51 lines
1.1 KiB
Python
51 lines
1.1 KiB
Python
import pickle
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import numpy as np
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import xgboost as xgb
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import os
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kRows = 100
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kCols = 10
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def generate_data():
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X = np.random.randn(kRows, kCols)
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y = np.random.randn(kRows)
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return X, y
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class TestPickling:
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def run_model_pickling(self, xgb_params):
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X, y = generate_data()
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dtrain = xgb.DMatrix(X, y)
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bst = xgb.train(xgb_params, dtrain)
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dump_0 = bst.get_dump(dump_format='json')
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assert dump_0
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filename = 'model.pkl'
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with open(filename, 'wb') as fd:
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pickle.dump(bst, fd)
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with open(filename, 'rb') as fd:
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bst = pickle.load(fd)
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with open(filename, 'wb') as fd:
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pickle.dump(bst, fd)
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with open(filename, 'rb') as fd:
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bst = pickle.load(fd)
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assert bst.get_dump(dump_format='json') == dump_0
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if os.path.exists(filename):
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os.remove(filename)
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def test_model_pickling_json(self):
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params = {
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'nthread': 1,
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'tree_method': 'hist',
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}
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self.run_model_pickling(params)
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