checkin copy

demo/guide-python/basic_walkthrough.py

@@ -3,7 +3,6 @@
 import scipy.sparse
 import pickle
 import xgboost as xgb
-import copy
 
 ### simple example
 # load file from text file, also binary buffer generated by xgboost

wrapper/xgboost.py

@@ -127,7 +127,6 @@ def __init__(self, data, label=None, missing=0.0, weight=None):
         weight : list or numpy 1-D array (optional)
             Weight for each instance.
         """
-
         # force into void_p, mac need to pass things in as void_p
         if data is None:
             self.handle = None
@@ -348,6 +347,46 @@ def __init__(self, params=None, cache=(), model_file=None):
     def __del__(self):
         xglib.XGBoosterFree(self.handle)
 
+    def __getstate__(self):
+        # can't pickle ctypes pointers
+        # put model content in bytearray
+        this = self.__dict__.copy()
+        handle = this['handle']
+        if handle is not None:
+            raw = self.save_raw()
+            this["handle"] = raw
+        return this
+
+    def __setstate__(self, state):
+        # reconstruct handle from raw data
+        handle = state['handle']
+        if handle is not None:
+            buf = handle
+            dmats = c_array(ctypes.c_void_p, [])
+            handle = ctypes.c_void_p(xglib.XGBoosterCreate(dmats, 0))            
+            length = ctypes.c_ulong(len(buf))
+            ptr = (ctypes.c_char * len(buf)).from_buffer(buf)
+            xglib.XGBoosterLoadModelFromBuffer(handle, ptr, length)
+            state['handle'] = handle            
+        self.__dict__.update(state)
+        self.set_param({'seed': 0})
+
+    def __copy__(self):
+        return self.__deepcopy__()
+
+    def __deepcopy__(self):
+        return Booster(model_file = self.save_raw())
+
+    def copy(self):
+        """
+        Copy the booster object
+        
+        Returns
+        --------
+        a copied booster model
+        """
+        return self.__copy__()
+
     def set_param(self, params, pv=None):
         if isinstance(params, collections.Mapping):
             params = params.items()
@@ -440,6 +479,11 @@ def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False):
         """
         Predict with data.
 
+        NOTE: This function is not thread safe.
+              For each booster object, predict can only be called from one thread.
+              If you want to run prediction using multiple thread, call bst.copy() to make copies
+              of model object and then call predict
+
         Parameters
         ----------
         data : DMatrix
@@ -874,18 +918,12 @@ def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True
 
         self._Booster = None
 
-    def __getstate__(self):
-        # can't pickle ctypes pointers so put _Booster in a bytearray object
-        this = self.__dict__.copy()  # don't modify in place
-        bst = this["_Booster"]
-        if bst is not None:
-            raw = this["_Booster"].save_raw()
-            this["_Booster"] = raw
-        return this
-
     def __setstate__(self, state):
+        # backward compatiblity code
+        # load booster from raw if it is raw
+        # the booster now support pickle
         bst = state["_Booster"]
-        if bst is not None:
+        if bst is not None and not isinstance(bst, Booster):
             state["_Booster"] = Booster(model_file=bst)
         self.__dict__.update(state)
 
@@ -977,7 +1015,6 @@ def predict_proba(self, X):
             classzero_probs = 1.0 - classone_probs
             return np.vstack((classzero_probs, classone_probs)).transpose()
 
-
 class XGBRegressor(XGBModel, XGBRegressor):
     __doc__ = """
     Implementation of the scikit-learn API for XGBoost regression