4d81c741e9
* Generate column matrix from gHistIndex. * Avoid synchronization with the sparse page once the cache is written. * Cleanups: Remove member variables/functions, change the update routine to look like approx and gpu_hist. * Remove pruner.
52 lines
1.2 KiB
Python
52 lines
1.2 KiB
Python
'''
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Demo for using feature weight to change column sampling
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=======================================================
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.. versionadded:: 1.3.0
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'''
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import numpy as np
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import xgboost
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from matplotlib import pyplot as plt
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import argparse
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def main(args):
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rng = np.random.RandomState(1994)
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kRows = 1000
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kCols = 10
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X = rng.randn(kRows, kCols)
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y = rng.randn(kRows)
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fw = np.ones(shape=(kCols,))
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for i in range(kCols):
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fw[i] *= float(i)
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dtrain = xgboost.DMatrix(X, y)
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dtrain.set_info(feature_weights=fw)
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bst = xgboost.train({'tree_method': 'hist',
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'colsample_bynode': 0.2},
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dtrain, num_boost_round=10,
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evals=[(dtrain, 'd')])
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feature_map = bst.get_fscore()
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# feature zero has 0 weight
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assert feature_map.get('f0', None) is None
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assert max(feature_map.values()) == feature_map.get('f9')
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if args.plot:
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xgboost.plot_importance(bst)
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plt.show()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--plot',
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type=int,
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default=1,
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help='Set to 0 to disable plotting the evaluation history.')
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args = parser.parse_args()
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main(args)
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