2257 lines
79 KiB
Python
2257 lines
79 KiB
Python
# pylint: disable=too-many-arguments, too-many-locals, no-name-in-module
|
|
# pylint: disable=missing-class-docstring, invalid-name
|
|
# pylint: disable=too-many-lines, fixme
|
|
# pylint: disable=too-few-public-methods
|
|
# pylint: disable=import-error
|
|
"""
|
|
Dask extensions for distributed training
|
|
----------------------------------------
|
|
|
|
See :doc:`Distributed XGBoost with Dask </tutorials/dask>` for simple tutorial. Also
|
|
:doc:`/python/dask-examples/index` 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
|
|
|
|
Optional dask configuration
|
|
===========================
|
|
|
|
- **xgboost.scheduler_address**: Specify the scheduler address, see :ref:`tracker-ip`.
|
|
|
|
.. versionadded:: 1.6.0
|
|
|
|
.. code-block:: python
|
|
|
|
dask.config.set({"xgboost.scheduler_address": "192.0.0.100"})
|
|
# We can also specify the port.
|
|
dask.config.set({"xgboost.scheduler_address": "192.0.0.100:12345"})
|
|
|
|
"""
|
|
import platform
|
|
import logging
|
|
import collections
|
|
import socket
|
|
from contextlib import contextmanager
|
|
from collections import defaultdict
|
|
from threading import Thread
|
|
from functools import partial, update_wrapper
|
|
from typing import TYPE_CHECKING, List, Tuple, Callable, Optional, Any, Union, Dict, Set
|
|
from typing import Sequence
|
|
from typing import Awaitable, Generator, TypeVar
|
|
|
|
import numpy
|
|
|
|
from . import rabit, config
|
|
|
|
from .callback import TrainingCallback
|
|
|
|
from .compat import LazyLoader
|
|
from .compat import scipy_sparse
|
|
from .compat import PANDAS_INSTALLED, DataFrame, Series, pandas_concat
|
|
from .compat import lazy_isinstance
|
|
|
|
from .core import DMatrix, DeviceQuantileDMatrix, Booster, _expect, DataIter
|
|
from .core import Objective, Metric
|
|
from .core import _deprecate_positional_args, _has_categorical
|
|
from .data import FeatureNames
|
|
from .training import train as worker_train
|
|
from .tracker import RabitTracker, get_host_ip
|
|
from .sklearn import XGBModel, XGBClassifier, XGBRegressorBase, XGBClassifierBase
|
|
from .sklearn import _wrap_evaluation_matrices, _objective_decorator, _check_rf_callback
|
|
from .sklearn import XGBRankerMixIn
|
|
from .sklearn import xgboost_model_doc
|
|
from .sklearn import _cls_predict_proba
|
|
from .sklearn import XGBRanker
|
|
|
|
if TYPE_CHECKING:
|
|
from dask import dataframe as dd
|
|
from dask import array as da
|
|
from dask import delayed as ddelayed
|
|
import dask
|
|
import distributed
|
|
else:
|
|
dd = LazyLoader("dd", globals(), "dask.dataframe")
|
|
da = LazyLoader("da", globals(), "dask.array")
|
|
ddelayed = LazyLoader("Delayed", globals(), "dask.delayed")
|
|
dask = LazyLoader("dask", globals(), "dask")
|
|
distributed = LazyLoader("distributed", globals(), "dask.distributed")
|
|
|
|
_DaskCollection = Union["da.Array", "dd.DataFrame", "dd.Series"]
|
|
|
|
try:
|
|
from mypy_extensions import TypedDict
|
|
|
|
TrainReturnT = TypedDict(
|
|
"TrainReturnT",
|
|
{
|
|
"booster": Booster,
|
|
"history": Dict,
|
|
},
|
|
)
|
|
except ImportError:
|
|
TrainReturnT = Dict[str, Any] # type:ignore
|
|
|
|
__all__ = [
|
|
"RabitContext",
|
|
"DaskDMatrix",
|
|
"DaskDeviceQuantileDMatrix",
|
|
"DaskXGBRegressor",
|
|
"DaskXGBClassifier",
|
|
"DaskXGBRanker",
|
|
"DaskXGBRFRegressor",
|
|
"DaskXGBRFClassifier",
|
|
"train",
|
|
"predict",
|
|
"inplace_predict",
|
|
]
|
|
|
|
# TODOs:
|
|
# - CV
|
|
#
|
|
# 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.
|
|
# - Use Any for type hint when the return value can be union of Awaitable and plain
|
|
# value. This is caused by Client.sync can return both types depending on context.
|
|
# Right now there's no good way to silent:
|
|
#
|
|
# await train(...)
|
|
#
|
|
# if train returns an Union type.
|
|
|
|
|
|
LOGGER = logging.getLogger("[xgboost.dask]")
|
|
|
|
|
|
def _multi_lock() -> Any:
|
|
"""MultiLock is only available on latest distributed. See:
|
|
|
|
https://github.com/dask/distributed/pull/4503
|
|
"""
|
|
try:
|
|
from distributed import MultiLock
|
|
except ImportError:
|
|
|
|
class MultiLock: # type:ignore
|
|
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
|
pass
|
|
|
|
def __enter__(self) -> "MultiLock":
|
|
return self
|
|
|
|
def __exit__(self, *args: Any, **kwargs: Any) -> None:
|
|
return
|
|
|
|
async def __aenter__(self) -> "MultiLock":
|
|
return self
|
|
|
|
async def __aexit__(self, *args: Any, **kwargs: Any) -> None:
|
|
return
|
|
|
|
return MultiLock
|
|
|
|
|
|
def _try_start_tracker(
|
|
n_workers: int,
|
|
addrs: List[Union[Optional[str], Optional[Tuple[str, int]]]],
|
|
) -> Dict[str, Union[int, str]]:
|
|
env: Dict[str, Union[int, str]] = {"DMLC_NUM_WORKER": n_workers}
|
|
try:
|
|
if isinstance(addrs[0], tuple):
|
|
host_ip = addrs[0][0]
|
|
port = addrs[0][1]
|
|
rabit_context = RabitTracker(
|
|
host_ip=get_host_ip(host_ip),
|
|
n_workers=n_workers,
|
|
port=port,
|
|
use_logger=False,
|
|
)
|
|
else:
|
|
addr = addrs[0]
|
|
assert isinstance(addr, str) or addr is None
|
|
host_ip = get_host_ip(addr)
|
|
rabit_context = RabitTracker(
|
|
host_ip=host_ip, n_workers=n_workers, use_logger=False, sortby="task"
|
|
)
|
|
env.update(rabit_context.worker_envs())
|
|
rabit_context.start(n_workers)
|
|
thread = Thread(target=rabit_context.join)
|
|
thread.daemon = True
|
|
thread.start()
|
|
except socket.error as e:
|
|
if len(addrs) < 2 or e.errno != 99:
|
|
raise
|
|
LOGGER.warning(
|
|
"Failed to bind address '%s', trying to use '%s' instead.",
|
|
str(addrs[0]),
|
|
str(addrs[1]),
|
|
)
|
|
env = _try_start_tracker(n_workers, addrs[1:])
|
|
|
|
return env
|
|
|
|
|
|
def _start_tracker(
|
|
n_workers: int,
|
|
addr_from_dask: Optional[str],
|
|
addr_from_user: Optional[Tuple[str, int]],
|
|
) -> Dict[str, Union[int, str]]:
|
|
"""Start Rabit tracker, recurse to try different addresses."""
|
|
env = _try_start_tracker(n_workers, [addr_from_user, addr_from_dask])
|
|
return env
|
|
|
|
|
|
def _assert_dask_support() -> None:
|
|
try:
|
|
import dask # pylint: disable=W0621,W0611
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Dask needs to be installed in order to use this module"
|
|
) from e
|
|
|
|
if platform.system() == "Windows":
|
|
msg = "Windows is not officially supported for dask/xgboost,"
|
|
msg += " contribution are welcomed."
|
|
LOGGER.warning(msg)
|
|
|
|
|
|
class RabitContext:
|
|
"""A context controlling rabit initialization and finalization."""
|
|
|
|
def __init__(self, args: List[bytes]) -> None:
|
|
self.args = args
|
|
worker = distributed.get_worker()
|
|
with distributed.worker_client() as client:
|
|
info = client.scheduler_info()
|
|
w = info["workers"][worker.address]
|
|
wid = w["id"]
|
|
# We use task ID for rank assignment which makes the RABIT rank consistent (but
|
|
# not the same as task ID is string and "10" is sorted before "2") with dask
|
|
# worker ID. This outsources the rank assignment to dask and prevents
|
|
# non-deterministic issue.
|
|
self.args.append(
|
|
(f"DMLC_TASK_ID=[xgboost.dask-{wid}]:" + str(worker.address)).encode()
|
|
)
|
|
|
|
def __enter__(self) -> None:
|
|
rabit.init(self.args)
|
|
assert rabit.is_distributed()
|
|
LOGGER.debug("-------------- rabit say hello ------------------")
|
|
|
|
def __exit__(self, *args: List) -> None:
|
|
rabit.finalize()
|
|
LOGGER.debug("--------------- rabit say bye ------------------")
|
|
|
|
|
|
def concat(value: Any) -> Any: # 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.csr_matrix):
|
|
return scipy_sparse.vstack(value, format="csr")
|
|
if scipy_sparse and isinstance(value[0], scipy_sparse.csc_matrix):
|
|
return scipy_sparse.vstack(value, format="csc")
|
|
if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
|
|
# other sparse format will be converted to CSR.
|
|
return scipy_sparse.vstack(value, format="csr")
|
|
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"
|
|
):
|
|
from cudf import concat as CUDF_concat # pylint: disable=import-error
|
|
|
|
return CUDF_concat(value, axis=0)
|
|
if lazy_isinstance(value[0], "cupy._core.core", "ndarray"):
|
|
import cupy
|
|
|
|
# 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: Optional["distributed.Client"]) -> "distributed.Client":
|
|
"""Simple wrapper around testing None."""
|
|
if not isinstance(client, (type(distributed.get_client()), type(None))):
|
|
raise TypeError(
|
|
_expect([type(distributed.get_client()), type(None)], type(client))
|
|
)
|
|
ret = distributed.get_client() if client is None else client
|
|
return ret
|
|
|
|
|
|
# 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`.
|
|
|
|
See doc for :py:obj:`xgboost.DMatrix` constructor for other parameters. DaskDMatrix
|
|
accepts only dask collection.
|
|
|
|
.. 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 :
|
|
Specify the dask client used for training. Use default client returned from dask
|
|
if it's set to None.
|
|
|
|
"""
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self,
|
|
client: "distributed.Client",
|
|
data: _DaskCollection,
|
|
label: Optional[_DaskCollection] = None,
|
|
*,
|
|
weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
missing: float = None,
|
|
silent: bool = False, # pylint: disable=unused-argument
|
|
feature_names: FeatureNames = None,
|
|
feature_types: Optional[List[str]] = None,
|
|
group: Optional[_DaskCollection] = None,
|
|
qid: Optional[_DaskCollection] = None,
|
|
label_lower_bound: Optional[_DaskCollection] = None,
|
|
label_upper_bound: Optional[_DaskCollection] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
enable_categorical: bool = False,
|
|
) -> None:
|
|
_assert_dask_support()
|
|
client = _xgb_get_client(client)
|
|
|
|
self.feature_names = feature_names
|
|
self.feature_types = feature_types
|
|
self.missing = missing
|
|
self.enable_categorical = enable_categorical
|
|
|
|
if qid is not None and weight is not None:
|
|
raise NotImplementedError("per-group weight is not implemented.")
|
|
if group is not None:
|
|
raise NotImplementedError(
|
|
"group structure is not implemented, use qid instead."
|
|
)
|
|
|
|
if len(data.shape) != 2:
|
|
raise ValueError(f"Expecting 2 dimensional input, got: {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._n_cols = data.shape[1]
|
|
assert isinstance(self._n_cols, int)
|
|
self.worker_map: Dict[str, "distributed.Future"] = defaultdict(list)
|
|
self.is_quantile: bool = False
|
|
|
|
self._init = client.sync(
|
|
self._map_local_data,
|
|
client,
|
|
data,
|
|
label=label,
|
|
weights=weight,
|
|
base_margin=base_margin,
|
|
qid=qid,
|
|
feature_weights=feature_weights,
|
|
label_lower_bound=label_lower_bound,
|
|
label_upper_bound=label_upper_bound,
|
|
)
|
|
|
|
def __await__(self) -> Generator:
|
|
return self._init.__await__()
|
|
|
|
async def _map_local_data(
|
|
self,
|
|
client: "distributed.Client",
|
|
data: _DaskCollection,
|
|
label: Optional[_DaskCollection] = None,
|
|
weights: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
qid: Optional[_DaskCollection] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
label_lower_bound: Optional[_DaskCollection] = None,
|
|
label_upper_bound: Optional[_DaskCollection] = None,
|
|
) -> "DaskDMatrix":
|
|
"""Obtain references to local data."""
|
|
|
|
def inconsistent(
|
|
left: List[Any], left_name: str, right: List[Any], right_name: str
|
|
) -> str:
|
|
msg = (
|
|
f"Partitions between {left_name} and {right_name} are not "
|
|
f"consistent: {len(left)} != {len(right)}. "
|
|
f"Please try to repartition/rechunk your data."
|
|
)
|
|
return msg
|
|
|
|
def check_columns(parts: numpy.ndarray) -> None:
|
|
# 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])"
|
|
)
|
|
|
|
def to_delayed(d: _DaskCollection) -> List[ddelayed.Delayed]:
|
|
"""Breaking data into partitions, a trick borrowed from dask_xgboost. `to_delayed`
|
|
downgrades high-level objects into numpy or pandas equivalents .
|
|
|
|
"""
|
|
d = client.persist(d)
|
|
delayed_obj = d.to_delayed()
|
|
if isinstance(delayed_obj, numpy.ndarray):
|
|
# da.Array returns an array to delayed objects
|
|
check_columns(delayed_obj)
|
|
delayed_list: List[ddelayed.Delayed] = delayed_obj.flatten().tolist()
|
|
else:
|
|
# dd.DataFrame
|
|
delayed_list = delayed_obj
|
|
return delayed_list
|
|
|
|
OpDelayed = TypeVar("OpDelayed", _DaskCollection, None)
|
|
|
|
def flatten_meta(meta: OpDelayed) -> OpDelayed:
|
|
if meta is not None:
|
|
meta_parts: List[ddelayed.Delayed] = to_delayed(meta)
|
|
return meta_parts
|
|
return None
|
|
|
|
X_parts = to_delayed(data)
|
|
y_parts = flatten_meta(label)
|
|
w_parts = flatten_meta(weights)
|
|
margin_parts = flatten_meta(base_margin)
|
|
qid_parts = flatten_meta(qid)
|
|
ll_parts = flatten_meta(label_lower_bound)
|
|
lu_parts = flatten_meta(label_upper_bound)
|
|
|
|
parts: Dict[str, List[ddelayed.Delayed]] = {"data": X_parts}
|
|
|
|
def append_meta(m_parts: Optional[List[ddelayed.Delayed]], name: str) -> None:
|
|
if m_parts is not None:
|
|
assert len(X_parts) == len(m_parts), inconsistent(
|
|
X_parts, "X", m_parts, name
|
|
)
|
|
parts[name] = m_parts
|
|
|
|
append_meta(y_parts, "label")
|
|
append_meta(w_parts, "weight")
|
|
append_meta(margin_parts, "base_margin")
|
|
append_meta(qid_parts, "qid")
|
|
append_meta(ll_parts, "label_lower_bound")
|
|
append_meta(lu_parts, "label_upper_bound")
|
|
# At this point, `parts` looks like:
|
|
# [(x0, x1, ..), (y0, y1, ..), ..] in delayed form
|
|
|
|
# turn into list of dictionaries.
|
|
packed_parts: List[Dict[str, ddelayed.Delayed]] = []
|
|
for i in range(len(X_parts)):
|
|
part_dict: Dict[str, ddelayed.Delayed] = {}
|
|
for key, value in parts.items():
|
|
part_dict[key] = value[i]
|
|
packed_parts.append(part_dict)
|
|
|
|
# delay the zipped result
|
|
# pylint: disable=no-member
|
|
delayed_parts: List[ddelayed.Delayed] = list(map(dask.delayed, packed_parts))
|
|
# At this point, the mental model should look like:
|
|
# [(x0, y0, ..), (x1, y1, ..), ..] in delayed form
|
|
|
|
# convert delayed objects into futures and make sure they are realized
|
|
fut_parts: List[distributed.Future] = client.compute(delayed_parts)
|
|
await distributed.wait(fut_parts) # async wait for parts to be computed
|
|
|
|
# maybe we can call dask.align_partitions here to ease the partition alignment?
|
|
|
|
for part in fut_parts:
|
|
# Each part is [x0, y0, w0, ...] in future form.
|
|
assert part.status == "finished", part.status
|
|
|
|
# Preserving the partition order for prediction.
|
|
self.partition_order = {}
|
|
for i, part in enumerate(fut_parts):
|
|
self.partition_order[part.key] = i
|
|
|
|
key_to_partition = {part.key: part for part in fut_parts}
|
|
who_has: Dict[str, Tuple[str, ...]] = await client.scheduler.who_has(
|
|
keys=[part.key for part in fut_parts]
|
|
)
|
|
|
|
worker_map: Dict[str, List[distributed.Future]] = defaultdict(list)
|
|
|
|
for key, workers in who_has.items():
|
|
worker_map[next(iter(workers))].append(key_to_partition[key])
|
|
|
|
self.worker_map = worker_map
|
|
|
|
if feature_weights is None:
|
|
self.feature_weights = None
|
|
else:
|
|
self.feature_weights = await client.compute(feature_weights).result()
|
|
|
|
return self
|
|
|
|
def _create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
|
|
"""Create a dictionary of objects that can be pickled for function
|
|
arguments.
|
|
|
|
"""
|
|
return {
|
|
"feature_names": self.feature_names,
|
|
"feature_types": self.feature_types,
|
|
"feature_weights": self.feature_weights,
|
|
"missing": self.missing,
|
|
"enable_categorical": self.enable_categorical,
|
|
"parts": self.worker_map.get(worker_addr, None),
|
|
"is_quantile": self.is_quantile,
|
|
}
|
|
|
|
def num_col(self) -> int:
|
|
return self._n_cols
|
|
|
|
|
|
_MapRetT = TypeVar("_MapRetT")
|
|
|
|
|
|
async def map_worker_partitions(
|
|
client: Optional["distributed.Client"],
|
|
func: Callable[..., _MapRetT],
|
|
*refs: Any,
|
|
workers: List[str],
|
|
) -> List[_MapRetT]:
|
|
"""Map a function onto partitions of each worker."""
|
|
# 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.
|
|
client = _xgb_get_client(client)
|
|
futures = []
|
|
for addr in workers:
|
|
args = []
|
|
for ref in refs:
|
|
if isinstance(ref, DaskDMatrix):
|
|
# pylint: disable=protected-access
|
|
args.append(ref._create_fn_args(addr))
|
|
else:
|
|
args.append(ref)
|
|
fut = client.submit(
|
|
func, *args, pure=False, workers=[addr], allow_other_workers=False
|
|
)
|
|
futures.append(fut)
|
|
results = await client.gather(futures)
|
|
return results
|
|
|
|
|
|
_DataParts = List[Dict[str, Any]]
|
|
|
|
|
|
def _get_worker_parts(list_of_parts: _DataParts) -> Dict[str, List[Any]]:
|
|
assert isinstance(list_of_parts, list)
|
|
result: Dict[str, List[Any]] = {}
|
|
|
|
def append(i: int, name: str) -> None:
|
|
if name in list_of_parts[i]:
|
|
part = list_of_parts[i][name]
|
|
else:
|
|
part = None
|
|
if part is not None:
|
|
if name not in result:
|
|
result[name] = []
|
|
result[name].append(part)
|
|
|
|
for i, _ in enumerate(list_of_parts):
|
|
append(i, "data")
|
|
append(i, "label")
|
|
append(i, "weight")
|
|
append(i, "base_margin")
|
|
append(i, "qid")
|
|
append(i, "label_lower_bound")
|
|
append(i, "label_upper_bound")
|
|
|
|
return result
|
|
|
|
|
|
class DaskPartitionIter(DataIter): # pylint: disable=R0902
|
|
"""A data iterator for `DaskDeviceQuantileDMatrix`."""
|
|
|
|
def __init__(
|
|
self,
|
|
data: List[Any],
|
|
label: Optional[List[Any]] = None,
|
|
weight: Optional[List[Any]] = None,
|
|
base_margin: Optional[List[Any]] = None,
|
|
qid: Optional[List[Any]] = None,
|
|
label_lower_bound: Optional[List[Any]] = None,
|
|
label_upper_bound: Optional[List[Any]] = None,
|
|
feature_names: FeatureNames = None,
|
|
feature_types: Optional[Union[Any, List[Any]]] = None,
|
|
) -> None:
|
|
self._data = data
|
|
self._label = label
|
|
self._weight = weight
|
|
self._base_margin = base_margin
|
|
self._qid = qid
|
|
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, collections.abc.Sequence)
|
|
|
|
types = (collections.abc.Sequence, type(None))
|
|
assert isinstance(self._label, types)
|
|
assert isinstance(self._weight, 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 _get(self, attr: str) -> Optional[Any]:
|
|
if getattr(self, attr) is not None:
|
|
return getattr(self, attr)[self._iter]
|
|
return None
|
|
|
|
def data(self) -> Any:
|
|
"""Utility function for obtaining current batch of data."""
|
|
return self._data[self._iter]
|
|
|
|
def reset(self) -> None:
|
|
"""Reset the iterator"""
|
|
self._iter = 0
|
|
|
|
def next(self, input_data: Callable) -> int:
|
|
"""Yield next batch of data"""
|
|
if self._iter == len(self._data):
|
|
# Return 0 when there's no more batch.
|
|
return 0
|
|
feature_names: FeatureNames = None
|
|
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._get("_label"),
|
|
weight=self._get("_weight"),
|
|
group=None,
|
|
qid=self._get("_qid"),
|
|
base_margin=self._get("_base_margin"),
|
|
label_lower_bound=self._get("_label_lower_bound"),
|
|
label_upper_bound=self._get("_label_upper_bound"),
|
|
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 all partitions/chunks of data
|
|
are merged by weighted GK sketching. So the number of partitions from dask may affect
|
|
training accuracy as GK generates bounded error for each merge. See doc string for
|
|
:py:obj:`xgboost.DeviceQuantileDMatrix` and :py:obj:`xgboost.DMatrix` for other
|
|
parameters.
|
|
|
|
.. versionadded:: 1.2.0
|
|
|
|
Parameters
|
|
----------
|
|
max_bin : Number of bins for histogram construction.
|
|
|
|
"""
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self,
|
|
client: "distributed.Client",
|
|
data: _DaskCollection,
|
|
label: Optional[_DaskCollection] = None,
|
|
*,
|
|
weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
missing: float = None,
|
|
silent: bool = False, # disable=unused-argument
|
|
feature_names: FeatureNames = None,
|
|
feature_types: Optional[Union[Any, List[Any]]] = None,
|
|
max_bin: int = 256,
|
|
group: Optional[_DaskCollection] = None,
|
|
qid: Optional[_DaskCollection] = None,
|
|
label_lower_bound: Optional[_DaskCollection] = None,
|
|
label_upper_bound: Optional[_DaskCollection] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
enable_categorical: bool = False,
|
|
) -> None:
|
|
super().__init__(
|
|
client=client,
|
|
data=data,
|
|
label=label,
|
|
weight=weight,
|
|
base_margin=base_margin,
|
|
group=group,
|
|
qid=qid,
|
|
label_lower_bound=label_lower_bound,
|
|
label_upper_bound=label_upper_bound,
|
|
missing=missing,
|
|
silent=silent,
|
|
feature_weights=feature_weights,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
enable_categorical=enable_categorical,
|
|
)
|
|
self.max_bin = max_bin
|
|
self.is_quantile = True
|
|
|
|
def _create_fn_args(self, worker_addr: str) -> Dict[str, Any]:
|
|
args = super()._create_fn_args(worker_addr)
|
|
args["max_bin"] = self.max_bin
|
|
return args
|
|
|
|
|
|
def _create_device_quantile_dmatrix(
|
|
feature_names: FeatureNames,
|
|
feature_types: Optional[Union[Any, List[Any]]],
|
|
feature_weights: Optional[Any],
|
|
missing: float,
|
|
nthread: int,
|
|
parts: Optional[_DataParts],
|
|
max_bin: int,
|
|
enable_categorical: bool,
|
|
) -> DeviceQuantileDMatrix:
|
|
worker = distributed.get_worker()
|
|
if parts is None:
|
|
msg = f"worker {worker.address} has an empty DMatrix."
|
|
LOGGER.warning(msg)
|
|
import cupy
|
|
|
|
d = DeviceQuantileDMatrix(
|
|
cupy.zeros((0, 0)),
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
max_bin=max_bin,
|
|
enable_categorical=enable_categorical,
|
|
)
|
|
return d
|
|
|
|
unzipped_dict = _get_worker_parts(parts)
|
|
it = DaskPartitionIter(**unzipped_dict)
|
|
|
|
dmatrix = DeviceQuantileDMatrix(
|
|
it,
|
|
missing=missing,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
nthread=nthread,
|
|
max_bin=max_bin,
|
|
enable_categorical=enable_categorical,
|
|
)
|
|
dmatrix.set_info(feature_weights=feature_weights)
|
|
return dmatrix
|
|
|
|
|
|
def _create_dmatrix(
|
|
feature_names: FeatureNames,
|
|
feature_types: Optional[Union[Any, List[Any]]],
|
|
feature_weights: Optional[Any],
|
|
missing: float,
|
|
nthread: int,
|
|
enable_categorical: bool,
|
|
parts: Optional[_DataParts],
|
|
) -> DMatrix:
|
|
"""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 = f"worker {worker.address} has an empty DMatrix."
|
|
LOGGER.warning(msg)
|
|
d = DMatrix(
|
|
numpy.empty((0, 0)),
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
enable_categorical=enable_categorical,
|
|
)
|
|
return d
|
|
|
|
T = TypeVar("T")
|
|
|
|
def concat_or_none(data: Sequence[Optional[T]]) -> Optional[T]:
|
|
if any(part is None for part in data):
|
|
return None
|
|
return concat(data)
|
|
|
|
unzipped_dict = _get_worker_parts(list_of_parts)
|
|
concated_dict: Dict[str, Any] = {}
|
|
for key, value in unzipped_dict.items():
|
|
v = concat_or_none(value)
|
|
concated_dict[key] = v
|
|
|
|
dmatrix = DMatrix(
|
|
**concated_dict,
|
|
missing=missing,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
nthread=nthread,
|
|
enable_categorical=enable_categorical,
|
|
feature_weights=feature_weights,
|
|
)
|
|
return dmatrix
|
|
|
|
|
|
def _dmatrix_from_list_of_parts(
|
|
is_quantile: bool, **kwargs: Any
|
|
) -> Union[DMatrix, DeviceQuantileDMatrix]:
|
|
if is_quantile:
|
|
return _create_device_quantile_dmatrix(**kwargs)
|
|
return _create_dmatrix(**kwargs)
|
|
|
|
|
|
async def _get_rabit_args(
|
|
n_workers: int, dconfig: Optional[Dict[str, Any]], client: "distributed.Client"
|
|
) -> List[bytes]:
|
|
"""Get rabit context arguments from data distribution in DaskDMatrix."""
|
|
# There are 3 possible different addresses:
|
|
# 1. Provided by user via dask.config
|
|
# 2. Guessed by xgboost `get_host_ip` function
|
|
# 3. From dask scheduler
|
|
# We try 1 and 3 if 1 is available, otherwise 2 and 3.
|
|
valid_config = ["scheduler_address"]
|
|
# See if user config is available
|
|
host_ip: Optional[str] = None
|
|
port: int = 0
|
|
if dconfig is not None:
|
|
for k in dconfig:
|
|
if k not in valid_config:
|
|
raise ValueError(f"Unknown configuration: {k}")
|
|
host_ip = dconfig.get("scheduler_address", None)
|
|
try:
|
|
host_ip, port = distributed.comm.get_address_host_port(host_ip)
|
|
except ValueError:
|
|
pass
|
|
if host_ip is not None:
|
|
user_addr = (host_ip, port)
|
|
else:
|
|
user_addr = None
|
|
|
|
# Try address from dask scheduler, this might not work, see
|
|
# https://github.com/dask/dask-xgboost/pull/40
|
|
try:
|
|
sched_addr = distributed.comm.get_address_host(client.scheduler.address)
|
|
sched_addr = sched_addr.strip("/:")
|
|
except Exception: # pylint: disable=broad-except
|
|
sched_addr = None
|
|
|
|
# make sure all workers are online so that we can obtain reliable scheduler_info
|
|
client.wait_for_workers(n_workers)
|
|
env = await client.run_on_scheduler(
|
|
_start_tracker, n_workers, sched_addr, user_addr
|
|
)
|
|
|
|
rabit_args = [f"{k}={v}".encode() for k, v in env.items()]
|
|
return rabit_args
|
|
|
|
|
|
def _get_dask_config() -> Optional[Dict[str, Any]]:
|
|
return dask.config.get("xgboost", default=None)
|
|
|
|
|
|
# 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: Optional[Sequence[Tuple[DaskDMatrix, str]]]
|
|
) -> List[str]:
|
|
X_worker_map: Set[str] = 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)
|
|
if e[0] is dtrain:
|
|
continue
|
|
worker_map = set(e[0].worker_map.keys())
|
|
X_worker_map = X_worker_map.union(worker_map)
|
|
return list(X_worker_map)
|
|
|
|
|
|
async def _train_async(
|
|
client: "distributed.Client",
|
|
global_config: Dict[str, Any],
|
|
dconfig: Optional[Dict[str, Any]],
|
|
params: Dict[str, Any],
|
|
dtrain: DaskDMatrix,
|
|
num_boost_round: int,
|
|
evals: Optional[Sequence[Tuple[DaskDMatrix, str]]],
|
|
obj: Optional[Objective],
|
|
feval: Optional[Metric],
|
|
early_stopping_rounds: Optional[int],
|
|
verbose_eval: Union[int, bool],
|
|
xgb_model: Optional[Booster],
|
|
callbacks: Optional[Sequence[TrainingCallback]],
|
|
custom_metric: Optional[Metric],
|
|
) -> Optional[TrainReturnT]:
|
|
workers = _get_workers_from_data(dtrain, evals)
|
|
_rabit_args = await _get_rabit_args(len(workers), dconfig, client)
|
|
|
|
if params.get("booster", None) == "gblinear":
|
|
raise NotImplementedError(
|
|
f"booster `{params['booster']}` is not yet supported for dask."
|
|
)
|
|
|
|
def dispatched_train(
|
|
parameters: Dict,
|
|
rabit_args: List[bytes],
|
|
train_id: int,
|
|
evals_name: List[str],
|
|
evals_id: List[int],
|
|
train_ref: dict,
|
|
*refs: dict,
|
|
) -> Optional[TrainReturnT]:
|
|
worker = distributed.get_worker()
|
|
local_param = parameters.copy()
|
|
n_threads = 0
|
|
for p in ["nthread", "n_jobs"]:
|
|
if (
|
|
local_param.get(p, None) is not None
|
|
and local_param.get(p, worker.nthreads) != worker.nthreads
|
|
):
|
|
LOGGER.info("Overriding `nthreads` defined in dask worker.")
|
|
n_threads = local_param[p]
|
|
break
|
|
if n_threads == 0 or n_threads is None:
|
|
n_threads = worker.nthreads
|
|
local_param.update({"nthread": n_threads, "n_jobs": n_threads})
|
|
local_history: TrainingCallback.EvalsLog = {}
|
|
with RabitContext(rabit_args), config.config_context(**global_config):
|
|
Xy = _dmatrix_from_list_of_parts(**train_ref, nthread=n_threads)
|
|
evals: List[Tuple[DMatrix, str]] = []
|
|
for i, ref in enumerate(refs):
|
|
if evals_id[i] == train_id:
|
|
evals.append((Xy, evals_name[i]))
|
|
continue
|
|
eval_Xy = _dmatrix_from_list_of_parts(**ref, nthread=n_threads)
|
|
evals.append((eval_Xy, evals_name[i]))
|
|
|
|
booster = worker_train(
|
|
params=local_param,
|
|
dtrain=Xy,
|
|
num_boost_round=num_boost_round,
|
|
evals_result=local_history,
|
|
evals=evals if len(evals) != 0 else None,
|
|
obj=obj,
|
|
feval=feval,
|
|
custom_metric=custom_metric,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
verbose_eval=verbose_eval,
|
|
xgb_model=xgb_model,
|
|
callbacks=callbacks,
|
|
)
|
|
if Xy.num_row() != 0:
|
|
ret: Optional[TrainReturnT] = {
|
|
"booster": booster,
|
|
"history": local_history,
|
|
}
|
|
else:
|
|
ret = None
|
|
return ret
|
|
|
|
async with _multi_lock()(workers, client):
|
|
if evals is not None:
|
|
evals_data = [d for d, n in evals]
|
|
evals_name = [n for d, n in evals]
|
|
evals_id = [id(d) for d in evals_data]
|
|
else:
|
|
evals_data = []
|
|
evals_name = []
|
|
evals_id = []
|
|
|
|
results = await map_worker_partitions(
|
|
client,
|
|
dispatched_train,
|
|
params,
|
|
_rabit_args,
|
|
id(dtrain),
|
|
evals_name,
|
|
evals_id,
|
|
*([dtrain] + evals_data),
|
|
workers=workers,
|
|
)
|
|
return list(filter(lambda ret: ret is not None, results))[0]
|
|
|
|
|
|
@_deprecate_positional_args
|
|
def train( # pylint: disable=unused-argument
|
|
client: "distributed.Client",
|
|
params: Dict[str, Any],
|
|
dtrain: DaskDMatrix,
|
|
num_boost_round: int = 10,
|
|
*,
|
|
evals: Optional[Sequence[Tuple[DaskDMatrix, str]]] = None,
|
|
obj: Optional[Objective] = None,
|
|
feval: Optional[Metric] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
xgb_model: Optional[Booster] = None,
|
|
verbose_eval: Union[int, bool] = True,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None,
|
|
custom_metric: Optional[Metric] = None,
|
|
) -> Any:
|
|
"""Train XGBoost model.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
.. note::
|
|
|
|
Other parameters are the same as :py:func:`xgboost.train` except for
|
|
`evals_result`, which is returned as part of function return value instead of
|
|
argument.
|
|
|
|
Parameters
|
|
----------
|
|
client :
|
|
Specify the dask client used for training. Use default client returned from dask
|
|
if it's set to None.
|
|
|
|
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)
|
|
args = locals()
|
|
return client.sync(
|
|
_train_async,
|
|
global_config=config.get_config(),
|
|
dconfig=_get_dask_config(),
|
|
**args,
|
|
)
|
|
|
|
|
|
def _can_output_df(is_df: bool, output_shape: Tuple) -> bool:
|
|
return is_df and len(output_shape) <= 2
|
|
|
|
|
|
def _maybe_dataframe(
|
|
data: Any, prediction: Any, columns: List[int], is_df: bool
|
|
) -> Any:
|
|
"""Return dataframe for prediction when applicable."""
|
|
if _can_output_df(is_df, prediction.shape):
|
|
# Need to preserve the index for dataframe.
|
|
# See issue: https://github.com/dmlc/xgboost/issues/6939
|
|
# In older versions of dask, the partition is actually a numpy array when input is
|
|
# dataframe.
|
|
index = getattr(data, "index", None)
|
|
if lazy_isinstance(data, "cudf.core.dataframe", "DataFrame"):
|
|
import cudf
|
|
|
|
if prediction.size == 0:
|
|
return cudf.DataFrame({}, columns=columns, dtype=numpy.float32)
|
|
|
|
prediction = cudf.DataFrame(
|
|
prediction, columns=columns, dtype=numpy.float32, index=index
|
|
)
|
|
else:
|
|
if prediction.size == 0:
|
|
return DataFrame({}, columns=columns, dtype=numpy.float32, index=index)
|
|
|
|
prediction = DataFrame(
|
|
prediction, columns=columns, dtype=numpy.float32, index=index
|
|
)
|
|
return prediction
|
|
|
|
|
|
async def _direct_predict_impl( # pylint: disable=too-many-branches
|
|
mapped_predict: Callable,
|
|
booster: "distributed.Future",
|
|
data: _DaskCollection,
|
|
base_margin: Optional[_DaskCollection],
|
|
output_shape: Tuple[int, ...],
|
|
meta: Dict[int, str],
|
|
) -> _DaskCollection:
|
|
columns = tuple(meta.keys())
|
|
if len(output_shape) >= 3 and isinstance(data, dd.DataFrame):
|
|
# Without this check, dask will finish the prediction silently even if output
|
|
# dimension is greater than 3. But during map_partitions, dask passes a
|
|
# `dd.DataFrame` as local input to xgboost, which is converted to csr_matrix by
|
|
# `_convert_unknown_data` since dd.DataFrame is not known to xgboost native
|
|
# binding.
|
|
raise ValueError(
|
|
"Use `da.Array` or `DaskDMatrix` when output has more than 2 dimensions."
|
|
)
|
|
if _can_output_df(isinstance(data, dd.DataFrame), output_shape):
|
|
if base_margin is not None and isinstance(base_margin, da.Array):
|
|
# Easier for map_partitions
|
|
base_margin_df: Optional[dd.DataFrame] = base_margin.to_dask_dataframe()
|
|
else:
|
|
base_margin_df = base_margin
|
|
predictions = dd.map_partitions(
|
|
mapped_predict,
|
|
booster,
|
|
data,
|
|
True,
|
|
columns,
|
|
base_margin_df,
|
|
meta=dd.utils.make_meta(meta),
|
|
)
|
|
# classification can return a dataframe, drop 1 dim when it's reg/binary
|
|
if len(output_shape) == 1:
|
|
predictions = predictions.iloc[:, 0]
|
|
else:
|
|
if base_margin is not None and isinstance(
|
|
base_margin, (dd.Series, dd.DataFrame)
|
|
):
|
|
# Easier for map_blocks
|
|
base_margin_array: Optional[da.Array] = base_margin.to_dask_array()
|
|
else:
|
|
base_margin_array = base_margin
|
|
# Input data is 2-dim array, output can be 1(reg, binary)/2(multi-class,
|
|
# contrib)/3(contrib, interaction)/4(interaction) dims.
|
|
if len(output_shape) == 1:
|
|
drop_axis: Union[int, List[int]] = [1] # drop from 2 to 1 dim.
|
|
new_axis: Union[int, List[int]] = []
|
|
else:
|
|
drop_axis = []
|
|
if isinstance(data, dd.DataFrame):
|
|
new_axis = list(range(len(output_shape) - 2))
|
|
else:
|
|
new_axis = [i + 2 for i in range(len(output_shape) - 2)]
|
|
if len(output_shape) == 2:
|
|
# Somehow dask fail to infer output shape change for 2-dim prediction, and
|
|
# `chunks = (None, output_shape[1])` doesn't work due to None is not
|
|
# supported in map_blocks.
|
|
chunks: Optional[List[Tuple]] = list(data.chunks)
|
|
assert isinstance(chunks, list)
|
|
chunks[1] = (output_shape[1],)
|
|
else:
|
|
chunks = None
|
|
predictions = da.map_blocks(
|
|
mapped_predict,
|
|
booster,
|
|
data,
|
|
False,
|
|
columns,
|
|
base_margin_array,
|
|
chunks=chunks,
|
|
drop_axis=drop_axis,
|
|
new_axis=new_axis,
|
|
dtype=numpy.float32,
|
|
)
|
|
return predictions
|
|
|
|
|
|
def _infer_predict_output(
|
|
booster: Booster, features: int, is_df: bool, inplace: bool, **kwargs: Any
|
|
) -> Tuple[Tuple[int, ...], Dict[int, str]]:
|
|
"""Create a dummy test sample to infer output shape for prediction."""
|
|
assert isinstance(features, int)
|
|
rng = numpy.random.RandomState(1994)
|
|
test_sample = rng.randn(1, features)
|
|
if inplace:
|
|
kwargs = kwargs.copy()
|
|
if kwargs.pop("predict_type") == "margin":
|
|
kwargs["output_margin"] = True
|
|
m = DMatrix(test_sample)
|
|
# generated DMatrix doesn't have feature name, so no validation.
|
|
test_predt = booster.predict(m, validate_features=False, **kwargs)
|
|
n_columns = test_predt.shape[1] if len(test_predt.shape) > 1 else 1
|
|
meta: Dict[int, str] = {}
|
|
if _can_output_df(is_df, test_predt.shape):
|
|
for i in range(n_columns):
|
|
meta[i] = "f4"
|
|
return test_predt.shape, meta
|
|
|
|
|
|
async def _get_model_future(
|
|
client: "distributed.Client", model: Union[Booster, Dict, "distributed.Future"]
|
|
) -> "distributed.Future":
|
|
if isinstance(model, Booster):
|
|
booster = await client.scatter(model, broadcast=True)
|
|
elif isinstance(model, dict):
|
|
booster = await client.scatter(model["booster"], broadcast=True)
|
|
elif isinstance(model, distributed.Future):
|
|
booster = model
|
|
if booster.type is not Booster:
|
|
raise TypeError(
|
|
f"Underlying type of model future should be `Booster`, got {booster.type}"
|
|
)
|
|
else:
|
|
raise TypeError(_expect([Booster, dict, distributed.Future], type(model)))
|
|
return booster
|
|
|
|
|
|
# pylint: disable=too-many-statements
|
|
async def _predict_async(
|
|
client: "distributed.Client",
|
|
global_config: Dict[str, Any],
|
|
model: Union[Booster, Dict, "distributed.Future"],
|
|
data: _DaskCollection,
|
|
output_margin: bool,
|
|
missing: float,
|
|
pred_leaf: bool,
|
|
pred_contribs: bool,
|
|
approx_contribs: bool,
|
|
pred_interactions: bool,
|
|
validate_features: bool,
|
|
iteration_range: Tuple[int, int],
|
|
strict_shape: bool,
|
|
) -> _DaskCollection:
|
|
_booster = await _get_model_future(client, model)
|
|
if not isinstance(data, (DaskDMatrix, da.Array, dd.DataFrame)):
|
|
raise TypeError(_expect([DaskDMatrix, da.Array, dd.DataFrame], type(data)))
|
|
|
|
def mapped_predict(
|
|
booster: Booster, partition: Any, is_df: bool, columns: List[int], _: Any
|
|
) -> Any:
|
|
with config.config_context(**global_config):
|
|
m = DMatrix(
|
|
data=partition,
|
|
missing=missing,
|
|
enable_categorical=_has_categorical(booster, partition)
|
|
)
|
|
predt = booster.predict(
|
|
data=m,
|
|
output_margin=output_margin,
|
|
pred_leaf=pred_leaf,
|
|
pred_contribs=pred_contribs,
|
|
approx_contribs=approx_contribs,
|
|
pred_interactions=pred_interactions,
|
|
validate_features=validate_features,
|
|
iteration_range=iteration_range,
|
|
strict_shape=strict_shape,
|
|
)
|
|
predt = _maybe_dataframe(partition, predt, columns, is_df)
|
|
return predt
|
|
|
|
# Predict on dask collection directly.
|
|
if isinstance(data, (da.Array, dd.DataFrame)):
|
|
_output_shape, meta = await client.compute(
|
|
client.submit(
|
|
_infer_predict_output,
|
|
_booster,
|
|
features=data.shape[1],
|
|
is_df=isinstance(data, dd.DataFrame),
|
|
inplace=False,
|
|
output_margin=output_margin,
|
|
pred_leaf=pred_leaf,
|
|
pred_contribs=pred_contribs,
|
|
approx_contribs=approx_contribs,
|
|
pred_interactions=pred_interactions,
|
|
strict_shape=strict_shape,
|
|
)
|
|
)
|
|
return await _direct_predict_impl(
|
|
mapped_predict, _booster, data, None, _output_shape, meta
|
|
)
|
|
|
|
output_shape, _ = await client.compute(
|
|
client.submit(
|
|
_infer_predict_output,
|
|
booster=_booster,
|
|
features=data.num_col(),
|
|
is_df=False,
|
|
inplace=False,
|
|
output_margin=output_margin,
|
|
pred_leaf=pred_leaf,
|
|
pred_contribs=pred_contribs,
|
|
approx_contribs=approx_contribs,
|
|
pred_interactions=pred_interactions,
|
|
strict_shape=strict_shape,
|
|
)
|
|
)
|
|
# Prediction on dask DMatrix.
|
|
partition_order = data.partition_order
|
|
feature_names = data.feature_names
|
|
feature_types = data.feature_types
|
|
missing = data.missing
|
|
|
|
def dispatched_predict(booster: Booster, part: Dict[str, Any]) -> numpy.ndarray:
|
|
data = part["data"]
|
|
base_margin = part.get("base_margin", None)
|
|
with config.config_context(**global_config):
|
|
m = DMatrix(
|
|
data,
|
|
missing=missing,
|
|
base_margin=base_margin,
|
|
feature_names=feature_names,
|
|
feature_types=feature_types,
|
|
)
|
|
predt = booster.predict(
|
|
m,
|
|
output_margin=output_margin,
|
|
pred_leaf=pred_leaf,
|
|
pred_contribs=pred_contribs,
|
|
approx_contribs=approx_contribs,
|
|
pred_interactions=pred_interactions,
|
|
validate_features=validate_features,
|
|
iteration_range=iteration_range,
|
|
strict_shape=strict_shape,
|
|
)
|
|
return predt
|
|
|
|
all_parts = []
|
|
all_orders = []
|
|
all_shapes = []
|
|
all_workers: List[str] = []
|
|
workers_address = list(data.worker_map.keys())
|
|
for worker_addr in workers_address:
|
|
list_of_parts = data.worker_map[worker_addr]
|
|
all_parts.extend(list_of_parts)
|
|
all_workers.extend(len(list_of_parts) * [worker_addr])
|
|
all_orders.extend([partition_order[part.key] for part in list_of_parts])
|
|
for w, part in zip(all_workers, all_parts):
|
|
s = client.submit(lambda part: part["data"].shape[0], part, workers=[w])
|
|
all_shapes.append(s)
|
|
|
|
parts_with_order = list(zip(all_parts, all_shapes, all_orders, all_workers))
|
|
parts_with_order = sorted(parts_with_order, key=lambda p: p[2])
|
|
all_parts = [part for part, shape, order, w in parts_with_order]
|
|
all_shapes = [shape for part, shape, order, w in parts_with_order]
|
|
all_workers = [w for part, shape, order, w in parts_with_order]
|
|
|
|
futures = []
|
|
for w, part in zip(all_workers, all_parts):
|
|
f = client.submit(dispatched_predict, _booster, part, workers=[w])
|
|
futures.append(f)
|
|
|
|
# Constructing a dask array from list of numpy arrays
|
|
# See https://docs.dask.org/en/latest/array-creation.html
|
|
arrays = []
|
|
all_shapes = await client.gather(all_shapes)
|
|
for i, rows in enumerate(all_shapes):
|
|
arrays.append(
|
|
da.from_delayed(
|
|
futures[i], shape=(rows,) + output_shape[1:], dtype=numpy.float32
|
|
)
|
|
)
|
|
predictions = da.concatenate(arrays, axis=0)
|
|
return predictions
|
|
|
|
|
|
def predict( # pylint: disable=unused-argument
|
|
client: "distributed.Client",
|
|
model: Union[TrainReturnT, Booster, "distributed.Future"],
|
|
data: Union[DaskDMatrix, _DaskCollection],
|
|
output_margin: bool = False,
|
|
missing: float = numpy.nan,
|
|
pred_leaf: bool = False,
|
|
pred_contribs: bool = False,
|
|
approx_contribs: bool = False,
|
|
pred_interactions: bool = False,
|
|
validate_features: bool = True,
|
|
iteration_range: Tuple[int, int] = (0, 0),
|
|
strict_shape: bool = False,
|
|
) -> Any:
|
|
"""Run prediction with a trained booster.
|
|
|
|
.. note::
|
|
|
|
Using ``inplace_predict`` might be faster when some features are not needed. See
|
|
:py:meth:`xgboost.Booster.predict` for details on various parameters. When output
|
|
has more than 2 dimensions (shap value, leaf with strict_shape), input should be
|
|
``da.Array`` or ``DaskDMatrix``.
|
|
|
|
.. versionadded:: 1.0.0
|
|
|
|
Parameters
|
|
----------
|
|
client:
|
|
Specify the dask client used for training. Use default client
|
|
returned from dask if it's set to None.
|
|
model:
|
|
The trained model. It can be a distributed.Future so user can
|
|
pre-scatter it onto all workers.
|
|
data:
|
|
Input data used for prediction. When input is a dataframe object,
|
|
prediction output is a series.
|
|
missing:
|
|
Used when input data is not DaskDMatrix. Specify the value
|
|
considered as missing.
|
|
|
|
Returns
|
|
-------
|
|
prediction: dask.array.Array/dask.dataframe.Series
|
|
When input data is ``dask.array.Array`` or ``DaskDMatrix``, the return value is an
|
|
array, when input data is ``dask.dataframe.DataFrame``, return value can be
|
|
``dask.dataframe.Series``, ``dask.dataframe.DataFrame``, depending on the output
|
|
shape.
|
|
|
|
"""
|
|
_assert_dask_support()
|
|
client = _xgb_get_client(client)
|
|
return client.sync(_predict_async, global_config=config.get_config(), **locals())
|
|
|
|
|
|
async def _inplace_predict_async( # pylint: disable=too-many-branches
|
|
client: "distributed.Client",
|
|
global_config: Dict[str, Any],
|
|
model: Union[Booster, Dict, "distributed.Future"],
|
|
data: _DaskCollection,
|
|
iteration_range: Tuple[int, int],
|
|
predict_type: str,
|
|
missing: float,
|
|
validate_features: bool,
|
|
base_margin: Optional[_DaskCollection],
|
|
strict_shape: bool,
|
|
) -> _DaskCollection:
|
|
client = _xgb_get_client(client)
|
|
booster = await _get_model_future(client, model)
|
|
if not isinstance(data, (da.Array, dd.DataFrame)):
|
|
raise TypeError(_expect([da.Array, dd.DataFrame], type(data)))
|
|
if base_margin is not None and not isinstance(
|
|
data, (da.Array, dd.DataFrame, dd.Series)
|
|
):
|
|
raise TypeError(_expect([da.Array, dd.DataFrame, dd.Series], type(base_margin)))
|
|
|
|
def mapped_predict(
|
|
booster: Booster,
|
|
partition: Any,
|
|
is_df: bool,
|
|
columns: List[int],
|
|
base_margin: Any,
|
|
) -> Any:
|
|
with config.config_context(**global_config):
|
|
prediction = booster.inplace_predict(
|
|
partition,
|
|
iteration_range=iteration_range,
|
|
predict_type=predict_type,
|
|
missing=missing,
|
|
base_margin=base_margin,
|
|
validate_features=validate_features,
|
|
strict_shape=strict_shape,
|
|
)
|
|
prediction = _maybe_dataframe(partition, prediction, columns, is_df)
|
|
return prediction
|
|
|
|
# await turns future into value.
|
|
shape, meta = await client.compute(
|
|
client.submit(
|
|
_infer_predict_output,
|
|
booster,
|
|
features=data.shape[1],
|
|
is_df=isinstance(data, dd.DataFrame),
|
|
inplace=True,
|
|
predict_type=predict_type,
|
|
iteration_range=iteration_range,
|
|
strict_shape=strict_shape,
|
|
)
|
|
)
|
|
return await _direct_predict_impl(
|
|
mapped_predict, booster, data, base_margin, shape, meta
|
|
)
|
|
|
|
|
|
def inplace_predict( # pylint: disable=unused-argument
|
|
client: "distributed.Client",
|
|
model: Union[TrainReturnT, Booster, "distributed.Future"],
|
|
data: _DaskCollection,
|
|
iteration_range: Tuple[int, int] = (0, 0),
|
|
predict_type: str = "value",
|
|
missing: float = numpy.nan,
|
|
validate_features: bool = True,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
strict_shape: bool = False,
|
|
) -> Any:
|
|
"""Inplace prediction. See doc in :py:meth:`xgboost.Booster.inplace_predict` for details.
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
Parameters
|
|
----------
|
|
client:
|
|
Specify the dask client used for training. Use default client
|
|
returned from dask if it's set to None.
|
|
model:
|
|
See :py:func:`xgboost.dask.predict` for details.
|
|
data :
|
|
dask collection.
|
|
iteration_range:
|
|
See :py:meth:`xgboost.Booster.predict` for details.
|
|
predict_type:
|
|
See :py:meth:`xgboost.Booster.inplace_predict` for details.
|
|
missing:
|
|
Value in the input data which needs to be present as a missing
|
|
value. If None, defaults to np.nan.
|
|
base_margin:
|
|
See :py:obj:`xgboost.DMatrix` for details.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
strict_shape:
|
|
See :py:meth:`xgboost.Booster.predict` for details.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Returns
|
|
-------
|
|
prediction :
|
|
When input data is ``dask.array.Array``, the return value is an array, when input
|
|
data is ``dask.dataframe.DataFrame``, return value can be
|
|
``dask.dataframe.Series``, ``dask.dataframe.DataFrame``, depending on the output
|
|
shape.
|
|
|
|
"""
|
|
_assert_dask_support()
|
|
client = _xgb_get_client(client)
|
|
# When used in asynchronous environment, the `client` object should have
|
|
# `asynchronous` attribute as True. When invoked by the skl interface, it's
|
|
# responsible for setting up the client.
|
|
return client.sync(
|
|
_inplace_predict_async, global_config=config.get_config(), **locals()
|
|
)
|
|
|
|
|
|
async def _async_wrap_evaluation_matrices(
|
|
client: "distributed.Client", **kwargs: Any
|
|
) -> Tuple[DaskDMatrix, Optional[List[Tuple[DaskDMatrix, str]]]]:
|
|
"""A switch function for async environment."""
|
|
|
|
def _inner(**kwargs: Any) -> DaskDMatrix:
|
|
m = DaskDMatrix(client=client, **kwargs)
|
|
return m
|
|
|
|
train_dmatrix, evals = _wrap_evaluation_matrices(create_dmatrix=_inner, **kwargs)
|
|
train_dmatrix = await train_dmatrix
|
|
if evals is None:
|
|
return train_dmatrix, evals
|
|
awaited = []
|
|
for e in evals:
|
|
if e[0] is train_dmatrix: # already awaited
|
|
awaited.append(e)
|
|
continue
|
|
awaited.append((await e[0], e[1]))
|
|
return train_dmatrix, awaited
|
|
|
|
|
|
@contextmanager
|
|
def _set_worker_client(
|
|
model: "DaskScikitLearnBase", client: "distributed.Client"
|
|
) -> Generator:
|
|
"""Temporarily set the client for sklearn model."""
|
|
try:
|
|
model.client = client
|
|
yield model
|
|
finally:
|
|
model.client = None
|
|
|
|
|
|
class DaskScikitLearnBase(XGBModel):
|
|
"""Base class for implementing scikit-learn interface with Dask"""
|
|
|
|
_client = None
|
|
|
|
async def _predict_async(
|
|
self,
|
|
data: _DaskCollection,
|
|
output_margin: bool,
|
|
validate_features: bool,
|
|
base_margin: Optional[_DaskCollection],
|
|
iteration_range: Optional[Tuple[int, int]],
|
|
) -> Any:
|
|
iteration_range = self._get_iteration_range(iteration_range)
|
|
if self._can_use_inplace_predict():
|
|
predts = await inplace_predict(
|
|
client=self.client,
|
|
model=self.get_booster(),
|
|
data=data,
|
|
iteration_range=iteration_range,
|
|
predict_type="margin" if output_margin else "value",
|
|
missing=self.missing,
|
|
base_margin=base_margin,
|
|
validate_features=validate_features,
|
|
)
|
|
if isinstance(predts, dd.DataFrame):
|
|
predts = predts.to_dask_array()
|
|
else:
|
|
test_dmatrix = await DaskDMatrix(
|
|
self.client, data=data, base_margin=base_margin, missing=self.missing
|
|
)
|
|
predts = await predict(
|
|
self.client,
|
|
model=self.get_booster(),
|
|
data=test_dmatrix,
|
|
output_margin=output_margin,
|
|
validate_features=validate_features,
|
|
iteration_range=iteration_range,
|
|
)
|
|
return predts
|
|
|
|
def predict(
|
|
self,
|
|
X: _DaskCollection,
|
|
output_margin: bool = False,
|
|
ntree_limit: Optional[int] = None,
|
|
validate_features: bool = True,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> Any:
|
|
_assert_dask_support()
|
|
msg = "`ntree_limit` is not supported on dask, use `iteration_range` instead."
|
|
assert ntree_limit is None, msg
|
|
return self.client.sync(
|
|
self._predict_async,
|
|
X,
|
|
output_margin=output_margin,
|
|
validate_features=validate_features,
|
|
base_margin=base_margin,
|
|
iteration_range=iteration_range,
|
|
)
|
|
|
|
async def _apply_async(
|
|
self,
|
|
X: _DaskCollection,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> Any:
|
|
iteration_range = self._get_iteration_range(iteration_range)
|
|
test_dmatrix = await DaskDMatrix(self.client, data=X, missing=self.missing)
|
|
predts = await predict(
|
|
self.client,
|
|
model=self.get_booster(),
|
|
data=test_dmatrix,
|
|
pred_leaf=True,
|
|
iteration_range=iteration_range,
|
|
)
|
|
return predts
|
|
|
|
def apply(
|
|
self,
|
|
X: _DaskCollection,
|
|
ntree_limit: Optional[int] = None,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> Any:
|
|
_assert_dask_support()
|
|
msg = "`ntree_limit` is not supported on dask, use `iteration_range` instead."
|
|
assert ntree_limit is None, msg
|
|
return self.client.sync(self._apply_async, X, iteration_range=iteration_range)
|
|
|
|
def __await__(self) -> Awaitable[Any]:
|
|
# Generate a coroutine wrapper to make this class awaitable.
|
|
async def _() -> Awaitable[Any]:
|
|
return self
|
|
|
|
return self._client_sync(_).__await__()
|
|
|
|
def __getstate__(self) -> Dict:
|
|
this = self.__dict__.copy()
|
|
if "_client" in this.keys():
|
|
del this["_client"]
|
|
return this
|
|
|
|
@property
|
|
def client(self) -> "distributed.Client":
|
|
"""The dask client used in this model. The `Client` object can not be serialized for
|
|
transmission, so if task is launched from a worker instead of directly from the
|
|
client process, this attribute needs to be set at that worker.
|
|
|
|
"""
|
|
|
|
client = _xgb_get_client(self._client)
|
|
return client
|
|
|
|
@client.setter
|
|
def client(self, clt: "distributed.Client") -> None:
|
|
# calling `worker_client' doesn't return the correct `asynchronous` attribute, so
|
|
# we have to pass it ourselves.
|
|
self._asynchronous = clt.asynchronous if clt is not None else False
|
|
self._client = clt
|
|
|
|
def _client_sync(self, func: Callable, **kwargs: Any) -> Any:
|
|
"""Get the correct client, when method is invoked inside a worker we
|
|
should use `worker_client' instead of default client.
|
|
|
|
"""
|
|
|
|
if self._client is None:
|
|
asynchronous = getattr(self, "_asynchronous", False)
|
|
try:
|
|
distributed.get_worker()
|
|
in_worker = True
|
|
except ValueError:
|
|
in_worker = False
|
|
if in_worker:
|
|
with distributed.worker_client() as client:
|
|
with _set_worker_client(self, client) as this:
|
|
ret = this.client.sync(
|
|
func, **kwargs, asynchronous=asynchronous
|
|
)
|
|
return ret
|
|
return ret
|
|
|
|
return self.client.sync(func, **kwargs, asynchronous=self.client.asynchronous)
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"""Implementation of the Scikit-Learn API for XGBoost.""", ["estimators", "model"]
|
|
)
|
|
class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
|
|
"""dummy doc string to workaround pylint, replaced by the decorator."""
|
|
async def _fit_async(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
sample_weight: Optional[_DaskCollection],
|
|
base_margin: Optional[_DaskCollection],
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]],
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
|
|
early_stopping_rounds: Optional[int],
|
|
verbose: bool,
|
|
xgb_model: Optional[Union[Booster, XGBModel]],
|
|
feature_weights: Optional[_DaskCollection],
|
|
callbacks: Optional[Sequence[TrainingCallback]],
|
|
) -> _DaskCollection:
|
|
params = self.get_xgb_params()
|
|
dtrain, evals = await _async_wrap_evaluation_matrices(
|
|
client=self.client,
|
|
X=X,
|
|
y=y,
|
|
group=None,
|
|
qid=None,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
feature_weights=feature_weights,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
base_margin_eval_set=base_margin_eval_set,
|
|
eval_group=None,
|
|
eval_qid=None,
|
|
missing=self.missing,
|
|
enable_categorical=self.enable_categorical,
|
|
)
|
|
|
|
if callable(self.objective):
|
|
obj: Optional[Callable] = _objective_decorator(self.objective)
|
|
else:
|
|
obj = None
|
|
model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
|
|
xgb_model, eval_metric, params, early_stopping_rounds, callbacks
|
|
)
|
|
results = await self.client.sync(
|
|
_train_async,
|
|
asynchronous=True,
|
|
client=self.client,
|
|
global_config=config.get_config(),
|
|
dconfig=_get_dask_config(),
|
|
params=params,
|
|
dtrain=dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
obj=obj,
|
|
feval=None,
|
|
custom_metric=metric,
|
|
verbose_eval=verbose,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
callbacks=callbacks,
|
|
xgb_model=model,
|
|
)
|
|
self._Booster = results["booster"]
|
|
self._set_evaluation_result(results["history"])
|
|
return self
|
|
|
|
# pylint: disable=missing-docstring, disable=unused-argument
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
*,
|
|
sample_weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: bool = True,
|
|
xgb_model: Optional[Union[Booster, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None,
|
|
) -> "DaskXGBRegressor":
|
|
_assert_dask_support()
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
return self._client_sync(self._fit_async, **args)
|
|
|
|
|
|
@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: _DaskCollection,
|
|
y: _DaskCollection,
|
|
sample_weight: Optional[_DaskCollection],
|
|
base_margin: Optional[_DaskCollection],
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]],
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
|
|
early_stopping_rounds: Optional[int],
|
|
verbose: bool,
|
|
xgb_model: Optional[Union[Booster, XGBModel]],
|
|
feature_weights: Optional[_DaskCollection],
|
|
callbacks: Optional[Sequence[TrainingCallback]],
|
|
) -> "DaskXGBClassifier":
|
|
params = self.get_xgb_params()
|
|
dtrain, evals = await _async_wrap_evaluation_matrices(
|
|
self.client,
|
|
X=X,
|
|
y=y,
|
|
group=None,
|
|
qid=None,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
feature_weights=feature_weights,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
base_margin_eval_set=base_margin_eval_set,
|
|
eval_group=None,
|
|
eval_qid=None,
|
|
missing=self.missing,
|
|
enable_categorical=self.enable_categorical,
|
|
)
|
|
|
|
# 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"
|
|
|
|
if callable(self.objective):
|
|
obj: Optional[Callable] = _objective_decorator(self.objective)
|
|
else:
|
|
obj = None
|
|
model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
|
|
xgb_model, eval_metric, params, early_stopping_rounds, callbacks
|
|
)
|
|
results = await self.client.sync(
|
|
_train_async,
|
|
asynchronous=True,
|
|
client=self.client,
|
|
global_config=config.get_config(),
|
|
dconfig=_get_dask_config(),
|
|
params=params,
|
|
dtrain=dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
obj=obj,
|
|
feval=None,
|
|
custom_metric=metric,
|
|
verbose_eval=verbose,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
callbacks=callbacks,
|
|
xgb_model=model,
|
|
)
|
|
self._Booster = results["booster"]
|
|
if not callable(self.objective):
|
|
self.objective = params["objective"]
|
|
self._set_evaluation_result(results["history"])
|
|
return self
|
|
|
|
# pylint: disable=unused-argument
|
|
def fit(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
*,
|
|
sample_weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: bool = True,
|
|
xgb_model: Optional[Union[Booster, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None,
|
|
) -> "DaskXGBClassifier":
|
|
_assert_dask_support()
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
return self._client_sync(self._fit_async, **args)
|
|
|
|
async def _predict_proba_async(
|
|
self,
|
|
X: _DaskCollection,
|
|
validate_features: bool,
|
|
base_margin: Optional[_DaskCollection],
|
|
iteration_range: Optional[Tuple[int, int]],
|
|
) -> _DaskCollection:
|
|
if self.objective == "multi:softmax":
|
|
raise ValueError(
|
|
"multi:softmax doesn't support `predict_proba`. "
|
|
"Switch to `multi:softproba` instead"
|
|
)
|
|
predts = await super()._predict_async(
|
|
data=X,
|
|
output_margin=False,
|
|
validate_features=validate_features,
|
|
base_margin=base_margin,
|
|
iteration_range=iteration_range,
|
|
)
|
|
vstack = update_wrapper(
|
|
partial(da.vstack, allow_unknown_chunksizes=True), da.vstack
|
|
)
|
|
return _cls_predict_proba(getattr(self, "n_classes_", 0), predts, vstack)
|
|
|
|
# pylint: disable=missing-function-docstring
|
|
def predict_proba(
|
|
self,
|
|
X: _DaskCollection,
|
|
ntree_limit: Optional[int] = None,
|
|
validate_features: bool = True,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> Any:
|
|
_assert_dask_support()
|
|
msg = "`ntree_limit` is not supported on dask, use `iteration_range` instead."
|
|
assert ntree_limit is None, msg
|
|
return self._client_sync(
|
|
self._predict_proba_async,
|
|
X=X,
|
|
validate_features=validate_features,
|
|
base_margin=base_margin,
|
|
iteration_range=iteration_range,
|
|
)
|
|
|
|
predict_proba.__doc__ = XGBClassifier.predict_proba.__doc__
|
|
|
|
async def _predict_async(
|
|
self,
|
|
data: _DaskCollection,
|
|
output_margin: bool,
|
|
validate_features: bool,
|
|
base_margin: Optional[_DaskCollection],
|
|
iteration_range: Optional[Tuple[int, int]],
|
|
) -> _DaskCollection:
|
|
pred_probs = await super()._predict_async(
|
|
data, output_margin, validate_features, base_margin, iteration_range
|
|
)
|
|
if output_margin:
|
|
return pred_probs
|
|
|
|
if len(pred_probs.shape) == 1:
|
|
preds = (pred_probs > 0.5).astype(int)
|
|
else:
|
|
assert len(pred_probs.shape) == 2
|
|
assert isinstance(pred_probs, da.Array)
|
|
# when using da.argmax directly, dask will construct a numpy based return
|
|
# array, which runs into error when computing GPU based prediction.
|
|
|
|
def _argmax(x: Any) -> Any:
|
|
return x.argmax(axis=1)
|
|
|
|
preds = da.map_blocks(_argmax, pred_probs, drop_axis=1)
|
|
return preds
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"""Implementation of the Scikit-Learn API for XGBoost Ranking.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
""",
|
|
["estimators", "model"],
|
|
end_note="""
|
|
.. note::
|
|
|
|
For dask implementation, group is not supported, use qid instead.
|
|
""",
|
|
)
|
|
class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, objective: str = "rank:pairwise", **kwargs: Any):
|
|
if callable(objective):
|
|
raise ValueError("Custom objective function not supported by XGBRanker.")
|
|
super().__init__(objective=objective, kwargs=kwargs)
|
|
|
|
async def _fit_async(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
group: Optional[_DaskCollection],
|
|
qid: Optional[_DaskCollection],
|
|
sample_weight: Optional[_DaskCollection],
|
|
base_margin: Optional[_DaskCollection],
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
|
|
eval_group: Optional[Sequence[_DaskCollection]],
|
|
eval_qid: Optional[Sequence[_DaskCollection]],
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]],
|
|
early_stopping_rounds: Optional[int],
|
|
verbose: bool,
|
|
xgb_model: Optional[Union[XGBModel, Booster]],
|
|
feature_weights: Optional[_DaskCollection],
|
|
callbacks: Optional[Sequence[TrainingCallback]],
|
|
) -> "DaskXGBRanker":
|
|
msg = "Use `qid` instead of `group` on dask interface."
|
|
if not (group is None and eval_group is None):
|
|
raise ValueError(msg)
|
|
if qid is None:
|
|
raise ValueError("`qid` is required for ranking.")
|
|
params = self.get_xgb_params()
|
|
dtrain, evals = await _async_wrap_evaluation_matrices(
|
|
self.client,
|
|
X=X,
|
|
y=y,
|
|
group=None,
|
|
qid=qid,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
feature_weights=feature_weights,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
base_margin_eval_set=base_margin_eval_set,
|
|
eval_group=None,
|
|
eval_qid=eval_qid,
|
|
missing=self.missing,
|
|
enable_categorical=self.enable_categorical,
|
|
)
|
|
if eval_metric is not None:
|
|
if callable(eval_metric):
|
|
raise ValueError(
|
|
"Custom evaluation metric is not yet supported for XGBRanker."
|
|
)
|
|
model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
|
|
xgb_model, eval_metric, params, early_stopping_rounds, callbacks
|
|
)
|
|
results = await self.client.sync(
|
|
_train_async,
|
|
asynchronous=True,
|
|
client=self.client,
|
|
global_config=config.get_config(),
|
|
dconfig=_get_dask_config(),
|
|
params=params,
|
|
dtrain=dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
obj=None,
|
|
feval=None,
|
|
custom_metric=metric,
|
|
verbose_eval=verbose,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
callbacks=callbacks,
|
|
xgb_model=model,
|
|
)
|
|
self._Booster = results["booster"]
|
|
self.evals_result_ = results["history"]
|
|
return self
|
|
|
|
# pylint: disable=unused-argument, arguments-differ
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
*,
|
|
group: Optional[_DaskCollection] = None,
|
|
qid: Optional[_DaskCollection] = None,
|
|
sample_weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
|
|
eval_group: Optional[Sequence[_DaskCollection]] = None,
|
|
eval_qid: Optional[Sequence[_DaskCollection]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
|
|
early_stopping_rounds: int = None,
|
|
verbose: bool = False,
|
|
xgb_model: Optional[Union[XGBModel, Booster]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None,
|
|
) -> "DaskXGBRanker":
|
|
_assert_dask_support()
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
return self._client_sync(self._fit_async, **args)
|
|
|
|
# FIXME(trivialfis): arguments differ due to additional parameters like group and qid.
|
|
fit.__doc__ = XGBRanker.fit.__doc__
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"""Implementation of the Scikit-Learn API for XGBoost Random Forest Regressor.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
""",
|
|
["model", "objective"],
|
|
extra_parameters="""
|
|
n_estimators : int
|
|
Number of trees in random forest to fit.
|
|
""",
|
|
)
|
|
class DaskXGBRFRegressor(DaskXGBRegressor):
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self,
|
|
*,
|
|
learning_rate: Optional[float] = 1,
|
|
subsample: Optional[float] = 0.8,
|
|
colsample_bynode: Optional[float] = 0.8,
|
|
reg_lambda: Optional[float] = 1e-5,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
subsample=subsample,
|
|
colsample_bynode=colsample_bynode,
|
|
reg_lambda=reg_lambda,
|
|
**kwargs,
|
|
)
|
|
|
|
def get_xgb_params(self) -> Dict[str, Any]:
|
|
params = super().get_xgb_params()
|
|
params["num_parallel_tree"] = self.n_estimators
|
|
return params
|
|
|
|
def get_num_boosting_rounds(self) -> int:
|
|
return 1
|
|
|
|
# pylint: disable=unused-argument
|
|
def fit(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
*,
|
|
sample_weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: bool = True,
|
|
xgb_model: Optional[Union[Booster, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None,
|
|
) -> "DaskXGBRFRegressor":
|
|
_assert_dask_support()
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
_check_rf_callback(early_stopping_rounds, callbacks)
|
|
super().fit(**args)
|
|
return self
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"""Implementation of the Scikit-Learn API for XGBoost Random Forest Classifier.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
""",
|
|
["model", "objective"],
|
|
extra_parameters="""
|
|
n_estimators : int
|
|
Number of trees in random forest to fit.
|
|
""",
|
|
)
|
|
class DaskXGBRFClassifier(DaskXGBClassifier):
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self,
|
|
*,
|
|
learning_rate: Optional[float] = 1,
|
|
subsample: Optional[float] = 0.8,
|
|
colsample_bynode: Optional[float] = 0.8,
|
|
reg_lambda: Optional[float] = 1e-5,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
subsample=subsample,
|
|
colsample_bynode=colsample_bynode,
|
|
reg_lambda=reg_lambda,
|
|
**kwargs,
|
|
)
|
|
|
|
def get_xgb_params(self) -> Dict[str, Any]:
|
|
params = super().get_xgb_params()
|
|
params["num_parallel_tree"] = self.n_estimators
|
|
return params
|
|
|
|
def get_num_boosting_rounds(self) -> int:
|
|
return 1
|
|
|
|
# pylint: disable=unused-argument
|
|
def fit(
|
|
self,
|
|
X: _DaskCollection,
|
|
y: _DaskCollection,
|
|
*,
|
|
sample_weight: Optional[_DaskCollection] = None,
|
|
base_margin: Optional[_DaskCollection] = None,
|
|
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: bool = True,
|
|
xgb_model: Optional[Union[Booster, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
|
|
feature_weights: Optional[_DaskCollection] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None,
|
|
) -> "DaskXGBRFClassifier":
|
|
_assert_dask_support()
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
_check_rf_callback(early_stopping_rounds, callbacks)
|
|
super().fit(**args)
|
|
return self
|