a1bcd33a3b
* Use typed array for models. * Change the memory snapshot format. * Add new C API for saving to raw format.
66 lines
1.7 KiB
Python
66 lines
1.7 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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import json
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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) -> str:
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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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config_0 = bst.save_config()
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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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config_1 = bst.save_config()
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assert config_0 == config_1
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return json.loads(config_0)
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def test_model_pickling_json(self):
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def check(config):
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updater = config["learner"]["gradient_booster"]["updater"]
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if params["tree_method"] == "exact":
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subsample = updater["grow_colmaker"]["train_param"]["subsample"]
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else:
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subsample = updater["grow_quantile_histmaker"]["train_param"]["subsample"]
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assert float(subsample) == 0.5
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params = {"nthread": 8, "tree_method": "hist", "subsample": 0.5}
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config = self.run_model_pickling(params)
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check(config)
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params = {"nthread": 8, "tree_method": "exact", "subsample": 0.5}
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config = self.run_model_pickling(params)
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check(config)
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