Reverting unnecessary changes. Removing unused default values. Renaming variables in find-cli tests.

spacy/cli/find_threshold.py

@@ -13,7 +13,6 @@ from ._util import app, Arg, Opt, import_code, setup_gpu
 from .. import util
 
 _DEFAULTS = {
-    "average": "micro",
     "n_trials": 10,
     "use_gpu": -1,
     "gold_preproc": False,

spacy/scorer.py

@@ -21,7 +21,13 @@ MISSING_VALUES = frozenset([None, 0, ""])
 class PRFScore:
     """A precision / recall / F score."""
 
-    def __init__(self, *, tp: int = 0, fp: int = 0, fn: int = 0) -> None:
+    def __init__(
+        self,
+        *,
+        tp: int = 0,
+        fp: int = 0,
+        fn: int = 0
+    ) -> None:
         self.tp = tp
         self.fp = fp
         self.fn = fn
@@ -37,9 +43,7 @@ class PRFScore:
 
     def __add__(self, other):
         return PRFScore(
-            tp=self.tp + other.tp,
-            fp=self.fp + other.fp,
-            fn=self.fn + other.fn,
+            tp=self.tp + other.tp, fp=self.fp + other.fp, fn=self.fn + other.fn,
         )
 
     def score_set(self, cand: set, gold: set) -> None:

spacy/tests/test_cli.py

@@ -889,8 +889,8 @@ def test_cli_find_threshold(capsys):
     def init_nlp(
         components: Tuple[Tuple[str, Dict[str, Any]], ...] = ()
     ) -> Tuple[Language, List[Example]]:
-        _nlp = English()
-        textcat: TextCategorizer = _nlp.add_pipe(  # type: ignore
+        new_nlp = English()
+        textcat: TextCategorizer = new_nlp.add_pipe(  # type: ignore
             factory_name="textcat_multilabel",
             name="tc_multi",
             config={"threshold": 0.9},
@@ -901,17 +901,17 @@ def test_cli_find_threshold(capsys):
 
         # Append additional components to pipeline.
         for cfn, comp_config in components:
-            comp = _nlp.add_pipe(cfn, config=comp_config)
+            comp = new_nlp.add_pipe(cfn, config=comp_config)
             if isinstance(comp, TextCategorizer):
                 for label in textcat_labels:
                     comp.add_label(label)
 
-        _examples = make_examples(_nlp)
-        _nlp.initialize(get_examples=lambda: _examples)
+        new_examples = make_examples(new_nlp)
+        new_nlp.initialize(get_examples=lambda: new_examples)
         for i in range(5):
-            _nlp.update(_examples)
+            new_nlp.update(new_examples)
 
-        return _nlp, _examples
+        return new_nlp, new_examples
 
     with make_tempdir() as docs_dir:
         # Check whether find_threshold() identifies lowest threshold above 0 as (first) ideal threshold, as this matches