Remove object subclassing

spacy/cli/pretrain.py

@@ -312,7 +312,7 @@ def create_pretraining_model(nlp, tok2vec, pretrain_config):
     return model
 
 
-class ProgressTracker(object):
+class ProgressTracker:
     def __init__(self, frequency=1000000):
         self.loss = 0.0
         self.prev_loss = 0.0

spacy/displacy/render.py

@@ -16,7 +16,7 @@ DEFAULT_LANG = "en"
 DEFAULT_DIR = "ltr"
 
 
-class DependencyRenderer(object):
+class DependencyRenderer:
     """Render dependency parses as SVGs."""
 
     style = "dep"
@@ -224,7 +224,7 @@ class DependencyRenderer(object):
         return sorted(list(levels))
 
 
-class EntityRenderer(object):
+class EntityRenderer:
     """Render named entities as HTML."""
 
     style = "ent"

spacy/errors.py

@@ -15,7 +15,7 @@ def add_codes(err_cls):
 # fmt: off
 
 @add_codes
-class Warnings(object):
+class Warnings:
     W004 = ("No text fixing enabled. Run `pip install ftfy` to enable fixing "
             "using ftfy.fix_text if necessary.")
     W005 = ("Doc object not parsed. This means displaCy won't be able to "
@@ -118,7 +118,7 @@ class Warnings(object):
 
 
 @add_codes
-class Errors(object):
+class Errors:
     E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
     E002 = ("Can't find factory for '{name}'. This usually happens when spaCy "
             "calls `nlp.create_pipe` with a component name that's not built "
@@ -538,7 +538,7 @@ class Errors(object):
 
 
 @add_codes
-class TempErrors(object):
+class TempErrors:
     T003 = ("Resizing pretrained Tagger models is not currently supported.")
     T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
             "issue tracker: http://github.com/explosion/spaCy/issues")

spacy/language.py

@@ -39,7 +39,7 @@ from . import about
 ENABLE_PIPELINE_ANALYSIS = False
 
 
-class BaseDefaults(object):
+class BaseDefaults:
     @classmethod
     def create_lemmatizer(cls, nlp=None, lookups=None):
         if lookups is None:
@@ -126,7 +126,7 @@ class BaseDefaults(object):
     paired_orth_variants = []
 
 
-class Language(object):
+class Language:
     """A text-processing pipeline. Usually you'll load this once per process,
     and pass the instance around your application.
 
@@ -531,10 +531,16 @@ class Language(object):
         if len(examples) == 0:
             return losses
         if not isinstance(examples, Iterable):
-            raise TypeError(Errors.E978.format(name="language", method="update", types=type(examples)))
+            raise TypeError(
+                Errors.E978.format(
+                    name="language", method="update", types=type(examples)
+                )
+            )
         wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)])
         if wrong_types:
-            raise TypeError(Errors.E978.format(name="language", method="update", types=wrong_types))
+            raise TypeError(
+                Errors.E978.format(name="language", method="update", types=wrong_types)
+            )
 
         if sgd is None:
             if self._optimizer is None:
@@ -580,10 +586,18 @@ class Language(object):
         if len(examples) == 0:
             return
         if not isinstance(examples, Iterable):
-            raise TypeError(Errors.E978.format(name="language", method="rehearse", types=type(examples)))
+            raise TypeError(
+                Errors.E978.format(
+                    name="language", method="rehearse", types=type(examples)
+                )
+            )
         wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)])
         if wrong_types:
-            raise TypeError(Errors.E978.format(name="language", method="rehearse", types=wrong_types))
+            raise TypeError(
+                Errors.E978.format(
+                    name="language", method="rehearse", types=wrong_types
+                )
+            )
         if sgd is None:
             if self._optimizer is None:
                 self._optimizer = create_default_optimizer()
@@ -692,10 +706,18 @@ class Language(object):
         DOCS: https://spacy.io/api/language#evaluate
         """
         if not isinstance(examples, Iterable):
-            raise TypeError(Errors.E978.format(name="language", method="evaluate", types=type(examples)))
+            raise TypeError(
+                Errors.E978.format(
+                    name="language", method="evaluate", types=type(examples)
+                )
+            )
         wrong_types = set([type(eg) for eg in examples if not isinstance(eg, Example)])
         if wrong_types:
-            raise TypeError(Errors.E978.format(name="language", method="evaluate", types=wrong_types))
+            raise TypeError(
+                Errors.E978.format(
+                    name="language", method="evaluate", types=wrong_types
+                )
+            )
         if scorer is None:
             scorer = Scorer(pipeline=self.pipeline)
         if component_cfg is None:
@@ -1043,7 +1065,7 @@ class Language(object):
         return self
 
 
-class component(object):
+class component:
     """Decorator for pipeline components. Can decorate both function components
     and class components and will automatically register components in the
     Language.factories. If the component is a class and needs access to the

spacy/lemmatizer.py

@@ -2,7 +2,7 @@ from .errors import Errors
 from .parts_of_speech import NAMES as UPOS_NAMES
 
 
-class Lemmatizer(object):
+class Lemmatizer:
     """
     The Lemmatizer supports simple part-of-speech-sensitive suffix rules and
     lookup tables.

spacy/lookups.py

@@ -10,7 +10,7 @@ from .strings import get_string_id
 UNSET = object()
 
 
-class Lookups(object):
+class Lookups:
     """Container for large lookup tables and dictionaries, e.g. lemmatization
     data or tokenizer exception lists. Lookups are available via vocab.lookups,
     so they can be accessed before the pipeline components are applied (e.g.

spacy/matcher/matcher.pyx

@@ -690,7 +690,7 @@ def _get_attr_values(spec, string_store):
 # These predicate helper classes are used to match the REGEX, IN, >= etc
 # extensions to the matcher introduced in #3173.
 
-class _RegexPredicate(object):
+class _RegexPredicate:
     operators = ("REGEX",)
 
     def __init__(self, i, attr, value, predicate, is_extension=False):
@@ -711,7 +711,7 @@ class _RegexPredicate(object):
         return bool(self.value.search(value))
 
 
-class _SetMemberPredicate(object):
+class _SetMemberPredicate:
     operators = ("IN", "NOT_IN")
 
     def __init__(self, i, attr, value, predicate, is_extension=False):
@@ -738,7 +738,7 @@ class _SetMemberPredicate(object):
         return repr(("SetMemberPredicate", self.i, self.attr, self.value, self.predicate))
 
 
-class _ComparisonPredicate(object):
+class _ComparisonPredicate:
     operators = ("==", "!=", ">=", "<=", ">", "<")
 
     def __init__(self, i, attr, value, predicate, is_extension=False):

spacy/ml/models/multi_task.py

@@ -94,7 +94,7 @@ def build_masked_language_model(vocab, wrapped_model, mask_prob=0.15):
     return mlm_model
 
 
-class _RandomWords(object):
+class _RandomWords:
     def __init__(self, vocab):
         self.words = [lex.text for lex in vocab if lex.prob != 0.0]
         self.probs = [lex.prob for lex in vocab if lex.prob != 0.0]

spacy/pipeline/entityruler.py

@@ -11,7 +11,7 @@ DEFAULT_ENT_ID_SEP = "||"
 
 
 @component("entity_ruler", assigns=["doc.ents", "token.ent_type", "token.ent_iob"])
-class EntityRuler(object):
+class EntityRuler:
     """The EntityRuler lets you add spans to the `Doc.ents` using token-based
     rules or exact phrase matches. It can be combined with the statistical
     `EntityRecognizer` to boost accuracy, or used on its own to implement a

spacy/pipeline/hooks.py

@@ -6,7 +6,7 @@ from ..util import link_vectors_to_models
 
 
 @component("sentencizer_hook", assigns=["doc.user_hooks"])
-class SentenceSegmenter(object):
+class SentenceSegmenter:
     """A simple spaCy hook, to allow custom sentence boundary detection logic
     (that doesn't require the dependency parse). To change the sentence
     boundary detection strategy, pass a generator function `strategy` on

spacy/pipeline/pipes.pyx

@@ -35,7 +35,7 @@ def _load_cfg(path):
         return {}
 
 
-class Pipe(object):
+class Pipe:
     """This class is not instantiated directly. Components inherit from it, and
     it defines the interface that components should follow to function as
     components in a spaCy analysis pipeline.

spacy/scorer.py

@@ -3,7 +3,7 @@ import numpy as np
 from .errors import Errors
 
 
-class PRFScore(object):
+class PRFScore:
     """
     A precision / recall / F score
     """
@@ -33,7 +33,7 @@ class PRFScore(object):
         return 2 * ((p * r) / (p + r + 1e-100))
 
 
-class ROCAUCScore(object):
+class ROCAUCScore:
     """
     An AUC ROC score.
     """
@@ -62,7 +62,7 @@ class ROCAUCScore(object):
         return self.saved_score
 
 
-class Scorer(object):
+class Scorer:
     """Compute evaluation scores."""
 
     def __init__(self, eval_punct=False, pipeline=None):

spacy/tests/parser/test_ner.py

@@ -378,7 +378,7 @@ def test_ner_warns_no_lookups():
         assert not record.list
 
 
-class BlockerComponent1(object):
+class BlockerComponent1:
     name = "my_blocker"
 
     def __init__(self, start, end):

spacy/tests/pipeline/test_analysis.py

@@ -20,7 +20,7 @@ def test_component_decorator_function():
 
 def test_component_decorator_class():
     @component(name="test")
-    class TestComponent(object):
+    class TestComponent:
         """docstring1"""
 
         foo = "bar"
@@ -97,7 +97,7 @@ def test_component_factories_from_nlp():
     """Test that class components can implement a from_nlp classmethod that
     gives them access to the nlp object and config via the factory."""
 
-    class TestComponent5(object):
+    class TestComponent5:
         def __call__(self, doc):
             return doc
 

spacy/tests/regression/test_issue5137.py

@@ -5,7 +5,7 @@ from spacy.tests.util import make_tempdir
 
 
 def test_issue5137():
-    class MyComponent(object):
+    class MyComponent:
         name = "my_component"
 
         def __init__(self, nlp, **cfg):

spacy/tests/regression/test_issue5230.py

@@ -24,7 +24,7 @@ def vectors():
 
 def custom_pipe():
     # create dummy pipe partially implementing interface -- only want to test to_disk
-    class SerializableDummy(object):
+    class SerializableDummy:
         def __init__(self, **cfg):
             if cfg:
                 self.cfg = cfg

spacy/tests/test_errors.py

@@ -6,7 +6,7 @@ from spacy.errors import add_codes
 
 
 @add_codes
-class Errors(object):
+class Errors:
     E001 = "error description"
 
 

spacy/tokens/_serialize.py

@@ -13,7 +13,7 @@ ALL_ATTRS = ("ORTH", "TAG", "HEAD", "DEP", "ENT_IOB", "ENT_TYPE", "ENT_KB_ID", "
 # fmt: on
 
 
-class DocBin(object):
+class DocBin:
     """Pack Doc objects for binary serialization.
 
     The DocBin class lets you efficiently serialize the information from a

spacy/tokens/underscore.py

@@ -4,7 +4,7 @@ import copy
 from ..errors import Errors
 
 
-class Underscore(object):
+class Underscore:
     mutable_types = (dict, list, set)
     doc_extensions = {}
     span_extensions = {}

spacy/util.py

@@ -997,7 +997,7 @@ class SimpleFrozenDict(dict):
         raise NotImplementedError(Errors.E095)
 
 
-class DummyTokenizer(object):
+class DummyTokenizer:
     # add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
     # allow serialization (see #1557)
     def to_bytes(self, **kwargs):

spacy/vectors.pyx

@@ -18,7 +18,7 @@ def unpickle_vectors(bytes_data):
     return Vectors().from_bytes(bytes_data)
 
 
-class GlobalRegistry(object):
+class GlobalRegistry:
     """Global store of vectors, to avoid repeatedly loading the data."""
     data = {}
 

website/docs/usage/linguistic-features.md

@@ -1004,7 +1004,7 @@ object. Let's say we have the following class as our tokenizer:
 import spacy
 from spacy.tokens import Doc
 
-class WhitespaceTokenizer(object):
+class WhitespaceTokenizer:
     def __init__(self, vocab):
         self.vocab = vocab
 

website/docs/usage/processing-pipelines.md

@@ -401,7 +401,7 @@ import spacy
 from spacy.matcher import PhraseMatcher
 from spacy.tokens import Span
 
-class EntityMatcher(object):
+class EntityMatcher:
     name = "entity_matcher"
 
     def __init__(self, nlp, terms, label):
@@ -683,7 +683,7 @@ to `Doc.user_span_hooks` and `Doc.user_token_hooks`.
 
 ```python
 ### Add custom similarity hooks
-class SimilarityModel(object):
+class SimilarityModel:
     def __init__(self, model):
         self._model = model
 

website/docs/usage/rule-based-matching.md

@@ -511,7 +511,7 @@ from spacy.tokens import Token
 
 # We're using a class because the component needs to be initialized with
 # the shared vocab via the nlp object
-class BadHTMLMerger(object):
+class BadHTMLMerger:
     def __init__(self, nlp):
         patterns = [
             [{"ORTH": "<"}, {"LOWER": "br"}, {"ORTH": ">"}],

website/docs/usage/saving-loading.md

@@ -193,7 +193,7 @@ add to that data and saves and loads the data to and from a JSON file.
 
 ```python
 ### {highlight="15-19,21-26"}
-class CustomComponent(object):
+class CustomComponent:
     name = "my_component"
 
     def __init__(self):
@@ -345,7 +345,7 @@ snek = """
               `'--....--'`
 """
 
-class SnekFactory(object):
+class SnekFactory:
     def __init__(self, nlp, **cfg):
         self.nlp = nlp
 
@@ -433,7 +433,7 @@ nlp = spacy.load("en_core_snek_sm", snek_style="cute")
 ```python
 SNEKS = {"basic": snek, "cute": cute_snek}  # collection of sneks
 
-class SnekFactory(object):
+class SnekFactory:
     def __init__(self, nlp, **cfg):
         self.nlp = nlp
         self.snek_style = cfg.get("snek_style", "basic")