[python][docs] several little fixes (#1533)

python-package/lightgbm/basic.py

@@ -1097,7 +1097,7 @@ class Dataset(object):
 
     def _set_predictor(self, predictor):
         """
-        Set predictor for continued training, not recommand for user to call this function.
+        Set predictor for continued training, not recommended for user to call this function.
         Please set init_model in engine.train or engine.cv
         """
         if predictor is self._predictor:

python-package/lightgbm/engine.py

@@ -45,7 +45,7 @@ def train(params, train_set, num_boost_round=100,
         If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
         Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples.
         To ignore the default metric in params, set it to the string ``"None"``
-    init_model : string or None, optional (default=None)
+    init_model : string, Booster or None, optional (default=None)
         Filename of LightGBM model or Booster instance used for continue training.
     feature_name : list of strings or 'auto', optional (default="auto")
         Feature names.
@@ -268,7 +268,7 @@ def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratifi
                 raise LightGBMError('Scikit-learn is required for lambdarank cv.')
             # lambdarank task, split according to groups
             group_info = full_data.get_group().astype(int)
-            flatted_group = np.repeat(range(len(group_info)), repeats=group_info)
+            flatted_group = np.repeat(range_(len(group_info)), repeats=group_info)
             group_kfold = _LGBMGroupKFold(n_splits=nfold)
             folds = group_kfold.split(X=np.zeros(num_data), groups=flatted_group)
         elif stratified:
@@ -352,7 +352,7 @@ def cv(params, train_set, num_boost_round=100,
         If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
         Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples.
         To ignore the default metric in params, set it to the string ``"None"``
-    init_model : string or None, optional (default=None)
+    init_model : string, Booster or None, optional (default=None)
         Filename of LightGBM model or Booster instance used for continue training.
     feature_name : list of strings or 'auto', optional (default="auto")
         Feature names.

python-package/lightgbm/plotting.py

@@ -10,7 +10,7 @@ from io import BytesIO
 import numpy as np
 
 from .basic import Booster
-from .compat import MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED, string_type
+from .compat import MATPLOTLIB_INSTALLED, GRAPHVIZ_INSTALLED, range_, string_type
 from .sklearn import LGBMModel
 
 
@@ -216,7 +216,7 @@ def plot_metric(booster, metric=None, dataset_names=None,
             raise KeyError('No given metric in eval results.')
         results = metrics_for_one[metric]
     num_iteration, max_result, min_result = len(results), max(results), min(results)
-    x_ = range(num_iteration)
+    x_ = range_(num_iteration)
     ax.plot(x_, results, label=name)
 
     for name in dataset_names:

python-package/lightgbm/sklearn.py

@@ -193,7 +193,7 @@ class LGBMModel(_LGBMModelBase):
             Number of parallel threads.
         silent : bool, optional (default=True)
             Whether to print messages while running boosting.
-        importance_type : str, optional (default='split')
+        importance_type : string, optional (default='split')
             The type of feature importance to be filled into ``feature_importances_``.
             If "split", result contains numbers of times the feature is used in a model.
             If "gain", result contains total gains of splits which use the feature.