Merge 60ea0d6 into 8bfcfb0

dedupe/api.py

@@ -1052,17 +1052,7 @@ def _read_training(self, training_file: TextIO) -> None:
         training_pairs = json.load(training_file,
                                    object_hook=serializer._from_json)
 
-        try:
-            self.mark_pairs(training_pairs)
-        except AttributeError as e:
-            if "Attempting to fingerprint with an index predicate without indexing records" in str(e):
-                raise UserWarning('Training data has records not known '
-                                  'to the active learner. Read training '
-                                  'in before initializing the active '
-                                  'learner with the sample method, or '
-                                  'use the prepare_training method.')
-            else:
-                raise
+        self.mark_pairs(training_pairs)
 
     def train(self,
               recall: float = 1.00,
@@ -1177,6 +1167,22 @@ def mark_pairs(self, labeled_pairs: TrainingData) -> None:
                                 }
             matcher.mark_pairs(labeled_examples)
 
+        .. note::
+           `mark_pairs` is primarily designed to be used with `uncertain_pairs`
+           to incrementally build a training set.
+
+           If you have existing training data, you should likely
+           format the data into the right form and supply the training
+           data with the `training_file` argument of the
+           `prepare_training` method.
+
+           If that is not possible or desirable, you can use
+           `mark_pairs` to train a linker using the `mark_pairs`
+           method. However, you must ensure that every record that
+           appears in the `labeled_pairs` argument appears in either
+           the data or `training_file` supplied to the
+           `prepare_method` argument.
+
         '''
         self._checkTrainingPairs(labeled_pairs)
 
@@ -1185,7 +1191,16 @@ def mark_pairs(self, labeled_pairs: TrainingData) -> None:
 
         if self.active_learner:
             examples, y = flatten_training(labeled_pairs)
-            self.active_learner.mark(examples, y)
+
+            try:
+                self.active_learner.mark(examples, y)
+            except dedupe.predicates.NoIndexError:
+                raise UserWarning('Training data has records not known '
+                                  'to the active learner. Make sure data '
+                                  'are in the data arguments of '
+                                  'prepare_training method or use '
+                                  'the training_file argument of '
+                                  'prepare_training')
 
     def _checkTrainingPairs(self, labeled_pairs: TrainingData) -> None:
         try:
@@ -1272,6 +1287,9 @@ def prepare_training(self,
 
         '''
 
+        # Reset active learner
+        self.active_learner = None
+
         if training_file:
             self._read_training(training_file)
         self._sample(data, sample_size, blocked_proportion)
@@ -1357,6 +1375,8 @@ def prepare_training(self,
                matcher.prepare_training(data_1, data_2, training_file=f)
 
         '''
+        # Reset active learner
+        self.active_learner = None
 
         if training_file:
             self._read_training(training_file)

dedupe/predicates.py

@@ -25,6 +25,10 @@
 PUNCTABLE = str.maketrans("", "", string.punctuation)
 
 
+class NoIndexError(AttributeError):
+    pass
+
+
 def strip_punc(s):
     return s.translate(PUNCTABLE)
 
@@ -162,8 +166,8 @@ def __call__(self, record, **kwargs):
             try:
                 doc_id = self.index._doc_to_id[doc]
             except AttributeError:
-                raise AttributeError("Attempting to block with an index "
-                                     "predicate without indexing records")
+                raise NoIndexError("Attempting to block with an index "
+                                   "predicate without indexing records")
 
             if doc_id in self.canopy:
                 block_key = self.canopy[doc_id]
@@ -216,8 +220,8 @@ def __call__(self, record, target=False, **kwargs):
                     else:
                         centers = self.index.search(doc, self.threshold)
                 except AttributeError:
-                    raise AttributeError("Attempting to block with an index "
-                                         "predicate without indexing records")
+                    raise NoIndexError("Attempting to block with an index "
+                                       "predicate without indexing records")
                 result = [str(center) for center in centers]
                 self._cache[(column, target)] = result
                 return result