Local Explainability

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 modeva modules

from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoLGBMClassifier

Load Dataset

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

Train a LGBM model

model = MoLGBMClassifier(verbose=-1)
model.fit(ds.train_x, ds.train_y)

MoLGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
                 importance_type='split', learning_rate=0.1, max_depth=-1,
                 min_child_samples=20, min_child_weight=0.001,
                 min_split_gain=0.0, n_estimators=100, n_jobs=None,
                 num_leaves=31, objective=None, random_state=None,
                 reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,
                 subsample_for_bin=200000, subsample_freq=0, verbose=-1)

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LIME

ts = TestSuite(ds, model)
results = ts.explain_lime(dataset="test", sample_index=0, centered=False, random_state=0)
results.plot()

Baseline-(Kernel) SHAP (a single baseline sample)

results = ts.explain_shap(dataset="test", sample_index=0,
                          baseline_dataset="train", baseline_sample_index=2024, random_state=0)
results.plot()

Baseline-(Kernel) SHAP (a group of baseline samples)

results = ts.explain_shap(dataset="test", sample_index=0,
                          baseline_dataset="train", baseline_sample_size=200, random_state=0)
results.plot()