1. 引入

特征的重要性，即feature importance，使用sklearn自带的一些模型，就能计算出来。
比如RandomForest取feature_importance的用法如下：

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer, load_iris
data = load_iris()
x_data = data.data
y_data = data.target
print(x_data.shape,y_data.shape)# (150, 4) (150,)
model = RandomForestClassifier()
model.fit(x_data,y_data)
model.feature_importances_ # array([0.09366231, 0.02290373, 0.44489138, 0.43854258])

模型训练后，通过调用模型的属性（feature_importances_），就能得到各个特征的重要性值，值越大说明特征重要性越高。

但有些模型并不自带计算feature_importances的机制，那该如何得到feature_importances呢？

2. LOFO与FLOFO

1. LOFO

LOFO是Leave One Feature Out的缩写，他计算特征重要性的思路是：遍历去掉每一个特征，用留下的特征训练模型，在验证集上评估模型效果，以此来衡量模型的重要性。

用验证集评估模型时，使用KFold的方式，K次训练、预测的过程，就能得到K个评估值，所以LOFO能输出特征重要性的均值与标准差。

如果不输入模型，参考1中LOFO的实现默认用LightGBM来进行评估。

2. FLOFO

FLOFO是Fast LOFO的意思。

LOFO的计算过程，需要循环迭代“移除一个特征，KFold训练评估”的整个过程，比较耗时。FLOFO是为了加速（简化）这个过程的。

FLOFO会使用全特征来训练好一个模型，然后依次循环迭代“对某一个特征值进行随机扰动，使用已经训练好的模型来验证”，这个过程不需要重新训练模型，所以会很快。FLOFO的重要性，就用扰动前的结果减去扰动后的结果。（结果在这里可以是AUC/ACC之类的值，这个扰动前后结果越大，说明特征越重要）

3. LOFO示例代码

下面对sklearn自带的breast_cancer数据集使用LOFO：

1. 导入依赖

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer, load_iris
from sklearn.model_selection import KFold
from lofo import LOFOImportance, Dataset, plot_importance

这里导入模型、KFold，和LOFO。要注意LOFO中大量使用dataframe，所以还要导入pandas。

2. 导入数据集

导入breast_cancer数据集，要注意需要将数据转换为dataframe格式。

data = load_breast_cancer(as_frame=True)# load as dataframe
df = data.data
df['target']=data.target.values
print(df.shape)# (569, 31)

breast_cancer是二分类数据集，即target中只有0和1两个数值。

3. 将数据集转换为LOFO的Dataset格式

必须要将dataframe的数据集包装为Dataset结构，才能调用LOFO的相关接口。

dataset = Dataset(df=df, target="target", features=[col for col in df.columns if col != 'target'])

参数 target 是df中表示y值的列名，features是df中的特征名list。

4. 获取任意模型对应的feature_importance

这里以 RandomForestClassifier 为例

model = RandomForestClassifier()
cv = KFold(n_splits=5, shuffle=True, random_state=666)
lofo_imp = LOFOImportance(dataset, cv=cv, scoring="f1",model=model)
importance_df = lofo_imp.get_importance()

以5Fold为例来对模型做验证，所以最终每个特征能得到5个importance值。importance_df的值如下

	feature	importance_mean	importance_std	val_imp_0	val_imp_1	val_imp_2	val_imp_3	val_imp_4
26	area error	0.0104953	0.0151496	0.0263158	0.0175439	0.0175439	0.00877193	-0.0176991
23	worst perimeter	0.00878746	0.0124054	0.0175439	0	-0.00877193	0.0263158	0.00884956
29	mean smoothness	0.00704859	0.00863287	-0.00877193	0.00877193	0.00877193	0.00877193	0.0176991
24	mean texture	0.00704859	0.0170287	-0.00877193	0.0350877	0	-0.00877193	0.0176991
1	mean radius	0.00527868	0.00702537	0	0.0175439	0	0	0.00884956
16	mean compactness	0.00355535	0.0143273	0	0	0.00877193	-0.0175439	0.0265487
4	perimeter error	0.0035243	0.0105341	-0.00877193	0.0175439	-0.00877193	0.00877193	0.00884956
9	worst area	0.00175439	0.00656431	0.00877193	0.00877193	0	-0.00877193	0
11	mean symmetry	0.00175439	0.00656431	0	0.00877193	0.00877193	-0.00877193	0
3	worst fractal dimension	0.00175439	0.00350877	0	0	0	0.00877193	0
22	worst radius	0.00175439	0.0085947	-0.00877193	0.0175439	0	0	0
8	radius error	0.00173886	0.00861375	-0.00877193	0.00877193	0.00877193	0.00877193	-0.00884956
17	texture error	3.10511e-05	0.0124494	-0.00877193	0	0.00877193	-0.0175439	0.0176991
19	mean concavity	1.55255e-05	0.00962338	0	0.00877193	0	-0.0175439	0.00884956
14	fractal dimension error	0	0	0	0	0	0	0
2	mean concave points	-1.55255e-05	0.00962338	0.0175439	0	-0.00877193	0	-0.00884956
7	worst concavity	-4.65766e-05	0.0147618	0	0.0175439	0.00877193	0	-0.0265487
10	concavity error	-0.00173886	0.00658923	0	0	-0.00877193	-0.00877193	0.00884956
0	mean fractal dimension	-0.00175439	0.00350877	0	0	0	-0.00877193	0
13	worst compactness	-0.00175439	0.00350877	-0.00877193	0	0	0	0
28	mean perimeter	-0.00350877	0.00701754	0	0	0	-0.0175439	0
12	smoothness error	-0.0035243	0.00431644	0	0	-0.00877193	0	-0.00884956
27	mean area	-0.00703307	0.00656853	-0.00877193	0	0	-0.0175439	-0.00884956
18	compactness error	-0.00880298	0.00788074	-0.00877193	0	-0.0175439	0	-0.0176991

5. 画出feature_importance图

LOFO自带了画图的接口，可以直接对importance_df做可视化

plot_importance(importance_df, figsize=(12, 20))

这样就能得到feature importanc的排序输出结果：

6. 多分类数据集测试

测试了iris数据集，发现程序可以正常运行，但importance_df中的值都为NaN，哪怕把scoring="f1"改为scoring="f1_macro"后就正常了。

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer, load_iris
from sklearn.model_selection import KFold
from lofo import LOFOImportance, Dataset, plot_importancedata = load_iris(as_frame=True)# load as dataframe
df = data.data
df['target']=data.target.values# model
model = RandomForestClassifier()
# dataset
dataset = Dataset(df=df, target="target", features=[col for col in df.columns if col != 'target'])
# get feature importance
cv = KFold(n_splits=5, shuffle=True, random_state=666)
lofo_imp = LOFOImportance(dataset, cv=cv, scoring="f1_macro",model=model)
importance_df = lofo_imp.get_importance()
print(importance_df)

7. 最终代码

综合上述过程，得到直接能运行的代码如下：

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer, load_iris
from sklearn.model_selection import KFold
from lofo import LOFOImportance, Dataset, plot_importancedata = load_breast_cancer(as_frame=True)# load as dataframe
df = data.data
df['target']=data.target.values# model
model = RandomForestClassifier()
# dataset
dataset = Dataset(df=df, target="target", features=[col for col in df.columns if col != 'target'])
# get feature importance
cv = KFold(n_splits=5, shuffle=True, random_state=666)
lofo_imp = LOFOImportance(dataset, cv=cv, scoring="f1",model=model)
importance_df = lofo_imp.get_importance()
print(importance_df)

4. FLOFO示例代码

Fast LOFO直接调用FLOFOImportance即可，参考代码如下：

from lofo import FLOFOImportance
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer, load_iris
from sklearn.model_selection import KFold
from lofo import LOFOImportance, Dataset, plot_importance
# step-01: prepare data
data = load_breast_cancer(as_frame=True)# load as dataframe
x_data = data.data.to_numpy()
y_data = data.target.values
df = data.data
df['target']=data.target.values
# repeat more data since FLOFO need > 1000 data
df=pd.DataFrame(pd.np.repeat(df.values,2,axis=0),columns=df.columns)
# step-02: train model
model = RandomForestClassifier()
model.fit(x_data,y_data)
# step-03: fast-lofo
lofo_imp = FLOFOImportance(validation_df=df, target="target", features=[col for col in df.columns if col != 'target'],scoring="f1",trained_model=model)
importance_df = lofo_imp.get_importance()
print(importance_df)

FLOFOImportance与LOFOImportance的几点区别：

1. FLOFOImportance不在需要将数据包装为Dataset结构
2. FLOFOImportance需要先训练模型，再调用FLOFO
3. FLOFOImportance接口使用与LOFO稍有不同

结论

1. RandomForest能直接对多分类数据计算feature_importances_
2. LOFO默认使用LightGBM来计算得到feature_importances_
3. 参考1中的LOFO库支持多分类数据集，但需要把scoring="f1"改为scoring="f1_macro"等支持多类别的评估准则
4. FLOFO（Fast LOFO）比LOFO运行更快

参考

1. https://github.com/aerdem4/lofo-importance
2. https://juejin.cn/post/7020237735516438564