ML：LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读
ML：LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略目录
LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略
LGBMClassifier
XGBClassifier
CatBoostClassifier
LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略LGBMClassifierLGBMClassifier.feature_importances_函数，采用split方式计算
LGBMC.feature_importances_
importance_type='split',
def feature_importances_(self):
"""Get feature importances.
Note
----
Feature importance in sklearn interface used to normalize to 1,it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now.
``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 booster_(self):
"""Get the underlying lightgbm Booster of this model."""
if self._Booster is None:
raise LGBMNotFittedError('No booster found. Need to call fit beforehand.')
return self._Booster
def num_feature(self):
"""Get number of features.
Returns
-------
num_feature : int
The number of features.
"""
out_num_feature = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumFeature(
self.handle,
ctypes.byref(out_num_feature)))
return out_num_feature.value
self.booster_.feature_importance
(importance_type=
self.importance_type)
def feature_importance(self, importance_type='split', iteration=None):
"""Get feature importances.
Parameters
----------
importance_type : string, optional (default="split"). How the importance is calculated.字符串，可选（默认值=“split”）。如何计算重要性。
If "split", result contains numbers of times the feature is used in a model. 如果“split”，则结果包含该特征在模型中使用的次数。
If "gain", result contains total gains of splits which use the feature.如果“gain”，则结果包含使用该特征的拆分的总增益。
iteration : int or None, optional (default=None).Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used.If
Returns
-------
result : numpy array
Array with feature importances.
"""
if iteration is None:
iteration = self.best_iteration
if importance_type == "split":
importance_type_int = 0
elif importance_type == "gain":
importance_type_int = 1
else:
importance_type_int = -1
result = np.zeros(self.num_feature(), dtype=np.float64)
_safe_call(_LIB.LGBM_BoosterFeatureImportance(
self.handle,
ctypes.c_int(iteration),
ctypes.c_int(importance_type_int),
result.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
if importance_type_int == 0:
return result.astype(int)
else:
return result
XGBClassifierXGBClassifier.feature_importances_函数，采用weight方式计算
XGBC.
feature_importances_
importance_type="weight" # 默认 gain、weight、cover、total_gain、total_cover
def feature_importances_(self):
"""
Feature importances property
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such as linear learners .仅当选择决策树模型作为基础学习者（`booster=gbtree`）时，才定义特征重要性。它不适用于其他基本学习者类型，例如线性学习者(`booster=gblinear`).
Returns
-------
feature_importances_ : array of shape ````
"""
if getattr(self, 'booster', None) is not None and self.booster != 'gbtree':
raise AttributeError('Feature importance is not defined for Booster type {}'
.format(self.booster))
b = self.get_booster()
score = b.get_score(importance_type=self.importance_type)
all_features =
all_features = np.array(all_features, dtype=np.float32)
return all_features / all_features.sum()
get_score
def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.一个特征用于在所有树上分割数据的次数。* 'gain': the average gain across all splits the feature is used in.使用该特征的所有拆分的平均增益。* 'cover': the average coverage across all splits the feature is used in.使用该特征的所有拆分的平均覆盖率。* 'total_gain': the total gain across all splits the feature is used in.该特征在所有分割中使用的总增益。* 'total_cover': the total coverage across all splits the feature is used in.使用该特征的所有拆分的总覆盖率。 .. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such as linear learners (`booster=gblinear`).
Parameters
----------
fmap: str (optional)
The name of feature map file.
importance_type: str, default 'weight'
One of the importance types defined above.
"""
if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}: raise ValueError('Feature importance is not defined for Booster type {}' .format(self.booster))
allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover']
if importance_type not in allowed_importance_types: msg = ("importance_type mismatch, got '{}', expected one of " + repr(allowed_importance_types))
raise ValueError(msg.format(importance_type))
# if it's weight, then omap stores the number of missing values
if importance_type == 'weight':
# do a simpler tree dump to save time
trees = self.get_dump(fmap, with_stats=False)
fmap = {}
for tree in trees:
for line in tree.split('\n'):
# look for the opening square bracket
arr = line.split('[')
# if no opening bracket (leaf node), ignore this line
if len(arr) == 1:
continue
# extract feature name from string between []
fid = arr.split(']').split('
if fid not in fmap:
# if the feature hasn't been seen yet
fmap = 1
else:
fmap += 1
return fmap
else:
average_over_splits = True
if importance_type == 'total_gain':
importance_type = 'gain'
average_over_splits = False
elif importance_type == 'total_cover':
importance_type = 'cover'
average_over_splits = False
trees = self.get_dump(fmap, with_stats=True)
importance_type += '='
fmap = {}
gmap = {}
for tree in trees:
for line in tree.split('\n'):
# look for the opening square bracket
arr = line.split('[')
# if no opening bracket (leaf node), ignore this line
if len(arr) == 1:
continue
# look for the closing bracket, extract only info within that bracket
fid = arr.split(']')
# extract gain or cover from string after closing bracket
g = float(fid.split(importance_type).split(','))
# extract feature name from string before closing bracket
fid = fid.split('
if fid not in fmap:
# if the feature hasn't been seen yet
fmap = 1
gmap = g
else:
fmap += 1
gmap += g
# calculate average value (gain/cover) for each feature
if average_over_splits:
for fid in gmap:
gmap = gmap / fmap
return gmap
CatBoostClassifierCatBoostClassifier.feature_importances_函数，采用is_groupwise_metric(loss)方式计算
CatC.feature_importances_
def feature_importances_(self):
loss = self._object._get_loss_function_name()
if loss and is_groupwise_metric(loss):
return np.array(getattr(self, "_loss_value_change", None))
else:
return np.array(getattr(self, "_prediction_values_change", None))
CatBoost简单地利用了在正常情况下(当我们包括特征时)使用模型获得的度量(损失函数)与不使用该特征的模型(模型建立大约与此功能从所有的树在合奏)。差别越大，特征就越重要。
https://www.yilongzhijia.cn/tupian/seo365t.jpg
页:
[1]