Understanding the engineered features in Autogluaon. about imodels

[mglowacki100]

I'm not sure if autogluon creates those names, but if you look for quick-fix, you need to one-hot encode categorical variables by yourself:

def dummification(df, col):
  dfz = pd.get_dummies(df[col], prefix=col)
  df = df.drop(columns=[col])
  return pd.concat([df, dfz], axis=1)

...
train_data = train_data.drop(columns='education-num') # education_num is just education encoded by 'ordinal'
categorical = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']

for c in categorical:
  train_data = dummification(train_data, c)

with this:

predictor.print_interpretable_rules(model_name='RuleFit_3')
                                                                                                                                                                          rule  coef
                                                                                                                                                                  capital-gain  0.00
                                                                                                                      capital-gain <= 6571.5 and education_ Prof-school <= 0.5 -0.47
                                                                                                                 capital-gain <= 7073.5 and occupation_ Exec-managerial <= 0.5 -0.44
                                                                                               fnlwgt <= 260314.5 and capital-gain <= 7073.5 and education_ Prof-school <= 0.5 -0.19
                                                                                  capital-gain <= 7268.5 and education_ Bachelors <= 0.5 and occupation_ Prof-specialty <= 0.5 -0.36
                                               capital-gain <= 6571.5 and education_ Bachelors <= 0.5 and occupation_ Prof-specialty <= 0.5 and workclass_ Self-emp-inc <= 0.5 -0.85
                                                                                                                                        age <= 42.5 and capital-gain <= 7073.5 -0.14
                                                                                                                                     age <= 38.5 and education_ Masters <= 0.5 -0.38
                                                                       capital-gain <= 7073.5 and marital-status_ Married-civ-spouse <= 0.5 and workclass_ Self-emp-inc <= 0.5 -0.37
                                                                                             age > 27.5 and marital-status_ Married-civ-spouse > 0.5 and hours-per-week > 38.5  0.86
                                                                                 age <= 62.5 and age > 27.5 and marital-status_ Married-civ-spouse > 0.5 and race_ White > 0.5  0.47
                                                               age > 29.5 and education_ HS-grad <= 0.5 and marital-status_ Married-civ-spouse > 0.5 and hours-per-week > 33.5  0.03
age > 33.5 and education_ 11th <= 0.5 and capital-gain <= 4782.0 and marital-status_ Married-civ-spouse > 0.5 and occupation_ Farming-fishing <= 0.5 and hours-per-week > 37.5  0.07
                                                                                             age > 42.5 and marital-status_ Married-civ-spouse > 0.5 and hours-per-week > 28.5  0.25
              age <= 52.0 and age > 27.5 and fnlwgt > 134350.5 and marital-status_ Married-civ-spouse > 0.5 and hours-per-week > 32.5 and occupation_ Machine-op-inspct <= 0.5  0.64
     fnlwgt > 104201.0 and capital-gain <= 7268.5 and marital-status_ Married-civ-spouse > 0.5 and hours-per-week > 35.5 and workclass_ ? <= 0.5 and workclass_ Private <= 0.5  0.17

where for categorical >0.5 means True, <=0.5 means False

[mglowacki100]

Hi @csinva, I see you're autogluon contributor, so two additional things regarding interpretable:

• GreedyTree, `HiearchicalShrinkageTree' - displays feature_1, feature_2, ... (there is warning: X has feature names but ... was fitted without feature names), I'm not 100% sure but it seems to me that feature_1 is first column in training dataframe, feature_2 second column and so on...
• BoostedRules doesn't display rules

[csinva]

Thanks @mglowacki100! I agee I think one-hot encoding is the best way to go for now.

That feature engineering is performed by autogluon not imodels. There isn't currently support for inverse transforming back to the original features, but we will try and add it soon!

[vinay-k12]

Thought of that but was thinking that this would increase training time hugely. But anyways, I'll run it on limited features.