From bcd66fc448d1160a6289a3696bd6070e9b66a9b0 Mon Sep 17 00:00:00 2001
From: Y-oHr-N <kon.y.ohr.n@gmail.com>
Date: Mon, 9 Mar 2020 03:36:53 +0900
Subject: [PATCH] Use parse_version

---
 optgbm/sklearn.py | 34 ++++++++++++++++++++--------------
 1 file changed, 20 insertions(+), 14 deletions(-)

diff --git a/optgbm/sklearn.py b/optgbm/sklearn.py
index 686b3de..821f7fc 100644
--- a/optgbm/sklearn.py
+++ b/optgbm/sklearn.py
@@ -4,6 +4,7 @@
 import logging
 import time
 
+from pkg_resources import parse_version
 from typing import Any
 from typing import Callable
 from typing import Dict
@@ -14,24 +15,15 @@
 import lightgbm as lgb
 import numpy as np
 import optuna
+import sklearn
 
 from sklearn.base import ClassifierMixin
 from sklearn.base import RegressorMixin
 from sklearn.model_selection import BaseCrossValidator
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import check_random_state
-from sklearn.utils import safe_indexing
 from sklearn.utils.validation import check_is_fitted
 
-try:  # lightgbm<=2.2.3
-    from lightgbm.sklearn import _eval_function_wrapper as _EvalFunctionWrapper
-    from lightgbm.sklearn import (
-        _objective_function_wrapper as _ObjectiveFunctionWrapper,
-    )
-except ImportError:
-    from lightgbm.sklearn import _EvalFunctionWrapper
-    from lightgbm.sklearn import _ObjectiveFunctionWrapper
-
 from .utils import check_cv
 from .utils import check_fit_params
 from .utils import check_X
@@ -40,6 +32,20 @@
 from .utils import RANDOM_STATE_TYPE
 from .utils import TWO_DIM_ARRAYLIKE_TYPE
 
+if parse_version(lgb.__version__) >= parse_version("2.3"):
+    from lightgbm.sklearn import _EvalFunctionWrapper
+    from lightgbm.sklearn import _ObjectiveFunctionWrapper
+else:
+    from lightgbm.sklearn import _eval_function_wrapper as _EvalFunctionWrapper
+    from lightgbm.sklearn import (
+        _objective_function_wrapper as _ObjectiveFunctionWrapper,
+    )
+
+if parse_version(sklearn.__version__) >= parse_version("0.22"):
+    from sklearn.utils import _safe_indexing as safe_indexing
+else:
+    from sklearn.utils import safe_indexing
+
 MAX_INT = np.iinfo(np.int32).max
 
 OBJECTIVE2METRIC = {
@@ -240,14 +246,14 @@ def __init__(
     def from_representations(
         cls, representations: List[str], weights: Optional[np.ndarray] = None
     ) -> "_VotingBooster":
-        try:  # lightgbm<=2.2.3
+        if parse_version(lgb.__version__) >= parse_version("2.3"):
             boosters = [
-                lgb.Booster(params={"model_str": model_str})
+                lgb.Booster(model_str=model_str, silent=True)
                 for model_str in representations
             ]
-        except TypeError:
+        else:
             boosters = [
-                lgb.Booster(model_str=model_str, silent=True)
+                lgb.Booster(params={"model_str": model_str})
                 for model_str in representations
             ]