(f)
|
|
# Get delayed objects
|
|
results = await client.gather(futures)
|
|
results = [t for l in results for t in l] # flatten into 1 dim list
|
|
# 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, *args, missing=numpy.nan):
|
|
'''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.
|
|
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, *args,
|
|
missing=missing)
|
|
|
|
|
|
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_weights, 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_weights[i]
|
|
if sample_weights 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
|
|
def fit(self, X, y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=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.
|
|
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_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
verbose=True):
|
|
dtrain = await DaskDMatrix(client=self.client,
|
|
data=X, label=y, weight=sample_weights,
|
|
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)
|
|
self._Booster = results['booster']
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.evals_result_ = results['history']
|
|
return self
|
|
|
|
# pylint: disable=missing-docstring
|
|
def fit(self, X, y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
verbose=True):
|
|
_assert_dask_support()
|
|
return self.client.sync(self._fit_async, X, y, sample_weights,
|
|
eval_set, sample_weight_eval_set,
|
|
verbose)
|
|
|
|
async def _predict_async(self, data): # pylint: disable=arguments-differ
|
|
test_dmatrix = await DaskDMatrix(client=self.client, data=data,
|
|
missing=self.missing)
|
|
pred_probs = await predict(client=self.client,
|
|
model=self.get_booster(), data=test_dmatrix)
|
|
return pred_probs
|
|
|
|
def predict(self, data):
|
|
_assert_dask_support()
|
|
return self.client.sync(self._predict_async, data)
|
|
|
|
|
|
@xgboost_model_doc(
|
|
'Implementation of the scikit-learn API for XGBoost classification.',
|
|
['estimators', 'model']
|
|
)
|
|
class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
|
|
async def _fit_async(self, X, y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
verbose=True):
|
|
dtrain = await DaskDMatrix(client=self.client,
|
|
data=X, label=y, weight=sample_weights,
|
|
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, verbose_eval=verbose)
|
|
self._Booster = results['booster']
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.evals_result_ = results['history']
|
|
return self
|
|
|
|
def fit(self, X, y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None,
|
|
verbose=True):
|
|
_assert_dask_support()
|
|
return self.client.sync(self._fit_async, X, y, sample_weights,
|
|
eval_set, sample_weight_eval_set, verbose)
|
|
|
|
async def _predict_proba_async(self, data):
|
|
_assert_dask_support()
|
|
|
|
test_dmatrix = await DaskDMatrix(client=self.client, data=data,
|
|
missing=self.missing)
|
|
pred_probs = await predict(client=self.client,
|
|
model=self.get_booster(), data=test_dmatrix)
|
|
return pred_probs
|
|
|
|
def predict_proba(self, data): # pylint: disable=arguments-differ,missing-docstring
|
|
_assert_dask_support()
|
|
return self.client.sync(self._predict_proba_async, data)
|
|
|
|
async def _predict_async(self, data):
|
|
_assert_dask_support()
|
|
|
|
test_dmatrix = await DaskDMatrix(client=self.client, data=data,
|
|
missing=self.missing)
|
|
pred_probs = await predict(client=self.client,
|
|
model=self.get_booster(), data=test_dmatrix)
|
|
|
|
if self.n_classes_ == 2:
|
|
preds = (pred_probs > 0.5).astype(int)
|
|
else:
|
|
preds = da.argmax(pred_probs, axis=1)
|
|
|
|
return preds
|
|
|
|
def predict(self, data): # pylint: disable=arguments-differ
|
|
_assert_dask_support()
|
|
return self.client.sync(self._predict_async, data)
|