55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import os
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import numpy as np
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import xgboost as xgb
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from sklearn.datasets import load_svmlight_file
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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)
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dtest = xgb.DMatrix(test)
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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, use_label_encoder=False)
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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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