add cv for python

wrapper/xgboost.py

@@ -448,11 +448,13 @@ def mknfold(dall, nfold, param, seed, evals=[], fpreproc = None):
         # run preprocessing on the data set if needed
         if fpreproc is not None:
             dtrain, dtest, tparam = fpreproc(dtrain, dtest, param.copy())
+        else:
+            tparam = param
         plst = tparam.items() + [('eval_metric', itm) for itm in evals]
         ret.append(CVPack(dtrain, dtest, plst))
     return ret
 
-def aggcv(rlist):
+def aggcv(rlist, show_stdv=True):
     """
     aggregate cross validation results
     """
@@ -468,11 +470,14 @@ def aggcv(rlist):
             cvmap[k].append(float(v))
     for k, v in sorted(cvmap.items(), key = lambda x:x[0]):
         v = np.array(v)
-        ret += '\t%s:%f+%f' % (k, np.mean(v), np.std(v))
+        if show_stdv:
+            ret += '\tcv-%s:%f+%f' % (k, np.mean(v), np.std(v))
+        else:
+            ret += '\tcv-%s:%f' % (k, np.mean(v))
     return ret
 
-def cv(params, dtrain, num_boost_round = 10, nfold=3, eval_metric = [], \
-        obj = None, feval = None, fpreproc = None):
+def cv(params, dtrain, num_boost_round = 10, nfold=3, metrics=[], \
+        obj = None, feval = None, fpreproc = None, show_stdv = True, seed = 0):
     """ cross validation  with given paramaters
         Args:
             params: dict
@@ -485,14 +490,21 @@ def cv(params, dtrain, num_boost_round = 10, nfold=3, eval_metric = [], \
                    folds to do cv
             evals: list or
                    list of items to be evaluated
-            obj:
-            feval:
+            obj: custom objective function
+            feval: custom evaluation function
             fpreproc: preprocessing function that takes dtrain, dtest,
                       param and return transformed version of dtrain, dtest, param
+            show_stdv: whether display standard deviation
+            seed: seed used to generate the folds
+
+        Returns: list(string) of evaluation history
     """
-    cvfolds = mknfold(dtrain, nfold, params, 0, eval_metric, fpreproc)
+    results = []
+    cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc)
     for i in range(num_boost_round):
         for f in cvfolds:
             f.update(i, obj)
-        res = aggcv([f.eval(i, feval) for f in cvfolds])
+        res = aggcv([f.eval(i, feval) for f in cvfolds], show_stdv)
         sys.stderr.write(res+'\n')
+        results.append(res)
+    return results