from sklearn.datasets import make_hastie_10_2
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.inspection import PartialDependenceDisplay
import matplotlib.pyplot as plt

# Создание набора данных
X, y = make_hastie_10_2(random_state=0)

# Обучение модели
clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0).fit(X, y)

# Определение признаков для PDP
features = [0, 1, (0, 1)]

# Создание PDP
pdp_display = PartialDependenceDisplay.from_estimator(clf, X, features)

# Показать PDP
plt.show()

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from alibi.explainers import ALE, plot_ale

# Загрузка и подготовка данных
data = fetch_california_housing()
X = data.data
y = data.target
feature_names = data.feature_names

# Разделение данных на обучающую и тестовую выборки
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Обучение моделей
lr = LinearRegression()
rf = RandomForestRegressor(n_estimators=100, random_state=42)
lr.fit(X_train, y_train)
rf.fit(X_train, y_train)

# Вычисление ALE для линейной регрессии
lr_ale = ALE(lr.predict, feature_names=feature_names, target_names=['Value in $100,000's'])
lr_exp = lr_ale.explain(X_train)

# Вычисление ALE для случайного леса
rf_ale = ALE(rf.predict, feature_names=feature_names, target_names=['Value in $100,000's'])
rf_exp = rf_ale.explain(X_train)

# Визуализация ALE
plot_ale(lr_exp, feature_names=feature_names)
plot_ale(rf_exp, feature_names=feature_names)