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"""
Demo for prediction using number of trees
=========================================
"""
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()
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