# 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 socket from collections import defaultdict from copy import deepcopy from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union from urllib.parse import urlparse import numpy as np import scipy.sparse as ss from .basic import _LIB, LightGBMError, _choose_param_value, _ConfigAliases, _log_info, _log_warning, _safe_call from .compat import (DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED, Client, LGBMNotFittedError, concat, dask_Array, dask_DataFrame, dask_Series, default_client, delayed, pd_DataFrame, pd_Series, wait) from .sklearn import LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _lgbmmodel_doc_fit, _lgbmmodel_doc_predict _DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series] _DaskMatrixLike = Union[dask_Array, dask_DataFrame] _DaskPart = Union[np.ndarray, pd_DataFrame, pd_Series, ss.spmatrix] _PredictionDtype = Union[Type[np.float32], Type[np.float64], Type[np.int32], Type[np.int64]] 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 _find_open_port(worker_ip: str, local_listen_port: int, ports_to_skip: Iterable[int]) -> int: """Find an open port. This function tries to find a free port on the machine it's run on. It is intended to be run once on each Dask worker, sequentially. Parameters ---------- worker_ip : str IP address for the Dask worker. local_listen_port : int First port to try when searching for open ports. ports_to_skip: Iterable[int] An iterable of integers referring to ports that should be skipped. Since multiple Dask workers can run on the same physical machine, this method may be called multiple times on the same machine. ``ports_to_skip`` is used to ensure that LightGBM doesn't try to use the same port for two worker processes running on the same machine. Returns ------- port : int A free port on the machine referenced by ``worker_ip``. """ max_tries = 1000 found_port = False for i in range(max_tries): out_port = local_listen_port + i if out_port in ports_to_skip: continue try: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((worker_ip, out_port)) found_port = True break # if unavailable, you'll get OSError: Address already in use except OSError: continue if not found_port: msg = "LightGBM tried %s:%d-%d and could not create a connection. Try setting local_listen_port to a different value." raise RuntimeError(msg % (worker_ip, local_listen_port, out_port)) return out_port def _find_ports_for_workers(client: Client, worker_addresses: Iterable[str], local_listen_port: int) -> Dict[str, int]: """Find an open port on each worker. LightGBM distributed training uses TCP sockets by default, and this method is used to identify open ports on each worker so LightGBM can reliable create those sockets. Parameters ---------- client : dask.distributed.Client Dask client. worker_addresses : Iterable[str] An iterable of addresses for workers in the cluster. These are strings of the form ``://:port``. local_listen_port : int First port to try when searching for open ports. Returns ------- result : Dict[str, int] Dictionary where keys are worker addresses and values are an open port for LightGBM to use. """ lightgbm_ports: Set[int] = set() worker_ip_to_port = {} for worker_address in worker_addresses: port = client.submit( func=_find_open_port, workers=[worker_address], worker_ip=urlparse(worker_address).hostname, local_listen_port=local_listen_port, ports_to_skip=lightgbm_ports ).result() lightgbm_ports.add(port) worker_ip_to_port[worker_address] = port return worker_ip_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('Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got %s.' % str(type(seq[0]))) 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 = 120, **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 try: model = model_factory(**params) if is_ranker: model.fit(data, label, sample_weight=weight, group=group, **kwargs) else: model.fit(data, label, sample_weight=weight, **kwargs) finally: _safe_call(_LIB.LGBM_NetworkFree()) 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: List[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 A list 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(",") 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 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[_DaskCollection] = None, group: Optional[_DaskCollection] = 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, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None) Weights of training data. group : Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] 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. **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('Parameter tree_learner set to %s, which is not allowed. Using "data" as default' % params['tree_learner']) params['tree_learner'] = 'data' if params['tree_learner'] not in {'data', 'data_parallel'}: _log_warning( 'Support for tree_learner %s in lightgbm.dask is experimental and may break in a future release. \n' 'Use "data" for a stable, well-tested interface.' % params['tree_learner'] ) # 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] # 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 return part # trigger error locally # 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]) 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_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 = set(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_address_to_port = _find_ports_for_workers( client=client, worker_addresses=worker_addresses, local_listen_port=local_listen_port ) machines = ','.join([ '%s:%d' % (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 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), return_model=(worker == master_worker), **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 moodel 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: 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 pred_proba or pred_contrib or pred_leaf: 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, raw_score: bool = False, pred_proba: bool = False, pred_leaf: bool = False, pred_contrib: bool = False, dtype: _PredictionDtype = np.float32, **kwargs: Any ) -> 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] 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): if pred_proba: kwargs['chunks'] = (data.chunks[0], (model.n_classes_,)) else: kwargs['drop_axis'] = 1 return data.map_blocks( _predict_part, model=model, raw_score=raw_score, pred_proba=pred_proba, pred_leaf=pred_leaf, pred_contrib=pred_contrib, dtype=dtype, **kwargs ) else: raise TypeError('Data must be either Dask Array or Dask DataFrame. Got %s.' % str(type(data))) 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) 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[_DaskCollection] = None, group: Optional[_DaskCollection] = None, **kwargs: Any ) -> "_DaskLGBMModel": 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) 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, group=group, **kwargs ) self.set_params(**model.get_params()) self._lgb_dask_copy_extra_params(model, self) return self def _lgb_dask_to_local(self, model_factory: Type[LGBMModel]) -> LGBMModel: params = self.get_params() 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() attributes = source.__dict__ extra_param_names = set(attributes.keys()).difference(params.keys()) for name in extra_param_names: setattr(dest, name, attributes[name]) class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel): """Distributed version of lightgbm.LGBMClassifier.""" 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[Callable, str]] = None, class_weight: Optional[Union[dict, str]] = None, min_split_gain: float = 0., min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1., subsample_freq: int = 0, colsample_bytree: float = 1., reg_alpha: float = 0., reg_lambda: float = 0., random_state: Optional[Union[int, np.random.RandomState]] = None, n_jobs: int = -1, silent: bool = True, 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, silent=silent, importance_type=importance_type, **kwargs ) _base_doc = LGBMClassifier.__init__.__doc__ _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs') _base_doc = ( _before_kwargs + 'client : dask.distributed.Client or None, optional (default=None)\n' + ' ' * 12 + '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.\n' + ' ' * 8 + _kwargs + _after_kwargs ) # the note on custom objective functions in LGBMModel.__init__ is not # currently relevant for the Dask estimators __init__.__doc__ = _base_doc[:_base_doc.find('Note\n')] def __getstate__(self) -> Dict[Any, Any]: return self._lgb_dask_getstate() def fit( self, X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskCollection] = None, **kwargs: Any ) -> "DaskLGBMClassifier": """Docstring is inherited from the lightgbm.LGBMClassifier.fit.""" return self._lgb_dask_fit( model_factory=LGBMClassifier, X=X, y=y, sample_weight=sample_weight, **kwargs ) _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, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)" ) # DaskLGBMClassifier does not support init_score, evaluation data, or early stopping _base_doc = (_base_doc[:_base_doc.find('init_score :')] + _base_doc[_base_doc.find('verbose :'):]) # DaskLGBMClassifier support for callbacks and init_model is not tested fit.__doc__ = ( _base_doc[:_base_doc.find('callbacks :')] + '**kwargs\n' + ' ' * 12 + 'Other parameters passed through to ``LGBMClassifier.fit()``.\n' ) def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMClassifier.predict.""" return _predict( model=self.to_local(), data=X, dtype=self.classes_.dtype, **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]" ) def predict_proba(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba.""" return _predict( model=self.to_local(), data=X, pred_proba=True, **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]" ) 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) class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel): """Distributed version of lightgbm.LGBMRegressor.""" 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[Callable, str]] = None, class_weight: Optional[Union[dict, str]] = None, min_split_gain: float = 0., min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1., subsample_freq: int = 0, colsample_bytree: float = 1., reg_alpha: float = 0., reg_lambda: float = 0., random_state: Optional[Union[int, np.random.RandomState]] = None, n_jobs: int = -1, silent: bool = True, 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, silent=silent, importance_type=importance_type, **kwargs ) _base_doc = LGBMRegressor.__init__.__doc__ _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs') _base_doc = ( _before_kwargs + 'client : dask.distributed.Client or None, optional (default=None)\n' + ' ' * 12 + '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.\n' + ' ' * 8 + _kwargs + _after_kwargs ) # the note on custom objective functions in LGBMModel.__init__ is not # currently relevant for the Dask estimators __init__.__doc__ = _base_doc[:_base_doc.find('Note\n')] def __getstate__(self) -> Dict[Any, Any]: return self._lgb_dask_getstate() def fit( self, X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskCollection] = None, **kwargs: Any ) -> "DaskLGBMRegressor": """Docstring is inherited from the lightgbm.LGBMRegressor.fit.""" return self._lgb_dask_fit( model_factory=LGBMRegressor, X=X, y=y, sample_weight=sample_weight, **kwargs ) _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, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)" ) # DaskLGBMRegressor does not support init_score, evaluation data, or early stopping _base_doc = (_base_doc[:_base_doc.find('init_score :')] + _base_doc[_base_doc.find('verbose :'):]) # DaskLGBMRegressor support for callbacks and init_model is not tested fit.__doc__ = ( _base_doc[:_base_doc.find('callbacks :')] + '**kwargs\n' + ' ' * 12 + 'Other parameters passed through to ``LGBMRegressor.fit()``.\n' ) def predict(self, X: _DaskMatrixLike, **kwargs) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMRegressor.predict.""" return _predict( model=self.to_local(), data=X, **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]" ) 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) class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel): """Distributed version of lightgbm.LGBMRanker.""" 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[Callable, str]] = None, class_weight: Optional[Union[dict, str]] = None, min_split_gain: float = 0., min_child_weight: float = 1e-3, min_child_samples: int = 20, subsample: float = 1., subsample_freq: int = 0, colsample_bytree: float = 1., reg_alpha: float = 0., reg_lambda: float = 0., random_state: Optional[Union[int, np.random.RandomState]] = None, n_jobs: int = -1, silent: bool = True, 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, silent=silent, importance_type=importance_type, **kwargs ) _base_doc = LGBMRanker.__init__.__doc__ _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition('**kwargs') _base_doc = ( _before_kwargs + 'client : dask.distributed.Client or None, optional (default=None)\n' + ' ' * 12 + '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.\n' + ' ' * 8 + _kwargs + _after_kwargs ) # the note on custom objective functions in LGBMModel.__init__ is not # currently relevant for the Dask estimators __init__.__doc__ = _base_doc[:_base_doc.find('Note\n')] def __getstate__(self) -> Dict[Any, Any]: return self._lgb_dask_getstate() def fit( self, X: _DaskMatrixLike, y: _DaskCollection, sample_weight: Optional[_DaskCollection] = None, init_score: Optional[_DaskCollection] = None, group: Optional[_DaskCollection] = None, **kwargs: Any ) -> "DaskLGBMRanker": """Docstring is inherited from the lightgbm.LGBMRanker.fit.""" if init_score is not None: raise RuntimeError('init_score is not currently supported in lightgbm.dask') return self._lgb_dask_fit( model_factory=LGBMRanker, X=X, y=y, sample_weight=sample_weight, group=group, **kwargs ) _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, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)", group_shape="Dask Array, Dask DataFrame, Dask Series of shape = [n_samples] or None, optional (default=None)" ) # DaskLGBMRanker does not support init_score, evaluation data, or early stopping _base_doc = (_base_doc[:_base_doc.find('init_score :')] + _base_doc[_base_doc.find('init_score :'):]) _base_doc = (_base_doc[:_base_doc.find('eval_set :')] + _base_doc[_base_doc.find('verbose :'):]) # DaskLGBMRanker support for callbacks and init_model is not tested fit.__doc__ = ( _base_doc[:_base_doc.find('callbacks :')] + '**kwargs\n' + ' ' * 12 + 'Other parameters passed through to ``LGBMRanker.fit()``.\n' ) def predict(self, X: _DaskMatrixLike, **kwargs: Any) -> dask_Array: """Docstring is inherited from the lightgbm.LGBMRanker.predict.""" return _predict(self.to_local(), X, **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]" ) 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)