# coding: utf-8
"""Distributed training with LightGBM and dask.distributed.
This module enables you to perform distributed training with LightGBM on
dask.Array and dask.DataFrame collections.
It is based on dask-lightgbm, which was based on dask-xgboost.
"""
import operator
import socket
from collections import defaultdict
from copy import deepcopy
from enum import Enum, auto
from functools import partial
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union
from urllib.parse import urlparse
import numpy as np
import scipy.sparse as ss
from .basic import LightGBMError, _choose_param_value, _ConfigAliases, _log_info, _log_warning
from .compat import (
DASK_INSTALLED,
PANDAS_INSTALLED,
SKLEARN_INSTALLED,
Client,
Future,
LGBMNotFittedError,
concat,
dask_Array,
dask_array_from_delayed,
dask_bag_from_delayed,
dask_DataFrame,
dask_Series,
default_client,
delayed,
pd_DataFrame,
pd_Series,
wait,
)
from .sklearn import (
LGBMClassifier,
LGBMModel,
LGBMRanker,
LGBMRegressor,
_LGBM_ScikitCustomObjectiveFunction,
_LGBM_ScikitEvalMetricType,
_lgbmmodel_doc_custom_eval_note,
_lgbmmodel_doc_fit,
_lgbmmodel_doc_predict,
)
__all__ = [
"DaskLGBMClassifier",
"DaskLGBMRanker",
"DaskLGBMRegressor",
]
_DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]
_DaskMatrixLike = Union[dask_Array, dask_DataFrame]
_DaskVectorLike = Union[dask_Array, dask_Series]
_DaskPart = Union[np.ndarray, pd_DataFrame, pd_Series, ss.spmatrix]
_PredictionDtype = Union[Type[np.float32], Type[np.float64], Type[np.int32], Type[np.int64]]
class _RemoteSocket:
def acquire(self) -> int:
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self.socket.bind(("", 0))
return self.socket.getsockname()[1]
def release(self) -> None:
self.socket.close()
def _acquire_port() -> Tuple[_RemoteSocket, int]:
s = _RemoteSocket()
port = s.acquire()
return s, port
class _DatasetNames(Enum):
"""Placeholder names used by lightgbm.dask internals to say 'also evaluate the training data'.
Avoid duplicating the training data when the validation set refers to elements of training data.
"""
TRAINSET = auto()
SAMPLE_WEIGHT = auto()
INIT_SCORE = auto()
GROUP = auto()
def _get_dask_client(client: Optional[Client]) -> Client:
"""Choose a Dask client to use.
Parameters
----------
client : dask.distributed.Client or None
Dask client.
Returns
-------
client : dask.distributed.Client
A Dask client.
"""
if client is None:
return default_client()
else:
return client
def _assign_open_ports_to_workers(
client: Client,
workers: List[str],
) -> Tuple[Dict[str, Future], Dict[str, int]]:
"""Assign an open port to each worker.
Returns
-------
worker_to_socket_future: dict
mapping from worker address to a future pointing to the remote socket.
worker_to_port: dict
mapping from worker address to an open port in the worker's host.
"""
# Acquire port in worker
worker_to_future = {}
for worker in workers:
worker_to_future[worker] = client.submit(
_acquire_port,
workers=[worker],
allow_other_workers=False,
pure=False,
)
# schedule futures to retrieve each element of the tuple
worker_to_socket_future = {}
worker_to_port_future = {}
for worker, socket_future in worker_to_future.items():
worker_to_socket_future[worker] = client.submit(operator.itemgetter(0), socket_future)
worker_to_port_future[worker] = client.submit(operator.itemgetter(1), socket_future)
# retrieve ports
worker_to_port = client.gather(worker_to_port_future)
return worker_to_socket_future, worker_to_port
def _concat(seq: List[_DaskPart]) -> _DaskPart:
if isinstance(seq[0], np.ndarray):
return np.concatenate(seq, axis=0)
elif isinstance(seq[0], (pd_DataFrame, pd_Series)):
return concat(seq, axis=0)
elif isinstance(seq[0], ss.spmatrix):
return ss.vstack(seq, format="csr")
else:
raise TypeError(
f"Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0]).__name__}."
)
def _remove_list_padding(*args: Any) -> List[List[Any]]:
return [[z for z in arg if z is not None] for arg in args]
def _pad_eval_names(lgbm_model: LGBMModel, required_names: List[str]) -> LGBMModel:
"""Append missing (key, value) pairs to a LightGBM model's evals_result_ and best_score_ OrderedDict attrs based on a set of required eval_set names.
Allows users to rely on expected eval_set names being present when fitting DaskLGBM estimators with ``eval_set``.
"""
for eval_name in required_names:
if eval_name not in lgbm_model.evals_result_:
lgbm_model.evals_result_[eval_name] = {}
if eval_name not in lgbm_model.best_score_:
lgbm_model.best_score_[eval_name] = {}
return lgbm_model
def _train_part(
params: Dict[str, Any],
model_factory: Type[LGBMModel],
list_of_parts: List[Dict[str, _DaskPart]],
machines: str,
local_listen_port: int,
num_machines: int,
return_model: bool,
time_out: int,
remote_socket: _RemoteSocket,
**kwargs: Any,
) -> Optional[LGBMModel]:
network_params = {
"machines": machines,
"local_listen_port": local_listen_port,
"time_out": time_out,
"num_machines": num_machines,
}
params.update(network_params)
is_ranker = issubclass(model_factory, LGBMRanker)
# Concatenate many parts into one
data = _concat([x["data"] for x in list_of_parts])
label = _concat([x["label"] for x in list_of_parts])
if "weight" in list_of_parts[0]:
weight = _concat([x["weight"] for x in list_of_parts])
else:
weight = None
if "group" in list_of_parts[0]:
group = _concat([x["group"] for x in list_of_parts])
else:
group = None
if "init_score" in list_of_parts[0]:
init_score = _concat([x["init_score"] for x in list_of_parts])
else:
init_score = None
# construct local eval_set data.
n_evals = max(len(x.get("eval_set", [])) for x in list_of_parts)
eval_names = kwargs.pop("eval_names", None)
eval_class_weight = kwargs.get("eval_class_weight")
local_eval_set = None
local_eval_names = None
local_eval_sample_weight = None
local_eval_init_score = None
local_eval_group = None
if n_evals:
has_eval_sample_weight = any(x.get("eval_sample_weight") is not None for x in list_of_parts)
has_eval_init_score = any(x.get("eval_init_score") is not None for x in list_of_parts)
local_eval_set = []
evals_result_names = []
if has_eval_sample_weight:
local_eval_sample_weight = []
if has_eval_init_score:
local_eval_init_score = []
if is_ranker:
local_eval_group = []
# store indices of eval_set components that were not contained within local parts.
missing_eval_component_idx = []
# consolidate parts of each individual eval component.
for i in range(n_evals):
x_e = []
y_e = []
w_e = []
init_score_e = []
g_e = []
for part in list_of_parts:
if not part.get("eval_set"):
continue
# require that eval_name exists in evaluated result data in case dropped due to padding.
# in distributed training the 'training' eval_set is not detected, will have name 'valid_<index>'.
if eval_names:
evals_result_name = eval_names[i]
else:
evals_result_name = f"valid_{i}"
eval_set = part["eval_set"][i]
if eval_set is _DatasetNames.TRAINSET:
x_e.append(part["data"])
y_e.append(part["label"])
else:
x_e.extend(eval_set[0])
y_e.extend(eval_set[1])
if evals_result_name not in evals_result_names:
evals_result_names.append(evals_result_name)
eval_weight = part.get("eval_sample_weight")
if eval_weight:
if eval_weight[i] is _DatasetNames.SAMPLE_WEIGHT:
w_e.append(part["weight"])
else:
w_e.extend(eval_weight[i])
eval_init_score = part.get("eval_init_score")
if eval_init_score:
if eval_init_score[i] is _DatasetNames.INIT_SCORE:
init_score_e.append(part["init_score"])
else:
init_score_e.extend(eval_init_score[i])
eval_group = part.get("eval_group")
if eval_group:
if eval_group[i] is _DatasetNames.GROUP:
g_e.append(part["group"])
else:
g_e.extend(eval_group[i])
# filter padding from eval parts then _concat each eval_set component.
x_e, y_e, w_e, init_score_e, g_e = _remove_list_padding(x_e, y_e, w_e, init_score_e, g_e)
if x_e:
local_eval_set.append((_concat(x_e), _concat(y_e)))
else:
missing_eval_component_idx.append(i)
continue
if w_e:
local_eval_sample_weight.append(_concat(w_e))
if init_score_e:
local_eval_init_score.append(_concat(init_score_e))
if g_e:
local_eval_group.append(_concat(g_e))
# reconstruct eval_set fit args/kwargs depending on which components of eval_set are on worker.
eval_component_idx = [i for i in range(n_evals) if i not in missing_eval_component_idx]
if eval_names:
local_eval_names = [eval_names[i] for i in eval_component_idx]
if eval_class_weight:
kwargs["eval_class_weight"] = [eval_class_weight[i] for i in eval_component_idx]
model = model_factory(**params)
if remote_socket is not None:
remote_socket.release()
try:
if is_ranker:
model.fit(
data,
label,
sample_weight=weight,
init_score=init_score,
group=group,
eval_set=local_eval_set,
eval_sample_weight=local_eval_sample_weight,
eval_init_score=local_eval_init_score,
eval_group=local_eval_group,
eval_names=local_eval_names,
**kwargs,
)
else:
model.fit(
data,
label,
sample_weight=weight,
init_score=init_score,
eval_set=local_eval_set,
eval_sample_weight=local_eval_sample_weight,
eval_init_score=local_eval_init_score,
eval_names=local_eval_names,
**kwargs,
)
finally:
if getattr(model, "fitted_", False):
model.booster_.free_network()
if n_evals:
# ensure that expected keys for evals_result_ and best_score_ exist regardless of padding.
model = _pad_eval_names(model, required_names=evals_result_names)
return model if return_model else None
def _split_to_parts(data: _DaskCollection, is_matrix: bool) -> List[_DaskPart]:
parts = data.to_delayed()
if isinstance(parts, np.ndarray):
if is_matrix:
assert parts.shape[1] == 1
else:
assert parts.ndim == 1 or parts.shape[1] == 1
parts = parts.flatten().tolist()
return parts
def _machines_to_worker_map(machines: str, worker_addresses: Iterable[str]) -> Dict[str, int]:
"""Create a worker_map from machines list.
Given ``machines`` and a list of Dask worker addresses, return a mapping where the keys are
``worker_addresses`` and the values are ports from ``machines``.
Parameters
----------
machines : str
A comma-delimited list of workers, of the form ``ip1:port,ip2:port``.
worker_addresses : list of str
An iterable of Dask worker addresses, of the form ``{protocol}{hostname}:{port}``, where ``port`` is the port Dask's scheduler uses to talk to that worker.
Returns
-------
result : Dict[str, int]
Dictionary where keys are work addresses in the form expected by Dask and values are a port for LightGBM to use.
"""
machine_addresses = machines.split(",")
if len(set(machine_addresses)) != len(machine_addresses):
raise ValueError(
f"Found duplicates in 'machines' ({machines}). Each entry in 'machines' must be a unique IP-port combination."
)
machine_to_port = defaultdict(set)
for address in machine_addresses:
host, port = address.split(":")
machine_to_port[host].add(int(port))
out = {}
for address in worker_addresses:
worker_host = urlparse(address).hostname
if not worker_host:
raise ValueError(f"Could not parse host name from worker address '{address}'")
out[address] = machine_to_port[worker_host].pop()
return out
def _train(
client: Client,
data: _DaskMatrixLike,
label: _DaskCollection,
params: Dict[str, Any],
model_factory: Type[LGBMModel],
sample_weight: Optional[_DaskVectorLike] = None,
init_score: Optional[_DaskCollection] = None,
group: Optional[_DaskVectorLike] = None,
eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,
eval_names: Optional[List[str]] = None,
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_class_weight: Optional[List[Union[dict, str]]] = None,
eval_init_score: Optional[List[_DaskCollection]] = None,
eval_group: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,
eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None,
**kwargs: Any,
) -> LGBMModel:
"""Inner train routine.
Parameters
----------
client : dask.distributed.Client
Dask client.
data : Dask Array or Dask DataFrame of shape = [n_samples, n_features]
Input feature matrix.
label : Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
params : dict
Parameters passed to constructor of the local underlying model.
model_factory : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class
Class of the local underlying model.
sample_weight : Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)
Weights of training data. Weights should be non-negative.
init_score : Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None)
Init score of training data.
group : Dask Array or Dask Series or None, optional (default=None)
Group/query data.
Only used in the learning-to-rank task.
sum(group) = n_samples.
For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
eval_set : list of (X, y) tuples of Dask data collections, or None, optional (default=None)
List of (X, y) tuple pairs to use as validation sets.
Note, that not all workers may receive chunks of every eval set within ``eval_set``. When the returned
lightgbm estimator is not trained using any chunks of a particular eval set, its corresponding component
of ``evals_result_`` and ``best_score_`` will be empty dictionaries.
eval_names : list of str, or None, optional (default=None)
Names of eval_set.
eval_sample_weight : list of Dask Array or Dask Series, or None, optional (default=None)
Weights for each validation set in eval_set. Weights should be non-negative.
eval_class_weight : list of dict or str, or None, optional (default=None)
Class weights, one dict or str for each validation set in eval_set.
eval_init_score : list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)
Initial model score for each validation set in eval_set.
eval_group : list of Dask Array or Dask Series, or None, optional (default=None)
Group/query for each validation set in eval_set.
eval_metric : str, callable, list or None, optional (default=None)
If str, it should be a built-in evaluation metric to use.
If callable, it should be a custom evaluation metric, see note below for more details.
If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both.
In either case, the ``metric`` from the Dask model parameters (or inferred from the objective) will be evaluated and used as well.
Default: 'l2' for DaskLGBMRegressor, 'binary(multi)_logloss' for DaskLGBMClassifier, 'ndcg' for DaskLGBMRanker.
eval_at : list or tuple of int, optional (default=None)
The evaluation positions of the specified ranking metric.
**kwargs
Other parameters passed to ``fit`` method of the local underlying model.
Returns
-------
model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class
Returns fitted underlying model.
Note
----
This method handles setting up the following network parameters based on information
about the Dask cluster referenced by ``client``.
* ``local_listen_port``: port that each LightGBM worker opens a listening socket on,
to accept connections from other workers. This can differ from LightGBM worker
to LightGBM worker, but does not have to.
* ``machines``: a comma-delimited list of all workers in the cluster, in the
form ``ip:port,ip:port``. If running multiple Dask workers on the same host, use different
ports for each worker. For example, for ``LocalCluster(n_workers=3)``, you might
pass ``"127.0.0.1:12400,127.0.0.1:12401,127.0.0.1:12402"``.
* ``num_machines``: number of LightGBM workers.
* ``timeout``: time in minutes to wait before closing unused sockets.
The default behavior of this function is to generate ``machines`` from the list of
Dask workers which hold some piece of the training data, and to search for an open
port on each worker to be used as ``local_listen_port``.
If ``machines`` is provided explicitly in ``params``, this function uses the hosts
and ports in that list directly, and does not do any searching. This means that if
any of the Dask workers are missing from the list or any of those ports are not free
when training starts, training will fail.
If ``local_listen_port`` is provided in ``params`` and ``machines`` is not, this function
constructs ``machines`` from the list of Dask workers which hold some piece of the
training data, assuming that each one will use the same ``local_listen_port``.
"""
params = deepcopy(params)
# capture whether local_listen_port or its aliases were provided
listen_port_in_params = any(alias in params for alias in _ConfigAliases.get("local_listen_port"))
# capture whether machines or its aliases were provided
machines_in_params = any(alias in params for alias in _ConfigAliases.get("machines"))
params = _choose_param_value(
main_param_name="tree_learner",
params=params,
default_value="data",
)
allowed_tree_learners = {
"data",
"data_parallel",
"feature",
"feature_parallel",
"voting",
"voting_parallel",
}
if params["tree_learner"] not in allowed_tree_learners:
_log_warning(
f'Parameter tree_learner set to {params["tree_learner"]}, which is not allowed. Using "data" as default'
)
params["tree_learner"] = "data"
# Some passed-in parameters can be removed:
# * 'num_machines': set automatically from Dask worker list
# * 'num_threads': overridden to match nthreads on each Dask process
for param_alias in _ConfigAliases.get("num_machines", "num_threads"):
if param_alias in params:
_log_warning(f"Parameter {param_alias} will be ignored.")
params.pop(param_alias)
# Split arrays/dataframes into parts. Arrange parts into dicts to enforce co-locality
data_parts = _split_to_parts(data=data, is_matrix=True)
label_parts = _split_to_parts(data=label, is_matrix=False)
parts = [{"data": x, "label": y} for (x, y) in zip(data_parts, label_parts)]
n_parts = len(parts)
if sample_weight is not None:
weight_parts = _split_to_parts(data=sample_weight, is_matrix=False)
for i in range(n_parts):
parts[i]["weight"] = weight_parts[i]
if group is not None:
group_parts = _split_to_parts(data=group, is_matrix=False)
for i in range(n_parts):
parts[i]["group"] = group_parts[i]
if init_score is not None:
init_score_parts = _split_to_parts(data=init_score, is_matrix=False)
for i in range(n_parts):
parts[i]["init_score"] = init_score_parts[i]
# evals_set will to be re-constructed into smaller lists of (X, y) tuples, where
# X and y are each delayed sub-lists of original eval dask Collections.
if eval_set:
# find maximum number of parts in an individual eval set so that we can
# pad eval sets when they come in different sizes.
n_largest_eval_parts = max(x[0].npartitions for x in eval_set)
eval_sets: Dict[
int, List[Union[_DatasetNames, Tuple[List[Optional[_DaskMatrixLike]], List[Optional[_DaskVectorLike]]]]]
] = defaultdict(list)
if eval_sample_weight:
eval_sample_weights: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict(
list
)
if eval_group:
eval_groups: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict(list)
if eval_init_score:
eval_init_scores: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskMatrixLike]]]]] = defaultdict(list)
for i, (X_eval, y_eval) in enumerate(eval_set):
n_this_eval_parts = X_eval.npartitions
# when individual eval set is equivalent to training data, skip recomputing parts.
if X_eval is data and y_eval is label:
for parts_idx in range(n_parts):
eval_sets[parts_idx].append(_DatasetNames.TRAINSET)
else:
eval_x_parts = _split_to_parts(data=X_eval, is_matrix=True)
eval_y_parts = _split_to_parts(data=y_eval, is_matrix=False)
for j in range(n_largest_eval_parts):
parts_idx = j % n_parts
# add None-padding for individual eval_set member if it is smaller than the largest member.
if j < n_this_eval_parts:
x_e = eval_x_parts[j]
y_e = eval_y_parts[j]
else:
x_e = None
y_e = None
if j < n_parts:
# first time a chunk of this eval set is added to this part.
eval_sets[parts_idx].append(([x_e], [y_e]))
else:
# append additional chunks of this eval set to this part.
eval_sets[parts_idx][-1][0].append(x_e) # type: ignore[index, union-attr]
eval_sets[parts_idx][-1][1].append(y_e) # type: ignore[index, union-attr]
if eval_sample_weight:
if eval_sample_weight[i] is sample_weight:
for parts_idx in range(n_parts):
eval_sample_weights[parts_idx].append(_DatasetNames.SAMPLE_WEIGHT)
else:
eval_w_parts = _split_to_parts(data=eval_sample_weight[i], is_matrix=False)
# ensure that all evaluation parts map uniquely to one part.
for j in range(n_largest_eval_parts):
if j < n_this_eval_parts:
w_e = eval_w_parts[j]
else:
w_e = None
parts_idx = j % n_parts
if j < n_parts:
eval_sample_weights[parts_idx].append([w_e])
else:
eval_sample_weights[parts_idx][-1].append(w_e) # type: ignore[union-attr]
if eval_init_score:
if eval_init_score[i] is init_score:
for parts_idx in range(n_parts):
eval_init_scores[parts_idx].append(_DatasetNames.INIT_SCORE)
else:
eval_init_score_parts = _split_to_parts(data=eval_init_score[i], is_matrix=False)
for j in range(n_largest_eval_parts):
if j < n_this_eval_parts:
init_score_e = eval_init_score_parts[j]
else:
init_score_e = None
parts_idx = j % n_parts
if j < n_parts:
eval_init_scores[parts_idx].append([init_score_e])
else:
eval_init_scores[parts_idx][-1].append(init_score_e) # type: ignore[union-attr]
if eval_group:
if eval_group[i] is group:
for parts_idx in range(n_parts):
eval_groups[parts_idx].append(_DatasetNames.GROUP)
else:
eval_g_parts = _split_to_parts(data=eval_group[i], is_matrix=False)
for j in range(n_largest_eval_parts):
if j < n_this_eval_parts:
g_e = eval_g_parts[j]
else:
g_e = None
parts_idx = j % n_parts
if j < n_parts:
eval_groups[parts_idx].append([g_e])
else:
eval_groups[parts_idx][-1].append(g_e) # type: ignore[union-attr]
# assign sub-eval_set components to worker parts.
for parts_idx, e_set in eval_sets.items():
parts[parts_idx]["eval_set"] = e_set
if eval_sample_weight:
parts[parts_idx]["eval_sample_weight"] = eval_sample_weights[parts_idx]
if eval_init_score:
parts[parts_idx]["eval_init_score"] = eval_init_scores[parts_idx]
if eval_group:
parts[parts_idx]["eval_group"] = eval_groups[parts_idx]
# Start computation in the background
parts = list(map(delayed, parts))
parts = client.compute(parts)
wait(parts)
for part in parts:
if part.status == "error": # type: ignore
# trigger error locally
return part # type: ignore[return-value]
# Find locations of all parts and map them to particular Dask workers
key_to_part_dict = {part.key: part for part in parts} # type: ignore
who_has = client.who_has(parts)
worker_map = defaultdict(list)
for key, workers in who_has.items():
worker_map[next(iter(workers))].append(key_to_part_dict[key])
# Check that all workers were provided some of eval_set. Otherwise warn user that validation
# data artifacts may not be populated depending on worker returning final estimator.
if eval_set:
for worker in worker_map:
has_eval_set = False
for part in worker_map[worker]:
if "eval_set" in part.result(): # type: ignore[attr-defined]
has_eval_set = True
break
if not has_eval_set:
_log_warning(
f"Worker {worker} was not allocated eval_set data. Therefore evals_result_ and best_score_ data may be unreliable. "
"Try rebalancing data across workers."
)
# assign general validation set settings to fit kwargs.
if eval_names:
kwargs["eval_names"] = eval_names
if eval_class_weight:
kwargs["eval_class_weight"] = eval_class_weight
if eval_metric:
kwargs["eval_metric"] = eval_metric
if eval_at:
kwargs["eval_at"] = eval_at
master_worker = next(iter(worker_map))
worker_ncores = client.ncores()
# resolve aliases for network parameters and pop the result off params.
# these values are added back in calls to `_train_part()`
params = _choose_param_value(
main_param_name="local_listen_port",
params=params,
default_value=12400,
)
local_listen_port = params.pop("local_listen_port")
params = _choose_param_value(
main_param_name="machines",
params=params,
default_value=None,
)
machines = params.pop("machines")
# figure out network params
worker_to_socket_future: Dict[str, Future] = {}
worker_addresses = worker_map.keys()
if machines is not None:
_log_info("Using passed-in 'machines' parameter")
worker_address_to_port = _machines_to_worker_map(
machines=machines,
worker_addresses=worker_addresses,
)
else:
if listen_port_in_params:
_log_info("Using passed-in 'local_listen_port' for all workers")
unique_hosts = {urlparse(a).hostname for a in worker_addresses}
if len(unique_hosts) < len(worker_addresses):
msg = (
"'local_listen_port' was provided in Dask training parameters, but at least one "
"machine in the cluster has multiple Dask worker processes running on it. Please omit "
"'local_listen_port' or pass 'machines'."
)
raise LightGBMError(msg)
worker_address_to_port = {address: local_listen_port for address in worker_addresses}
else:
_log_info("Finding random open ports for workers")
worker_to_socket_future, worker_address_to_port = _assign_open_ports_to_workers(
client, list(worker_map.keys())
)
machines = ",".join(
[f"{urlparse(worker_address).hostname}:{port}" for worker_address, port in worker_address_to_port.items()]
)
num_machines = len(worker_address_to_port)
# Tell each worker to train on the parts that it has locally
#
# This code treats ``_train_part()`` calls as not "pure" because:
# 1. there is randomness in the training process unless parameters ``seed``
# and ``deterministic`` are set
# 2. even with those parameters set, the output of one ``_train_part()`` call
# relies on global state (it and all the other LightGBM training processes
# coordinate with each other)
futures_classifiers = [
client.submit(
_train_part,
model_factory=model_factory,
params={**params, "num_threads": worker_ncores[worker]},
list_of_parts=list_of_parts,
machines=machines,
local_listen_port=worker_address_to_port[worker],
num_machines=num_machines,
time_out=params.get("time_out", 120),
remote_socket=worker_to_socket_future.get(worker, None),
return_model=(worker == master_worker),
workers=[worker],
allow_other_workers=False,
pure=False,
**kwargs,
)
for worker, list_of_parts in worker_map.items()
]
results = client.gather(futures_classifiers)
results = [v for v in results if v]
model = results[0]
# if network parameters were changed during training, remove them from the
# returned model so that they're generated dynamically on every run based
# on the Dask cluster you're connected to and which workers have pieces of
# the training data
if not listen_port_in_params:
for param in _ConfigAliases.get("local_listen_port"):
model._other_params.pop(param, None)
if not machines_in_params:
for param in _ConfigAliases.get("machines"):
model._other_params.pop(param, None)
for param in _ConfigAliases.get("num_machines", "timeout"):
model._other_params.pop(param, None)
return model
def _predict_part(
part: _DaskPart,
model: LGBMModel,
raw_score: bool,
pred_proba: bool,
pred_leaf: bool,
pred_contrib: bool,
**kwargs: Any,
) -> _DaskPart:
result: _DaskPart
if part.shape[0] == 0:
result = np.array([])
elif pred_proba:
result = model.predict_proba(
part,
raw_score=raw_score,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
**kwargs,
)
else:
result = model.predict(
part,
raw_score=raw_score,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
**kwargs,
)
# dask.DataFrame.map_partitions() expects each call to return a pandas DataFrame or Series
if isinstance(part, pd_DataFrame):
if len(result.shape) == 2:
result = pd_DataFrame(result, index=part.index)
else:
result = pd_Series(result, index=part.index, name="predictions")
return result
def _predict(
model: LGBMModel,
data: _DaskMatrixLike,
client: Client,
raw_score: bool = False,
pred_proba: bool = False,
pred_leaf: bool = False,
pred_contrib: bool = False,
dtype: _PredictionDtype = np.float32,
**kwargs: Any,
) -> Union[dask_Array, List[dask_Array]]:
"""Inner predict routine.
Parameters
----------
model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class
Fitted underlying model.
data : Dask Array or Dask DataFrame of shape = [n_samples, n_features]
Input feature matrix.
raw_score : bool, optional (default=False)
Whether to predict raw scores.
pred_proba : bool, optional (default=False)
Should method return results of ``predict_proba`` (``pred_proba=True``) or ``predict`` (``pred_proba=False``).
pred_leaf : bool, optional (default=False)
Whether to predict leaf index.
pred_contrib : bool, optional (default=False)
Whether to predict feature contributions.
dtype : np.dtype, optional (default=np.float32)
Dtype of the output.
**kwargs
Other parameters passed to ``predict`` or ``predict_proba`` method.
Returns
-------
predicted_result : Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]
The predicted values.
X_leaves : Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]
If ``pred_leaf=True``, the predicted leaf of every tree for each sample.
X_SHAP_values : Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]
If ``pred_contrib=True``, the feature contributions for each sample.
"""
if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):
raise LightGBMError("dask, pandas and scikit-learn are required for lightgbm.dask")
if isinstance(data, dask_DataFrame):
return data.map_partitions(
_predict_part,
model=model,
raw_score=raw_score,
pred_proba=pred_proba,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
**kwargs,
).values
elif isinstance(data, dask_Array):
# for multi-class classification with sparse matrices, pred_contrib predictions
# are returned as a list of sparse matrices (one per class)
num_classes = model._n_classes
if num_classes > 2 and pred_contrib and isinstance(data._meta, ss.spmatrix):
predict_function = partial(
_predict_part,
model=model,
raw_score=False,
pred_proba=pred_proba,
pred_leaf=False,
pred_contrib=True,
**kwargs,
)
delayed_chunks = data.to_delayed()
bag = dask_bag_from_delayed(delayed_chunks[:, 0])
@delayed
def _extract(items: List[Any], i: int) -> Any:
return items[i]
preds = bag.map_partitions(predict_function)
# pred_contrib output will have one column per feature,
# plus one more for the base value
num_cols = model.n_features_ + 1
nrows_per_chunk = data.chunks[0]
out: List[List[dask_Array]] = [[] for _ in range(num_classes)]
# need to tell Dask the expected type and shape of individual preds
pred_meta = data._meta
for j, partition in enumerate(preds.to_delayed()):
for i in range(num_classes):
part = dask_array_from_delayed(
value=_extract(partition, i),
shape=(nrows_per_chunk[j], num_cols),
meta=pred_meta,
)
out[i].append(part)
# by default, dask.array.concatenate() concatenates sparse arrays into a COO matrix
# the code below is used instead to ensure that the sparse type is preserved during concatentation
if isinstance(pred_meta, ss.csr_matrix):
concat_fn = partial(ss.vstack, format="csr")
elif isinstance(pred_meta, ss.csc_matrix):
concat_fn = partial(ss.vstack, format="csc")
else:
concat_fn = ss.vstack
# At this point, `out` is a list of lists of delayeds (each of which points to a matrix).
# Concatenate them to return a list of Dask Arrays.
out_arrays: List[dask_Array] = []
for i in range(num_classes):
out_arrays.append(
dask_array_from_delayed(
value=delayed(concat_fn)(out[i]),
shape=(data.shape[0], num_cols),
meta=pred_meta,
)
)
return out_arrays
data_row = client.compute(data[[0]]).result()
predict_fn = partial(
_predict_part,
model=model,
raw_score=raw_score,
pred_proba=pred_proba,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
**kwargs,
)
pred_row = predict_fn(data_row)
chunks: Tuple[int, ...] = (data.chunks[0],)
map_blocks_kwargs = {}
if len(pred_row.shape) > 1:
chunks += (pred_row.shape[1],)
else:
map_blocks_kwargs["drop_axis"] = 1
return data.map_blocks(
predict_fn,
chunks=chunks,
meta=pred_row,
dtype=dtype,
**map_blocks_kwargs,
)
else:
raise TypeError(f"Data must be either Dask Array or Dask DataFrame. Got {type(data).__name__}.")
class _DaskLGBMModel:
@property
def client_(self) -> Client:
""":obj:`dask.distributed.Client`: Dask client.
This property can be passed in the constructor or updated
with ``model.set_params(client=client)``.
"""
if not getattr(self, "fitted_", False):
raise LGBMNotFittedError("Cannot access property client_ before calling fit().")
return _get_dask_client(client=self.client)
def _lgb_dask_getstate(self) -> Dict[Any, Any]:
"""Remove un-picklable attributes before serialization."""
client = self.__dict__.pop("client", None)
self._other_params.pop("client", None) # type: ignore[attr-defined]
out = deepcopy(self.__dict__)
out.update({"client": None})
self.client = client
return out
def _lgb_dask_fit(
self,
model_factory: Type[LGBMModel],
X: _DaskMatrixLike,
y: _DaskCollection,
sample_weight: Optional[_DaskVectorLike] = None,
init_score: Optional[_DaskCollection] = None,
group: Optional[_DaskVectorLike] = None,
eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,
eval_names: Optional[List[str]] = None,
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_class_weight: Optional[List[Union[dict, str]]] = None,
eval_init_score: Optional[List[_DaskCollection]] = None,
eval_group: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,
eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None,
**kwargs: Any,
) -> "_DaskLGBMModel":
if not DASK_INSTALLED:
raise LightGBMError("dask is required for lightgbm.dask")
if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):
raise LightGBMError("dask, pandas and scikit-learn are required for lightgbm.dask")
params = self.get_params(True) # type: ignore[attr-defined]
params.pop("client", None)
model = _train(
client=_get_dask_client(self.client),
data=X,
label=y,
params=params,
model_factory=model_factory,
sample_weight=sample_weight,
init_score=init_score,
group=group,
eval_set=eval_set,
eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_class_weight=eval_class_weight,
eval_init_score=eval_init_score,
eval_group=eval_group,
eval_metric=eval_metric,
eval_at=eval_at,
**kwargs,
)
self.set_params(**model.get_params()) # type: ignore[attr-defined]
self._lgb_dask_copy_extra_params(model, self) # type: ignore[attr-defined]
return self
def _lgb_dask_to_local(self, model_factory: Type[LGBMModel]) -> LGBMModel:
params = self.get_params() # type: ignore[attr-defined]
params.pop("client", None)
model = model_factory(**params)
self._lgb_dask_copy_extra_params(self, model)
model._other_params.pop("client", None)
return model
@staticmethod
def _lgb_dask_copy_extra_params(
source: Union["_DaskLGBMModel", LGBMModel],
dest: Union["_DaskLGBMModel", LGBMModel],
) -> None:
params = source.get_params() # type: ignore[union-attr]
attributes = source.__dict__
extra_param_names = set(attributes.keys()).difference(params.keys())
for name in extra_param_names:
setattr(dest, name, attributes[name])
[docs]
class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):
"""Distributed version of lightgbm.LGBMClassifier."""
[docs]
def __init__(
self,
boosting_type: str = "gbdt",
num_leaves: int = 31,
max_depth: int = -1,
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
class_weight: Optional[Union[dict, str]] = None,
min_split_gain: float = 0.0,
min_child_weight: float = 1e-3,
min_child_samples: int = 20,
subsample: float = 1.0,
subsample_freq: int = 0,
colsample_bytree: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 0.0,
random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None,
n_jobs: Optional[int] = None,
importance_type: str = "split",
client: Optional[Client] = None,
**kwargs: Any,
):
"""Docstring is inherited from the lightgbm.LGBMClassifier.__init__."""
self.client = client
super().__init__(
boosting_type=boosting_type,
num_leaves=num_leaves,
max_depth=max_depth,
learning_rate=learning_rate,
n_estimators=n_estimators,
subsample_for_bin=subsample_for_bin,
objective=objective,
class_weight=class_weight,
min_split_gain=min_split_gain,
min_child_weight=min_child_weight,
min_child_samples=min_child_samples,
subsample=subsample,
subsample_freq=subsample_freq,
colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha,
reg_lambda=reg_lambda,
random_state=random_state,
n_jobs=n_jobs,
importance_type=importance_type,
**kwargs,
)
_base_doc = LGBMClassifier.__init__.__doc__
_before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore
__init__.__doc__ = f"""
{_before_kwargs}client : dask.distributed.Client or None, optional (default=None)
{' ':4}Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.
{_kwargs}{_after_kwargs}
"""
def __getstate__(self) -> Dict[Any, Any]:
return self._lgb_dask_getstate()
[docs]
def fit( # type: ignore[override]
self,
X: _DaskMatrixLike,
y: _DaskCollection,
sample_weight: Optional[_DaskVectorLike] = None,
init_score: Optional[_DaskCollection] = None,
eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,
eval_names: Optional[List[str]] = None,
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_class_weight: Optional[List[Union[dict, str]]] = None,
eval_init_score: Optional[List[_DaskCollection]] = None,
eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,
**kwargs: Any,
) -> "DaskLGBMClassifier":
"""Docstring is inherited from the lightgbm.LGBMClassifier.fit."""
self._lgb_dask_fit(
model_factory=LGBMClassifier,
X=X,
y=y,
sample_weight=sample_weight,
init_score=init_score,
eval_set=eval_set,
eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_class_weight=eval_class_weight,
eval_init_score=eval_init_score,
eval_metric=eval_metric,
**kwargs,
)
return self
_base_doc = _lgbmmodel_doc_fit.format(
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]",
sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)",
init_score_shape="Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None)",
group_shape="Dask Array or Dask Series or None, optional (default=None)",
eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
eval_init_score_shape="list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)",
eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
)
# DaskLGBMClassifier does not support group, eval_group.
_base_doc = _base_doc[: _base_doc.find("group :")] + _base_doc[_base_doc.find("eval_set :") :]
_base_doc = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :]
# DaskLGBMClassifier support for callbacks and init_model is not tested
fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
Other parameters passed through to ``LGBMClassifier.fit()``.
Returns
-------
self : lightgbm.DaskLGBMClassifier
Returns self.
{_lgbmmodel_doc_custom_eval_note}
"""
[docs]
def predict(
self,
X: _DaskMatrixLike, # type: ignore[override]
raw_score: bool = False,
start_iteration: int = 0,
num_iteration: Optional[int] = None,
pred_leaf: bool = False,
pred_contrib: bool = False,
validate_features: bool = False,
**kwargs: Any,
) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMClassifier.predict."""
return _predict(
model=self.to_local(),
data=X,
dtype=self.classes_.dtype,
client=_get_dask_client(self.client),
raw_score=raw_score,
start_iteration=start_iteration,
num_iteration=num_iteration,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
validate_features=validate_features,
**kwargs,
)
predict.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted value for each sample.",
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_result",
predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]",
X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]",
)
[docs]
def predict_proba(
self,
X: _DaskMatrixLike, # type: ignore[override]
raw_score: bool = False,
start_iteration: int = 0,
num_iteration: Optional[int] = None,
pred_leaf: bool = False,
pred_contrib: bool = False,
validate_features: bool = False,
**kwargs: Any,
) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba."""
return _predict(
model=self.to_local(),
data=X,
pred_proba=True,
client=_get_dask_client(self.client),
raw_score=raw_score,
start_iteration=start_iteration,
num_iteration=num_iteration,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
validate_features=validate_features,
**kwargs,
)
predict_proba.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted probability for each class for each sample.",
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_probability",
predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]",
X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]",
)
[docs]
def to_local(self) -> LGBMClassifier:
"""Create regular version of lightgbm.LGBMClassifier from the distributed version.
Returns
-------
model : lightgbm.LGBMClassifier
Local underlying model.
"""
return self._lgb_dask_to_local(LGBMClassifier)
[docs]
class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):
"""Distributed version of lightgbm.LGBMRegressor."""
[docs]
def __init__(
self,
boosting_type: str = "gbdt",
num_leaves: int = 31,
max_depth: int = -1,
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
class_weight: Optional[Union[dict, str]] = None,
min_split_gain: float = 0.0,
min_child_weight: float = 1e-3,
min_child_samples: int = 20,
subsample: float = 1.0,
subsample_freq: int = 0,
colsample_bytree: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 0.0,
random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None,
n_jobs: Optional[int] = None,
importance_type: str = "split",
client: Optional[Client] = None,
**kwargs: Any,
):
"""Docstring is inherited from the lightgbm.LGBMRegressor.__init__."""
self.client = client
super().__init__(
boosting_type=boosting_type,
num_leaves=num_leaves,
max_depth=max_depth,
learning_rate=learning_rate,
n_estimators=n_estimators,
subsample_for_bin=subsample_for_bin,
objective=objective,
class_weight=class_weight,
min_split_gain=min_split_gain,
min_child_weight=min_child_weight,
min_child_samples=min_child_samples,
subsample=subsample,
subsample_freq=subsample_freq,
colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha,
reg_lambda=reg_lambda,
random_state=random_state,
n_jobs=n_jobs,
importance_type=importance_type,
**kwargs,
)
_base_doc = LGBMRegressor.__init__.__doc__
_before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore
__init__.__doc__ = f"""
{_before_kwargs}client : dask.distributed.Client or None, optional (default=None)
{' ':4}Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.
{_kwargs}{_after_kwargs}
"""
def __getstate__(self) -> Dict[Any, Any]:
return self._lgb_dask_getstate()
[docs]
def fit( # type: ignore[override]
self,
X: _DaskMatrixLike,
y: _DaskCollection,
sample_weight: Optional[_DaskVectorLike] = None,
init_score: Optional[_DaskVectorLike] = None,
eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,
eval_names: Optional[List[str]] = None,
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_init_score: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,
**kwargs: Any,
) -> "DaskLGBMRegressor":
"""Docstring is inherited from the lightgbm.LGBMRegressor.fit."""
self._lgb_dask_fit(
model_factory=LGBMRegressor,
X=X,
y=y,
sample_weight=sample_weight,
init_score=init_score,
eval_set=eval_set,
eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score,
eval_metric=eval_metric,
**kwargs,
)
return self
_base_doc = _lgbmmodel_doc_fit.format(
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]",
sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)",
init_score_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)",
group_shape="Dask Array or Dask Series or None, optional (default=None)",
eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
eval_init_score_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
)
# DaskLGBMRegressor does not support group, eval_class_weight, eval_group.
_base_doc = _base_doc[: _base_doc.find("group :")] + _base_doc[_base_doc.find("eval_set :") :]
_base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :]
_base_doc = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :]
# DaskLGBMRegressor support for callbacks and init_model is not tested
fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
Other parameters passed through to ``LGBMRegressor.fit()``.
Returns
-------
self : lightgbm.DaskLGBMRegressor
Returns self.
{_lgbmmodel_doc_custom_eval_note}
"""
[docs]
def predict(
self,
X: _DaskMatrixLike, # type: ignore[override]
raw_score: bool = False,
start_iteration: int = 0,
num_iteration: Optional[int] = None,
pred_leaf: bool = False,
pred_contrib: bool = False,
validate_features: bool = False,
**kwargs: Any,
) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMRegressor.predict."""
return _predict(
model=self.to_local(),
data=X,
client=_get_dask_client(self.client),
raw_score=raw_score,
start_iteration=start_iteration,
num_iteration=num_iteration,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
validate_features=validate_features,
**kwargs,
)
predict.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted value for each sample.",
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_result",
predicted_result_shape="Dask Array of shape = [n_samples]",
X_leaves_shape="Dask Array of shape = [n_samples, n_trees]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1]",
)
[docs]
def to_local(self) -> LGBMRegressor:
"""Create regular version of lightgbm.LGBMRegressor from the distributed version.
Returns
-------
model : lightgbm.LGBMRegressor
Local underlying model.
"""
return self._lgb_dask_to_local(LGBMRegressor)
[docs]
class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):
"""Distributed version of lightgbm.LGBMRanker."""
[docs]
def __init__(
self,
boosting_type: str = "gbdt",
num_leaves: int = 31,
max_depth: int = -1,
learning_rate: float = 0.1,
n_estimators: int = 100,
subsample_for_bin: int = 200000,
objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
class_weight: Optional[Union[dict, str]] = None,
min_split_gain: float = 0.0,
min_child_weight: float = 1e-3,
min_child_samples: int = 20,
subsample: float = 1.0,
subsample_freq: int = 0,
colsample_bytree: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 0.0,
random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None,
n_jobs: Optional[int] = None,
importance_type: str = "split",
client: Optional[Client] = None,
**kwargs: Any,
):
"""Docstring is inherited from the lightgbm.LGBMRanker.__init__."""
self.client = client
super().__init__(
boosting_type=boosting_type,
num_leaves=num_leaves,
max_depth=max_depth,
learning_rate=learning_rate,
n_estimators=n_estimators,
subsample_for_bin=subsample_for_bin,
objective=objective,
class_weight=class_weight,
min_split_gain=min_split_gain,
min_child_weight=min_child_weight,
min_child_samples=min_child_samples,
subsample=subsample,
subsample_freq=subsample_freq,
colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha,
reg_lambda=reg_lambda,
random_state=random_state,
n_jobs=n_jobs,
importance_type=importance_type,
**kwargs,
)
_base_doc = LGBMRanker.__init__.__doc__
_before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore
__init__.__doc__ = f"""
{_before_kwargs}client : dask.distributed.Client or None, optional (default=None)
{' ':4}Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.
{_kwargs}{_after_kwargs}
"""
def __getstate__(self) -> Dict[Any, Any]:
return self._lgb_dask_getstate()
[docs]
def fit( # type: ignore[override]
self,
X: _DaskMatrixLike,
y: _DaskCollection,
sample_weight: Optional[_DaskVectorLike] = None,
init_score: Optional[_DaskVectorLike] = None,
group: Optional[_DaskVectorLike] = None,
eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,
eval_names: Optional[List[str]] = None,
eval_sample_weight: Optional[List[_DaskVectorLike]] = None,
eval_init_score: Optional[List[_DaskVectorLike]] = None,
eval_group: Optional[List[_DaskVectorLike]] = None,
eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,
eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5),
**kwargs: Any,
) -> "DaskLGBMRanker":
"""Docstring is inherited from the lightgbm.LGBMRanker.fit."""
self._lgb_dask_fit(
model_factory=LGBMRanker,
X=X,
y=y,
sample_weight=sample_weight,
init_score=init_score,
group=group,
eval_set=eval_set,
eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score,
eval_group=eval_group,
eval_metric=eval_metric,
eval_at=eval_at,
**kwargs,
)
return self
_base_doc = _lgbmmodel_doc_fit.format(
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]",
sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)",
init_score_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)",
group_shape="Dask Array or Dask Series or None, optional (default=None)",
eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
eval_init_score_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)",
)
# DaskLGBMRanker does not support eval_class_weight or early stopping
_base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :]
_base_doc = (
_base_doc[: _base_doc.find("feature_name :")]
+ "eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5))\n"
+ f"{' ':8}The evaluation positions of the specified metric.\n"
+ f"{' ':4}{_base_doc[_base_doc.find('feature_name :'):]}"
)
# DaskLGBMRanker support for callbacks and init_model is not tested
fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
Other parameters passed through to ``LGBMRanker.fit()``.
Returns
-------
self : lightgbm.DaskLGBMRanker
Returns self.
{_lgbmmodel_doc_custom_eval_note}
"""
[docs]
def predict(
self,
X: _DaskMatrixLike, # type: ignore[override]
raw_score: bool = False,
start_iteration: int = 0,
num_iteration: Optional[int] = None,
pred_leaf: bool = False,
pred_contrib: bool = False,
validate_features: bool = False,
**kwargs: Any,
) -> dask_Array:
"""Docstring is inherited from the lightgbm.LGBMRanker.predict."""
return _predict(
model=self.to_local(),
data=X,
client=_get_dask_client(self.client),
raw_score=raw_score,
start_iteration=start_iteration,
num_iteration=num_iteration,
pred_leaf=pred_leaf,
pred_contrib=pred_contrib,
validate_features=validate_features,
**kwargs,
)
predict.__doc__ = _lgbmmodel_doc_predict.format(
description="Return the predicted value for each sample.",
X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]",
output_name="predicted_result",
predicted_result_shape="Dask Array of shape = [n_samples]",
X_leaves_shape="Dask Array of shape = [n_samples, n_trees]",
X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1]",
)
[docs]
def to_local(self) -> LGBMRanker:
"""Create regular version of lightgbm.LGBMRanker from the distributed version.
Returns
-------
model : lightgbm.LGBMRanker
Local underlying model.
"""
return self._lgb_dask_to_local(LGBMRanker)