1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
|
# cython: cdivision=True
# cython: boundscheck=False
# cython: wraparound=False
# cython: language_level=3
"""This module contains utility routines."""
# Author: Nicolas Hug
from cython.parallel import prange
from ...base import is_classifier
from .binning import _BinMapper
from .common cimport G_H_DTYPE_C
from .common cimport Y_DTYPE_C
def get_equivalent_estimator(estimator, lib='lightgbm'):
"""Return an unfitted estimator from another lib with matching hyperparams.
This utility function takes care of renaming the sklearn parameters into
their LightGBM, XGBoost or CatBoost equivalent parameters.
# unmapped XGB parameters:
# - min_samples_leaf
# - min_data_in_bin
# - min_split_gain (there is min_split_loss though?)
# unmapped Catboost parameters:
# max_leaves
# min_*
"""
if lib not in ('lightgbm', 'xgboost', 'catboost'):
raise ValueError('accepted libs are lightgbm, xgboost, and catboost. '
' got {}'.format(lib))
sklearn_params = estimator.get_params()
if sklearn_params['loss'] == 'auto':
raise ValueError('auto loss is not accepted. We need to know if '
'the problem is binary or multiclass classification.')
if sklearn_params['early_stopping']:
raise NotImplementedError('Early stopping should be deactivated.')
lightgbm_loss_mapping = {
'least_squares': 'regression_l2',
'least_absolute_deviation': 'regression_l1',
'binary_crossentropy': 'binary',
'categorical_crossentropy': 'multiclass'
}
lightgbm_params = {
'objective': lightgbm_loss_mapping[sklearn_params['loss']],
'learning_rate': sklearn_params['learning_rate'],
'n_estimators': sklearn_params['max_iter'],
'num_leaves': sklearn_params['max_leaf_nodes'],
'max_depth': sklearn_params['max_depth'],
'min_child_samples': sklearn_params['min_samples_leaf'],
'reg_lambda': sklearn_params['l2_regularization'],
'max_bin': sklearn_params['max_bins'],
'min_data_in_bin': 1,
'min_child_weight': 1e-3,
'min_sum_hessian_in_leaf': 1e-3,
'min_split_gain': 0,
'verbosity': 10 if sklearn_params['verbose'] else -10,
'boost_from_average': True,
'enable_bundle': False, # also makes feature order consistent
'min_data_in_bin': 1,
'subsample_for_bin': _BinMapper().subsample,
}
if sklearn_params['loss'] == 'categorical_crossentropy':
# LightGBM multiplies hessians by 2 in multiclass loss.
lightgbm_params['min_sum_hessian_in_leaf'] *= 2
lightgbm_params['learning_rate'] *= 2
# XGB
xgboost_loss_mapping = {
'least_squares': 'reg:linear',
'least_absolute_deviation': 'LEAST_ABSOLUTE_DEV_NOT_SUPPORTED',
'binary_crossentropy': 'reg:logistic',
'categorical_crossentropy': 'multi:softmax'
}
xgboost_params = {
'tree_method': 'hist',
'grow_policy': 'lossguide', # so that we can set max_leaves
'objective': xgboost_loss_mapping[sklearn_params['loss']],
'learning_rate': sklearn_params['learning_rate'],
'n_estimators': sklearn_params['max_iter'],
'max_leaves': sklearn_params['max_leaf_nodes'],
'max_depth': sklearn_params['max_depth'] or 0,
'lambda': sklearn_params['l2_regularization'],
'max_bin': sklearn_params['max_bins'],
'min_child_weight': 1e-3,
'verbosity': 2 if sklearn_params['verbose'] else 0,
'silent': sklearn_params['verbose'] == 0,
'n_jobs': -1,
}
# Catboost
catboost_loss_mapping = {
'least_squares': 'RMSE',
# catboost does not support MAE when leaf_estimation_method is Newton
'least_absolute_deviation': 'LEAST_ASBOLUTE_DEV_NOT_SUPPORTED',
'binary_crossentropy': 'Logloss',
'categorical_crossentropy': 'MultiClass'
}
catboost_params = {
'loss_function': catboost_loss_mapping[sklearn_params['loss']],
'learning_rate': sklearn_params['learning_rate'],
'iterations': sklearn_params['max_iter'],
'depth': sklearn_params['max_depth'],
'reg_lambda': sklearn_params['l2_regularization'],
'max_bin': sklearn_params['max_bins'],
'feature_border_type': 'Median',
'leaf_estimation_method': 'Newton',
'verbose': bool(sklearn_params['verbose']),
}
if lib == 'lightgbm':
from lightgbm import LGBMRegressor
from lightgbm import LGBMClassifier
if is_classifier(estimator):
return LGBMClassifier(**lightgbm_params)
else:
return LGBMRegressor(**lightgbm_params)
elif lib == 'xgboost':
from xgboost import XGBRegressor
from xgboost import XGBClassifier
if is_classifier(estimator):
return XGBClassifier(**xgboost_params)
else:
return XGBRegressor(**xgboost_params)
else:
from catboost import CatBoostRegressor
from catboost import CatBoostClassifier
if is_classifier(estimator):
return CatBoostClassifier(**catboost_params)
else:
return CatBoostRegressor(**catboost_params)
def sum_parallel(G_H_DTYPE_C [:] array):
cdef:
Y_DTYPE_C out = 0.
int i = 0
for i in prange(array.shape[0], schedule='static', nogil=True):
out += array[i]
return out
|
