Cleanup the train function. (#7377)
* Move attribute setter to callback. * Remove the internal train function. * Remove unnecessary initialization.
This commit is contained in:
Jiaming Yuan
GitHub
parent
154b15060e
commit
c74df31bf9
@ -11,7 +11,7 @@ from typing import Sequence
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import numpy
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from . import rabit
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from .core import Booster, DMatrix, XGBoostError
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from .core import Booster, DMatrix, XGBoostError, _get_booster_layer_trees
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from .compat import STRING_TYPES
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@ -149,6 +149,25 @@ class CallbackContainer:
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assert isinstance(model.cvfolds, list), msg
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else:
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assert isinstance(model, Booster), msg
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if not self.is_cv:
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num_parallel_tree, _ = _get_booster_layer_trees(model)
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if model.attr('best_score') is not None:
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model.best_score = float(cast(str, model.attr('best_score')))
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model.best_iteration = int(cast(str, model.attr('best_iteration')))
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# num_class is handled internally
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model.set_attr(
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best_ntree_limit=str((model.best_iteration + 1) * num_parallel_tree)
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)
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model.best_ntree_limit = int(cast(str, model.attr("best_ntree_limit")))
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else:
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# Due to compatibility with version older than 1.4, these attributes are
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# added to Python object even if early stopping is not used.
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model.best_iteration = model.num_boosted_rounds() - 1
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model.set_attr(best_iteration=str(model.best_iteration))
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model.best_ntree_limit = (model.best_iteration + 1) * num_parallel_tree
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model.set_attr(best_ntree_limit=str(model.best_ntree_limit))
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return model
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def before_iteration(
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@ -5,10 +5,10 @@
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import copy
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import os
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import warnings
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from typing import Optional, Dict, Any, Union, Tuple, cast, Sequence
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from typing import Optional, Dict, Any, Union, Tuple, Sequence
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import numpy as np
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from .core import Booster, DMatrix, XGBoostError, _get_booster_layer_trees
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from .core import Booster, DMatrix, XGBoostError, _deprecate_positional_args
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from .core import Metric, Objective
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from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold)
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from . import callback
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@ -45,93 +45,12 @@ def _configure_custom_metric(
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return eval_metric
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def _train_internal(
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params: Dict[str, Any],
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dtrain: DMatrix,
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num_boost_round: int = 10,
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evals: Optional[Sequence[Tuple[DMatrix, str]]] = None,
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obj: Optional[Objective] = None,
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feval: Optional[Metric] = None,
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custom_metric: Optional[Metric] = None,
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xgb_model: Optional[Union[str, os.PathLike, Booster, bytearray]] = None,
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callbacks: Optional[Sequence[callback.TrainingCallback]] = None,
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evals_result: callback.TrainingCallback.EvalsLog = None,
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maximize: Optional[bool] = None,
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verbose_eval: Optional[Union[bool, int]] = True,
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early_stopping_rounds: Optional[int] = None,
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) -> Booster:
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"""internal training function"""
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callbacks = [] if callbacks is None else copy.copy(list(callbacks))
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metric_fn = _configure_custom_metric(feval, custom_metric)
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evals = list(evals) if evals else []
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bst = Booster(params, [dtrain] + [d[0] for d in evals])
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if xgb_model is not None:
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bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
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start_iteration = 0
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_assert_new_callback(callbacks)
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if verbose_eval:
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verbose_eval = 1 if verbose_eval is True else verbose_eval
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callbacks.append(callback.EvaluationMonitor(period=verbose_eval))
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if early_stopping_rounds:
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callbacks.append(
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callback.EarlyStopping(rounds=early_stopping_rounds, maximize=maximize)
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)
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cb_container = callback.CallbackContainer(
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callbacks,
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metric=metric_fn,
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# For old `feval` parameter, the behavior is unchanged. For the new
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# `custom_metric`, it will receive proper prediction result when custom objective
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# is not used.
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output_margin=callable(obj) or metric_fn is feval,
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)
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bst = cb_container.before_training(bst)
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for i in range(start_iteration, num_boost_round):
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if cb_container.before_iteration(bst, i, dtrain, evals):
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break
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bst.update(dtrain, i, obj)
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if cb_container.after_iteration(bst, i, dtrain, evals):
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break
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bst = cb_container.after_training(bst)
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if evals_result is not None:
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evals_result.update(cb_container.history)
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# These should be moved into callback functions `after_training`, but until old
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# callbacks are removed, the train function is the only place for setting the
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# attributes.
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num_parallel_tree, _ = _get_booster_layer_trees(bst)
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if bst.attr('best_score') is not None:
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bst.best_score = float(cast(str, bst.attr('best_score')))
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bst.best_iteration = int(cast(str, bst.attr('best_iteration')))
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# num_class is handled internally
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bst.set_attr(
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best_ntree_limit=str((bst.best_iteration + 1) * num_parallel_tree)
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)
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bst.best_ntree_limit = int(cast(str, bst.attr("best_ntree_limit")))
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else:
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# Due to compatibility with version older than 1.4, these attributes are added
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# to Python object even if early stopping is not used.
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bst.best_iteration = bst.num_boosted_rounds() - 1
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bst.set_attr(best_iteration=str(bst.best_iteration))
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bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
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bst.set_attr(best_ntree_limit=str(bst.best_ntree_limit))
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# Copy to serialise and unserialise booster to reset state and free
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# training memory
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return bst.copy()
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@_deprecate_positional_args
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def train(
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params: Dict[str, Any],
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dtrain: DMatrix,
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num_boost_round: int = 10,
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*,
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evals: Optional[Sequence[Tuple[DMatrix, str]]] = None,
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obj: Optional[Objective] = None,
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feval: Optional[Metric] = None,
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@ -223,22 +142,48 @@ def train(
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-------
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Booster : a trained booster model
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"""
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bst = _train_internal(
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params,
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dtrain,
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num_boost_round=num_boost_round,
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evals=evals,
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obj=obj,
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feval=feval,
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xgb_model=xgb_model,
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callbacks=callbacks,
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verbose_eval=verbose_eval,
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evals_result=evals_result,
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maximize=maximize,
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early_stopping_rounds=early_stopping_rounds,
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custom_metric=custom_metric,
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callbacks = [] if callbacks is None else copy.copy(list(callbacks))
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metric_fn = _configure_custom_metric(feval, custom_metric)
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evals = list(evals) if evals else []
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bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
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start_iteration = 0
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_assert_new_callback(callbacks)
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if verbose_eval:
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verbose_eval = 1 if verbose_eval is True else verbose_eval
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callbacks.append(callback.EvaluationMonitor(period=verbose_eval))
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if early_stopping_rounds:
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callbacks.append(
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callback.EarlyStopping(rounds=early_stopping_rounds, maximize=maximize)
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)
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cb_container = callback.CallbackContainer(
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callbacks,
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metric=metric_fn,
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# For old `feval` parameter, the behavior is unchanged. For the new
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# `custom_metric`, it will receive proper prediction result when custom objective
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# is not used.
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output_margin=callable(obj) or metric_fn is feval,
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)
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return bst
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bst = cb_container.before_training(bst)
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for i in range(start_iteration, num_boost_round):
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if cb_container.before_iteration(bst, i, dtrain, evals):
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break
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bst.update(dtrain, i, obj)
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if cb_container.after_iteration(bst, i, dtrain, evals):
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break
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bst = cb_container.after_training(bst)
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if evals_result is not None:
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evals_result.update(cb_container.history)
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# Copy to serialise and unserialise booster to reset state and free
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# training memory
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return bst.copy()
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class CVPack:
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