From fcb833373b3d2c027ed2fb13f12b53d31e1a14f1 Mon Sep 17 00:00:00 2001
From: HangLi <lihang00@gmail.com>
Date: Thu, 23 Apr 2015 16:25:31 -0700
Subject: [PATCH] reorder parameters

---
 wrapper/xgboost.py | 35 +++++++++++++++++++----------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/wrapper/xgboost.py b/wrapper/xgboost.py
index fac4da186..5c8199c4d 100644
--- a/wrapper/xgboost.py
+++ b/wrapper/xgboost.py
@@ -753,8 +753,6 @@ class XGBModel(BaseEstimator):
     ----------
     max_depth : int
         Maximum tree depth for base learners.
-    min_child_weight : int
-        minimum sum of instance weight(hessian) needed in a child.
     learning_rate : float
         Boosting learning rate (xgb's "eta")
     n_estimators : int
@@ -763,33 +761,38 @@ class XGBModel(BaseEstimator):
         Whether to print messages while running boosting.
     objective : string
         Specify the learning task and the corresponding learning objective.
+
+    nthread : int
+        Number of parallel threads used to run xgboost.
+    min_child_weight : int
+        minimum sum of instance weight(hessian) needed in a child.        
     subsample : float
         Subsample ratio of the training instance.
     colsample_bytree : float
         Subsample ratio of columns when constructing each tree.
     eval_metric : string
         Evaluation metrics for validation data.
-    nthread : int
-        Number of parallel threads used to run xgboost.
     seed : int
         Random number seed.
     """
-    def __init__(self, max_depth=3, min_child_weight=1, learning_rate=0.1, n_estimators=100, 
-                 silent=True, objective="reg:linear", subsample=1, colsample_bytree=1, eval_metric='error',
-                 nthread=-1, seed=0):
+    def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear", 
+                 nthread=-1, min_child_weight=1, subsample=1, colsample_bytree=1, 
+                 eval_metric='error', seed=0):
         if not SKLEARN_INSTALLED:
             raise Exception('sklearn needs to be installed in order to use this module')
         self.max_depth = max_depth
-        self.min_child_weight = min_child_weight
         self.learning_rate = learning_rate
-        self.silent = silent
         self.n_estimators = n_estimators
+        self.silent = silent
         self.objective = objective
+        
+        self.nthread = nthread
+        self.min_child_weight = min_child_weight
         self.subsample = subsample
         self.colsample_bytree = colsample_bytree
         self.eval_metric = eval_metric
-        self.nthread = nthread
         self.seed = seed
+        
         self._Booster = Booster()
 
     def get_xgb_params(self):
@@ -812,12 +815,12 @@ class XGBModel(BaseEstimator):
 
 
 class XGBClassifier(XGBModel, ClassifierMixin):
-    def __init__(self, max_depth=3, min_child_weight=1, learning_rate=0.1, n_estimators=100, 
-                 silent=True, objective="binary:logistic", subsample=1, colsample_bytree=1, eval_metric='error',
-                 nthread=-1, seed=0):
-        super(XGBClassifier, self).__init__(max_depth, min_child_weight, learning_rate, n_estimators, 
-                                            silent, objective, subsample, colsample_bytree,eval_metric,
-                                            nthread, seed)
+    def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic", 
+                 nthread=-1, min_child_weight=1, subsample=1, colsample_bytree=1,
+                 eval_metric='error', seed=0):
+        super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective, 
+                                            nthread, min_child_weight, subsample, colsample_bytree,
+                                            eval_metric, seed)
 
     def fit(self, X, y, sample_weight=None):
         y_values = list(np.unique(y))