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Unverified Commit d670a4d6 authored by José Morales's avatar José Morales Committed by GitHub
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[python-package] use 2d collections for predictions, grads and hess in... 

[python-package] use 2d collections for predictions, grads and hess in multiclass custom objective (#4925)

* reshape predictions, grad and hess in multiclass custom objective

* add sklearn test. move custom obj to utils. docs for numpy

* use num_model_per_iteration to get num_classes

* update docs and dask multiclass custom objective test

* move reshaping to __inner_predict. add test for feval

* add missing note. remove extra line
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Showing with 143 additions and 81 deletions
python-package/lightgbm/basic.py
View file @ d670a4d6
......@@ -2947,22 +2947,21 @@ class Booster:
Should accept two parameters: preds, train_data,
and return (grad, hess).
preds : numpy 1-D array
preds : numpy 1-D array or numpy 2-D array (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.
train_data : Dataset
The training dataset.
grad : list, numpy 1-D array or pandas Series
grad : numpy 1-D array or numpy 2-D array (for multi-class task)
The value of the first order derivative (gradient) of the loss
with respect to the elements of preds for each sample point.
hess : list, numpy 1-D array or pandas Series
hess : numpy 1-D array or numpy 2-D array (for multi-class task)
The value of the second order derivative (Hessian) of the loss
with respect to the elements of preds 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.
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
Returns
-------
......@@ -3000,6 +2999,9 @@ class Booster:
if not self.__set_objective_to_none:
self.reset_parameter({"objective": "none"}).__set_objective_to_none = True
grad, hess = fobj(self.__inner_predict(0), self.train_set)
if self.num_model_per_iteration() > 1:
grad = grad.ravel(order='F')
hess = hess.ravel(order='F')
return self.__boost(grad, hess)
def __boost(self, grad, hess):
......@@ -3009,16 +3011,15 @@ class Booster:
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.
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
Parameters
----------
grad : list, numpy 1-D array or pandas Series
grad : numpy 1-D array or numpy 2-D array (for multi-class task)
The value of the first order derivative (gradient) of the loss
with respect to the elements of score for each sample point.
hess : list, numpy 1-D array or pandas Series
hess : numpy 1-D array or numpy 2-D array (for multi-class task)
The value of the second order derivative (Hessian) of the loss
with respect to the elements of score for each sample point.
......@@ -3160,8 +3161,8 @@ class Booster:
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].
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
Returns
-------
......@@ -3195,7 +3196,7 @@ class Booster:
Should accept two parameters: preds, eval_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : numpy 1-D array
preds : numpy 1-D array or numpy 2-D array (for multi-class task)
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.
......@@ -3208,8 +3209,8 @@ class Booster:
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].
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
Returns
-------
......@@ -3228,7 +3229,7 @@ class Booster:
Should accept two parameters: preds, eval_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
preds : numpy 1-D array
preds : numpy 1-D array or numpy 2-D array (for multi-class task)
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.
......@@ -3241,8 +3242,8 @@ class Booster:
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].
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
Returns
-------
......@@ -3868,7 +3869,11 @@ class Booster:
if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]):
raise ValueError(f"Wrong length of predict results for data {data_idx}")
self.__is_predicted_cur_iter[data_idx] = True
return self.__inner_predict_buffer[data_idx]
result = self.__inner_predict_buffer[data_idx]
if self.__num_class > 1:
num_data = result.size // self.__num_class
result = result.reshape(num_data, self.__num_class, order='F')
return result
def __get_eval_info(self):
"""Get inner evaluation count and names."""
......
python-package/lightgbm/sklearn.py
View file @ d670a4d6
......@@ -59,7 +59,7 @@ class _ObjectiveFunctionWrapper:
y_true : numpy 1-D array of shape = [n_samples]
The target values.
y_pred : numpy 1-D array of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of 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.
......@@ -69,18 +69,17 @@ class _ObjectiveFunctionWrapper:
sum(group) = n_samples.
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 : list, numpy 1-D array or pandas Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task)
The value of the first order derivative (gradient) of the loss
with respect to the elements of y_pred for each sample point.
hess : list, numpy 1-D array or pandas Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task)
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 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.
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
"""
self.func = func
......@@ -89,17 +88,17 @@ class _ObjectiveFunctionWrapper:
Parameters
----------
preds : numpy 1-D array of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
The predicted values.
dataset : Dataset
The training dataset.
Returns
-------
grad : list, numpy 1-D array or pandas Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task)
The value of the first order derivative (gradient) of the loss
with respect to the elements of preds for each sample point.
hess : list, numpy 1-D array or pandas Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task)
The value of the second order derivative (Hessian) of the loss
with respect to the elements of preds for each sample point.
"""
......@@ -114,20 +113,13 @@ class _ObjectiveFunctionWrapper:
"""weighted for objective"""
weight = dataset.get_weight()
if weight is not None:
"""only one class"""
if len(weight) == len(grad):
grad = np.multiply(grad, weight)
hess = np.multiply(hess, weight)
else:
num_data = len(weight)
num_class = len(grad) // num_data
if num_class * num_data != len(grad):
raise ValueError("Length of grad and hess should equal to num_class * num_data")
for k in range(num_class):
for i in range(num_data):
idx = k * num_data + i
grad[idx] *= weight[i]
hess[idx] *= weight[i]
if grad.ndim == 2: # multi-class
num_data = grad.shape[0]
if weight.size != num_data:
raise ValueError("grad and hess should be of shape [n_samples, n_classes]")
weight = weight.reshape(num_data, 1)
grad *= weight
hess *= weight
return grad, hess
......@@ -152,7 +144,7 @@ class _EvalFunctionWrapper:
y_true : numpy 1-D array of shape = [n_samples]
The target values.
y_pred : numpy 1-D array of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array 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.
......@@ -173,8 +165,8 @@ class _EvalFunctionWrapper:
.. note::
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].
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
"""
self.func = func
......@@ -183,7 +175,7 @@ class _EvalFunctionWrapper:
Parameters
----------
preds : numpy 1-D array of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
The predicted values.
dataset : Dataset
The training dataset.
......@@ -287,7 +279,7 @@ _lgbmmodel_doc_custom_eval_note = """
y_true : numpy 1-D array of shape = [n_samples]
The target values.
y_pred : numpy 1-D array of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of 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.
......@@ -306,8 +298,8 @@ _lgbmmodel_doc_custom_eval_note = """
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
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].
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
"""
_lgbmmodel_doc_predict = (
......@@ -464,7 +456,7 @@ class LGBMModel(_LGBMModelBase):
y_true : numpy 1-D array of shape = [n_samples]
The target values.
y_pred : numpy 1-D array of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of 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.
......@@ -474,16 +466,15 @@ class LGBMModel(_LGBMModelBase):
sum(group) = n_samples.
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 : list, numpy 1-D array or pandas Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
The value of the first order derivative (gradient) of the loss
with respect to the elements of y_pred for each sample point.
hess : list, numpy 1-D array or pandas Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
The value of the second order derivative (Hessian) of the loss
with respect to the elements of y_pred for each sample point.
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.
For multi-class task, preds are a [n_samples, n_classes] numpy 2-D array,
and grad and hess should be returned in the same format.
"""
if not SKLEARN_INSTALLED:
raise LightGBMError('scikit-learn is required for lightgbm.sklearn. '
......
tests/python_package_test/test_basic.py
View file @ d670a4d6
......@@ -7,13 +7,14 @@ from pathlib import Path
import numpy as np
import pytest
from scipy import sparse
from sklearn.datasets import dump_svmlight_file, load_svmlight_file
from sklearn.datasets import dump_svmlight_file, load_svmlight_file, make_blobs
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
import lightgbm as lgb
from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame, pd_Series
from .utils import load_breast_cancer
from .utils import load_breast_cancer, sklearn_multiclass_custom_objective, softmax
def test_basic(tmp_path):
......@@ -587,7 +588,7 @@ def _bad_gradients(preds, _):
def _good_gradients(preds, _):
return np.random.randn(len(preds)), np.random.rand(len(preds))
return np.random.randn(*preds.shape), np.random.rand(*preds.shape)
def test_custom_objective_safety():
......@@ -609,3 +610,51 @@ def test_custom_objective_safety():
good_bst_multi.update(fobj=_good_gradients)
with pytest.raises(ValueError, match=re.escape(f"number of models per one iteration ({nclass})")):
bad_bst_multi.update(fobj=_bad_gradients)
def test_multiclass_custom_objective():
def custom_obj(y_pred, ds):
y_true = ds.get_label()
return sklearn_multiclass_custom_objective(y_true, y_pred)
centers = [[-4, -4], [4, 4], [-4, 4]]
X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
ds = lgb.Dataset(X, y)
params = {'objective': 'multiclass', 'num_class': 3, 'num_leaves': 7}
builtin_obj_bst = lgb.train(params, ds, num_boost_round=10)
builtin_obj_preds = builtin_obj_bst.predict(X)
custom_obj_bst = lgb.train(params, ds, num_boost_round=10, fobj=custom_obj)
custom_obj_preds = softmax(custom_obj_bst.predict(X))
np.testing.assert_allclose(builtin_obj_preds, custom_obj_preds, rtol=0.01)
def test_multiclass_custom_eval():
def custom_eval(y_pred, ds):
y_true = ds.get_label()
return 'custom_logloss', log_loss(y_true, y_pred), False
centers = [[-4, -4], [4, 4], [-4, 4]]
X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0)
train_ds = lgb.Dataset(X_train, y_train)
valid_ds = lgb.Dataset(X_valid, y_valid, reference=train_ds)
params = {'objective': 'multiclass', 'num_class': 3, 'num_leaves': 7}
eval_result = {}
bst = lgb.train(
params,
train_ds,
num_boost_round=10,
valid_sets=[train_ds, valid_ds],
valid_names=['train', 'valid'],
feval=custom_eval,
callbacks=[lgb.record_evaluation(eval_result)],
keep_training_booster=True,
)
for key, ds in zip(['train', 'valid'], [train_ds, valid_ds]):
np.testing.assert_allclose(eval_result[key]['multi_logloss'], eval_result[key]['custom_logloss'])
_, metric, value, _ = bst.eval(ds, key, feval=custom_eval)[1] # first element is multi_logloss
assert metric == 'custom_logloss'
np.testing.assert_allclose(value, eval_result[key][metric][-1])
tests/python_package_test/test_dask.py
View file @ d670a4d6
......@@ -15,6 +15,8 @@ import pytest
import lightgbm as lgb
from .utils import sklearn_multiclass_custom_objective
if not platform.startswith('linux'):
pytest.skip('lightgbm.dask is currently supported in Linux environments', allow_module_level=True)
if machine() != 'x86_64':
......@@ -271,25 +273,6 @@ def _objective_logistic_regression(y_true, y_pred):
return grad, hess
def _objective_logloss(y_true, y_pred):
num_rows = len(y_true)
num_class = len(np.unique(y_true))
# operate on preds as [num_data, num_classes] matrix
y_pred = y_pred.reshape(-1, num_class, order='F')
row_wise_max = np.max(y_pred, axis=1).reshape(num_rows, 1)
preds = y_pred - row_wise_max
prob = np.exp(preds) / np.sum(np.exp(preds), axis=1).reshape(num_rows, 1)
grad_update = np.zeros_like(preds)
grad_update[np.arange(num_rows), y_true.astype(np.int32)] = -1.0
grad = prob + grad_update
factor = num_class / (num_class - 1)
hess = factor * prob * (1 - prob)
# reshape back to 1-D array, grouped by class id and then row id
grad = grad.T.reshape(-1)
hess = hess.T.reshape(-1)
return grad, hess
@pytest.mark.parametrize('output', data_output)
@pytest.mark.parametrize('task', ['binary-classification', 'multiclass-classification'])
@pytest.mark.parametrize('boosting_type', boosting_types)
......@@ -507,7 +490,7 @@ def test_classifier_custom_objective(output, task, cluster):
})
elif task == 'multiclass-classification':
params.update({
'objective': _objective_logloss,
'objective': sklearn_multiclass_custom_objective,
'num_classes': 3
})
......
tests/python_package_test/test_sklearn.py
View file @ d670a4d6
......@@ -7,7 +7,7 @@ import joblib
import numpy as np
import pytest
from sklearn.base import clone
from sklearn.datasets import load_svmlight_file, make_multilabel_classification
from sklearn.datasets import load_svmlight_file, make_blobs, make_multilabel_classification
from sklearn.ensemble import StackingClassifier, StackingRegressor
from sklearn.metrics import log_loss, mean_squared_error
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
......@@ -18,7 +18,7 @@ from sklearn.utils.validation import check_is_fitted
import lightgbm as lgb
from .utils import (load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud, make_ranking,
make_synthetic_regression)
make_synthetic_regression, sklearn_multiclass_custom_objective, softmax)
decreasing_generator = itertools.count(0, -1)
......@@ -1280,3 +1280,20 @@ def test_training_succeeds_when_data_is_dataframe_and_label_is_column_array(task
preds_1d = model_1d.predict(X)
preds_2d = model_2d.predict(X)
np.testing.assert_array_equal(preds_1d, preds_2d)
def test_multiclass_custom_objective():
centers = [[-4, -4], [4, 4], [-4, 4]]
X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
params = {'n_estimators': 10, 'num_leaves': 7}
builtin_obj_model = lgb.LGBMClassifier(**params)
builtin_obj_model.fit(X, y)
builtin_obj_preds = builtin_obj_model.predict_proba(X)
custom_obj_model = lgb.LGBMClassifier(objective=sklearn_multiclass_custom_objective, **params)
custom_obj_model.fit(X, y)
custom_obj_preds = softmax(custom_obj_model.predict(X, raw_score=True))
np.testing.assert_allclose(builtin_obj_preds, custom_obj_preds, rtol=0.01)
assert not callable(builtin_obj_model.objective_)
assert callable(custom_obj_model.objective_)
tests/python_package_test/utils.py
View file @ d670a4d6
......@@ -114,3 +114,20 @@ def make_ranking(n_samples=100, n_features=20, n_informative=5, gmax=2,
@lru_cache(maxsize=None)
def make_synthetic_regression(n_samples=100):
return sklearn.datasets.make_regression(n_samples, n_features=4, n_informative=2, random_state=42)
def softmax(x):
row_wise_max = np.max(x, axis=1).reshape(-1, 1)
exp_x = np.exp(x - row_wise_max)
return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)
def sklearn_multiclass_custom_objective(y_true, y_pred):
num_rows, num_class = y_pred.shape
prob = softmax(y_pred)
grad_update = np.zeros_like(prob)
grad_update[np.arange(num_rows), y_true.astype(np.int32)] = -1.0
grad = prob + grad_update
factor = num_class / (num_class - 1)
hess = factor * prob * (1 - prob)
return grad, hess
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