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Random Subgroups python package

Making predictions with subgroups

random-subgroups is a machine learning package compatible with scikit-learn.

It uses ensembles of weak estimators, as in random forests, for classification and regression tasks. The main difference from the random forests algorithm is that it uses subgroups as estimators.

The subgroup discovery implementation of this package is made on top of the pysubgroup package. It uses many of the features from pysubgroup, but it also extends it with different quality measures (more suitable for prediction) and different search strategies.

Installation:

pip install random-subgroups

Example of the classifier:

from randomsubgroups import RandomSubgroupClassifier
from sklearn import datasets

data = datasets.load_breast_cancer()
y = data.target
X = data.data

sg_classifier = RandomSubgroupClassifier(n_estimators=30)

sg_classifier.fit(X, y)
>>> classifiers_df = sg_classifier.show_models()

Target: 0; Model: Col26>=0.27 AND Col7>=0.06
Target: 0; Model: Col3>=435.17 AND Col6>=0.11
Target: 0; Model: Col20>=18.22 AND Col3>=806.60
Target: 0; Model: Col16>=0.02 AND Col20>=15.87
Target: 0; Model: Col17>=0.01 AND Col20>=17.91
Target: 0; Model: Col20>=17.50 AND Col22>=118.60
Target: 0; Model: Col23>=1004.60 AND Col7>=0.05
Target: 0; Model: Col0>=15.33 AND Col13>=21.73
Target: 0; Model: Col22>=124.16
Target: 0; Model: Col13>=18.88 AND Col3>=716.60
Target: 0; Model: Col12>=1.39 AND Col22>=123.11
Target: 0; Model: Col23>=1030.0 AND Col6>=0.05
Target: 0; Model: Col27>=0.15 AND Col3>=358.90
Target: 0; Model: Col15>=0.01 AND Col23>=883.99
Target: 0; Model: Col0>=10.98 AND Col27>=0.16
Target: 1; Model: Col22<105.0 AND Col27<0.16
Target: 1; Model: Col20<15.53 AND Col26<0.35
Target: 1; Model: Col7<0.05
Target: 1; Model: Col13<42.86 AND Col27<0.12
Target: 1; Model: Col23<771.47 AND Col27<0.12
Target: 1; Model: Col20<17.79 AND Col25<0.20
Target: 1; Model: Col20<15.49 AND Col27<0.15
Target: 1; Model: Col13<29.40 AND Col1<19.98
Target: 1; Model: Col20<15.75 AND Col6<0.08
Target: 1; Model: Col20<15.63 AND Col27<0.20
Target: 1; Model: Col22<104.79 AND Col29<0.10
Target: 1; Model: Col20<14.69 AND Col6<0.12
Target: 1; Model: Col27<0.12 AND Col3<693.70
Target: 1; Model: Col20<16.00 AND Col6<0.09
Target: 1; Model: Col27<0.11 AND Col7<0.06
>>> sg_classifier.show_decision(X[5])

The predicted value is: 0
From a total of 6 estimators.

The subgroups used in the prediction are:

 Predicting target 0
Col0>=10.98 AND Col27>=0.16 ---> 0
Col26>=0.27 AND Col7>=0.06 ---> 0
Col27>=0.15 AND Col3>=358.90 ---> 0
Col3>=435.17 AND Col6>=0.11 ---> 0

 Predicting target 1
Col13<29.40 AND Col1<19.98 ---> 1
Col20<15.63 AND Col27<0.20 ---> 1

The targets of the subgroups used in the prediction have the following distribution:

output

Example of the regressor:

from randomsubgroups import RandomSubgroupRegressor
from sklearn import datasets

data = datasets.load_diabetes()
y = data.target
X = data.data

sg_regressor = RandomSubgroupRegressor(n_estimators=30)

sg_regressor.fit(X, y)
>>> regressors_df = sg_regressor.show_models()

Target: 98.35; Model: Col2<-0.03 AND Col5<0.05
Target: 104.24844720496894; Model: Col6>=-0.01 AND Col7<-0.00
Target: 107.71686746987952; Model: Col6>=-0.02 AND Col7<-0.00
Target: 109.73033707865169; Model: Col3<0.06 AND Col8<-0.01
Target: 191.60625; Model: Col2>=0.00 AND Col3>=-0.03
Target: 192.41304347826087; Model: Col3>=-0.02 AND Col7>=0.03
Target: 199.28795811518324; Model: Col8>=0.01
Target: 202.17094017094018; Model: Col3>=0.04 AND Col4>=-0.05
Target: 206.8709677419355; Model: Col2>=0.02 AND Col7>=-0.04
Target: 211.1290322580645; Model: Col2>=-0.02 AND Col8>=0.01
Target: 212.44036697247705; Model: Col4>=-0.01 AND Col8>=0.03
Target: 212.8655462184874; Model: Col7>=-0.00 AND Col8>=0.02
Target: 213.66935483870967; Model: Col7>=-0.01 AND Col8>=0.03
Target: 216.0079365079365; Model: Col3>=-0.02 AND Col6<-0.01
Target: 218.92233009708738; Model: Col0>=-0.03 AND Col8>=0.03
Target: 219.56435643564356; Model: Col2>=0.02 AND Col7>=-0.00
Target: 220.40740740740742; Model: Col2>=0.02 AND Col6<-0.02
Target: 220.46153846153845; Model: Col3>=-0.04 AND Col8>=0.03
Target: 222.0222222222222; Model: Col8>=0.02 AND Col9>=0.01
Target: 222.92592592592592; Model: Col2>=0.00 AND Col3>=0.03
Target: 224.375; Model: Col6<0.00 AND Col9>=0.02
Target: 224.3939393939394; Model: Col2>=0.02 AND Col7>=-0.00
Target: 224.70833333333334; Model: Col3>=0.02 AND Col8>=0.01
Target: 226.5257731958763; Model: Col2>=-0.00 AND Col8>=0.02
Target: 233.0185185185185; Model: Col2>=0.02 AND Col7>=-0.00
Target: 239.25882352941176; Model: Col2>=0.00 AND Col9>=0.02
Target: 243.9375; Model: Col2>=0.03 AND Col3>=0.01
Target: 247.63492063492063; Model: Col2>=-0.01 AND Col9>=0.05
Target: 248.56756756756758; Model: Col2>=0.03 AND Col9>=0.02
Target: 260.29411764705884; Model: Col2>=0.06 AND Col8>=-0.01
>>> sg_regressor.show_decision(X[0])

The predicted value is: 220.93552644658507
From a total of 12 estimators.

The subgroups used in the prediction are:
Col2>=0.00 AND Col3>=-0.03 ---> 191.60625
Col8>=0.01 ---> 199.28795811518324
Col2>=0.02 AND Col7>=-0.04 ---> 206.8709677419355
Col2>=-0.02 AND Col8>=0.01 ---> 211.1290322580645
Col3>=-0.02 AND Col6<-0.01 ---> 216.0079365079365
Col2>=0.02 AND Col7>=-0.00 ---> 219.56435643564356
Col2>=0.02 AND Col6<-0.02 ---> 220.40740740740742
Col2>=0.02 AND Col7>=-0.00 ---> 224.3939393939394
Col3>=0.02 AND Col8>=0.01 ---> 224.70833333333334
Col2>=0.02 AND Col7>=-0.00 ---> 233.0185185185185
Col2>=0.03 AND Col3>=0.01 ---> 243.9375
Col2>=0.06 AND Col8>=-0.01 ---> 260.29411764705884

The targets of the subgroups used in the prediction have the following distribution:

output