1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""
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Demo for prediction using number of trees
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=========================================
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"""
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import os
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import numpy as np
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from sklearn.datasets import load_svmlight_file
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import xgboost as xgb
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CURRENT_DIR = os.path.dirname(__file__)
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train = os.path.join(CURRENT_DIR, "../data/agaricus.txt.train")
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test = os.path.join(CURRENT_DIR, "../data/agaricus.txt.test")
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def native_interface():
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# load data in do training
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dtrain = xgb.DMatrix(train + "?format=libsvm")
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dtest = xgb.DMatrix(test + "?format=libsvm")
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param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
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watchlist = [(dtest, "eval"), (dtrain, "train")]
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num_round = 3
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bst = xgb.train(param, dtrain, num_round, watchlist)
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print("start testing prediction from first n trees")
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# predict using first 1 tree
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label = dtest.get_label()
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ypred1 = bst.predict(dtest, iteration_range=(0, 1))
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# by default, we predict using all the trees
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ypred2 = bst.predict(dtest)
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print("error of ypred1=%f" % (np.sum((ypred1 > 0.5) != label) / float(len(label))))
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print("error of ypred2=%f" % (np.sum((ypred2 > 0.5) != label) / float(len(label))))
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def sklearn_interface():
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X_train, y_train = load_svmlight_file(train)
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X_test, y_test = load_svmlight_file(test)
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clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1)
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clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
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assert clf.n_classes_ == 2
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print("start testing prediction from first n trees")
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# predict using first 1 tree
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ypred1 = clf.predict(X_test, iteration_range=(0, 1))
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# by default, we predict using all the trees
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ypred2 = clf.predict(X_test)
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print(
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"error of ypred1=%f" % (np.sum((ypred1 > 0.5) != y_test) / float(len(y_test)))
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)
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print(
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"error of ypred2=%f" % (np.sum((ypred2 > 0.5) != y_test) / float(len(y_test)))
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)
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if __name__ == "__main__":
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native_interface()
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sklearn_interface()
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