Tidy up and auto-format

spacy/_ml.py

@@ -674,14 +674,14 @@ def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
     with Model.define_operators({">>": chain, "**": clone}):
         # context encoder
         tok2vec = Tok2Vec(
-                width=hidden_width,
-                embed_size=embed_width,
-                pretrained_vectors=pretrained_vectors,
-                cnn_maxout_pieces=cnn_maxout_pieces,
-                subword_features=True,
-                conv_depth=conv_depth,
-                bilstm_depth=0,
-            )
+            width=hidden_width,
+            embed_size=embed_width,
+            pretrained_vectors=pretrained_vectors,
+            cnn_maxout_pieces=cnn_maxout_pieces,
+            subword_features=True,
+            conv_depth=conv_depth,
+            bilstm_depth=0,
+        )
 
         model = (
             tok2vec

spacy/cli/debug_data.py

@@ -8,7 +8,7 @@ import sys
 import srsly
 from wasabi import Printer, MESSAGES
 
-from ..gold import GoldCorpus, read_json_object
+from ..gold import GoldCorpus
 from ..syntax import nonproj
 from ..util import load_model, get_lang_class
 
@@ -95,13 +95,19 @@ def debug_data(
         corpus = GoldCorpus(train_path, dev_path)
         try:
             train_docs = list(corpus.train_docs(nlp))
-            train_docs_unpreprocessed = list(corpus.train_docs_without_preprocessing(nlp))
+            train_docs_unpreprocessed = list(
+                corpus.train_docs_without_preprocessing(nlp)
+            )
         except ValueError as e:
-            loading_train_error_message = "Training data cannot be loaded: {}".format(str(e))
+            loading_train_error_message = "Training data cannot be loaded: {}".format(
+                str(e)
+            )
         try:
             dev_docs = list(corpus.dev_docs(nlp))
         except ValueError as e:
-            loading_dev_error_message = "Development data cannot be loaded: {}".format(str(e))
+            loading_dev_error_message = "Development data cannot be loaded: {}".format(
+                str(e)
+            )
     if loading_train_error_message or loading_dev_error_message:
         if loading_train_error_message:
             msg.fail(loading_train_error_message)
@@ -158,11 +164,15 @@ def debug_data(
     )
     if gold_train_data["n_misaligned_words"] > 0:
         msg.warn(
-            "{} misaligned tokens in the training data".format(gold_train_data["n_misaligned_words"])
+            "{} misaligned tokens in the training data".format(
+                gold_train_data["n_misaligned_words"]
+            )
         )
     if gold_dev_data["n_misaligned_words"] > 0:
         msg.warn(
-            "{} misaligned tokens in the dev data".format(gold_dev_data["n_misaligned_words"])
+            "{} misaligned tokens in the dev data".format(
+                gold_dev_data["n_misaligned_words"]
+            )
         )
     most_common_words = gold_train_data["words"].most_common(10)
     msg.text(
@@ -184,7 +194,9 @@ def debug_data(
 
     if "ner" in pipeline:
         # Get all unique NER labels present in the data
-        labels = set(label for label in gold_train_data["ner"] if label not in ("O", "-"))
+        labels = set(
+            label for label in gold_train_data["ner"] if label not in ("O", "-")
+        )
         label_counts = gold_train_data["ner"]
         model_labels = _get_labels_from_model(nlp, "ner")
         new_labels = [l for l in labels if l not in model_labels]
@@ -222,7 +234,9 @@ def debug_data(
             )
 
         if gold_train_data["ws_ents"]:
-            msg.fail("{} invalid whitespace entity spans".format(gold_train_data["ws_ents"]))
+            msg.fail(
+                "{} invalid whitespace entity spans".format(gold_train_data["ws_ents"])
+            )
             has_ws_ents_error = True
 
         for label in new_labels:
@@ -323,33 +337,36 @@ def debug_data(
             "Found {} sentence{} with an average length of {:.1f} words.".format(
                 gold_train_data["n_sents"],
                 "s" if len(train_docs) > 1 else "",
-                gold_train_data["n_words"] / gold_train_data["n_sents"]
+                gold_train_data["n_words"] / gold_train_data["n_sents"],
             )
         )
 
         # profile labels
         labels_train = [label for label in gold_train_data["deps"]]
-        labels_train_unpreprocessed = [label for label in gold_train_unpreprocessed_data["deps"]]
+        labels_train_unpreprocessed = [
+            label for label in gold_train_unpreprocessed_data["deps"]
+        ]
         labels_dev = [label for label in gold_dev_data["deps"]]
 
         if gold_train_unpreprocessed_data["n_nonproj"] > 0:
             msg.info(
                 "Found {} nonprojective train sentence{}".format(
                     gold_train_unpreprocessed_data["n_nonproj"],
-                    "s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else ""
+                    "s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else "",
                 )
             )
         if gold_dev_data["n_nonproj"] > 0:
             msg.info(
                 "Found {} nonprojective dev sentence{}".format(
                     gold_dev_data["n_nonproj"],
-                    "s" if gold_dev_data["n_nonproj"] > 1 else ""
+                    "s" if gold_dev_data["n_nonproj"] > 1 else "",
                 )
             )
 
         msg.info(
             "{} {} in train data".format(
-                len(labels_train_unpreprocessed), "label" if len(labels_train) == 1 else "labels"
+                len(labels_train_unpreprocessed),
+                "label" if len(labels_train) == 1 else "labels",
             )
         )
         msg.info(
@@ -373,43 +390,45 @@ def debug_data(
                 )
                 has_low_data_warning = True
 
-
         # rare labels in projectivized train
         rare_projectivized_labels = []
         for label in gold_train_data["deps"]:
             if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
-                rare_projectivized_labels.append("{}: {}".format(label, str(gold_train_data["deps"][label])))
+                rare_projectivized_labels.append(
+                    "{}: {}".format(label, str(gold_train_data["deps"][label]))
+                )
 
         if len(rare_projectivized_labels) > 0:
-                msg.warn(
-                    "Low number of examples for {} label{} in the "
-                    "projectivized dependency trees used for training. You may "
-                    "want to projectivize labels such as punct before "
-                    "training in order to improve parser performance.".format(
-                        len(rare_projectivized_labels),
-                        "s" if len(rare_projectivized_labels) > 1 else "")
+            msg.warn(
+                "Low number of examples for {} label{} in the "
+                "projectivized dependency trees used for training. You may "
+                "want to projectivize labels such as punct before "
+                "training in order to improve parser performance.".format(
+                    len(rare_projectivized_labels),
+                    "s" if len(rare_projectivized_labels) > 1 else "",
                 )
-                msg.warn(
-                    "Projectivized labels with low numbers of examples: "
-                    "{}".format("\n".join(rare_projectivized_labels)),
-                    show=verbose
-                )
-                has_low_data_warning = True
+            )
+            msg.warn(
+                "Projectivized labels with low numbers of examples: "
+                "{}".format("\n".join(rare_projectivized_labels)),
+                show=verbose,
+            )
+            has_low_data_warning = True
 
         # labels only in train
         if set(labels_train) - set(labels_dev):
             msg.warn(
                 "The following labels were found only in the train data: "
                 "{}".format(", ".join(set(labels_train) - set(labels_dev))),
-                show=verbose
+                show=verbose,
             )
 
         # labels only in dev
         if set(labels_dev) - set(labels_train):
             msg.warn(
-                "The following labels were found only in the dev data: " +
-                ", ".join(set(labels_dev) - set(labels_train)),
-                show=verbose
+                "The following labels were found only in the dev data: "
+                + ", ".join(set(labels_dev) - set(labels_train)),
+                show=verbose,
             )
 
         if has_low_data_warning:
@@ -422,8 +441,10 @@ def debug_data(
         # multiple root labels
         if len(gold_train_unpreprocessed_data["roots"]) > 1:
             msg.warn(
-                "Multiple root labels ({}) ".format(", ".join(gold_train_unpreprocessed_data["roots"])) +
-                "found in training data. spaCy's parser uses a single root "
+                "Multiple root labels ({}) ".format(
+                    ", ".join(gold_train_unpreprocessed_data["roots"])
+                )
+                + "found in training data. spaCy's parser uses a single root "
                 "label ROOT so this distinction will not be available."
             )
 
@@ -432,14 +453,14 @@ def debug_data(
             msg.fail(
                 "Found {} nonprojective projectivized train sentence{}".format(
                     gold_train_data["n_nonproj"],
-                    "s" if gold_train_data["n_nonproj"] > 1 else ""
+                    "s" if gold_train_data["n_nonproj"] > 1 else "",
                 )
             )
         if gold_train_data["n_cycles"] > 0:
             msg.fail(
                 "Found {} projectivized train sentence{} with cycles".format(
                     gold_train_data["n_cycles"],
-                    "s" if gold_train_data["n_cycles"] > 1 else ""
+                    "s" if gold_train_data["n_cycles"] > 1 else "",
                 )
             )
 

spacy/cli/init_model.py

@@ -114,7 +114,7 @@ def read_attrs_from_deprecated(freqs_loc, clusters_loc):
             probs, _ = read_freqs(freqs_loc)
         msg.good("Counted frequencies")
     else:
-        probs, _ = ({}, DEFAULT_OOV_PROB)
+        probs, _ = ({}, DEFAULT_OOV_PROB)  # noqa: F841
     if clusters_loc:
         with msg.loading("Reading clusters..."):
             clusters = read_clusters(clusters_loc)

spacy/errors.py

@@ -429,6 +429,7 @@ class Errors(object):
     E155 = ("The `nlp` object should have access to pre-trained word vectors, cf. "
             "https://spacy.io/usage/models#languages.")
 
+
 @add_codes
 class TempErrors(object):
     T003 = ("Resizing pre-trained Tagger models is not currently supported.")

spacy/lang/ko/__init__.py

@@ -1,10 +1,8 @@
 # encoding: utf8
 from __future__ import unicode_literals, print_function
 
-import re
 import sys
 
-
 from .stop_words import STOP_WORDS
 from .tag_map import TAG_MAP
 from ...attrs import LANG
@@ -32,7 +30,7 @@ else:
     from typing import NamedTuple
 
     class Morpheme(NamedTuple):
-        
+
         surface = str("")
         lemma = str("")
         tag = str("")

spacy/lang/zh/__init__.py

@@ -8,6 +8,7 @@ from ..tokenizer_exceptions import BASE_EXCEPTIONS
 from .stop_words import STOP_WORDS
 from .tag_map import TAG_MAP
 
+
 class ChineseDefaults(Language.Defaults):
     lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
     lex_attr_getters[LANG] = lambda text: "zh"
@@ -45,4 +46,4 @@ class Chinese(Language):
             return Doc(self.vocab, words=words, spaces=spaces)
 
 
-__all__ = ["Chinese"]
+__all__ = ["Chinese"]

spacy/lang/zh/tag_map.py

@@ -1,8 +1,8 @@
 # coding: utf8
 from __future__ import unicode_literals
 
-from ...symbols import POS, PUNCT, SYM, ADJ, CONJ, CCONJ, NUM, DET, ADV, ADP, X, VERB
-from ...symbols import NOUN, PROPN, PART, INTJ, SPACE, PRON, AUX
+from ...symbols import POS, PUNCT, ADJ, CONJ, CCONJ, NUM, DET, ADV, ADP, X, VERB
+from ...symbols import NOUN, PART, INTJ, PRON
 
 # The Chinese part-of-speech tagger uses the OntoNotes 5 version of the Penn Treebank tag set.
 # We also map the tags to the simpler Google Universal POS tag set.
@@ -43,5 +43,5 @@ TAG_MAP = {
     "JJ": {POS: ADJ},
     "P": {POS: ADP},
     "PN": {POS: PRON},
-    "PU": {POS: PUNCT}
-}
+    "PU": {POS: PUNCT},
+}

spacy/scorer.py

@@ -160,14 +160,15 @@ class Scorer(object):
                     cand_deps.add((gold_i, gold_head, token.dep_.lower()))
         if "-" not in [token[-1] for token in gold.orig_annot]:
             # Find all NER labels in gold and doc
-            ent_labels = set([x[0] for x in gold_ents]
-                    + [k.label_ for k in doc.ents])
+            ent_labels = set([x[0] for x in gold_ents] + [k.label_ for k in doc.ents])
             # Set up all labels for per type scoring and prepare gold per type
             gold_per_ents = {ent_label: set() for ent_label in ent_labels}
             for ent_label in ent_labels:
                 if ent_label not in self.ner_per_ents:
                     self.ner_per_ents[ent_label] = PRFScore()
-                gold_per_ents[ent_label].update([x for x in gold_ents if x[0] == ent_label])
+                gold_per_ents[ent_label].update(
+                    [x for x in gold_ents if x[0] == ent_label]
+                )
             # Find all candidate labels, for all and per type
             cand_ents = set()
             cand_per_ents = {ent_label: set() for ent_label in ent_labels}

spacy/tests/regression/test_issue4002.py

@@ -1,7 +1,6 @@
 # coding: utf8
 from __future__ import unicode_literals
 
-import pytest
 from spacy.matcher import PhraseMatcher
 from spacy.tokens import Doc
 

spacy/tests/regression/test_issue4104.py

@@ -3,12 +3,13 @@ from __future__ import unicode_literals
 
 from ..util import get_doc
 
+
 def test_issue4104(en_vocab):
     """Test that English lookup lemmatization of spun & dry are correct
     expected mapping = {'dry': 'dry', 'spun': 'spin', 'spun-dry': 'spin-dry'}
-	"""
-    text = 'dry spun spun-dry'
+    """
+    text = "dry spun spun-dry"
     doc = get_doc(en_vocab, [t for t in text.split(" ")])
     # using a simple list to preserve order
-    expected = ['dry', 'spin', 'spin-dry']
+    expected = ["dry", "spin", "spin-dry"]
     assert [token.lemma_ for token in doc] == expected

spacy/tests/test_gold.py

@@ -6,6 +6,7 @@ from spacy.gold import spans_from_biluo_tags, GoldParse
 from spacy.tokens import Doc
 import pytest
 
+
 def test_gold_biluo_U(en_vocab):
     words = ["I", "flew", "to", "London", "."]
     spaces = [True, True, True, False, True]
@@ -32,14 +33,18 @@ def test_gold_biluo_BIL(en_vocab):
     tags = biluo_tags_from_offsets(doc, entities)
     assert tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
 
+
 def test_gold_biluo_overlap(en_vocab):
     words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
     spaces = [True, True, True, True, True, False, True]
     doc = Doc(en_vocab, words=words, spaces=spaces)
-    entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
-    (len("I flew to "), len("I flew to San Francisco"), "LOC")]
+    entities = [
+        (len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
+        (len("I flew to "), len("I flew to San Francisco"), "LOC"),
+    ]
     with pytest.raises(ValueError):
-        tags = biluo_tags_from_offsets(doc, entities)
+        biluo_tags_from_offsets(doc, entities)
+
 
 def test_gold_biluo_misalign(en_vocab):
     words = ["I", "flew", "to", "San", "Francisco", "Valley."]

spacy/tests/test_scorer.py

@@ -7,67 +7,62 @@ from spacy.scorer import Scorer
 from .util import get_doc
 
 test_ner_cardinal = [
-    [
-        "100 - 200",
-        {
-            "entities": [
-                [0, 3, "CARDINAL"],
-                [6, 9, "CARDINAL"]
-            ]
-        }
-    ]
+    ["100 - 200", {"entities": [[0, 3, "CARDINAL"], [6, 9, "CARDINAL"]]}]
 ]
 
 test_ner_apple = [
     [
         "Apple is looking at buying U.K. startup for $1 billion",
-        {
-            "entities": [
-                (0, 5, "ORG"),
-                (27, 31, "GPE"),
-                (44, 54, "MONEY"),
-            ]
-        }
+        {"entities": [(0, 5, "ORG"), (27, 31, "GPE"), (44, 54, "MONEY")]},
     ]
 ]
 
+
 def test_ner_per_type(en_vocab):
     # Gold and Doc are identical
     scorer = Scorer()
     for input_, annot in test_ner_cardinal:
-        doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0, 1, 'CARDINAL'], [2, 3, 'CARDINAL']])
-        gold = GoldParse(doc, entities = annot['entities'])
+        doc = get_doc(
+            en_vocab,
+            words=input_.split(" "),
+            ents=[[0, 1, "CARDINAL"], [2, 3, "CARDINAL"]],
+        )
+        gold = GoldParse(doc, entities=annot["entities"])
         scorer.score(doc, gold)
     results = scorer.scores
 
-    assert results['ents_p'] == 100
-    assert results['ents_f'] == 100
-    assert results['ents_r'] == 100
-    assert results['ents_per_type']['CARDINAL']['p'] == 100
-    assert results['ents_per_type']['CARDINAL']['f'] == 100
-    assert results['ents_per_type']['CARDINAL']['r'] == 100
+    assert results["ents_p"] == 100
+    assert results["ents_f"] == 100
+    assert results["ents_r"] == 100
+    assert results["ents_per_type"]["CARDINAL"]["p"] == 100
+    assert results["ents_per_type"]["CARDINAL"]["f"] == 100
+    assert results["ents_per_type"]["CARDINAL"]["r"] == 100
 
     # Doc has one missing and one extra entity
     # Entity type MONEY is not present in Doc
     scorer = Scorer()
     for input_, annot in test_ner_apple:
-        doc = get_doc(en_vocab, words = input_.split(' '), ents = [[0, 1, 'ORG'], [5, 6, 'GPE'], [6, 7, 'ORG']])
-        gold = GoldParse(doc, entities = annot['entities'])
+        doc = get_doc(
+            en_vocab,
+            words=input_.split(" "),
+            ents=[[0, 1, "ORG"], [5, 6, "GPE"], [6, 7, "ORG"]],
+        )
+        gold = GoldParse(doc, entities=annot["entities"])
         scorer.score(doc, gold)
     results = scorer.scores
 
-    assert results['ents_p'] == approx(66.66666)
-    assert results['ents_r'] == approx(66.66666)
-    assert results['ents_f'] == approx(66.66666)
-    assert 'GPE' in results['ents_per_type']
-    assert 'MONEY' in results['ents_per_type']
-    assert 'ORG' in results['ents_per_type']
-    assert results['ents_per_type']['GPE']['p'] == 100
-    assert results['ents_per_type']['GPE']['r'] == 100
-    assert results['ents_per_type']['GPE']['f'] == 100
-    assert results['ents_per_type']['MONEY']['p'] == 0
-    assert results['ents_per_type']['MONEY']['r'] == 0
-    assert results['ents_per_type']['MONEY']['f'] == 0
-    assert results['ents_per_type']['ORG']['p'] == 50
-    assert results['ents_per_type']['ORG']['r'] == 100
-    assert results['ents_per_type']['ORG']['f'] == approx(66.66666)
+    assert results["ents_p"] == approx(66.66666)
+    assert results["ents_r"] == approx(66.66666)
+    assert results["ents_f"] == approx(66.66666)
+    assert "GPE" in results["ents_per_type"]
+    assert "MONEY" in results["ents_per_type"]
+    assert "ORG" in results["ents_per_type"]
+    assert results["ents_per_type"]["GPE"]["p"] == 100
+    assert results["ents_per_type"]["GPE"]["r"] == 100
+    assert results["ents_per_type"]["GPE"]["f"] == 100
+    assert results["ents_per_type"]["MONEY"]["p"] == 0
+    assert results["ents_per_type"]["MONEY"]["r"] == 0
+    assert results["ents_per_type"]["MONEY"]["f"] == 0
+    assert results["ents_per_type"]["ORG"]["p"] == 50
+    assert results["ents_per_type"]["ORG"]["r"] == 100
+    assert results["ents_per_type"]["ORG"]["f"] == approx(66.66666)