合并 EBMs

在本笔记本中，我们将拟合多个可解释增强模型 (EBM)，然后将它们合并到一个 EBM 模型中。合并后的 EBM 模型保留了组成 EBM 模型的透明箱可解释性，使我们能够查看合并模型的全局和局部解释。

本笔记本可在 GitHub 上的 示例文件夹 中找到。

# install interpret if not already installed
try:
    import interpret
except ModuleNotFoundError:
    !pip install --quiet interpret pandas scikit-learn

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from interpret import show

from interpret import set_visualize_provider
from interpret.provider import InlineProvider
set_visualize_provider(InlineProvider())

df = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
    header=None)
df.columns = [
    "Age", "WorkClass", "fnlwgt", "Education", "EducationNum",
    "MaritalStatus", "Occupation", "Relationship", "Race", "Gender",
    "CapitalGain", "CapitalLoss", "HoursPerWeek", "NativeCountry", "Income"
]
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

seed = 42
np.random.seed(seed)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)

探索数据集

from interpret.data import ClassHistogram

hist = ClassHistogram().explain_data(X_train, y_train, name = 'Train Data')
show(hist)

训练多个 EBM 模型

from interpret.glassbox import ExplainableBoostingClassifier

# Fitting multiple EBM models with different training datasets and random seeds
seed = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)
ebm1 = ExplainableBoostingClassifier(random_state=seed)
ebm1.fit(X_train, y_train)  

seed +=10
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)
ebm2 = ExplainableBoostingClassifier(random_state=seed)
ebm2.fit(X_train, y_train)  

seed +=10
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)
ebm3 = ExplainableBoostingClassifier(random_state=seed)
ebm3.fit(X_train, y_train)

ExplainableBoostingClassifier(random_state=21)

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show(ebm1.explain_global(name='EBM 1'))

合并多个已训练的 EBM 模型

from interpret.glassbox import merge_ebms

merged_ebm = merge_ebms([ebm1, ebm2 , ebm3])
show(merged_ebm.explain_global(name='Merged EBM'))