Add multi-label classification to predict

Now ``SS3.predict``' takes an extra argument, ``multilabel``. When
``multilabel=True`` (False by default), ``predict`` performs a
multi-label classification on the given documents, and thus, for
each document returns the list of labels (instead of a single label).

pyss3/__init__.py

@@ -1837,7 +1837,7 @@ def predict_proba(self, x_test, prep=True, leave_pbar=True):
 
     def predict(
         self, x_test, def_cat=STR_MOST_PROBABLE,
-        labels=True, prep=True, leave_pbar=True
+        labels=True, multilabel=False, prep=True, leave_pbar=True
     ):
         """
         Classify a list of documents.
@@ -1851,6 +1851,10 @@ def predict(
         :param labels: whether to return the list of category names or just
                        category indexes
         :type labels: bool
+        :param multilabel: whether to perform multi-label classification or not.
+                           if enabled, for each document returns a ``list`` of labels
+                           instead of a single label (``str``).
+        :type multilabel: bool
         :param prep: enables input preprocessing (default: True)
         :type prep: bool
         :param leave_pbar: controls whether to leave the progress bar or
@@ -1864,52 +1868,31 @@ def predict(
         if not self.__categories__:
             raise EmptyModelError
 
-        categories = self.get_categories()
-
         if not def_cat or def_cat == STR_UNKNOWN:
-            def_cat = len(self.__categories__)
-            categories.append(STR_UNKNOWN_CATEGORY)
             Print.info(
                 "default category was set to 'unknown' (its index will be %d)"
-                %
-                def_cat,
+                % self.get_category_index(STR_UNKNOWN_CATEGORY),
                 offset=1
             )
         else:
             if def_cat == STR_MOST_PROBABLE:
-                def_cat = self.__get_most_probable_category__()
                 Print.info(
                     "default category was automatically set to '%s' "
-                    "(the most probable one)"
-                    %
-                    self.get_category_name(def_cat),
+                    "(the most probable one)" % self.get_most_probable_category(),
                     offset=1
                 )
             else:
-                def_cat = self.get_category_index(def_cat)
-                Print.info(
-                    "default category was set to '%s'"
-                    %
-                    self.get_category_name(def_cat),
-                    offset=1
-                )
+                Print.info("default category was set to '%s'" % def_cat, offset=1)
 
         stime = time()
         Print.info("about to start classifying test documents", offset=1)
-        classify = self.classify
+        classify = self.classify_label if not multilabel else self.classify_multilabel
 
         y_pred = [
-            r[0][0] if r[0][1] else def_cat
-            for r in [
-                classify(doc, prep=prep)
-                for doc in tqdm(
-                    x_test, desc=" Classification", leave=leave_pbar
-                )
-            ]
+            classify(doc, def_cat=def_cat, labels=labels, prep=prep)
+            for doc in tqdm(x_test, desc=" Classification", leave=leave_pbar)
         ]
 
-        if labels:
-            y_pred[:] = [categories[y] for y in y_pred]
         Print.info("finished --time: %.1fs" % (time() - stime), offset=1)
         return y_pred