Merge pull request #163 from shengshuyang/master

create a SaveLogger.save() method.

pystruct/tests/test_utils/test_utils_logging.py

@@ -0,0 +1,44 @@
+import numpy as np
+from tempfile import mkstemp
+
+from sklearn.datasets import load_iris
+from sklearn.cross_validation import train_test_split
+
+from pystruct.models import GraphCRF
+from pystruct.learners import NSlackSSVM
+from pystruct.utils import SaveLogger
+from pystruct.inference import get_installed
+
+from nose.tools import assert_less, assert_almost_equal
+
+# we always try to get the fastest installed inference method
+inference_method = get_installed(["qpbo", "ad3", "lp"])[0]
+
+def test_logging():
+    iris = load_iris()
+    X, y = iris.data, iris.target
+
+    X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
+    Y = y.reshape(-1, 1)
+
+    X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
+    _, file_name = mkstemp()
+
+    pbl = GraphCRF(n_features=4, n_states=3, inference_method='lp')
+    logger = SaveLogger(file_name)
+    svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger)
+    svm.fit(X_train, y_train)
+
+    score_current = svm.score(X_test, y_test)
+    score_auto_saved = logger.load().score(X_test, y_test)
+
+    alt_file_name = file_name + "alt"
+    logger.save(svm, alt_file_name)
+    logger.file_name = alt_file_name
+    logger.load()
+    score_manual_saved = logger.load().score(X_test, y_test)
+
+    assert_less(.97, score_current)
+    assert_less(.97, score_auto_saved)
+    assert_less(.97, score_manual_saved)
+    assert_almost_equal(score_auto_saved, score_manual_saved)

pystruct/utils/logging.py

@@ -50,15 +50,19 @@ def __call__(self, learner, iteration=0):
                 file_name = file_name % iteration
             if self.verbose > 0:
                 print("saving %s to file %s" % (learner, file_name))
-            with open(file_name, "wb") as f:
-                if hasattr(learner, 'inference_cache_'):
-                    # don't store the large inference cache!
-                    learner.inference_cache_, tmp = (None,
-                                                     learner.inference_cache_)
-                    pickle.dump(learner, f, -1)
-                    learner.inference_cache_ = tmp
-                else:
-                    pickle.dump(learner, f, -1)
+            self.save(learner, file_name)
+
+    def save(self, learner, file_name):
+        """Save the model to location specified in file_name."""
+        with open(file_name, "wb") as f:
+            if hasattr(learner, 'inference_cache_'):
+                # don't store the large inference cache!
+                learner.inference_cache_, tmp = (None,
+                                                 learner.inference_cache_)
+                pickle.dump(learner, f, -1)
+                learner.inference_cache_ = tmp
+            else:
+                pickle.dump(learner, f, -1)
 
     def load(self):
         """Load the model stoed in file_name and return it."""