-
Notifications
You must be signed in to change notification settings - Fork 3.8k
/
sklearn.py
1001 lines (884 loc) · 49.1 KB
/
sklearn.py
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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# coding: utf-8
"""Scikit-learn wrapper interface for LightGBM."""
import copy
import warnings
from inspect import signature
import numpy as np
from .basic import Dataset, LightGBMError, _ConfigAliases
from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase,
LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase,
_LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckSampleWeight,
_LGBMAssertAllFinite, _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
DataFrame, DataTable)
from .engine import train
class _ObjectiveFunctionWrapper:
"""Proxy class for objective function."""
def __init__(self, func):
"""Construct a proxy class.
This class transforms objective function to match objective function with signature ``new_func(preds, dataset)``
as expected by ``lightgbm.engine.train``.
Parameters
----------
func : callable
Expects a callable with signature ``func(y_true, y_pred)`` or ``func(y_true, y_pred, group)
and returns (grad, hess):
y_true : array-like of shape = [n_samples]
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
group : array-like
Group/query data.
Only used in the learning-to-rank task.
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 : 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.
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.
.. 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.
"""
self.func = func
def __call__(self, preds, dataset):
"""Call passed function with appropriate arguments.
Parameters
----------
preds : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
dataset : Dataset
The training dataset.
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.
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.
"""
labels = dataset.get_label()
argc = len(signature(self.func).parameters)
if argc == 2:
grad, hess = self.func(labels, preds)
elif argc == 3:
grad, hess = self.func(labels, preds, dataset.get_group())
else:
raise TypeError("Self-defined objective function should have 2 or 3 arguments, got %d" % argc)
"""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]
return grad, hess
class _EvalFunctionWrapper:
"""Proxy class for evaluation function."""
def __init__(self, func):
"""Construct a proxy class.
This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)``
as expected by ``lightgbm.engine.train``.
Parameters
----------
func : callable
Expects a callable with following signatures:
``func(y_true, y_pred)``,
``func(y_true, y_pred, weight)``
or ``func(y_true, y_pred, weight, group)``
and returns (eval_name, eval_result, is_higher_better) or
list of (eval_name, eval_result, is_higher_better):
y_true : array-like of shape = [n_samples]
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
weight : array-like of shape = [n_samples]
The weight of samples.
group : array-like
Group/query data.
Only used in the learning-to-rank task.
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.
eval_name : string
The name of evaluation function (without whitespaces).
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
.. 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].
"""
self.func = func
def __call__(self, preds, dataset):
"""Call passed function with appropriate arguments.
Parameters
----------
preds : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
dataset : Dataset
The training dataset.
Returns
-------
eval_name : string
The name of evaluation function (without whitespaces).
eval_result : float
The eval result.
is_higher_better : bool
Is eval result higher better, e.g. AUC is ``is_higher_better``.
"""
labels = dataset.get_label()
argc = len(signature(self.func).parameters)
if argc == 2:
return self.func(labels, preds)
elif argc == 3:
return self.func(labels, preds, dataset.get_weight())
elif argc == 4:
return self.func(labels, preds, dataset.get_weight(), dataset.get_group())
else:
raise TypeError("Self-defined eval function should have 2, 3 or 4 arguments, got %d" % argc)
class LGBMModel(_LGBMModelBase):
"""Implementation of the scikit-learn API for LightGBM."""
def __init__(self, boosting_type='gbdt', num_leaves=31, max_depth=-1,
learning_rate=0.1, n_estimators=100,
subsample_for_bin=200000, objective=None, class_weight=None,
min_split_gain=0., min_child_weight=1e-3, min_child_samples=20,
subsample=1., subsample_freq=0, colsample_bytree=1.,
reg_alpha=0., reg_lambda=0., random_state=None,
n_jobs=-1, silent=True, importance_type='split', **kwargs):
r"""Construct a gradient boosting model.
Parameters
----------
boosting_type : string, optional (default='gbdt')
'gbdt', traditional Gradient Boosting Decision Tree.
'dart', Dropouts meet Multiple Additive Regression Trees.
'goss', Gradient-based One-Side Sampling.
'rf', Random Forest.
num_leaves : int, optional (default=31)
Maximum tree leaves for base learners.
max_depth : int, optional (default=-1)
Maximum tree depth for base learners, <=0 means no limit.
learning_rate : float, optional (default=0.1)
Boosting learning rate.
You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate
in training using ``reset_parameter`` callback.
Note, that this will ignore the ``learning_rate`` argument in training.
n_estimators : int, optional (default=100)
Number of boosted trees to fit.
subsample_for_bin : int, optional (default=200000)
Number of samples for constructing bins.
objective : string, callable or None, optional (default=None)
Specify the learning task and the corresponding learning objective or
a custom objective function to be used (see note below).
Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker.
class_weight : dict, 'balanced' or None, optional (default=None)
Weights associated with classes in the form ``{class_label: weight}``.
Use this parameter only for multi-class classification task;
for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters.
Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities.
You may want to consider performing probability calibration
(https://scikit-learn.org/stable/modules/calibration.html) of your model.
The 'balanced' mode uses the values of y to automatically adjust weights
inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``.
If None, all classes are supposed to have weight one.
Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method)
if ``sample_weight`` is specified.
min_split_gain : float, optional (default=0.)
Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : float, optional (default=1e-3)
Minimum sum of instance weight (hessian) needed in a child (leaf).
min_child_samples : int, optional (default=20)
Minimum number of data needed in a child (leaf).
subsample : float, optional (default=1.)
Subsample ratio of the training instance.
subsample_freq : int, optional (default=0)
Frequence of subsample, <=0 means no enable.
colsample_bytree : float, optional (default=1.)
Subsample ratio of columns when constructing each tree.
reg_alpha : float, optional (default=0.)
L1 regularization term on weights.
reg_lambda : float, optional (default=0.)
L2 regularization term on weights.
random_state : int, RandomState object or None, optional (default=None)
Random number seed.
If int, this number is used to seed the C++ code.
If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code.
If None, default seeds in C++ code are used.
n_jobs : int, optional (default=-1)
Number of parallel threads.
silent : bool, optional (default=True)
Whether to print messages while running boosting.
importance_type : string, optional (default='split')
The type of feature importance to be filled into ``feature_importances_``.
If 'split', result contains numbers of times the feature is used in a model.
If 'gain', result contains total gains of splits which use the feature.
**kwargs
Other parameters for the model.
Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.
.. warning::
\*\*kwargs is not supported in sklearn, it may cause unexpected issues.
Note
----
A custom objective function can be provided for the ``objective`` parameter.
In this case, it should have the signature
``objective(y_true, y_pred) -> grad, hess`` or
``objective(y_true, y_pred, group) -> grad, hess``:
y_true : array-like of shape = [n_samples]
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
group : array-like
Group/query data.
Only used in the learning-to-rank task.
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 : 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.
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.
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.
"""
if not SKLEARN_INSTALLED:
raise LightGBMError('Scikit-learn is required for this module')
self.boosting_type = boosting_type
self.objective = objective
self.num_leaves = num_leaves
self.max_depth = max_depth
self.learning_rate = learning_rate
self.n_estimators = n_estimators
self.subsample_for_bin = subsample_for_bin
self.min_split_gain = min_split_gain
self.min_child_weight = min_child_weight
self.min_child_samples = min_child_samples
self.subsample = subsample
self.subsample_freq = subsample_freq
self.colsample_bytree = colsample_bytree
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.random_state = random_state
self.n_jobs = n_jobs
self.silent = silent
self.importance_type = importance_type
self._Booster = None
self._evals_result = None
self._best_score = None
self._best_iteration = None
self._other_params = {}
self._objective = objective
self.class_weight = class_weight
self._class_weight = None
self._class_map = None
self._n_features = None
self._n_features_in = None
self._classes = None
self._n_classes = None
self.set_params(**kwargs)
def _more_tags(self):
return {
'allow_nan': True,
'X_types': ['2darray', 'sparse', '1dlabels'],
'_xfail_checks': {
'check_no_attributes_set_in_init':
'scikit-learn incorrectly asserts that private attributes '
'cannot be set in __init__: '
'(see https://github.com/microsoft/LightGBM/issues/2628)'
}
}
def get_params(self, deep=True):
"""Get parameters for this estimator.
Parameters
----------
deep : bool, optional (default=True)
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
params = super().get_params(deep=deep)
params.update(self._other_params)
return params
def set_params(self, **params):
"""Set the parameters of this estimator.
Parameters
----------
**params
Parameter names with their new values.
Returns
-------
self : object
Returns self.
"""
for key, value in params.items():
setattr(self, key, value)
if hasattr(self, '_' + key):
setattr(self, '_' + key, value)
self._other_params[key] = value
return self
def fit(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_class_weight=None, eval_init_score=None, eval_group=None,
eval_metric=None, early_stopping_rounds=None, verbose=True,
feature_name='auto', categorical_feature='auto',
callbacks=None, init_model=None):
"""Build a gradient boosting model from the training set (X, y).
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
Input feature matrix.
y : array-like of shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
sample_weight : array-like of shape = [n_samples] or None, optional (default=None)
Weights of training data.
init_score : array-like of shape = [n_samples] or None, optional (default=None)
Init score of training data.
group : array-like or None, optional (default=None)
Group/query data.
Only used in the learning-to-rank task.
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.
eval_set : list or None, optional (default=None)
A list of (X, y) tuple pairs to use as validation sets.
eval_names : list of strings or None, optional (default=None)
Names of eval_set.
eval_sample_weight : list of arrays or None, optional (default=None)
Weights of eval data.
eval_class_weight : list or None, optional (default=None)
Class weights of eval data.
eval_init_score : list of arrays or None, optional (default=None)
Init score of eval data.
eval_group : list of arrays or None, optional (default=None)
Group data of eval data.
eval_metric : string, callable, list or None, optional (default=None)
If string, it should be a built-in evaluation metric to use.
If callable, it should be a custom evaluation metric, see note below for more details.
If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both.
In either case, the ``metric`` from the model parameters will be evaluated and used as well.
Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker.
early_stopping_rounds : int or None, optional (default=None)
Activates early stopping. The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.
To check only the first metric, set the ``first_metric_only`` parameter to ``True``
in additional parameters ``**kwargs`` of the model constructor.
verbose : bool or int, optional (default=True)
Requires at least one evaluation data.
If True, the eval metric on the eval set is printed at each boosting stage.
If int, the eval metric on the eval set is printed at every ``verbose`` boosting stage.
The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
.. rubric:: Example
With ``verbose`` = 4 and at least one item in ``eval_set``,
an evaluation metric is printed every 4 (instead of 1) boosting stages.
feature_name : list of strings or 'auto', optional (default='auto')
Feature names.
If 'auto' and data is pandas DataFrame, data columns names are used.
categorical_feature : list of strings or int, or 'auto', optional (default='auto')
Categorical features.
If list of int, interpreted as indices.
If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
All values in categorical features should be less than int32 max value (2147483647).
Large values could be memory consuming. Consider using consecutive integers starting from zero.
All negative values in categorical features will be treated as missing values.
The output cannot be monotonically constrained with respect to a categorical feature.
callbacks : list of callback functions or None, optional (default=None)
List of callback functions that are applied at each iteration.
See Callbacks in Python API for more information.
init_model : string, Booster, LGBMModel or None, optional (default=None)
Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training.
Returns
-------
self : object
Returns self.
Note
----
Custom eval function expects a callable with following signatures:
``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or
``func(y_true, y_pred, weight, group)``
and returns (eval_name, eval_result, is_higher_better) or
list of (eval_name, eval_result, is_higher_better):
y_true : array-like of shape = [n_samples]
The target values.
y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
The predicted values.
weight : array-like of shape = [n_samples]
The weight of samples.
group : array-like
Group/query data.
Only used in the learning-to-rank task.
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.
eval_name : string
The name of evaluation function (without whitespaces).
eval_result : float
The eval result.
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].
"""
if self._objective is None:
if isinstance(self, LGBMRegressor):
self._objective = "regression"
elif isinstance(self, LGBMClassifier):
self._objective = "binary"
elif isinstance(self, LGBMRanker):
self._objective = "lambdarank"
else:
raise ValueError("Unknown LGBMModel type.")
if callable(self._objective):
self._fobj = _ObjectiveFunctionWrapper(self._objective)
else:
self._fobj = None
evals_result = {}
params = self.get_params()
# user can set verbose with kwargs, it has higher priority
if not any(verbose_alias in params for verbose_alias in _ConfigAliases.get("verbosity")) and self.silent:
params['verbose'] = -1
params.pop('silent', None)
params.pop('importance_type', None)
params.pop('n_estimators', None)
params.pop('class_weight', None)
if isinstance(params['random_state'], np.random.RandomState):
params['random_state'] = params['random_state'].randint(np.iinfo(np.int32).max)
for alias in _ConfigAliases.get('objective'):
params.pop(alias, None)
if self._n_classes is not None and self._n_classes > 2:
for alias in _ConfigAliases.get('num_class'):
params.pop(alias, None)
params['num_class'] = self._n_classes
if hasattr(self, '_eval_at'):
for alias in _ConfigAliases.get('eval_at'):
params.pop(alias, None)
params['eval_at'] = self._eval_at
params['objective'] = self._objective
if self._fobj:
params['objective'] = 'None' # objective = nullptr for unknown objective
# Do not modify original args in fit function
# Refer to https://github.com/microsoft/LightGBM/pull/2619
eval_metric_list = copy.deepcopy(eval_metric)
if not isinstance(eval_metric_list, list):
eval_metric_list = [eval_metric_list]
# Separate built-in from callable evaluation metrics
eval_metrics_callable = [_EvalFunctionWrapper(f) for f in eval_metric_list if callable(f)]
eval_metrics_builtin = [m for m in eval_metric_list if isinstance(m, str)]
# register default metric for consistency with callable eval_metric case
original_metric = self._objective if isinstance(self._objective, str) else None
if original_metric is None:
# try to deduce from class instance
if isinstance(self, LGBMRegressor):
original_metric = "l2"
elif isinstance(self, LGBMClassifier):
original_metric = "multi_logloss" if self._n_classes > 2 else "binary_logloss"
elif isinstance(self, LGBMRanker):
original_metric = "ndcg"
# overwrite default metric by explicitly set metric
for metric_alias in _ConfigAliases.get("metric"):
if metric_alias in params:
original_metric = params.pop(metric_alias)
# concatenate metric from params (or default if not provided in params) and eval_metric
original_metric = [original_metric] if isinstance(original_metric, (str, type(None))) else original_metric
params['metric'] = [e for e in eval_metrics_builtin if e not in original_metric] + original_metric
params['metric'] = [metric for metric in params['metric'] if metric is not None]
if not isinstance(X, (DataFrame, DataTable)):
_X, _y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
if sample_weight is not None:
sample_weight = _LGBMCheckSampleWeight(sample_weight, _X)
else:
_X, _y = X, y
if self._class_weight is None:
self._class_weight = self.class_weight
if self._class_weight is not None:
class_sample_weight = _LGBMComputeSampleWeight(self._class_weight, y)
if sample_weight is None or len(sample_weight) == 0:
sample_weight = class_sample_weight
else:
sample_weight = np.multiply(sample_weight, class_sample_weight)
self._n_features = _X.shape[1]
# copy for consistency
self._n_features_in = self._n_features
def _construct_dataset(X, y, sample_weight, init_score, group, params,
categorical_feature='auto'):
return Dataset(X, label=y, weight=sample_weight, group=group,
init_score=init_score, params=params,
categorical_feature=categorical_feature)
train_set = _construct_dataset(_X, _y, sample_weight, init_score, group, params,
categorical_feature=categorical_feature)
valid_sets = []
if eval_set is not None:
def _get_meta_data(collection, name, i):
if collection is None:
return None
elif isinstance(collection, list):
return collection[i] if len(collection) > i else None
elif isinstance(collection, dict):
return collection.get(i, None)
else:
raise TypeError('{} should be dict or list'.format(name))
if isinstance(eval_set, tuple):
eval_set = [eval_set]
for i, valid_data in enumerate(eval_set):
# reduce cost for prediction training data
if valid_data[0] is X and valid_data[1] is y:
valid_set = train_set
else:
valid_weight = _get_meta_data(eval_sample_weight, 'eval_sample_weight', i)
valid_class_weight = _get_meta_data(eval_class_weight, 'eval_class_weight', i)
if valid_class_weight is not None:
if isinstance(valid_class_weight, dict) and self._class_map is not None:
valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()}
valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1])
if valid_weight is None or len(valid_weight) == 0:
valid_weight = valid_class_sample_weight
else:
valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
valid_init_score = _get_meta_data(eval_init_score, 'eval_init_score', i)
valid_group = _get_meta_data(eval_group, 'eval_group', i)
valid_set = _construct_dataset(valid_data[0], valid_data[1],
valid_weight, valid_init_score, valid_group, params)
valid_sets.append(valid_set)
if isinstance(init_model, LGBMModel):
init_model = init_model.booster_
self._Booster = train(params, train_set,
self.n_estimators, valid_sets=valid_sets, valid_names=eval_names,
early_stopping_rounds=early_stopping_rounds,
evals_result=evals_result, fobj=self._fobj, feval=eval_metrics_callable,
verbose_eval=verbose, feature_name=feature_name,
callbacks=callbacks, init_model=init_model)
if evals_result:
self._evals_result = evals_result
if early_stopping_rounds is not None and early_stopping_rounds > 0:
self._best_iteration = self._Booster.best_iteration
self._best_score = self._Booster.best_score
self.fitted_ = True
# free dataset
self._Booster.free_dataset()
del train_set, valid_sets
return self
def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
pred_leaf=False, pred_contrib=False, **kwargs):
"""Return the predicted value for each sample.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
Input features matrix.
raw_score : bool, optional (default=False)
Whether to predict raw scores.
start_iteration : int, optional (default=0)
Start index of the iteration to predict.
If <= 0, starts from the first iteration.
num_iteration : int or None, optional (default=None)
Total number of iterations used in the prediction.
If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
otherwise, all iterations from ``start_iteration`` are used (no limits).
If <= 0, all iterations from ``start_iteration`` are used (no limits).
pred_leaf : bool, optional (default=False)
Whether to predict leaf index.
pred_contrib : bool, optional (default=False)
Whether to predict feature contributions.
.. note::
If you want to get more explanations for your model's predictions using SHAP values,
like SHAP interaction values,
you can install the shap package (https://github.com/slundberg/shap).
Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra
column, where the last column is the expected value.
**kwargs
Other parameters for the prediction.
Returns
-------
predicted_result : array-like of shape = [n_samples] or shape = [n_samples, n_classes]
The predicted values.
X_leaves : array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]
If ``pred_leaf=True``, the predicted leaf of every tree for each sample.
X_SHAP_values : array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects
If ``pred_contrib=True``, the feature contributions for each sample.
"""
if self._n_features is None:
raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.")
if not isinstance(X, (DataFrame, DataTable)):
X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
n_features = X.shape[1]
if self._n_features != n_features:
raise ValueError("Number of features of the model must "
"match the input. Model n_features_ is %s and "
"input n_features is %s "
% (self._n_features, n_features))
return self._Booster.predict(X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration,
pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs)
@property
def n_features_(self):
""":obj:`int`: The number of features of fitted model."""
if self._n_features is None:
raise LGBMNotFittedError('No n_features found. Need to call fit beforehand.')
return self._n_features
@property
def n_features_in_(self):
""":obj:`int`: The number of features of fitted model."""
if self._n_features_in is None:
raise LGBMNotFittedError('No n_features_in found. Need to call fit beforehand.')
return self._n_features_in
@property
def best_score_(self):
""":obj:`dict` or :obj:`None`: The best score of fitted model."""
if self._n_features is None:
raise LGBMNotFittedError('No best_score found. Need to call fit beforehand.')
return self._best_score
@property
def best_iteration_(self):
""":obj:`int` or :obj:`None`: The best iteration of fitted model if ``early_stopping_rounds`` has been specified."""
if self._n_features is None:
raise LGBMNotFittedError('No best_iteration found. Need to call fit with early_stopping_rounds beforehand.')
return self._best_iteration
@property
def objective_(self):
""":obj:`string` or :obj:`callable`: The concrete objective used while fitting this model."""
if self._n_features is None:
raise LGBMNotFittedError('No objective found. Need to call fit beforehand.')
return self._objective
@property
def booster_(self):
"""Booster: The underlying Booster of this model."""
if self._Booster is None:
raise LGBMNotFittedError('No booster found. Need to call fit beforehand.')
return self._Booster
@property
def evals_result_(self):
""":obj:`dict` or :obj:`None`: The evaluation results if ``early_stopping_rounds`` has been specified."""
if self._n_features is None:
raise LGBMNotFittedError('No results found. Need to call fit with eval_set beforehand.')
return self._evals_result
@property
def feature_importances_(self):
""":obj:`array` of shape = [n_features]: The feature importances (the higher, the more important).
.. note::
``importance_type`` attribute is passed to the function
to configure the type of importance values to be extracted.
"""
if self._n_features is None:
raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.')
return self._Booster.feature_importance(importance_type=self.importance_type)
@property
def feature_name_(self):
""":obj:`array` of shape = [n_features]: The names of features."""
if self._n_features is None:
raise LGBMNotFittedError('No feature_name found. Need to call fit beforehand.')
return self._Booster.feature_name()
class LGBMRegressor(LGBMModel, _LGBMRegressorBase):
"""LightGBM regressor."""
def fit(self, X, y,
sample_weight=None, init_score=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_metric=None, early_stopping_rounds=None,
verbose=True, feature_name='auto', categorical_feature='auto',
callbacks=None, init_model=None):
"""Docstring is inherited from the LGBMModel."""
super().fit(X, y, sample_weight=sample_weight, init_score=init_score,
eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score, eval_metric=eval_metric,
early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name,
categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model)
return self
_base_doc = LGBMModel.fit.__doc__
_base_doc = (_base_doc[:_base_doc.find('group :')]
+ _base_doc[_base_doc.find('eval_set :'):])
_base_doc = (_base_doc[:_base_doc.find('eval_class_weight :')]
+ _base_doc[_base_doc.find('eval_init_score :'):])
fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
+ _base_doc[_base_doc.find('eval_metric :'):])
class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
"""LightGBM classifier."""
def fit(self, X, y,
sample_weight=None, init_score=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_class_weight=None, eval_init_score=None, eval_metric=None,
early_stopping_rounds=None, verbose=True,
feature_name='auto', categorical_feature='auto',
callbacks=None, init_model=None):
"""Docstring is inherited from the LGBMModel."""
_LGBMAssertAllFinite(y)
_LGBMCheckClassificationTargets(y)
self._le = _LGBMLabelEncoder().fit(y)
_y = self._le.transform(y)
self._class_map = dict(zip(self._le.classes_, self._le.transform(self._le.classes_)))
if isinstance(self.class_weight, dict):
self._class_weight = {self._class_map[k]: v for k, v in self.class_weight.items()}
self._classes = self._le.classes_
self._n_classes = len(self._classes)
if self._n_classes > 2:
# Switch to using a multiclass objective in the underlying LGBM instance
ova_aliases = {"multiclassova", "multiclass_ova", "ova", "ovr"}
if self._objective not in ova_aliases and not callable(self._objective):
self._objective = "multiclass"
if not callable(eval_metric):
if isinstance(eval_metric, (str, type(None))):
eval_metric = [eval_metric]
if self._n_classes > 2:
for index, metric in enumerate(eval_metric):
if metric in {'logloss', 'binary_logloss'}:
eval_metric[index] = "multi_logloss"
elif metric in {'error', 'binary_error'}:
eval_metric[index] = "multi_error"
else:
for index, metric in enumerate(eval_metric):
if metric in {'logloss', 'multi_logloss'}:
eval_metric[index] = 'binary_logloss'
elif metric in {'error', 'multi_error'}:
eval_metric[index] = 'binary_error'
# do not modify args, as it causes errors in model selection tools
valid_sets = None
if eval_set is not None:
if isinstance(eval_set, tuple):
eval_set = [eval_set]
valid_sets = [None] * len(eval_set)
for i, (valid_x, valid_y) in enumerate(eval_set):
if valid_x is X and valid_y is y:
valid_sets[i] = (valid_x, _y)
else:
valid_sets[i] = (valid_x, self._le.transform(valid_y))
super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets,
eval_names=eval_names, eval_sample_weight=eval_sample_weight,
eval_class_weight=eval_class_weight, eval_init_score=eval_init_score,
eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds,
verbose=verbose, feature_name=feature_name, categorical_feature=categorical_feature,
callbacks=callbacks, init_model=init_model)
return self
_base_doc = LGBMModel.fit.__doc__
_base_doc = (_base_doc[:_base_doc.find('group :')]
+ _base_doc[_base_doc.find('eval_set :'):])
fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
+ _base_doc[_base_doc.find('eval_metric :'):])
def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
pred_leaf=False, pred_contrib=False, **kwargs):
"""Docstring is inherited from the LGBMModel."""
result = self.predict_proba(X, raw_score, start_iteration, num_iteration,
pred_leaf, pred_contrib, **kwargs)
if callable(self._objective) or raw_score or pred_leaf or pred_contrib:
return result
else:
class_index = np.argmax(result, axis=1)
return self._le.inverse_transform(class_index)
predict.__doc__ = LGBMModel.predict.__doc__
def predict_proba(self, X, raw_score=False, start_iteration=0, num_iteration=None,
pred_leaf=False, pred_contrib=False, **kwargs):
"""Return the predicted probability for each class for each sample.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
Input features matrix.
raw_score : bool, optional (default=False)
Whether to predict raw scores.
start_iteration : int, optional (default=0)
Start index of the iteration to predict.
If <= 0, starts from the first iteration.
num_iteration : int or None, optional (default=None)
Total number of iterations used in the prediction.
If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
otherwise, all iterations from ``start_iteration`` are used (no limits).
If <= 0, all iterations from ``start_iteration`` are used (no limits).
pred_leaf : bool, optional (default=False)
Whether to predict leaf index.
pred_contrib : bool, optional (default=False)
Whether to predict feature contributions.
.. note::
If you want to get more explanations for your model's predictions using SHAP values,
like SHAP interaction values,
you can install the shap package (https://github.com/slundberg/shap).
Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra
column, where the last column is the expected value.
**kwargs
Other parameters for the prediction.
Returns
-------
predicted_probability : array-like of shape = [n_samples, n_classes]
The predicted probability for each class for each sample.
X_leaves : array-like of shape = [n_samples, n_trees * n_classes]
If ``pred_leaf=True``, the predicted leaf of every tree for each sample.
X_SHAP_values : array-like of shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects
If ``pred_contrib=True``, the feature contributions for each sample.
"""
result = super().predict(X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs)
if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib):
warnings.warn("Cannot compute class probabilities or labels "
"due to the usage of customized objective function.\n"
"Returning raw scores instead.")
return result
elif self._n_classes > 2 or raw_score or pred_leaf or pred_contrib:
return result
else:
return np.vstack((1. - result, result)).transpose()
@property
def classes_(self):
""":obj:`array` of shape = [n_classes]: The class label array."""
if self._classes is None:
raise LGBMNotFittedError('No classes found. Need to call fit beforehand.')
return self._classes
@property
def n_classes_(self):
""":obj:`int`: The number of classes."""
if self._n_classes is None:
raise LGBMNotFittedError('No classes found. Need to call fit beforehand.')
return self._n_classes
class LGBMRanker(LGBMModel):
"""LightGBM ranker."""
def fit(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_group=None, eval_metric=None,
eval_at=(1, 2, 3, 4, 5), early_stopping_rounds=None, verbose=True,
feature_name='auto', categorical_feature='auto',
callbacks=None, init_model=None):
"""Docstring is inherited from the LGBMModel."""
# check group data
if group is None:
raise ValueError("Should set group for ranking task")
if eval_set is not None:
if eval_group is None:
raise ValueError("Eval_group cannot be None when eval_set is not None")
elif len(eval_group) != len(eval_set):
raise ValueError("Length of eval_group should be equal to eval_set")
elif (isinstance(eval_group, dict)
and any(i not in eval_group or eval_group[i] is None for i in range(len(eval_group)))
or isinstance(eval_group, list)
and any(group is None for group in eval_group)):
raise ValueError("Should set group for all eval datasets for ranking task; "
"if you use dict, the index should start from 0")
self._eval_at = eval_at
super().fit(X, y, sample_weight=sample_weight, init_score=init_score, group=group,
eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score, eval_group=eval_group, eval_metric=eval_metric,
early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name,
categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model)
return self
_base_doc = LGBMModel.fit.__doc__
fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')]
+ _base_doc[_base_doc.find('eval_init_score :'):])
_base_doc = fit.__doc__
_before_early_stop, _early_stop, _after_early_stop = _base_doc.partition('early_stopping_rounds :')
fit.__doc__ = (_before_early_stop
+ 'eval_at : iterable of int, optional (default=(1, 2, 3, 4, 5))\n'
+ ' ' * 12 + 'The evaluation positions of the specified metric.\n'