使用树的数量进行预测的演示

import os

import numpy as np
from sklearn.datasets import load_svmlight_file

import xgboost as xgb

CURRENT_DIR = os.path.dirname(__file__)
train = os.path.join(CURRENT_DIR, "../data/agaricus.txt.train")
test = os.path.join(CURRENT_DIR, "../data/agaricus.txt.test")


def native_interface():
    # load data in do training
    dtrain = xgb.DMatrix(train + "?format=libsvm")
    dtest = xgb.DMatrix(test + "?format=libsvm")
    param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
    watchlist = [(dtest, "eval"), (dtrain, "train")]
    num_round = 3
    bst = xgb.train(param, dtrain, num_round, watchlist)

    print("start testing prediction from first n trees")
    # predict using first 1 tree
    label = dtest.get_label()
    ypred1 = bst.predict(dtest, iteration_range=(0, 1))
    # by default, we predict using all the trees
    ypred2 = bst.predict(dtest)

    print("error of ypred1=%f" % (np.sum((ypred1 > 0.5) != label) / float(len(label))))
    print("error of ypred2=%f" % (np.sum((ypred2 > 0.5) != label) / float(len(label))))


def sklearn_interface():
    X_train, y_train = load_svmlight_file(train)
    X_test, y_test = load_svmlight_file(test)
    clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1)
    clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
    assert clf.n_classes_ == 2

    print("start testing prediction from first n trees")
    # predict using first 1 tree
    ypred1 = clf.predict(X_test, iteration_range=(0, 1))
    # by default, we predict using all the trees
    ypred2 = clf.predict(X_test)

    print(
        "error of ypred1=%f" % (np.sum((ypred1 > 0.5) != y_test) / float(len(y_test)))
    )
    print(
        "error of ypred2=%f" % (np.sum((ypred2 > 0.5) != y_test) / float(len(y_test)))
    )


if __name__ == "__main__":
    native_interface()
    sklearn_interface()