[python] use the same zip function in 2 and 3 Python (#1681)

python-package/lightgbm/basic.py

@@ -251,7 +251,7 @@ def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorica
         else:
             if len(cat_cols) != len(pandas_categorical):
                 raise ValueError('train and valid dataset categorical_feature do not match.')
-            for col, category in zip(cat_cols, pandas_categorical):
+            for col, category in zip_(cat_cols, pandas_categorical):
                 if list(data[col].cat.categories) != list(category):
                     data[col] = data[col].cat.set_categories(category)
         if len(cat_cols):  # cat_cols is pandas Index object

python-package/lightgbm/engine.py

@@ -12,7 +12,7 @@
 from . import callback
 from .basic import Booster, Dataset, LightGBMError, _InnerPredictor
 from .compat import (SKLEARN_INSTALLED, _LGBMGroupKFold, _LGBMStratifiedKFold,
-                     integer_types, range_, string_type)
+                     integer_types, range_, zip_, string_type)
 
 
 def train(params, train_set, num_boost_round=100,
@@ -187,7 +187,7 @@ def train(params, train_set, num_boost_round=100,
         booster = Booster(params=params, train_set=train_set)
         if is_valid_contain_train:
             booster.set_train_data_name(train_data_name)
-        for valid_set, name_valid_set in zip(reduced_valid_sets, name_valid_sets):
+        for valid_set, name_valid_set in zip_(reduced_valid_sets, name_valid_sets):
             booster.add_valid(valid_set, name_valid_set)
     finally:
         train_set._reverse_update_params()
@@ -285,7 +285,7 @@ def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratifi
             kstep = int(num_data / nfold)
             test_id = [randidx[i: i + kstep] for i in range_(0, num_data, kstep)]
             train_id = [np.concatenate([test_id[i] for i in range_(nfold) if k != i]) for k in range_(nfold)]
-            folds = zip(train_id, test_id)
+            folds = zip_(train_id, test_id)
 
     ret = CVBooster()
     for train_idx, test_idx in folds:

python-package/lightgbm/plotting.py

@@ -10,7 +10,8 @@
 import numpy as np
 
 from .basic import Booster
-from .compat import MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED, LGBMDeprecationWarning, range_, string_type
+from .compat import (MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED, LGBMDeprecationWarning,
+                     range_, zip_, string_type)
 from .sklearn import LGBMModel
 
 
@@ -86,12 +87,12 @@ def plot_importance(booster, ax=None, height=0.2,
     if not len(importance):
         raise ValueError("Booster's feature_importance is empty.")
 
-    tuples = sorted(zip(feature_name, importance), key=lambda x: x[1])
+    tuples = sorted(zip_(feature_name, importance), key=lambda x: x[1])
     if ignore_zero:
         tuples = [x for x in tuples if x[1] > 0]
     if max_num_features is not None and max_num_features > 0:
         tuples = tuples[-max_num_features:]
-    labels, values = zip(*tuples)
+    labels, values = zip_(*tuples)
 
     if ax is None:
         if figsize is not None:
@@ -101,7 +102,7 @@ def plot_importance(booster, ax=None, height=0.2,
     ylocs = np.arange(len(values))
     ax.barh(ylocs, values, align='center', height=height, **kwargs)
 
-    for x, y in zip(values, ylocs):
+    for x, y in zip_(values, ylocs):
         ax.text(x + 1, y, x, va='center')
 
     ax.set_yticks(ylocs)