Adapt test_incremental.py to spaCy v2.2

tests/e2e/test_incremental.py

@@ -119,16 +119,17 @@ def test_incremental():
     featurizer = Featurizer(session, [PartTemp])
 
     featurizer.apply(split=0, train=True, parallelism=PARALLEL)
-    assert session.query(Feature).count() == 70
-    assert session.query(FeatureKey).count() == 512
+    assert session.query(Feature).count() == len(train_cands[0])
+    assert session.query(FeatureKey).count() == 526
+    num_feature_keys = session.query(FeatureKey).count()
 
     F_train = featurizer.get_feature_matrices(train_cands)
-    assert F_train[0].shape == (70, 512)
-    assert len(featurizer.get_keys()) == 512
+    assert F_train[0].shape == (len(train_cands[0]), num_feature_keys)
+    assert len(featurizer.get_keys()) == num_feature_keys
 
     # Test Dropping FeatureKey
     featurizer.drop_keys(["CORE_e1_LENGTH_1"])
-    assert session.query(FeatureKey).count() == 512
+    assert session.query(FeatureKey).count() == num_feature_keys
 
     stg_temp_lfs = [
         LF_storage_row,
@@ -142,12 +143,12 @@ def test_incremental():
     labeler = Labeler(session, [PartTemp])
 
     labeler.apply(split=0, lfs=[stg_temp_lfs], train=True, parallelism=PARALLEL)
-    assert session.query(Label).count() == 70
+    assert session.query(Label).count() == len(train_cands[0])
 
     # Only 5 because LF_operating_row doesn't apply to the first test doc
     assert session.query(LabelKey).count() == 5
     L_train = labeler.get_label_matrices(train_cands)
-    assert L_train[0].shape == (70, 5)
+    assert L_train[0].shape == (len(train_cands[0]), 5)
     assert len(labeler.get_keys()) == 5
 
     docs_path = "tests/data/html/112823.html"
@@ -180,21 +181,22 @@ def test_incremental():
     # Grab candidate lists
     train_cands = candidate_extractor.get_candidates(split=0)
     assert len(train_cands) == 1
-    assert len(train_cands[0]) == 1502
+    assert len(train_cands[0]) == 1501
 
     # Test if existing candidates are skipped.
     candidate_extractor.apply(new_docs, split=0, parallelism=PARALLEL, clear=False)
     train_cands = candidate_extractor.get_candidates(split=0)
     assert len(train_cands) == 1
-    assert len(train_cands[0]) == 1502
+    assert len(train_cands[0]) == 1501
 
     # Update features
     featurizer.update(new_docs, parallelism=PARALLEL)
-    assert session.query(Feature).count() == 1502
-    assert session.query(FeatureKey).count() == 2573
+    assert session.query(Feature).count() == len(train_cands[0])
+    assert session.query(FeatureKey).count() == 2526
+    num_feature_keys = session.query(FeatureKey).count()
     F_train = featurizer.get_feature_matrices(train_cands)
-    assert F_train[0].shape == (1502, 2573)
-    assert len(featurizer.get_keys()) == 2573
+    assert F_train[0].shape == (len(train_cands[0]), num_feature_keys)
+    assert len(featurizer.get_keys()) == num_feature_keys
 
     # Update LF_storage_row. Now it always returns ABSTAIN.
     @labeling_function(name="LF_storage_row")
@@ -213,11 +215,12 @@ def LF_storage_row_updated(c):
     # Update Labels
     labeler.update(docs, lfs=[stg_temp_lfs], parallelism=PARALLEL)
     labeler.update(new_docs, lfs=[stg_temp_lfs], parallelism=PARALLEL)
-    assert session.query(Label).count() == 1502
+    assert session.query(Label).count() == len(train_cands[0])
     # Only 5 because LF_storage_row doesn't apply to any doc (always ABSTAIN)
     assert session.query(LabelKey).count() == 5
+    num_label_keys = session.query(LabelKey).count()
     L_train = labeler.get_label_matrices(train_cands)
-    assert L_train[0].shape == (1502, 5)
+    assert L_train[0].shape == (len(train_cands[0]), num_label_keys)
 
     # Test clear
     featurizer.clear(train=True)