[PYTHON] Refactor trainnig API to use callback

Makefile

@@ -131,7 +131,7 @@ rcpplint:
 	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
 
 lint: rcpplint
-	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin
+	python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin python-package
 
 clean:
 	$(RM) -rf build build_plugin lib bin *~ */*~ */*/*~ */*/*/*~ */*.o */*/*.o */*/*/*.o xgboost

python-package/xgboost/compat.py

@@ -1,5 +1,5 @@
 # coding: utf-8
-# pylint: disable=unused-import, invalid-name, wrong-import-position
+# pylint: disable= invalid-name,  unused-import
 """For compatibility"""
 
 from __future__ import absolute_import
@@ -14,12 +14,14 @@
     STRING_TYPES = str,
 
     def py_str(x):
+        """convert c string back to python string"""
         return x.decode('utf-8')
 else:
     # pylint: disable=invalid-name
     STRING_TYPES = basestring,
 
     def py_str(x):
+        """convert c string back to python string"""
         return x
 
 try:

python-package/xgboost/core.py

@@ -1,5 +1,6 @@
 # coding: utf-8
-# pylint: disable=too-many-arguments, too-many-branches
+# pylint: disable=too-many-arguments, too-many-branches, invalid-name
+# pylint: disable=too-many-branches, too-many-lines, W0141
 """Core XGBoost Library."""
 from __future__ import absolute_import
 
@@ -21,6 +22,19 @@ class XGBoostError(Exception):
     """Error throwed by xgboost trainer."""
     pass
 
+class EarlyStopException(Exception):
+    """Exception to signal early stopping."""
+
+# Callback environment used by callbacks
+CallbackEnv = collections.namedtuple(
+    "XGBoostCallbackEnv",
+    ["booster",
+     "iteration",
+     "begin_iteration",
+     "end_iteration",
+     "rank",
+     "evaluation_result_list"])
+
 
 def from_pystr_to_cstr(data):
     """Convert a list of Python str to C pointer
@@ -657,7 +671,7 @@ def __setstate__(self, state):
     def __copy__(self):
         return self.__deepcopy__(None)
 
-    def __deepcopy__(self, memo):
+    def __deepcopy__(self, _):
         return Booster(model_file=self.save_raw())
 
     def copy(self):
@@ -975,7 +989,6 @@ def load_model(self, fname):
             _check_call(_LIB.XGBoosterLoadModelFromBuffer(self.handle, ptr, length))
 
     def dump_model(self, fout, fmap='', with_stats=False):
-        # pylint: disable=consider-using-enumerate
         """
         Dump model into a text file.
 
@@ -1143,10 +1156,12 @@ def _validate_features(self, data):
                 msg = 'feature_names mismatch: {0} {1}'
 
                 if dat_missing:
-                    msg += '\nexpected ' + ', '.join(str(s) for s in dat_missing) + ' in input data'
+                    msg += ('\nexpected ' + ', '.join(str(s) for s in dat_missing) +
+                            ' in input data')
 
                 if my_missing:
-                    msg += '\ntraining data did not have the following fields: ' + ', '.join(str(s) for s in my_missing)
+                    msg += ('\ntraining data did not have the following fields: ' +
+                            ', '.join(str(s) for s in my_missing))
 
                 raise ValueError(msg.format(self.feature_names,
                                             data.feature_names))
@@ -1161,23 +1176,25 @@ def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True)
             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.
+            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.
+        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))
+        regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".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]
+        n_unique = len(np.unique(values))
         bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)
 
         nph = np.histogram(values, bins=bins)
@@ -1187,7 +1204,8 @@ def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True)
         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).")
+            sys.stderr.write(
+                "Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
             return nph
         else:
             return nph

python-package/xgboost/rabit.py

@@ -1,3 +1,6 @@
+# coding: utf-8
+# pylint: disable= invalid-name
+
 """Distributed XGBoost Rabit related API."""
 from __future__ import absolute_import
 import sys
@@ -179,7 +182,7 @@ def allreduce(data, op, prepare_fun=None):
     else:
         func_ptr = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
 
-        def pfunc(args):
+        def pfunc(_):
             """prepare function."""
             prepare_fun(data)
         _LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),

python-package/xgboost/sklearn.py

@@ -1,5 +1,5 @@
 # coding: utf-8
-# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme
+# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912
 """Scikit-Learn Wrapper interface for XGBoost."""
 from __future__ import absolute_import
 
@@ -42,6 +42,7 @@ def _objective_decorator(func):
             ``dmatrix.get_label()``
     """
     def inner(preds, dmatrix):
+        """internal function"""
         labels = dmatrix.get_label()
         return func(labels, preds)
     return inner
@@ -183,7 +184,7 @@ def get_xgb_params(self):
 
     def fit(self, X, y, eval_set=None, eval_metric=None,
             early_stopping_rounds=None, verbose=True):
-        # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init, redefined-variable-type
+        # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
         """
         Fit the gradient boosting model
 
@@ -351,7 +352,7 @@ def __init__(self, max_depth=3, learning_rate=0.1,
 
     def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
             early_stopping_rounds=None, verbose=True):
-        # pylint: disable = attribute-defined-outside-init,arguments-differ, redefined-variable-type
+        # pylint: disable = attribute-defined-outside-init,arguments-differ
         """
         Fit gradient boosting classifier