# coding: utf-8 # pylint: disable=too-many-locals, too-many-arguments, invalid-name # pylint: disable=too-many-branches, too-many-statements """Training Library containing training routines.""" import numpy as np from .core import Booster, STRING_TYPES, XGBoostError, CallbackEnv from .core import EarlyStopException from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold) from . import rabit from . import callback def _train_internal(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, xgb_model=None, callbacks=None): """internal training function""" callbacks = [] if callbacks is None else callbacks evals = list(evals) params = params.copy() if isinstance(params, dict) \ and 'eval_metric' in params \ and isinstance(params['eval_metric'], list): params = dict((k, v) for k, v in params.items()) eval_metrics = params['eval_metric'] params.pop("eval_metric", None) params = list(params.items()) for eval_metric in eval_metrics: params += [('eval_metric', eval_metric)] bst = Booster(params, [dtrain] + [d[0] for d in evals]) nboost = 0 num_parallel_tree = 1 if xgb_model is not None: bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model) nboost = len(bst.get_dump()) _params = dict(params) if isinstance(params, list) else params if 'num_parallel_tree' in _params and _params[ 'num_parallel_tree'] is not None: num_parallel_tree = _params['num_parallel_tree'] nboost //= num_parallel_tree if 'num_class' in _params and _params['num_class'] is not None: nboost //= _params['num_class'] # Distributed code: Load the checkpoint from rabit. version = bst.load_rabit_checkpoint() assert rabit.get_world_size() != 1 or version == 0 rank = rabit.get_rank() start_iteration = int(version / 2) nboost += start_iteration callbacks_before_iter = [ cb for cb in callbacks if cb.__dict__.get('before_iteration', False)] callbacks_after_iter = [ cb for cb in callbacks if not cb.__dict__.get('before_iteration', False)] for i in range(start_iteration, num_boost_round): for cb in callbacks_before_iter: cb(CallbackEnv(model=bst, cvfolds=None, iteration=i, begin_iteration=start_iteration, end_iteration=num_boost_round, rank=rank, evaluation_result_list=None)) # Distributed code: need to resume to this point. # Skip the first update if it is a recovery step. if version % 2 == 0: bst.update(dtrain, i, obj) bst.save_rabit_checkpoint() version += 1 assert rabit.get_world_size() == 1 or version == rabit.version_number() nboost += 1 evaluation_result_list = [] # check evaluation result. if evals: bst_eval_set = bst.eval_set(evals, i, feval) if isinstance(bst_eval_set, STRING_TYPES): msg = bst_eval_set else: msg = bst_eval_set.decode() res = [x.split(':') for x in msg.split()] evaluation_result_list = [(k, float(v)) for k, v in res[1:]] try: for cb in callbacks_after_iter: cb(CallbackEnv(model=bst, cvfolds=None, iteration=i, begin_iteration=start_iteration, end_iteration=num_boost_round, rank=rank, evaluation_result_list=evaluation_result_list)) except EarlyStopException: break # do checkpoint after evaluation, in case evaluation also updates booster. bst.save_rabit_checkpoint() version += 1 if bst.attr('best_score') is not None: bst.best_score = float(bst.attr('best_score')) bst.best_iteration = int(bst.attr('best_iteration')) else: bst.best_iteration = nboost - 1 bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree # Copy to serialise and unserialise booster to reset state and free training memory return bst.copy() def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, evals_result=None, verbose_eval=True, xgb_model=None, callbacks=None): # pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init """Train a booster with given parameters. Parameters ---------- params : dict Booster params. dtrain : DMatrix Data to be trained. num_boost_round: int Number of boosting iterations. evals: list of pairs (DMatrix, string) List of validation sets for which metrics will evaluated during training. Validation metrics will help us track the performance of the model. obj : function Customized objective function. feval : function Customized evaluation function. maximize : bool Whether to maximize feval. early_stopping_rounds: int Activates early stopping. Validation metric needs to improve at least once in every **early_stopping_rounds** round(s) to continue training. Requires at least one item in **evals**. The method returns the model from the last iteration (not the best one). If there's more than one item in **evals**, the last entry will be used for early stopping. If there's more than one metric in the **eval_metric** parameter given in **params**, the last metric will be used for early stopping. If early stopping occurs, the model will have three additional fields: ``bst.best_score``, ``bst.best_iteration`` and ``bst.best_ntree_limit``. (Use ``bst.best_ntree_limit`` to get the correct value if ``num_parallel_tree`` and/or ``num_class`` appears in the parameters) evals_result: dict This dictionary stores the evaluation results of all the items in watchlist. Example: with a watchlist containing ``[(dtest,'eval'), (dtrain,'train')]`` and a parameter containing ``('eval_metric': 'logloss')``, the **evals_result** returns .. code-block:: python {'train': {'logloss': ['0.48253', '0.35953']}, 'eval': {'logloss': ['0.480385', '0.357756']}} verbose_eval : bool or int Requires at least one item in **evals**. If **verbose_eval** is True then the evaluation metric on the validation set is printed at each boosting stage. If **verbose_eval** is an integer then the evaluation metric on the validation set is printed at every given **verbose_eval** boosting stage. The last boosting stage / the boosting stage found by using **early_stopping_rounds** is also printed. Example: with ``verbose_eval=4`` and at least one item in **evals**, an evaluation metric is printed every 4 boosting stages, instead of every boosting stage. xgb_model : file name of stored xgb model or 'Booster' instance Xgb model to be loaded before training (allows training continuation). callbacks : list of callback functions List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using :ref:`Callback API `. Example: .. code-block:: python [xgb.callback.reset_learning_rate(custom_rates)] Returns ------- Booster : a trained booster model """ callbacks = [] if callbacks is None else callbacks # Most of legacy advanced options becomes callbacks if isinstance(verbose_eval, bool) and verbose_eval: callbacks.append(callback.print_evaluation()) else: if isinstance(verbose_eval, int): callbacks.append(callback.print_evaluation(verbose_eval)) if early_stopping_rounds is not None: callbacks.append(callback.early_stop(early_stopping_rounds, maximize=maximize, verbose=bool(verbose_eval))) if evals_result is not None: callbacks.append(callback.record_evaluation(evals_result)) return _train_internal(params, dtrain, num_boost_round=num_boost_round, evals=evals, obj=obj, feval=feval, xgb_model=xgb_model, callbacks=callbacks) class CVPack(object): """"Auxiliary datastruct to hold one fold of CV.""" def __init__(self, dtrain, dtest, param): """"Initialize the CVPack""" self.dtrain = dtrain self.dtest = dtest self.watchlist = [(dtrain, 'train'), (dtest, 'test')] self.bst = Booster(param, [dtrain, dtest]) def update(self, iteration, fobj): """"Update the boosters for one iteration""" self.bst.update(self.dtrain, iteration, fobj) def eval(self, iteration, feval): """"Evaluate the CVPack for one iteration.""" return self.bst.eval_set(self.watchlist, iteration, feval) def groups_to_rows(groups, boundaries): """ Given group row boundaries, convert ground indexes to row indexes :param groups: list of groups for testing :param boundaries: rows index limits of each group :return: row in group """ return np.concatenate([np.arange(boundaries[g], boundaries[g+1]) for g in groups]) def mkgroupfold(dall, nfold, param, evals=(), fpreproc=None, shuffle=True): """ Make n folds for cross-validation maintaining groups :return: cross-validation folds """ # we have groups for pairwise ranking... get a list of the group indexes group_boundaries = dall.get_uint_info('group_ptr') group_sizes = np.diff(group_boundaries) if shuffle is True: idx = np.random.permutation(len(group_sizes)) else: idx = np.arange(len(group_sizes)) # list by fold of test group indexes out_group_idset = np.array_split(idx, nfold) # list by fold of train group indexes in_group_idset = [np.concatenate([out_group_idset[i] for i in range(nfold) if k != i]) for k in range(nfold)] # from the group indexes, convert them to row indexes in_idset = [groups_to_rows(in_groups, group_boundaries) for in_groups in in_group_idset] out_idset = [groups_to_rows(out_groups, group_boundaries) for out_groups in out_group_idset] # build the folds by taking the appropriate slices ret = [] for k in range(nfold): # perform the slicing using the indexes determined by the above methods dtrain = dall.slice(in_idset[k], allow_groups=True) dtrain.set_group(group_sizes[in_group_idset[k]]) dtest = dall.slice(out_idset[k], allow_groups=True) dtest.set_group(group_sizes[out_group_idset[k]]) # run preprocessing on the data set if needed if fpreproc is not None: dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy()) else: tparam = param plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals] ret.append(CVPack(dtrain, dtest, plst)) return ret def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False, folds=None, shuffle=True): """ Make an n-fold list of CVPack from random indices. """ evals = list(evals) np.random.seed(seed) if stratified is False and folds is None: # Do standard k-fold cross validation. Automatically determine the folds. if len(dall.get_uint_info('group_ptr')) > 1: return mkgroupfold(dall, nfold, param, evals=evals, fpreproc=fpreproc, shuffle=shuffle) if shuffle is True: idx = np.random.permutation(dall.num_row()) else: idx = np.arange(dall.num_row()) out_idset = np.array_split(idx, nfold) in_idset = [np.concatenate([out_idset[i] for i in range(nfold) if k != i]) for k in range(nfold)] elif folds is not None: # Use user specified custom split using indices try: in_idset = [x[0] for x in folds] out_idset = [x[1] for x in folds] except TypeError: # Custom stratification using Sklearn KFoldSplit object splits = list(folds.split(X=dall.get_label(), y=dall.get_label())) in_idset = [x[0] for x in splits] out_idset = [x[1] for x in splits] nfold = len(out_idset) else: # Do standard stratefied shuffle k-fold split sfk = XGBStratifiedKFold(n_splits=nfold, shuffle=True, random_state=seed) splits = list(sfk.split(X=dall.get_label(), y=dall.get_label())) in_idset = [x[0] for x in splits] out_idset = [x[1] for x in splits] nfold = len(out_idset) ret = [] for k in range(nfold): # perform the slicing using the indexes determined by the above methods dtrain = dall.slice(in_idset[k]) dtest = dall.slice(out_idset[k]) # run preprocessing on the data set if needed if fpreproc is not None: dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy()) else: tparam = param plst = list(tparam.items()) + [('eval_metric', itm) for itm in evals] ret.append(CVPack(dtrain, dtest, plst)) return ret def aggcv(rlist): # pylint: disable=invalid-name """ Aggregate cross-validation results. If verbose_eval is true, progress is displayed in every call. If verbose_eval is an integer, progress will only be displayed every `verbose_eval` trees, tracked via trial. """ cvmap = {} idx = rlist[0].split()[0] for line in rlist: arr = line.split() assert idx == arr[0] for metric_idx, it in enumerate(arr[1:]): if not isinstance(it, STRING_TYPES): it = it.decode() k, v = it.split(':') if (metric_idx, k) not in cvmap: cvmap[(metric_idx, k)] = [] cvmap[(metric_idx, k)].append(float(v)) msg = idx results = [] for (metric_idx, k), v in sorted(cvmap.items(), key=lambda x: x[0][0]): v = np.array(v) if not isinstance(msg, STRING_TYPES): msg = msg.decode() mean, std = np.mean(v), np.std(v) results.extend([(k, mean, std)]) return results def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None, metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, callbacks=None, shuffle=True): # pylint: disable = invalid-name """Cross-validation with given parameters. Parameters ---------- params : dict Booster params. dtrain : DMatrix Data to be trained. num_boost_round : int Number of boosting iterations. nfold : int Number of folds in CV. stratified : bool Perform stratified sampling. folds : a KFold or StratifiedKFold instance or list of fold indices Sklearn KFolds or StratifiedKFolds object. Alternatively may explicitly pass sample indices for each fold. For ``n`` folds, **folds** should be a length ``n`` list of tuples. Each tuple is ``(in,out)`` where ``in`` is a list of indices to be used as the training samples for the ``n`` th fold and ``out`` is a list of indices to be used as the testing samples for the ``n`` th fold. metrics : string or list of strings Evaluation metrics to be watched in CV. obj : function Custom objective function. feval : function Custom evaluation function. maximize : bool Whether to maximize feval. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every **early_stopping_rounds** round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the **eval_metric** parameter given in **params**, the last metric will be used for early stopping. fpreproc : function Preprocessing function that takes (dtrain, dtest, param) and returns transformed versions of those. as_pandas : bool, default True Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return np.ndarray verbose_eval : bool, int, or None, default None Whether to display the progress. If None, progress will be displayed when np.ndarray is returned. If True, progress will be displayed at boosting stage. If an integer is given, progress will be displayed at every given `verbose_eval` boosting stage. show_stdv : bool, default True Whether to display the standard deviation in progress. Results are not affected, and always contains std. seed : int Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callback functions List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using :ref:`Callback API `. Example: .. code-block:: python [xgb.callback.reset_learning_rate(custom_rates)] shuffle : bool Shuffle data before creating folds. Returns ------- evaluation history : list(string) """ if stratified is True and not SKLEARN_INSTALLED: raise XGBoostError('sklearn needs to be installed in order to use stratified cv') if isinstance(metrics, str): metrics = [metrics] if isinstance(params, list): _metrics = [x[1] for x in params if x[0] == 'eval_metric'] params = dict(params) if 'eval_metric' in params: params['eval_metric'] = _metrics else: params = dict((k, v) for k, v in params.items()) if (not metrics) and 'eval_metric' in params: if isinstance(params['eval_metric'], list): metrics = params['eval_metric'] else: metrics = [params['eval_metric']] params.pop("eval_metric", None) results = {} cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc, stratified, folds, shuffle) # setup callbacks callbacks = [] if callbacks is None else callbacks if early_stopping_rounds is not None: callbacks.append(callback.early_stop(early_stopping_rounds, maximize=maximize, verbose=False)) if isinstance(verbose_eval, bool) and verbose_eval: callbacks.append(callback.print_evaluation(show_stdv=show_stdv)) else: if isinstance(verbose_eval, int): callbacks.append(callback.print_evaluation(verbose_eval, show_stdv=show_stdv)) callbacks_before_iter = [ cb for cb in callbacks if cb.__dict__.get('before_iteration', False)] callbacks_after_iter = [ cb for cb in callbacks if not cb.__dict__.get('before_iteration', False)] for i in range(num_boost_round): for cb in callbacks_before_iter: cb(CallbackEnv(model=None, cvfolds=cvfolds, iteration=i, begin_iteration=0, end_iteration=num_boost_round, rank=0, evaluation_result_list=None)) for fold in cvfolds: fold.update(i, obj) res = aggcv([f.eval(i, feval) for f in cvfolds]) for key, mean, std in res: if key + '-mean' not in results: results[key + '-mean'] = [] if key + '-std' not in results: results[key + '-std'] = [] results[key + '-mean'].append(mean) results[key + '-std'].append(std) try: for cb in callbacks_after_iter: cb(CallbackEnv(model=None, cvfolds=cvfolds, iteration=i, begin_iteration=0, end_iteration=num_boost_round, rank=0, evaluation_result_list=res)) except EarlyStopException as e: for k in results: results[k] = results[k][:(e.best_iteration + 1)] break if as_pandas: try: import pandas as pd results = pd.DataFrame.from_dict(results) except ImportError: pass return results