H-statistics

Experiment initialization and data preparation

from piml import Experiment
from piml.models import XGB2Regressor

exp = Experiment()
exp.data_loader(data="Friedman", silent=True)
exp.data_summary(silent=True)
exp.data_prepare(target="target", task_type="regression", silent=True)

Train Model

exp.model_train(model=XGB2Regressor(n_estimators=100), name="XGB2")

Run H-statistics with 2000 subsampled data and grid size of 5

res = exp.model_explain(model="XGB2", show="hstats", sample_size=2000, grid_size=5,
                        return_data=True, figsize=(5, 4))
res.data

	Feature 1	Feature 2	Importance
0	X0	X1	8.354665e-02
1	X0	X3	5.772886e-03
2	X3	X4	4.769194e-03
3	X1	X4	4.488876e-03
4	X1	X3	3.939141e-03
5	X2	X4	2.891201e-03
6	X0	X4	2.615382e-03
7	X2	X3	1.110027e-03
8	X1	X2	9.062784e-04
9	X0	X2	4.224594e-04
10	X4	X7	4.187721e-04
11	X6	X9	2.826716e-04
12	X1	X6	2.798646e-04
13	X1	X9	2.139691e-04
14	X0	X9	1.499676e-04
15	X2	X9	1.367038e-04
16	X3	X9	1.256837e-04
17	X0	X6	1.022405e-04
18	X3	X6	1.017541e-04
19	X2	X5	3.553405e-06
20	X4	X6	2.510080e-06
21	X1	X5	2.003126e-06
22	X2	X6	2.001398e-06
23	X0	X8	9.355216e-07
24	X1	X8	8.842721e-07
25	X7	X9	3.703580e-07
26	X2	X8	3.405027e-07
27	X4	X8	2.302398e-07
28	X0	X7	2.020537e-07
29	X5	X8	6.266068e-08
30	X5	X7	4.271688e-08
31	X0	X5	3.382035e-09
32	X7	X8	2.910548e-09
33	X5	X6	1.166214e-09
34	X6	X7	5.757503e-10
35	X6	X8	4.158681e-10
36	X5	X9	2.689033e-10
37	X8	X9	2.289872e-10
38	X4	X5	1.034555e-12
39	X4	X9	5.418748e-13
40	X2	X7	1.989873e-13
41	X3	X8	1.310739e-13
42	X3	X5	1.203739e-13
43	X1	X7	7.804507e-14
44	X3	X7	5.885018e-14

Estimated memory usage: 11 MB