在 h2O 中使用分类特征时如何获得适当的特征重要性信息

Question

When I have categorical features in my dataset, h20 implies one-hot encoding and start the training process. When I call summary method to see the feature importance tho, it treats each encoded categorical feature as a feature. My question is that how can I get the feature importance information for the original features?

#import libraries
import pandas as pd
import h2o
import random
from h2o.estimators.glm import H2OGeneralizedLinearEstimator

#initiate h20
h2o.init(ip ='localhost')   
h2o.remove_all()  

#load a fake data
training_data = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/glm_test/gamma_dispersion_factor_9_10kRows.csv")


#Spesifcy the predictors (x) and the response (y). I add a dummy categorical column named "0"
myPredictors = ["abs.C1.", "abs.C2.", "abs.C3.", "abs.C4.", "abs.C5.", '0']
myResponse = "resp"

#add a dummy column consisting of random string values
train = h2o.as_list(training_data)
train = pd.concat([train, pd.DataFrame(random.choices(['ya','ne','agh','c','de'],  k=len(training_data)))], axis=1)
train = h2o.H2OFrame(train)


#define linear regression method
def linearRegression(df, predictors, response):
    
    model = H2OGeneralizedLinearEstimator(family="gaussian",  lambda_ = 0, standardize = True)
    model.train(x=predictors, y=response, training_frame=df)
    print(model.summary)

linearRegression(train, myPredictors, myResponse)   

Once I run the model, here's the summary of feature importance reported by h20 .

Variable Importances: 
variable    relative_importance scaled_importance   percentage
0   abs.C5. 1.508031    1.000000    0.257004
1   abs.C4. 1.364653    0.904924    0.232569
2   abs.C3. 1.158184    0.768011    0.197382
3   abs.C2. 0.766653    0.508380    0.130656
4   abs.C1. 0.471997    0.312989    0.080440
5   0.de    0.275667    0.182799    0.046980
6   0.ne    0.210085    0.139311    0.035803
7   0.ya    0.078100    0.051789    0.013310
8   0.c 0.034353    0.022780    0.005855

Is there a method that I'd get the feature importance for column 0 . Note that in real, I have way more categorical feature, this is just a MWE.

Answer 1

As mentioned in the comments, there are several problems with this approach overall, the main of which is that importances are a tricky matter even with standard analysis, and further complicating things is likely to produce misleading results. One-hot encoding is not too problematic with GLMs, but it could be with, say, RFs. With that out of the way...

Given a dataframe (which model.summary seemingly produces):

import pandas as pd

df = pd.DataFrame([
    ['abs.C5.', 1.508031, 1.000000, 0.257004],
    ['abs.C4.', 1.364653, 0.904924, 0.232569],
    ['abs.C3.', 1.158184, 0.768011, 0.197382],
    ['abs.C2.', 0.766653, 0.508380, 0.130656],
    ['abs.C1.', 0.471997, 0.312989, 0.080440],
    ['0.de', 0.275667, 0.182799, 0.046980],
    ['0.ne', 0.210085, 0.139311, 0.035803],
    ['0.ya', 0.078100, 0.051789, 0.013310],
    ['0.c', 0.034353, 0.022780, 0.005855],
], columns=['variable', 'relative_importance', 'scaled_importance', 'percentage'])

df
#   variable    relative_importance scaled_importance   percentage  orig
# 0 abs.C5. 1.508031    1.000000    0.257004    abs
# 1 abs.C4. 1.364653    0.904924    0.232569    abs
# 2 abs.C3. 1.158184    0.768011    0.197382    abs
# 3 abs.C2. 0.766653    0.508380    0.130656    abs
# 4 abs.C1. 0.471997    0.312989    0.080440    abs
# 5 0.de    0.275667    0.182799    0.046980    0
# 6 0.ne    0.210085    0.139311    0.035803    0
# 7 0.ya    0.078100    0.051789    0.013310    0
# 8 0.c 0.034353    0.022780    0.005855    0

You can add an artificial column for groupby and then aggregate (sum):

df['original_variable'] = df['variable'].apply(lambda x: x.split('.')[0])
df.groupby('original_variable')['percentage'].sum()

# orig
# 0      0.101948
# abs    0.898051
# Name: percentage, dtype: float64

Or do it in one line so not to alter the dataframe, to the same effect:

df.groupby(df['variable'].apply(lambda x: x.split('.')[0]))['percentage'].sum()