GAMINet Classification

Installation

# To install the required package, use the following command:
# !pip install modeva

Authentication

# To get authentication, use the following command: (To get full access please replace the token to your own token)
# from modeva.utils.authenticate import authenticate
# authenticate(auth_code='eaaa4301-b140-484c-8e93-f9f633c8bacb')

Import required modules

from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoGAMINetClassifier

Load and prepare dataset

ds = DataSet()
ds.load(name="TaiwanCredit")
ds.set_random_split()
ds.set_target("FlagDefault")

ds.scale_numerical(method="minmax")
ds.preprocess()

Train model

model = MoGAMINetClassifier(random_state=0)
model.fit(ds.train_x, ds.train_y.ravel())

MoGAMINetClassifier(device='cpu', name='MoGAMINetClassifier')

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Basic accuracy analysis

ts = TestSuite(ds, model)
results = ts.diagnose_accuracy_table()
results.table

	AUC	ACC	F1	LogLoss	Precision	Recall	Brier
train	0.782959	0.817333	0.451177	0.429718	0.680257	0.337516	0.134997
test	0.782681	0.826500	0.464231	0.420069	0.698142	0.347726	0.130807
GAP	-0.000278	0.009167	0.013054	-0.009649	0.017886	0.010209	-0.004191

Feature importance analysis

results = ts.interpret_fi()
results.plot()

Global effects interpretation

For numerical feature

results = ts.interpret_effects(features="PAY_1")
results.plot()

For categorical feature

results = ts.interpret_effects(features="EDUCATION")
results.plot()

For 2 features

results = ts.interpret_effects(features=("PAY_1", "PAY_2"))
results.plot()

Local feature importance analysis

results = ts.interpret_local_fi(sample_index=1, centered=True)
results.plot()

Another sample in train set

results = ts.interpret_local_ei(dataset='train', sample_index=1)
results.plot()