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Unverified Commit 1a367c65 authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
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[docs][python] update some docs related to custom objective (#4245)

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python-package/lightgbm/basic.py
View file @ 1a367c65
......@@ -2600,14 +2600,17 @@ class Booster:
preds : list or numpy 1-D array
The predicted values.
Predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task.
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.
The value of the first order derivative (gradient) of the loss
with respect to the elements of preds for each sample point.
hess : list or numpy 1-D array
The value of the second order derivative (Hessian) for each sample point.
The value of the second order derivative (Hessian) of the loss
with respect to the elements of preds for each sample point.
For binary task, the preds is probability of positive class (or margin in case of specified ``fobj``).
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.
......@@ -2656,7 +2659,8 @@ class Booster:
.. note::
For binary task, the score is probability of positive class (or margin in case of custom objective).
Score is returned before any transformation,
e.g. it is raw margin instead of probability of positive class for binary task.
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]
and you should group grad and hess in this way as well.
......@@ -2664,9 +2668,11 @@ class Booster:
Parameters
----------
grad : list or numpy 1-D array
The first order derivative (gradient).
The value of the first order derivative (gradient) of the loss
with respect to the elements of score for each sample point.
hess : list or numpy 1-D array
The second order derivative (Hessian).
The value of the second order derivative (Hessian) of the loss
with respect to the elements of score for each sample point.
Returns
-------
......@@ -2788,6 +2794,8 @@ class Booster:
preds : list or numpy 1-D array
The predicted values.
If ``fobj`` is specified, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
eval_data : Dataset
The evaluation dataset.
eval_name : string
......@@ -2797,7 +2805,6 @@ class Booster:
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For binary task, the preds is probability of positive class (or margin in case of specified ``fobj``).
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].
......@@ -2835,6 +2842,8 @@ class Booster:
preds : list or numpy 1-D array
The predicted values.
If ``fobj`` is specified, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
train_data : Dataset
The training dataset.
eval_name : string
......@@ -2844,7 +2853,6 @@ class Booster:
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For binary task, the preds is probability of positive class (or margin in case of specified ``fobj``).
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].
......@@ -2867,6 +2875,8 @@ class Booster:
preds : list or numpy 1-D array
The predicted values.
If ``fobj`` is specified, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
valid_data : Dataset
The validation dataset.
eval_name : string
......@@ -2876,7 +2886,6 @@ class Booster:
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For binary task, the preds is probability of positive class (or margin in case of specified ``fobj``).
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].
......
python-package/lightgbm/engine.py
View file @ 1a367c65
......@@ -39,14 +39,17 @@ def train(params, train_set, num_boost_round=100,
preds : list or numpy 1-D array
The predicted values.
Predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task.
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.
The value of the first order derivative (gradient) of the loss
with respect to the elements of preds for each sample point.
hess : list or numpy 1-D array
The value of the second order derivative (Hessian) for each sample point.
The value of the second order derivative (Hessian) of the loss
with respect to the elements of preds for each sample point.
For binary task, the preds is margin.
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.
......@@ -58,6 +61,8 @@ def train(params, train_set, num_boost_round=100,
preds : list or numpy 1-D array
The predicted values.
If ``fobj`` is specified, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
train_data : Dataset
The training dataset.
eval_name : string
......@@ -67,7 +72,6 @@ def train(params, train_set, num_boost_round=100,
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For binary task, the preds is probability of positive class (or margin in case of specified ``fobj``).
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,
......@@ -428,14 +432,17 @@ def cv(params, train_set, num_boost_round=100,
preds : list or numpy 1-D array
The predicted values.
Predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task.
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.
The value of the first order derivative (gradient) of the loss
with respect to the elements of preds for each sample point.
hess : list or numpy 1-D array
The value of the second order derivative (Hessian) for each sample point.
The value of the second order derivative (Hessian) of the loss
with respect to the elements of preds for each sample point.
For binary task, the preds is margin.
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.
......@@ -447,6 +454,8 @@ def cv(params, train_set, num_boost_round=100,
preds : list or numpy 1-D array
The predicted values.
If ``fobj`` is specified, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
train_data : Dataset
The training dataset.
eval_name : string
......@@ -456,7 +465,6 @@ def cv(params, train_set, num_boost_round=100,
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For binary task, the preds is probability of positive class (or margin in case of specified ``fobj``).
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,
......
python-package/lightgbm/sklearn.py
View file @ 1a367c65
......@@ -32,6 +32,8 @@ class _ObjectiveFunctionWrapper:
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
Predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task.
group : array-like
Group/query data.
Only used in the learning-to-rank task.
......@@ -39,13 +41,14 @@ class _ObjectiveFunctionWrapper:
For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the first order derivative (gradient) for each sample point.
The value of the first order derivative (gradient) of the loss
with respect to the elements of y_pred for each sample point.
hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the second order derivative (Hessian) for each sample point.
The value of the second order derivative (Hessian) of the loss
with respect to the elements of y_pred for each sample point.
.. note::
For binary task, the y_pred is margin.
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
and you should group grad and hess in this way as well.
......@@ -65,9 +68,11 @@ class _ObjectiveFunctionWrapper:
Returns
-------
grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the first order derivative (gradient) for each sample point.
The value of the first order derivative (gradient) of the loss
with respect to the elements of preds for each sample point.
hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the second order derivative (Hessian) for each sample point.
The value of the second order derivative (Hessian) of the loss
with respect to the elements of preds for each sample point.
"""
labels = dataset.get_label()
argc = len(signature(self.func).parameters)
......@@ -120,6 +125,8 @@ class _EvalFunctionWrapper:
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
In case of custom ``objective``, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
weight : array-like of shape = [n_samples]
The weight of samples.
group : array-like
......@@ -137,7 +144,6 @@ class _EvalFunctionWrapper:
.. note::
For binary task, the y_pred is probability of positive class (or margin in case of custom ``objective``).
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].
"""
......@@ -272,6 +278,8 @@ _lgbmmodel_doc_custom_eval_note = """
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
In case of custom ``objective``, predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task in this case.
weight : array-like of shape = [n_samples]
The weight of samples.
group : array-like
......@@ -287,7 +295,6 @@ _lgbmmodel_doc_custom_eval_note = """
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
For binary task, the y_pred is probability of positive class (or margin in case of custom ``objective``).
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].
"""
......@@ -434,6 +441,8 @@ class LGBMModel(_LGBMModelBase):
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
Predicted values are returned before any transformation,
e.g. they are raw margin instead of probability of positive class for binary task.
group : array-like
Group/query data.
Only used in the learning-to-rank task.
......@@ -441,11 +450,12 @@ class LGBMModel(_LGBMModelBase):
For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the first order derivative (gradient) for each sample point.
The value of the first order derivative (gradient) of the loss
with respect to the elements of y_pred for each sample point.
hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The value of the second order derivative (Hessian) for each sample point.
The value of the second order derivative (Hessian) of the loss
with respect to the elements of y_pred for each sample point.
For binary task, the y_pred is margin.
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
and you should group grad and hess in this way as well.
......
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