add python interface for xgboost

python/example/demo.py

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+#!/usr/bin/python
+import sys
+import scipy.sparse
+# append the path to xgboost
+sys.path.append('../')
+import xgboost as xgb
+
+### simple example
+# load file from text file, also binary buffer generated by xgboost
+dtrain = xgb.DMatrix('agaricus.txt.train')
+dtest = xgb.DMatrix('agaricus.txt.test')
+
+# specify parameters via map, definition are same as c++ version
+param = {'bst:max_depth':4, 'bst:eta':1, 'silent':1, 'loss_type':2 }
+
+# specify validations set to watch performance
+evallist  = [(dtest,'eval'), (dtrain,'train')]
+num_round = 2
+bst = xgb.train( param, dtrain, num_round, evallist )
+
+# this is prediction
+preds = bst.predict( dtest )
+labels = dtest.get_label()
+print 'error=%f' % (  sum(1 for i in xrange(len(preds)) if int(preds[i]>0.5)!=labels[i]) /float(len(preds)))
+bst.save_model('0001.model')
+
+
+###
+# build dmatrix in python iteratively
+#
+print 'start running example of build DMatrix in python'
+dtrain = xgb.DMatrix()
+labels = []
+for l in open('agaricus.txt.train'):
+    arr = l.split()
+    labels.append( int(arr[0]))
+    feats = []
+    for it in arr[1:]:
+        k,v = it.split(':')
+        feats.append( (int(k), float(v)) )
+    dtrain.add_row( feats )
+dtrain.set_label( labels )
+evallist  = [(dtest,'eval'), (dtrain,'train')]
+
+bst = xgb.train( param, dtrain, num_round, evallist )
+
+###
+# build dmatrix from scipy.sparse
+print 'start running example of build DMatrix from scipy.sparse'
+labels = []
+row = []; col = []; dat = []
+i = 0
+for l in open('agaricus.txt.train'):
+    arr = l.split()
+    labels.append( int(arr[0]))
+    for it in arr[1:]:
+        k,v = it.split(':')
+        row.append(i); col.append(int(k)); dat.append(float(v))
+    i += 1
+
+csr = scipy.sparse.csr_matrix( (dat, (row,col)) )
+dtrain = xgb.DMatrix( csr )
+dtrain.set_label(labels)
+evallist  = [(dtest,'eval'), (dtrain,'train')]
+bst = xgb.train( param, dtrain, num_round, evallist )
+
+print 'start running example of build DMatrix from numpy array'
+# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation,then convert to DMatrix
+npymat = csr.todense()
+dtrain = xgb.DMatrix( npymat )
+dtrain.set_label(labels)
+evallist  = [(dtest,'eval'), (dtrain,'train')]
+bst = xgb.train( param, dtrain, num_round, evallist )
+