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[python] fixed picklability of sklearn models with custom obj and upd…
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…ated docstings for custom obj (#2191)

* refactored joblib test

* fixed picklability of sklearn models with custom obj and updated docstings for custom obj

* pickled model should be able to predict without refitting
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@StrikerRUS @guolinke
StrikerRUS authored and guolinke committed May 27, 2019
1 parent e5b6e50 commit 2459362
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Showing 4 changed files with 255 additions and 105 deletions.
63 changes: 55 additions & 8 deletions python-package/lightgbm/basic.py
View file
Expand Up @@ -59,7 +59,7 @@ def is_numeric(obj):


def is_numpy_1d_array(data):
"""Check whether data is a 1-D numpy array."""
"""Check whether data is a numpy 1-D array."""
return isinstance(data, np.ndarray) and len(data.shape) == 1


Expand All @@ -69,7 +69,7 @@ def is_1d_list(data):


def list_to_1d_numpy(data, dtype=np.float32, name='list'):
"""Convert data to 1-D numpy array."""
"""Convert data to numpy 1-D array."""
if is_numpy_1d_array(data):
if data.dtype == dtype:
return data
Expand Down Expand Up @@ -1853,9 +1853,20 @@ def update(self, train_set=None, fobj=None):
If None, last training data is used.
fobj : callable or None, optional (default=None)
Customized objective function.
Should accept two parameters: preds, train_data,
and return (grad, hess).
preds : list or numpy 1-D array
The predicted values.
train_data : Dataset
The training dataset.
grad : list or numpy 1-D array
The value of the first order derivative (gradient) for each sample point.
hess : list or numpy 1-D array
The value of the second order derivative (Hessian) for each sample point.
For multi-class task, the score is group by class_id first, then group by row_id.
If you want to get i-th row score in j-th class, the access way is score[j * num_data + i]
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is score[j * num_data + i]
and you should group grad and hess in this way as well.
Returns
Expand Down Expand Up @@ -1902,9 +1913,9 @@ def __boost(self, grad, hess):
Parameters
----------
grad : 1-D numpy array or 1-D list
grad : list or numpy 1-D array
The first order derivative (gradient).
hess : 1-D numpy array or 1-D list
hess : list or numpy 1-D array
The second order derivative (Hessian).
Returns
Expand Down Expand Up @@ -1994,8 +2005,20 @@ def eval(self, data, name, feval=None):
Name of the data.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
Should accept two parameters: preds, eval_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : list or numpy 1-D array
The predicted values.
eval_data : Dataset
The evaluation dataset.
eval_name : string
The name of evaluation function.
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
Expand Down Expand Up @@ -2030,6 +2053,18 @@ def eval_train(self, feval=None):
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : list or numpy 1-D array
The predicted values.
train_data : Dataset
The training dataset.
eval_name : string
The name of evaluation function.
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
Expand All @@ -2047,8 +2082,20 @@ def eval_valid(self, feval=None):
----------
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
Should accept two parameters: preds, valid_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : list or numpy 1-D array
The predicted values.
valid_data : Dataset
The validation dataset.
eval_name : string
The name of evaluation function.
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
Expand Down
58 changes: 57 additions & 1 deletion python-package/lightgbm/engine.py
View file
Expand Up @@ -39,10 +39,38 @@ def train(params, train_set, num_boost_round=100,
Names of ``valid_sets``.
fobj : callable or None, optional (default=None)
Customized objective function.
Should accept two parameters: preds, train_data,
and return (grad, hess).
preds : list or numpy 1-D array
The predicted values.
train_data : Dataset
The training dataset.
grad : list or numpy 1-D array
The value of the first order derivative (gradient) for each sample point.
hess : list or numpy 1-D array
The value of the second order derivative (Hessian) for each sample point.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is score[j * num_data + i]
and you should group grad and hess in this way as well.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : list or numpy 1-D array
The predicted values.
train_data : Dataset
The training dataset.
eval_name : string
The name of evaluation function.
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
Expand Down Expand Up @@ -373,11 +401,39 @@ def cv(params, train_set, num_boost_round=100,
Evaluation metrics to be monitored while CV.
If not None, the metric in ``params`` will be overridden.
fobj : callable or None, optional (default=None)
Custom objective function.
Customized objective function.
Should accept two parameters: preds, train_data,
and return (grad, hess).
preds : list or numpy 1-D array
The predicted values.
train_data : Dataset
The training dataset.
grad : list or numpy 1-D array
The value of the first order derivative (gradient) for each sample point.
hess : list or numpy 1-D array
The value of the second order derivative (Hessian) for each sample point.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is score[j * num_data + i]
and you should group grad and hess in this way as well.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : list or numpy 1-D array
The predicted values.
train_data : Dataset
The training dataset.
eval_name : string
The name of evaluation function.
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
Expand Down

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