Merge pull request #299 from jseabold/pickle-xgbooster

ENH: Pickle xgbooster enhancments. Thanks!

demo/guide-python/sklearn_examples.py

@@ -4,18 +4,17 @@ Created on 1 Apr 2015
 
 @author: Jamie Hall
 '''
-
+import pickle
 import xgboost as xgb
 
 import numpy as np
 from sklearn.cross_validation import KFold
-from sklearn.grid_search import GridSearchCV
 from sklearn.metrics import confusion_matrix, mean_squared_error
+from sklearn.grid_search import GridSearchCV
 from sklearn.datasets import load_iris, load_digits, load_boston
 
 rng = np.random.RandomState(31337)
 
-
 print("Zeros and Ones from the Digits dataset: binary classification")
 digits = load_digits(2)
 y = digits['target']
@@ -60,4 +59,9 @@ clf.fit(X,y)
 print(clf.best_score_)
 print(clf.best_params_)
 
-
+# The sklearn API models are picklable
+print("Pickling sklearn API models")
+# must open in binary format to pickle
+pickle.dump(clf, open("best_boston.pkl", "wb"))
+clf2 = pickle.load(open("best_boston.pkl", "rb"))
+print(np.allclose(clf.predict(X), clf2.predict(X)))

demo/guide-python/sklearn_parallel.py

@@ -0,0 +1,35 @@
+import os
+
+if __name__ == "__main__":
+    # NOTE: on posix systems, this *has* to be here and in the
+    # `__name__ == "__main__"` clause to run XGBoost in parallel processes
+    # using fork, if XGBoost was built with OpenMP support. Otherwise, if you
+    # build XGBoost without OpenMP support, you can use fork, which is the
+    # default backend for joblib, and omit this.
+    try:
+        from multiprocessing import set_start_method
+    except ImportError:
+        raise ImportError("Unable to import multiprocessing.set_start_method."
+                          " This example only runs on Python 3.4")
+    set_start_method("forkserver")
+
+    import numpy as np
+    from sklearn.grid_search import GridSearchCV
+    from sklearn.datasets import load_boston
+    import xgboost as xgb
+
+    rng = np.random.RandomState(31337)
+
+    print("Parallel Parameter optimization")
+    boston = load_boston()
+
+    os.environ["OMP_NUM_THREADS"] = "2"  # or to whatever you want
+    y = boston['target']
+    X = boston['data']
+    xgb_model = xgb.XGBRegressor()
+    clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
+                                   'n_estimators': [50, 100, 200]}, verbose=1,
+                       n_jobs=2)
+    clf.fit(X, y)
+    print(clf.best_score_)
+    print(clf.best_params_)

wrapper/README.md

@@ -7,6 +7,8 @@ Python
 * To make the python module, type ```./build.sh``` in the root directory of project
 * Install with `python setup.py install` from this directory.
 * Refer also to the walk through example in [demo folder](../demo/guide-python)
+* **NOTE**: if you want to run XGBoost in parallel using the fork backend for joblib/multiprocessing, you must build XGBoost without support for OpenMP by `make no_omp=1`. Otherwise, use the forkserver (in Python 3.4) or spawn backend. See the sklearn_examples.py demo.
+
 
 R
 =====

wrapper/xgboost.py

@@ -15,7 +15,6 @@ import re
 import ctypes
 import platform
 import collections
-from io import BytesIO
 
 import numpy as np
 import scipy.sparse
@@ -492,7 +491,7 @@ class Booster(object):
     def save_raw(self):
         """
         Save the model to a in memory buffer represetation
-        
+
         Returns
         -------
         a in memory buffer represetation of the model
@@ -876,12 +875,12 @@ class XGBModel(XGBModelBase):
         self._Booster = None
 
     def __getstate__(self):
-        # can't pickle ctypes pointers so put _Booster in a BytesIO obj
+        # can't pickle ctypes pointers so put _Booster in a bytearray object
-        this = self.__dict__.copy()  # don't modify in place        
+        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        
+            this["_Booster"] = raw
         return this
 
     def __setstate__(self, state):
@@ -894,7 +893,7 @@ class XGBModel(XGBModelBase):
         """
         get the underlying xgboost Booster of this model
         will raise an exception when fit was not called
-        
+
         Returns
         -------
         booster : a xgboost booster of underlying model
@@ -902,7 +901,7 @@ class XGBModel(XGBModelBase):
         if self._Booster is None:
             raise XGBError('need to call fit beforehand')
         return self._Booster
-    
+
     def get_xgb_params(self):
         xgb_params = self.get_params()