113 lines
4.3 KiB
Python
Executable File
113 lines
4.3 KiB
Python
Executable File
#!/usr/bin/python
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import sys
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import numpy as np
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import scipy.sparse
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# append the path to xgboost, you may need to change the following line
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# alternatively, you can add the path to PYTHONPATH environment variable
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sys.path.append('../')
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import xgboost as xgb
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### simple example
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# load file from text file, also binary buffer generated by xgboost
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dtrain = xgb.DMatrix('agaricus.txt.train')
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dtest = xgb.DMatrix('agaricus.txt.test')
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# specify parameters via map, definition are same as c++ version
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param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
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# specify validations set to watch performance
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evallist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, evallist)
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# this is prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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print ('error=%f' % ( sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds))))
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bst.save_model('0001.model')
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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','featmap.txt')
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###
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# build dmatrix from scipy.sparse
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print ('start running example of build DMatrix from scipy.sparse')
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labels = []
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row = []; col = []; dat = []
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i = 0
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for l in open('agaricus.txt.train'):
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arr = l.split()
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labels.append( int(arr[0]))
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for it in arr[1:]:
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k,v = it.split(':')
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row.append(i); col.append(int(k)); dat.append(float(v))
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i += 1
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csr = scipy.sparse.csr_matrix( (dat, (row,col)) )
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dtrain = xgb.DMatrix( csr )
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dtrain.set_label(labels)
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evallist = [(dtest,'eval'), (dtrain,'train')]
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bst = xgb.train( param, dtrain, num_round, evallist )
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print ('start running example of build DMatrix from numpy array')
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# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation,then convert to DMatrix
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npymat = csr.todense()
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dtrain = xgb.DMatrix( npymat)
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dtrain.set_label(labels)
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evallist = [(dtest,'eval'), (dtrain,'train')]
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bst = xgb.train( param, dtrain, num_round, evallist )
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###
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# advanced: cutomsized loss function
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#
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print ('start running example to used cutomized objective function')
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# note: for customized objective function, we leave objective as default
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# note: what we are getting is margin value in prediction
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# you must know what you are doing
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param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1 }
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# user define objective function, given prediction, return gradient and second order gradient
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# this is loglikelihood loss
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def logregobj(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds))
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grad = preds - labels
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hess = preds * (1.0-preds)
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return grad, hess
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# user defined evaluation function, return a pair metric_name, result
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# NOTE: when you do customized loss function, the default prediction value is margin
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# this may make buildin evalution metric not function properly
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# for example, we are doing logistic loss, the prediction is score before logistic transformation
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# the buildin evaluation error assumes input is after logistic transformation
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# Take this in mind when you use the customization, and maybe you need write customized evaluation function
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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# return a pair metric_name, result
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# since preds are margin(before logistic transformation, cutoff at 0)
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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# training with customized objective, we can also do step by step training
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# simply look at xgboost.py's implementation of train
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bst = xgb.train(param, dtrain, num_round, evallist, logregobj, evalerror)
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###
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# advanced: start from a initial base prediction
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#
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print ('start running example to start from a initial prediction')
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# specify parameters via map, definition are same as c++ version
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param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
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# train xgboost for 1 round
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bst = xgb.train( param, dtrain, 1, evallist )
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# Note: we need the margin value instead of transformed prediction in set_base_margin
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# do predict with output_margin=True, will always give you margin values before logistic transformation
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ptrain = bst.predict(dtrain, output_margin=True)
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ptest = bst.predict(dtest, output_margin=True)
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dtrain.set_base_margin(ptrain)
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dtest.set_base_margin(ptest)
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print ('this is result of running from initial prediction')
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bst = xgb.train( param, dtrain, 1, evallist )
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