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Fix #3663: Allow sklearn API to use callbacks (#3682)

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* Fix #3663: Allow sklearn API to use callbacks

* Fix lint

* Add Callback API to Python API doc
This commit is contained in:
mrgutkun
2018-09-07 23:51:26 +03:00
committed by Philip Hyunsu Cho
parent 5a8bbb39a1
commit 4b43810f51
4 changed files with 104 additions and 53 deletions
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41
python-package/xgboost/training.py
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@@ -137,34 +137,35 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
Whether to maximize feval.
early_stopping_rounds: int
Activates early stopping. Validation error needs to decrease at least
every <early_stopping_rounds> round(s) to continue training.
Requires at least one item in evals.
every **early_stopping_rounds** round(s) to continue training.
Requires at least one item in **evals**.
If there's more than one, will use the last.
Returns the model from the last iteration (not the best one).
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)
``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
Example: with a watchlist containing
``[(dtest,'eval'), (dtrain,'train')]`` and
a parameter containing ``('eval_metric': 'logloss')``,
the **evals_result** returns
.. code-block:: none
.. 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.
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
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.
learning_rates: list or function (deprecated - use callback API instead)
List of learning rate for each boosting round
@@ -175,12 +176,17 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
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 xgb.callback module.
Example: [xgb.callback.reset_learning_rate(custom_rates)]
It is possible to use predefined callbacks by using
:ref:`Callback API <callback_api>`.
Example:
.. code-block:: python
[xgb.callback.reset_learning_rate(custom_rates)]
Returns
-------
booster : a trained booster model
Booster : a trained booster model
"""
callbacks = [] if callbacks is None else callbacks
@@ -334,7 +340,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
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.
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.
@@ -368,10 +374,11 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
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 xgb.callback module.
It is possible to use predefined callbacks by using
:ref:`Callback API <callback_api>`.
Example:
.. code-block:: none
.. code-block:: python
[xgb.callback.reset_learning_rate(custom_rates)]
shuffle : bool