# 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-xgboost package. """ import logging from collections import defaultdict from urllib.parse import urlparse import numpy as np import pandas as pd from dask import array as da from dask import dataframe as dd from dask import delayed from dask.distributed import default_client, get_worker, wait from .basic import _LIB, _safe_call from .sklearn import LGBMClassifier, LGBMRegressor import scipy.sparse as ss logger = logging.getLogger(__name__) def _parse_host_port(address): parsed = urlparse(address) return parsed.hostname, parsed.port def _build_network_params(worker_addresses, local_worker_ip, local_listen_port, time_out): """Build network parameters suitable for LightGBM C backend. Parameters ---------- worker_addresses : iterable of str - collection of worker addresses in `://:port` format local_worker_ip : str local_listen_port : int time_out : int Returns ------- params: dict """ addr_port_map = {addr: (local_listen_port + i) for i, addr in enumerate(worker_addresses)} params = { 'machines': ','.join('%s:%d' % (_parse_host_port(addr)[0], port) for addr, port in addr_port_map.items()), 'local_listen_port': addr_port_map[local_worker_ip], 'time_out': time_out, 'num_machines': len(addr_port_map) } return params def _concat(seq): if isinstance(seq[0], np.ndarray): return np.concatenate(seq, axis=0) elif isinstance(seq[0], (pd.DataFrame, pd.Series)): return pd.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, model_factory, list_of_parts, worker_addresses, return_model, local_listen_port=12400, time_out=120, **kwargs): network_params = _build_network_params(worker_addresses, get_worker().address, local_listen_port, time_out) params.update(network_params) # Concatenate many parts into one parts = tuple(zip(*list_of_parts)) data = _concat(parts[0]) label = _concat(parts[1]) weight = _concat(parts[2]) if len(parts) == 3 else None try: model = model_factory(**params) 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, is_matrix): parts = data.to_delayed() if isinstance(parts, np.ndarray): assert (parts.shape[1] == 1) if is_matrix else (parts.ndim == 1 or parts.shape[1] == 1) parts = parts.flatten().tolist() return parts def _train(client, data, label, params, model_factory, weight=None, **kwargs): """Inner train routine. Parameters ---------- client: dask.Client - client X : dask array of shape = [n_samples, n_features] Input feature matrix. y : dask array of shape = [n_samples] The target values (class labels in classification, real numbers in regression). params : dict model_factory : lightgbm.LGBMClassifier or lightgbm.LGBMRegressor class sample_weight : array-like of shape = [n_samples] or None, optional (default=None) Weights of training data. """ # Split arrays/dataframes into parts. Arrange parts into tuples to enforce co-locality data_parts = _split_to_parts(data, is_matrix=True) label_parts = _split_to_parts(label, is_matrix=False) if weight is None: parts = list(map(delayed, zip(data_parts, label_parts))) else: weight_parts = _split_to_parts(weight, is_matrix=False) parts = list(map(delayed, zip(data_parts, label_parts, weight_parts))) # Start computation in the background parts = client.compute(parts) wait(parts) for part in parts: if part.status == 'error': return part # trigger error locally # Find locations of all parts and map them to particular Dask workers key_to_part_dict = dict([(part.key, part) for part in parts]) 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() if 'tree_learner' not in params or params['tree_learner'].lower() not in {'data', 'feature', 'voting'}: logger.warning('Parameter tree_learner not set or set to incorrect value ' '(%s), using "data" as default', params.get("tree_learner", None)) params['tree_learner'] = 'data' # 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, worker_addresses=list(worker_map.keys()), local_listen_port=params.get('local_listen_port', 12400), 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] return results[0] def _predict_part(part, model, proba, **kwargs): data = part.values if isinstance(part, pd.DataFrame) else part if data.shape[0] == 0: result = np.array([]) elif proba: result = model.predict_proba(data, **kwargs) else: result = model.predict(data, **kwargs) if isinstance(part, pd.DataFrame): if proba: result = pd.DataFrame(result, index=part.index) else: result = pd.Series(result, index=part.index, name='predictions') return result def _predict(model, data, proba=False, dtype=np.float32, **kwargs): """Inner predict routine. Parameters ---------- model : data : dask array of shape = [n_samples, n_features] Input feature matrix. proba : bool Should method return results of predict_proba (proba == True) or predict (proba == False) dtype : np.dtype Dtype of the output kwargs : other parameters passed to predict or predict_proba method """ if isinstance(data, dd._Frame): return data.map_partitions(_predict_part, model=model, proba=proba, **kwargs).values elif isinstance(data, da.Array): if proba: kwargs['chunks'] = (data.chunks[0], (model.n_classes_,)) else: kwargs['drop_axis'] = 1 return data.map_blocks(_predict_part, model=model, proba=proba, dtype=dtype, **kwargs) else: raise TypeError('Data must be either Dask array or dataframe. Got %s.' % str(type(data))) class _LGBMModel: def _fit(self, model_factory, X, y=None, sample_weight=None, client=None, **kwargs): """Docstring is inherited from the LGBMModel.""" if client is None: client = default_client() params = self.get_params(True) model = _train(client, X, y, params, model_factory, sample_weight, **kwargs) self.set_params(**model.get_params()) self._copy_extra_params(model, self) return self def _to_local(self, model_factory): model = model_factory(**self.get_params()) self._copy_extra_params(self, model) return model @staticmethod def _copy_extra_params(source, dest): 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(_LGBMModel, LGBMClassifier): """Distributed version of lightgbm.LGBMClassifier.""" def fit(self, X, y=None, sample_weight=None, client=None, **kwargs): """Docstring is inherited from the LGBMModel.""" return self._fit(LGBMClassifier, X, y, sample_weight, client, **kwargs) fit.__doc__ = LGBMClassifier.fit.__doc__ def predict(self, X, **kwargs): """Docstring is inherited from the lightgbm.LGBMClassifier.predict.""" return _predict(self.to_local(), X, dtype=self.classes_.dtype, **kwargs) predict.__doc__ = LGBMClassifier.predict.__doc__ def predict_proba(self, X, **kwargs): """Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba.""" return _predict(self.to_local(), X, proba=True, **kwargs) predict_proba.__doc__ = LGBMClassifier.predict_proba.__doc__ def to_local(self): """Create regular version of lightgbm.LGBMClassifier from the distributed version. Returns ------- model : lightgbm.LGBMClassifier """ return self._to_local(LGBMClassifier) class DaskLGBMRegressor(_LGBMModel, LGBMRegressor): """Docstring is inherited from the lightgbm.LGBMRegressor.""" def fit(self, X, y=None, sample_weight=None, client=None, **kwargs): """Docstring is inherited from the lightgbm.LGBMRegressor.fit.""" return self._fit(LGBMRegressor, X, y, sample_weight, client, **kwargs) fit.__doc__ = LGBMRegressor.fit.__doc__ def predict(self, X, **kwargs): """Docstring is inherited from the lightgbm.LGBMRegressor.predict.""" return _predict(self.to_local(), X, **kwargs) predict.__doc__ = LGBMRegressor.predict.__doc__ def to_local(self): """Create regular version of lightgbm.LGBMRegressor from the distributed version. Returns ------- model : lightgbm.LGBMRegressor """ return self._to_local(LGBMRegressor)