CV returns ndarray or DataFrame

python-package/xgboost/training.py

@@ -1,5 +1,6 @@
 # coding: utf-8
 # pylint: disable=too-many-locals, too-many-arguments, invalid-name
+# pylint: disable=too-many-branches
 """Training Library containing training routines."""
 from __future__ import absolute_import
 
@@ -179,16 +180,16 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None):
     return ret
 
 
-def aggcv(rlist, show_stdv=True):
+def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True):
     # pylint: disable=invalid-name
     """
     Aggregate cross-validation results.
     """
     cvmap = {}
-    ret = rlist[0].split()[0]
+    idx = rlist[0].split()[0]
     for line in rlist:
         arr = line.split()
-        assert ret == arr[0]
+        assert idx == arr[0]
         for it in arr[1:]:
             if not isinstance(it, STRING_TYPES):
                 it = it.decode()
@@ -196,19 +197,50 @@ def aggcv(rlist, show_stdv=True):
             if k not in cvmap:
                 cvmap[k] = []
             cvmap[k].append(float(v))
+
+    msg = idx
+
+    if show_stdv:
+        fmt = '\tcv-{0}:{1}+{2}'
+    else:
+        fmt = '\tcv-{0}:{1}'
+
+    index = []
+    results = []
     for k, v in sorted(cvmap.items(), key=lambda x: x[0]):
         v = np.array(v)
-        if not isinstance(ret, STRING_TYPES):
+        if not isinstance(msg, STRING_TYPES):
-            ret = ret.decode()
+            msg = msg.decode()
-        if show_stdv:
+        mean, std = np.mean(v), np.std(v)
-            ret += '\tcv-%s:%f+%f' % (k, np.mean(v), np.std(v))
+        msg += fmt.format(k, mean, std)
-        else:
+
-            ret += '\tcv-%s:%f' % (k, np.mean(v))
+        index.extend([k + '-mean', k + '-std'])
-    return ret
+        results.extend([mean, std])
+
+
+
+    if as_pandas:
+        try:
+            import pandas as pd
+            results = pd.Series(results, index=index)
+        except ImportError:
+            if show_progress is None:
+                show_progress = True
+    else:
+        # if show_progress is default (None),
+        # result will be np.ndarray as it can't hold column name
+        if show_progress is None:
+            show_progress = True
+
+    if show_progress:
+        sys.stderr.write(msg + '\n')
+
+    return results
 
 
 def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
-       obj=None, feval=None, fpreproc=None, show_stdv=True, seed=0):
+       obj=None, feval=None, fpreproc=None, as_pandas=True,
+       show_progress=None, show_stdv=True, seed=0):
     # pylint: disable = invalid-name
     """Cross-validation with given paramaters.
 
@@ -231,8 +263,15 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
     fpreproc : function
         Preprocessing function that takes (dtrain, dtest, param) and returns
         transformed versions of those.
-    show_stdv : bool
+    as_pandas : bool, default True
-        Whether to display the standard deviation.
+        Return pd.DataFrame when pandas is installed.
+        If False or pandas is not installed, return np.ndarray
+    show_progress : bool or None, default None
+        Whether to display the progress. If None, progress will be displayed
+        when np.ndarray is returned.
+    show_stdv : bool, default True
+        Whether to display the standard deviation in progress.
+        Results are not affected, and always contains std.
     seed : int
         Seed used to generate the folds (passed to numpy.random.seed).
 
@@ -245,8 +284,19 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
     for i in range(num_boost_round):
         for fold in cvfolds:
             fold.update(i, obj)
-        res = aggcv([f.eval(i, feval) for f in cvfolds], show_stdv)
+        res = aggcv([f.eval(i, feval) for f in cvfolds],
-        sys.stderr.write(res + '\n')
+                    show_stdv=show_stdv, show_progress=show_progress,
+                    as_pandas=as_pandas)
         results.append(res)
+
+    if as_pandas:
+        try:
+            import pandas as pd
+            results = pd.DataFrame(results)
+        except ImportError:
+            results = np.array(results)
+    else:
+        results = np.array(results)
+
     return results
 

scripts/travis_script.sh

@@ -64,7 +64,7 @@ if [ ${TASK} == "python-package" -o ${TASK} == "python-package3" ]; then
         conda create -n myenv python=2.7
     fi
     source activate myenv
-    conda install numpy scipy matplotlib nose
+    conda install numpy scipy pandas matplotlib nose
     python -m pip install graphviz
 
     make all CXX=${CXX} || exit -1

tests/python/test_basic.py

@@ -127,6 +127,36 @@ class TestBasic(unittest.TestCase):
         data = np.array([['a', 'b'], ['c', 'd']])
         self.assertRaises(ValueError, xgb.DMatrix, data)
 
+    def test_cv(self):
+        dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
+        params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+
+        import pandas as pd
+        cv = xgb.cv(params, dm, num_boost_round=10, nfold=10)
+        assert isinstance(cv, pd.DataFrame)
+        exp = pd.Index([u'test-error-mean', u'test-error-std',
+                        u'train-error-mean', u'train-error-std'])
+        assert cv.columns.equals(exp)
+
+        # show progress log (result is the same as above)
+        cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
+                    show_progress=True)
+        assert isinstance(cv, pd.DataFrame)
+        exp = pd.Index([u'test-error-mean', u'test-error-std',
+                        u'train-error-mean', u'train-error-std'])
+        assert cv.columns.equals(exp)
+        cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
+                    show_progress=True, show_stdv=False)
+        assert isinstance(cv, pd.DataFrame)
+        exp = pd.Index([u'test-error-mean', u'test-error-std',
+                        u'train-error-mean', u'train-error-std'])
+        assert cv.columns.equals(exp)
+
+        # return np.ndarray
+        cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
+        assert isinstance(cv, np.ndarray)
+        assert cv.shape == (10, 4)
+
     def test_plotting(self):
         bst2 = xgb.Booster(model_file='xgb.model')
         # plotting