75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
"""
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Getting started with XGBoost
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============================
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This is a simple example of using the native XGBoost interface, there are other
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interfaces in the Python package like scikit-learn interface and Dask interface.
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See :doc:`/python/python_intro` and :doc:`/tutorials/index` for other references.
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"""
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import os
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import pickle
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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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# Make sure the demo knows where to load the data.
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CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
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XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
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DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, "demo")
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# X is a scipy csr matrix, XGBoost supports many other input types,
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X, y = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.train"))
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dtrain = xgb.DMatrix(X, y)
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# validation set
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X_test, y_test = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.test"))
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dtest = xgb.DMatrix(X_test, y_test)
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# specify parameters via map, definition are same as c++ version
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param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
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# specify validations set to watch performance
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watchlist = [(dtest, "eval"), (dtrain, "train")]
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# number of boosting rounds
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num_round = 2
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bst = xgb.train(param, dtrain, num_boost_round=num_round, evals=watchlist)
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# run prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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print(
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"error=%f"
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% (
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sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i])
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/ float(len(preds))
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)
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)
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bst.save_model("model-0.json")
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# dump model
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bst.dump_model("dump.raw.txt")
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# dump model with feature map
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bst.dump_model("dump.nice.txt", os.path.join(DEMO_DIR, "data/featmap.txt"))
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# save dmatrix into binary buffer
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dtest.save_binary("dtest.dmatrix")
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# save model
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bst.save_model("model-1.json")
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# load model and data in
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bst2 = xgb.Booster(model_file="model-1.json")
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dtest2 = xgb.DMatrix("dtest.dmatrix")
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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# alternatively, you can pickle the booster
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pks = pickle.dumps(bst2)
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# load model and data in
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bst3 = pickle.loads(pks)
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preds3 = bst3.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds3 - preds)) == 0
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