add type hints to init for LGBMModel (#4420)

python-package/lightgbm/sklearn.py

@@ -2,6 +2,7 @@
 """Scikit-learn wrapper interface for LightGBM."""
 import copy
 from inspect import signature
+from typing import Callable, Dict, Optional, Union
 
 import numpy as np
 
@@ -348,13 +349,30 @@ def __call__(self, preds, dataset):
 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):
+ def __init__(
+ self,
+ boosting_type: str = 'gbdt',
+ num_leaves: int = 31,
+ max_depth: int = -1,
+ learning_rate: float = 0.1,
+ n_estimators: int = 100,
+ subsample_for_bin: int = 200000,
+ objective: Optional[Union[str, Callable]] = None,
+ class_weight: Optional[Union[Dict, str]] = None,
+ min_split_gain: float = 0.,
+ min_child_weight: float = 1e-3,
+ min_child_samples: int = 20,
+ subsample: float = 1.,
+ subsample_freq: int = 0,
+ colsample_bytree: float = 1.,
+ reg_alpha: float = 0.,
+ reg_lambda: float = 0.,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
+ n_jobs: int = -1,
+ silent: bool = True,
+ importance_type: str = 'split',
+ **kwargs
+ ):
  r"""Construct a gradient boosting model.
 
  Parameters