Hotfix

src/fonduer/features/feature_libs/tree_structs.py

@@ -42,7 +42,7 @@ def corenlp_to_xmltree(s, prune_root=True):
     if not isinstance(s, dict):
         try:
             s = s.__dict__ if hasattr(s, "__dict__") else dict(s)
-        except Exception as e:
+        except Exception:
             raise ValueError("Cannot convert input object to dict")
 
     # Use the dep_parents array as a guide: ensure it is present and a list of
@@ -53,7 +53,7 @@ def corenlp_to_xmltree(s, prune_root=True):
         )
     try:
         dep_parents = list(map(int, s["dep_parents"]))
-    except Exception as e:
+    except Exception:
         raise ValueError("'dep_parents' attribute must be a list of ints")
 
     # Also ensure that we are using CoreNLP-native indexing

src/fonduer/learning/utils.py

@@ -29,7 +29,7 @@ def save_marginals(session, X, marginals, training=True):
     # Make sure that we are working with a numpy array
     try:
         shape = marginals.shape
-    except Exception as e:
+    except Exception:
         marginals = np.array(marginals)
         shape = marginals.shape
 
@@ -82,7 +82,7 @@ def reshape_marginals(marginals):
     # Make sure training marginals are a numpy array first
     try:
         shape = marginals.shape
-    except Exception as e:
+    except Exception:
         marginals = np.array(marginals)
         shape = marginals.shape
 

src/fonduer/parser/parser.py

@@ -484,7 +484,7 @@ def _parse_sentence(self, paragraph, node, state):
                 if styles is not None:
                     for x in list(context_node.attrib.items()):
                         if x[0] == "class":
-                            exp = r"(." + x[1] + ")([\n\s\r]*)\{(.*?)\}"
+                            exp = r"(." + x[1] + r")([\n\s\r]*)\{(.*?)\}"
                             r = re.compile(exp, re.DOTALL)
                             if r.search(styles.text) is not None:
                                 if cur_style_index is not None:

src/fonduer/parser/spacy_parser.py

@@ -11,7 +11,7 @@
     from spacy.cli import download
     from spacy import util
     from spacy.tokens import Doc
-except Exception as e:
+except Exception:
     raise Exception("spaCy not installed. Use `pip install spacy`.")
 
 

src/fonduer/parser/visual_linker.py

@@ -24,7 +24,7 @@ def __init__(self, time=False, verbose=False):
         self.links = None
         self.pdf_dim = None
         delimiters = (
-            u"([\(\)\,\?\u2212\u201C\u201D\u2018\u2019\u00B0\*']|(?<!http):|\.$|\.\.\.)"
+            r"([\(\)\,\?\u2212\u201C\u201D\u2018\u2019\u00B0\*']|(?<!http):|\.$|\.\.\.)"
         )
         self.separators = re.compile(delimiters)
 
@@ -289,7 +289,7 @@ def _calculate_offset(self, listA, listB, seedSize, maxOffset):
         for i in range(seedSize):
             try:
                 offsets.append(wordsB.index(wordsA[i]) - i)
-            except Exception as e:
+            except Exception:
                 pass
         return int(np.median(offsets))
 

tests/e2e/test_e2e.py

@@ -483,7 +483,7 @@ def test_e2e(caplog):
     assert L_train_gold[0].shape == (3684, 1)
 
     gen_model = LabelModel(k=2)
-    gen_model.train(L_train[0], n_epochs=500, print_every=100)
+    gen_model.train_model(L_train[0], n_epochs=500, print_every=100)
 
     train_marginals = gen_model.predict_proba(L_train[0])[:, 1]
 
@@ -537,7 +537,7 @@ def test_e2e(caplog):
     assert L_train[0].shape == (3684, 16)
 
     gen_model = LabelModel(k=2)
-    gen_model.train(L_train[0], n_epochs=500, print_every=100)
+    gen_model.train_model(L_train[0], n_epochs=500, print_every=100)
 
     train_marginals = gen_model.predict_proba(L_train[0])[:, 1]
 

tests/shared/hardware_matchers.py

@@ -67,7 +67,7 @@ def part_file_name_conditions(attr):
     )
 
 
-add_rgx = "^[A-Z0-9\-]{5,15}$"
+add_rgx = r"^[A-Z0-9\-]{5,15}$"
 
 part_file_name_lambda_matcher = LambdaFunctionMatcher(func=part_file_name_conditions)
 part_file_name_matcher = Intersect(

tests/shared/hardware_spaces.py

@@ -236,7 +236,7 @@ def __init__(self, n_max=2, split_tokens=["-", "/"]):
     def apply(self, session, context):
         for ts in MentionNgrams.apply(self, session, context):
             m = re.match(
-                u"^([\+\-\u2010\u2011\u2012\u2013\u2014\u2212\uf02d])?(\s*)(\d+)$",
+                r"^([\+\-\u2010\u2011\u2012\u2013\u2014\u2212\uf02d])?(\s*)(\d+)$",
                 ts.get_span(),
                 re.U,
             )