FuseKernel Classification

For classification FuseKernel fits the fused kernel ridge regression on one-hot targets and reads probabilities off a temperature-calibrated softmax. This example fuses the XGBoost

Open In Colab 

# %%
# 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='YOUR_LICENSE_KEY')

# %%
# Import required modules
import warnings
warnings.filterwarnings("ignore")

import numpy as np
import pandas as pd
from modeva import DataSet, ModelZoo, TestSuite

# %%
# Load and prepare dataset
ds = DataSet()
ds.load(name="TaiwanCredit")
ds.set_random_split()

# %%
# Train FuseKernel
# ----------------------------------------------------------
# For classification FuseKernel fits the fused KRR on one-hot targets and reads
# probabilities off a temperature-calibrated softmax. Binary and multiclass are
# both supported, and the simplex weights are selected on the held-out query by
# accuracy.
from modeva.models import MoFuseKernelClassifier

model = MoFuseKernelClassifier(
    name="FuseKernel-Cls",
    use_xgb=True, use_spectral=True,
    spectral_params={"H": 4, "K": 8, "kernel": "laplace", "epochs": 50},
    interpret_ref_size=400, interpret_n_grid=20,
    random_state=0,
)

mz = ModelZoo(dataset=ds)
mz.add_model(model)
mz.train_all()
mz.leaderboard()

# %%
# Accuracy
# ----------------------------------------------------------
# AUC, accuracy, F1, log-loss and Brier, train vs test.
ts = TestSuite(ds, model)
ts.diagnose_accuracy_table().table

# %%
# Probabilities
# ----------------------------------------------------------
# ``predict_proba`` returns calibrated class probabilities (rows sum to 1).
proba = model.predict_proba(ds.test_x)
pd.DataFrame(proba[:5], columns=[f"P(class={c})" for c in model.classes_])

# %%
# Inherent Interpretation (FANOVA)
# ----------------------------------------------------------
# The same functional-ANOVA decomposition applies to the classification scores.
results = ts.interpret_fi()
results.table

# %%
results = ts.interpret_ei()
results.table

# %%
results = ts.interpret_effects(features="PAY_1")
results.plot(results.get_figure_names()[0])

# %%
results = ts.interpret_local_fi(sample_index=1)
results.table

# %%
# Reliability
# ----------------------------------------------------------
# Conformal prediction sets and coverage for the classifier.
ts.diagnose_reliability().table