Wrapping Arbitrary Classifier

This example requires full licence, and the program will break if you use the trial licence.

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

import numpy as np
import pandas as pd
from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoClassifier
from lightgbm import LGBMClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

Scripts to build a model

data = load_breast_cancer()
X, y = data.data, data.target  # Use California housing dataset
train_idx, test_idx = train_test_split(np.arange(data.data.shape[0]),
                                       test_size=0.2, random_state=42)

estimator = LGBMClassifier(verbose=-1)
estimator.fit(X[train_idx], y[train_idx])

LGBMClassifier(verbose=-1)

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Wrap the data into Modeva

ds = DataSet()
ds.load_dataframe(pd.concat([pd.DataFrame(data.data, columns=data.feature_names),
                  pd.DataFrame(data.target, columns=[data.target_names[1]])], axis=1))
ds.set_train_idx(train_idx)
ds.set_test_idx(test_idx)

Wrap the model into Modeva

def predict_proba_func(X):
    # X should be numpy array, and output should be of shape (X.shape[0], 2)
    return estimator.predict_proba(X)

model = MoClassifier(name="LGBM-arbitrary",
                     predict_proba_function=predict_proba_func)

Create test suite for diagnostics

ts = TestSuite(ds, model)

Permutation feature importance

result = ts.explain_pfi()
result.plot()

LIME for local explanation

result = ts.explain_lime(sample_index=0)
result.plot()