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Unverified Commit 796ba803 authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
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[docs] updated docs about output values (#3037)

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python-package/lightgbm/basic.py
View file @ 796ba803
...@@ -2133,6 +2133,7 @@ class Booster(object): ...@@ -2133,6 +2133,7 @@ class Booster(object):
hess : list or numpy 1-D array 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) 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. 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] 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. and you should group grad and hess in this way as well.
...@@ -2181,6 +2182,7 @@ class Booster(object): ...@@ -2181,6 +2182,7 @@ class Booster(object):
.. note:: .. note::
For binary task, the score is probability of positive class (or margin in case of custom objective).
For multi-class task, the score is group by class_id first, then group by row_id. 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] 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. and you should group grad and hess in this way as well.
...@@ -2321,6 +2323,7 @@ class Booster(object): ...@@ -2321,6 +2323,7 @@ class Booster(object):
is_higher_better : bool is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``. 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. 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]. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
...@@ -2367,6 +2370,7 @@ class Booster(object): ...@@ -2367,6 +2370,7 @@ class Booster(object):
is_higher_better : bool is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``. 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. 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]. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
...@@ -2398,6 +2402,7 @@ class Booster(object): ...@@ -2398,6 +2402,7 @@ class Booster(object):
is_higher_better : bool is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``. 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. 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]. 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 @ 796ba803
...@@ -50,6 +50,7 @@ def train(params, train_set, num_boost_round=100, ...@@ -50,6 +50,7 @@ def train(params, train_set, num_boost_round=100,
hess : list or numpy 1-D array 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) 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. 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] 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. and you should group grad and hess in this way as well.
...@@ -70,6 +71,7 @@ def train(params, train_set, num_boost_round=100, ...@@ -70,6 +71,7 @@ def train(params, train_set, num_boost_round=100,
is_higher_better : bool is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``. 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. 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]. 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, To ignore the default metric corresponding to the used objective,
...@@ -418,6 +420,7 @@ def cv(params, train_set, num_boost_round=100, ...@@ -418,6 +420,7 @@ def cv(params, train_set, num_boost_round=100,
hess : list or numpy 1-D array 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) 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. 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] 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. and you should group grad and hess in this way as well.
...@@ -438,6 +441,7 @@ def cv(params, train_set, num_boost_round=100, ...@@ -438,6 +441,7 @@ def cv(params, train_set, num_boost_round=100,
is_higher_better : bool is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``. 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. 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]. 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, To ignore the default metric corresponding to the used objective,
......
python-package/lightgbm/sklearn.py
View file @ 796ba803
...@@ -43,6 +43,7 @@ class _ObjectiveFunctionWrapper(object): ...@@ -43,6 +43,7 @@ class _ObjectiveFunctionWrapper(object):
.. note:: .. 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. 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] 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. and you should group grad and hess in this way as well.
...@@ -130,6 +131,7 @@ class _EvalFunctionWrapper(object): ...@@ -130,6 +131,7 @@ class _EvalFunctionWrapper(object):
.. note:: .. 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. 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]. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].
""" """
...@@ -292,6 +294,7 @@ class LGBMModel(_LGBMModelBase): ...@@ -292,6 +294,7 @@ class LGBMModel(_LGBMModelBase):
hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) 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) 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. 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] 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. and you should group grad and hess in this way as well.
...@@ -477,6 +480,7 @@ class LGBMModel(_LGBMModelBase): ...@@ -477,6 +480,7 @@ class LGBMModel(_LGBMModelBase):
is_higher_better : bool is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``. 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. 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]. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].
""" """
......
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