77d17f6264
* Add support for Gamma regression * Use base_score to replace the lp_bias * Remove the lp_bias config block * Add a demo for running gamma regression in Python * Typo fix * Revise the description for objective * Add a script to generate the autoclaims dataset
26 lines
1.0 KiB
Python
Executable File
26 lines
1.0 KiB
Python
Executable File
#!/usr/bin/python
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import xgboost as xgb
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import numpy as np
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# this script demonstrates how to fit gamma regression model (with log link function)
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# in xgboost, before running the demo you need to generate the autoclaims dataset
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# by running gen_autoclaims.R located in xgboost/demo/data.
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data = np.genfromtxt('../data/autoclaims.csv', delimiter=',')
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dtrain = xgb.DMatrix(data[0:4741, 0:34], data[0:4741, 34])
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dtest = xgb.DMatrix(data[4741:6773, 0:34], data[4741:6773, 34])
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# for gamma regression, we need to set the objective to 'reg:gamma', it also suggests
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# to set the base_score to a value between 1 to 5 if the number of iteration is small
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param = {'silent':1, 'objective':'reg:gamma', 'booster':'gbtree', 'base_score':3}
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# the rest of settings are the same
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 30
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# training and evaluation
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bst = xgb.train(param, dtrain, num_round, watchlist)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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print ('test deviance=%f' % (2 * np.sum((labels - preds) / preds - np.log(labels) + np.log(preds))))
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