Merge pull request dmlc#1017 from Far0n/hist

[py] split value histograms

python-package/xgboost/core.py

@@ -3,16 +3,18 @@
 """Core XGBoost Library."""
 from __future__ import absolute_import
 
+import sys
 import os
 import ctypes
 import collections
+import re
 
 import numpy as np
 import scipy.sparse
 
 from .libpath import find_lib_path
 
-from .compat import STRING_TYPES, PY3, DataFrame, py_str
+from .compat import STRING_TYPES, PY3, DataFrame, py_str, PANDAS_INSTALLED
 
 
 class XGBoostError(Exception):
@@ -1058,3 +1060,44 @@ def _validate_features(self, data):
 
                 raise ValueError(msg.format(self.feature_names,
                                             data.feature_names))
+
+    def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True):
+        """Get split value histogram of a feature
+        Parameters
+        ----------
+        feature: str
+            The name of the feature.
+        fmap: str (optional)
+            The name of feature map file.
+        bin: int, default None
+            The maximum number of bins.
+            Number of bins equals number of unique split values n_unique, if bins == None or bins > n_unique.
+        as_pandas : bool, default True
+            Return pd.DataFrame when pandas is installed.
+            If False or pandas is not installed, return numpy ndarray.
+
+        Returns
+        -------
+        a histogram of used splitting values for the specified feature either as numpy array or pandas DataFrame.
+        """
+        xgdump = self.get_dump(fmap=fmap)
+        values = []
+        regexp = re.compile("\[{0}<([\d.Ee+-]+)\]".format(feature))
+        for i in range(len(xgdump)):
+            m = re.findall(regexp, xgdump[i])
+            values.extend(map(float, m))
+
+        n_unique = np.unique(values).shape[0]
+        bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)
+
+        nph = np.histogram(values, bins=bins)
+        nph = np.column_stack((nph[1][1:], nph[0]))
+        nph = nph[nph[:, 1] > 0]
+
+        if as_pandas and PANDAS_INSTALLED:
+            return DataFrame(nph, columns=['SplitValue', 'Count'])
+        elif as_pandas and not PANDAS_INSTALLED:
+            sys.stderr.write("Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
+            return nph
+        else:
+            return nph

tests/python/test_with_sklearn.py

@@ -240,3 +240,22 @@ def test_sklearn_nfolds_cv():
     cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed)
     assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
     assert cv2.iloc[-1, 0] == cv3.iloc[-1, 0]
+
+
+def test_split_value_histograms():
+    digits_2class = load_digits(2)
+
+    X = digits_2class['data']
+    y = digits_2class['target']
+
+    dm = xgb.DMatrix(X, label=y)
+    params = {'max_depth': 6, 'eta': 0.01, 'silent': 1, 'objective': 'binary:logistic'}
+
+    gbdt = xgb.train(params, dm, num_boost_round=10)
+    assert gbdt.get_split_value_histogram("not_there", as_pandas=True).shape[0] == 0
+    assert gbdt.get_split_value_histogram("not_there", as_pandas=False).shape[0] == 0
+    assert gbdt.get_split_value_histogram("f28", bins=0).shape[0] == 1
+    assert gbdt.get_split_value_histogram("f28", bins=1).shape[0] == 1
+    assert gbdt.get_split_value_histogram("f28", bins=2).shape[0] == 2
+    assert gbdt.get_split_value_histogram("f28", bins=5).shape[0] == 2
+    assert gbdt.get_split_value_histogram("f28", bins=None).shape[0] == 2