add cv for python

demo/guide-python/README.md

@@ -4,3 +4,4 @@ XGBoost Python Feature Walkthrough
 * [Cutomize loss function, and evaluation metric](custom_objective.py)
 * [Boosting from existing prediction](boost_from_prediction.py)
 * [Predicting using first n trees](predict_first_ntree.py)
+* [Cross validation](cross_validation.py)

demo/guide-python/cross_validation.py

@@ -0,0 +1,63 @@
+#!/usr/bin/python
+import sys
+import numpy as np
+sys.path.append('../../wrapper')
+import xgboost as xgb
+
+### load data in do training
+dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+num_round = 2
+
+print ('running cross validation')
+# do cross validation, this will print result out as
+# [iteration]  metric_name:mean_value+std_value
+# std_value is standard deviation of the metric
+xgb.cv(param, dtrain, num_round, nfold=5,
+       metrics={'error'}, seed = 0)
+
+print ('running cross validation, disable standard deviation display')
+# do cross validation, this will print result out as
+# [iteration]  metric_name:mean_value+std_value
+# std_value is standard deviation of the metric
+xgb.cv(param, dtrain, num_round, nfold=5,
+       metrics={'error'}, seed = 0, show_stdv = False)
+
+print ('running cross validation, with preprocessing function')
+# define the preprocessing function
+# used to return the preprocessed training, test data, and parameter
+# we can use this to do weight rescale, etc.
+# as a example, we try to set scale_pos_weight
+def fpreproc(dtrain, dtest, param):
+    label = dtrain.get_label()
+    ratio = float(np.sum(label == 0)) / np.sum(label==1)
+    param['scale_pos_weight'] = ratio
+    return (dtrain, dtest, param)
+
+# do cross validation, for each fold
+# the dtrain, dtest, param will be passed into fpreproc
+# then the return value of fpreproc will be used to generate
+# results of that fold
+xgb.cv(param, dtrain, num_round, nfold=5,
+       metrics={'auc'}, seed = 0, fpreproc = fpreproc)
+
+###
+# you can also do cross validation with cutomized loss function
+# See custom_objective.py
+##
+print ('running cross validation, with cutomsized loss function')
+def logregobj(preds, dtrain):
+    labels = dtrain.get_label()
+    preds = 1.0 / (1.0 + np.exp(-preds))
+    grad = preds - labels
+    hess = preds * (1.0-preds)
+    return grad, hess
+def evalerror(preds, dtrain):
+    labels = dtrain.get_label()
+    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
+
+param = {'max_depth':2, 'eta':1, 'silent':1} 
+# train with customized objective
+xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
+       obj = logregobj, feval=evalerror)
+