使用 sklearn 接口的示例集合

要了解XGBoost的scikit-learn估计器接口的介绍，请参阅 使用 Scikit-Learn 估计器接口。

创建于 2015年4月1日

@作者: Jamie Hall

import pickle

import numpy as np
from sklearn.datasets import fetch_california_housing, load_digits, load_iris
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.model_selection import GridSearchCV, KFold, train_test_split

import xgboost as xgb

rng = np.random.RandomState(31337)

print("Zeros and Ones from the Digits dataset: binary classification")
digits = load_digits(n_class=2)
y = digits["target"]
X = digits["data"]
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
    xgb_model = xgb.XGBClassifier(n_jobs=1).fit(X[train_index], y[train_index])
    predictions = xgb_model.predict(X[test_index])
    actuals = y[test_index]
    print(confusion_matrix(actuals, predictions))

print("Iris: multiclass classification")
iris = load_iris()
y = iris["target"]
X = iris["data"]
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
    xgb_model = xgb.XGBClassifier(n_jobs=1).fit(X[train_index], y[train_index])
    predictions = xgb_model.predict(X[test_index])
    actuals = y[test_index]
    print(confusion_matrix(actuals, predictions))

print("California Housing: regression")
X, y = fetch_california_housing(return_X_y=True)
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
    xgb_model = xgb.XGBRegressor(n_jobs=1).fit(X[train_index], y[train_index])
    predictions = xgb_model.predict(X[test_index])
    actuals = y[test_index]
    print(mean_squared_error(actuals, predictions))

print("Parameter optimization")
xgb_model = xgb.XGBRegressor(n_jobs=1)
clf = GridSearchCV(
    xgb_model,
    {"max_depth": [2, 4], "n_estimators": [50, 100]},
    verbose=1,
    n_jobs=1,
    cv=3,
)
clf.fit(X, y)
print(clf.best_score_)
print(clf.best_params_)

# The sklearn API models are picklable
print("Pickling sklearn API models")
# must open in binary format to pickle
pickle.dump(clf, open("best_calif.pkl", "wb"))
clf2 = pickle.load(open("best_calif.pkl", "rb"))
print(np.allclose(clf.predict(X), clf2.predict(X)))

# Early-stopping

X = digits["data"]
y = digits["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = xgb.XGBClassifier(n_jobs=1, early_stopping_rounds=10, eval_metric="auc")
clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])