diff --git a/.github/contributors/kevinlu1248.md b/.github/contributors/kevinlu1248.md new file mode 100644 index 000000000..fc974ec95 --- /dev/null +++ b/.github/contributors/kevinlu1248.md @@ -0,0 +1,106 @@ +# spaCy contributor agreement + +This spaCy Contributor Agreement (**"SCA"**) is based on the +[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf). +The SCA applies to any contribution that you make to any product or project +managed by us (the **"project"**), and sets out the intellectual property rights +you grant to us in the contributed materials. The term **"us"** shall mean +[ExplosionAI GmbH](https://explosion.ai/legal). The term +**"you"** shall mean the person or entity identified below. + +If you agree to be bound by these terms, fill in the information requested +below and include the filled-in version with your first pull request, under the +folder [`.github/contributors/`](/.github/contributors/). The name of the file +should be your GitHub username, with the extension `.md`. For example, the user +example_user would create the file `.github/contributors/example_user.md`. + +Read this agreement carefully before signing. These terms and conditions +constitute a binding legal agreement. + +## Contributor Agreement + +1. The term "contribution" or "contributed materials" means any source code, +object code, patch, tool, sample, graphic, specification, manual, +documentation, or any other material posted or submitted by you to the project. + +2. With respect to any worldwide copyrights, or copyright applications and +registrations, in your contribution: + + * you hereby assign to us joint ownership, and to the extent that such + assignment is or becomes invalid, ineffective or unenforceable, you hereby + grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge, + royalty-free, unrestricted license to exercise all rights under those + copyrights. This includes, at our option, the right to sublicense these same + rights to third parties through multiple levels of sublicensees or other + licensing arrangements; + + * you agree that each of us can do all things in relation to your + contribution as if each of us were the sole owners, and if one of us makes + a derivative work of your contribution, the one who makes the derivative + work (or has it made will be the sole owner of that derivative work; + + * you agree that you will not assert any moral rights in your contribution + against us, our licensees or transferees; + + * you agree that we may register a copyright in your contribution and + exercise all ownership rights associated with it; and + + * you agree that neither of us has any duty to consult with, obtain the + consent of, pay or render an accounting to the other for any use or + distribution of your contribution. + +3. With respect to any patents you own, or that you can license without payment +to any third party, you hereby grant to us a perpetual, irrevocable, +non-exclusive, worldwide, no-charge, royalty-free license to: + + * make, have made, use, sell, offer to sell, import, and otherwise transfer + your contribution in whole or in part, alone or in combination with or + included in any product, work or materials arising out of the project to + which your contribution was submitted, and + + * at our option, to sublicense these same rights to third parties through + multiple levels of sublicensees or other licensing arrangements. + +4. Except as set out above, you keep all right, title, and interest in your +contribution. The rights that you grant to us under these terms are effective +on the date you first submitted a contribution to us, even if your submission +took place before the date you sign these terms. + +5. You covenant, represent, warrant and agree that: + + * Each contribution that you submit is and shall be an original work of + authorship and you can legally grant the rights set out in this SCA; + + * to the best of your knowledge, each contribution will not violate any + third party's copyrights, trademarks, patents, or other intellectual + property rights; and + + * each contribution shall be in compliance with U.S. export control laws and + other applicable export and import laws. You agree to notify us if you + become aware of any circumstance which would make any of the foregoing + representations inaccurate in any respect. We may publicly disclose your + participation in the project, including the fact that you have signed the SCA. + +6. This SCA is governed by the laws of the State of California and applicable +U.S. Federal law. Any choice of law rules will not apply. + +7. Please place an “x” on one of the applicable statement below. Please do NOT +mark both statements: + + * [x] I am signing on behalf of myself as an individual and no other person + or entity, including my employer, has or will have rights with respect to my + contributions. + + * [ ] I am signing on behalf of my employer or a legal entity and I have the + actual authority to contractually bind that entity. + +## Contributor Details + +| Field | Entry | +|------------------------------- | -------------------- | +| Name | Kevin Lu| +| Company name (if applicable) | | +| Title or role (if applicable) | Student| +| Date | | +| GitHub username | kevinlu1248| +| Website (optional) | | diff --git a/spacy/cli/debug_data.py b/spacy/cli/debug_data.py index 279f34f16..7a4a093e2 100644 --- a/spacy/cli/debug_data.py +++ b/spacy/cli/debug_data.py @@ -187,12 +187,17 @@ def debug_data( n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values()) msg.warn( "{} words in training data without vectors ({:0.2f}%)".format( - n_missing_vectors, - n_missing_vectors / gold_train_data["n_words"], + n_missing_vectors, n_missing_vectors / gold_train_data["n_words"], ), ) msg.text( - "10 most common words without vectors: {}".format(_format_labels(gold_train_data["words_missing_vectors"].most_common(10), counts=True)), show=verbose, + "10 most common words without vectors: {}".format( + _format_labels( + gold_train_data["words_missing_vectors"].most_common(10), + counts=True, + ) + ), + show=verbose, ) else: msg.info("No word vectors present in the model") diff --git a/spacy/cli/init_model.py b/spacy/cli/init_model.py index 537afd10f..7fdd39932 100644 --- a/spacy/cli/init_model.py +++ b/spacy/cli/init_model.py @@ -18,6 +18,8 @@ from wasabi import msg from ..vectors import Vectors from ..errors import Errors, Warnings from ..util import ensure_path, get_lang_class, load_model, OOV_RANK +from ..lookups import Lookups + try: import ftfy @@ -49,6 +51,7 @@ DEFAULT_OOV_PROB = -20 str, ), model_name=("Optional name for the model meta", "option", "mn", str), + omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool), base_model=("Base model (for languages with custom tokenizers)", "option", "b", str), ) def init_model( @@ -62,6 +65,7 @@ def init_model( prune_vectors=-1, vectors_name=None, model_name=None, + omit_extra_lookups=False, base_model=None, ): """ @@ -95,6 +99,15 @@ def init_model( with msg.loading("Creating model..."): nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model) + + # Create empty extra lexeme tables so the data from spacy-lookups-data + # isn't loaded if these features are accessed + if omit_extra_lookups: + nlp.vocab.lookups_extra = Lookups() + nlp.vocab.lookups_extra.add_table("lexeme_cluster") + nlp.vocab.lookups_extra.add_table("lexeme_prob") + nlp.vocab.lookups_extra.add_table("lexeme_settings") + msg.good("Successfully created model") if vectors_loc is not None: add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name) diff --git a/spacy/cli/train.py b/spacy/cli/train.py index 7cb2d9745..6ce095c15 100644 --- a/spacy/cli/train.py +++ b/spacy/cli/train.py @@ -17,6 +17,7 @@ from .._ml import create_default_optimizer from ..util import use_gpu as set_gpu from ..gold import GoldCorpus from ..compat import path2str +from ..lookups import Lookups from .. import util from .. import about @@ -57,6 +58,7 @@ from .. import about textcat_arch=("Textcat model architecture", "option", "ta", str), textcat_positive_label=("Textcat positive label for binary classes with two labels", "option", "tpl", str), tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path), + omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool), verbose=("Display more information for debug", "flag", "VV", bool), debug=("Run data diagnostics before training", "flag", "D", bool), # fmt: on @@ -96,6 +98,7 @@ def train( textcat_arch="bow", textcat_positive_label=None, tag_map_path=None, + omit_extra_lookups=False, verbose=False, debug=False, ): @@ -247,6 +250,14 @@ def train( # Update tag map with provided mapping nlp.vocab.morphology.tag_map.update(tag_map) + # Create empty extra lexeme tables so the data from spacy-lookups-data + # isn't loaded if these features are accessed + if omit_extra_lookups: + nlp.vocab.lookups_extra = Lookups() + nlp.vocab.lookups_extra.add_table("lexeme_cluster") + nlp.vocab.lookups_extra.add_table("lexeme_prob") + nlp.vocab.lookups_extra.add_table("lexeme_settings") + if vectors: msg.text("Loading vector from model '{}'".format(vectors)) _load_vectors(nlp, vectors) diff --git a/spacy/errors.py b/spacy/errors.py index f0b8592df..0750ab616 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -8,7 +8,7 @@ def add_codes(err_cls): class ErrorsWithCodes(err_cls): def __getattribute__(self, code): msg = super().__getattribute__(code) - if code.startswith('__'): # python system attributes like __class__ + if code.startswith("__"): # python system attributes like __class__ return msg else: return "[{code}] {msg}".format(code=code, msg=msg) @@ -116,6 +116,7 @@ class Warnings(object): " to check the alignment. Misaligned entities ('-') will be " "ignored during training.") + @add_codes class Errors(object): E001 = ("No component '{name}' found in pipeline. Available names: {opts}") diff --git a/spacy/lang/da/__init__.py b/spacy/lang/da/__init__.py index 92eec44b2..0190656e5 100644 --- a/spacy/lang/da/__init__.py +++ b/spacy/lang/da/__init__.py @@ -9,7 +9,6 @@ from .morph_rules import MORPH_RULES from ..tag_map import TAG_MAP from ..tokenizer_exceptions import BASE_EXCEPTIONS -from ..norm_exceptions import BASE_NORMS from ...language import Language from ...attrs import LANG from ...util import update_exc diff --git a/spacy/lang/de/stop_words.py b/spacy/lang/de/stop_words.py index 69134124f..0c8b375e0 100644 --- a/spacy/lang/de/stop_words.py +++ b/spacy/lang/de/stop_words.py @@ -47,7 +47,7 @@ kleines kommen kommt können könnt konnte könnte konnten kurz lang lange leicht leider lieber los machen macht machte mag magst man manche manchem manchen mancher manches mehr -mein meine meinem meinen meiner meines mich mir mit mittel mochte möchte mochten +mein meine meinem meinen meiner meines mich mir mit mittel mochte möchte mochten mögen möglich mögt morgen muss muß müssen musst müsst musste mussten na nach nachdem nahm natürlich neben nein neue neuen neun neunte neunten neunter diff --git a/spacy/lang/de/syntax_iterators.py b/spacy/lang/de/syntax_iterators.py index 13bb857ca..73c1b1a6e 100644 --- a/spacy/lang/de/syntax_iterators.py +++ b/spacy/lang/de/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -28,7 +28,7 @@ def noun_chunks(obj): "og", "app", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -38,7 +38,7 @@ def noun_chunks(obj): close_app = doc.vocab.strings.add("nk") rbracket = 0 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if i < rbracket: continue if word.pos in (NOUN, PROPN, PRON) and word.dep in np_deps: diff --git a/spacy/lang/el/syntax_iterators.py b/spacy/lang/el/syntax_iterators.py index b5811c337..10fa94f8c 100644 --- a/spacy/lang/el/syntax_iterators.py +++ b/spacy/lang/el/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases. Works on both Doc and Span. """ @@ -14,7 +14,7 @@ def noun_chunks(obj): # obj tag corrects some DEP tagger mistakes. # Further improvement of the models will eliminate the need for this tag. labels = ["nsubj", "obj", "iobj", "appos", "ROOT", "obl"] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -24,7 +24,7 @@ def noun_chunks(obj): nmod = doc.vocab.strings.add("nmod") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/en/syntax_iterators.py b/spacy/lang/en/syntax_iterators.py index dbb2d6c9f..91152bd50 100644 --- a/spacy/lang/en/syntax_iterators.py +++ b/spacy/lang/en/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -20,7 +20,7 @@ def noun_chunks(obj): "attr", "ROOT", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -29,7 +29,7 @@ def noun_chunks(obj): conj = doc.vocab.strings.add("conj") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/en/tokenizer_exceptions.py b/spacy/lang/en/tokenizer_exceptions.py index 62de81912..6a553052b 100644 --- a/spacy/lang/en/tokenizer_exceptions.py +++ b/spacy/lang/en/tokenizer_exceptions.py @@ -197,7 +197,7 @@ for word in ["who", "what", "when", "where", "why", "how", "there", "that"]: _exc[orth + "d"] = [ {ORTH: orth, LEMMA: word, NORM: word}, - {ORTH: "d", NORM: "'d"} + {ORTH: "d", NORM: "'d"}, ] _exc[orth + "'d've"] = [ diff --git a/spacy/lang/es/punctuation.py b/spacy/lang/es/punctuation.py index 42335237c..f989221c2 100644 --- a/spacy/lang/es/punctuation.py +++ b/spacy/lang/es/punctuation.py @@ -5,7 +5,6 @@ from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES from ..char_classes import LIST_ICONS, CURRENCY, LIST_UNITS, PUNCT from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA from ..char_classes import merge_chars -from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES _list_units = [u for u in LIST_UNITS if u != "%"] diff --git a/spacy/lang/es/syntax_iterators.py b/spacy/lang/es/syntax_iterators.py index 0badddca1..5fda35211 100644 --- a/spacy/lang/es/syntax_iterators.py +++ b/spacy/lang/es/syntax_iterators.py @@ -5,8 +5,8 @@ from ...symbols import NOUN, PROPN, PRON, VERB, AUX from ...errors import Errors -def noun_chunks(obj): - doc = obj.doc +def noun_chunks(doclike): + doc = doclike.doc if not doc.is_parsed: raise ValueError(Errors.E029) @@ -21,7 +21,7 @@ def noun_chunks(obj): np_right_deps = [doc.vocab.strings.add(label) for label in right_labels] stop_deps = [doc.vocab.strings.add(label) for label in stop_labels] token = doc[0] - while token and token.i < len(doc): + while token and token.i < len(doclike): if token.pos in [PROPN, NOUN, PRON]: left, right = noun_bounds( doc, token, np_left_deps, np_right_deps, stop_deps diff --git a/spacy/lang/fa/syntax_iterators.py b/spacy/lang/fa/syntax_iterators.py index dbb2d6c9f..91152bd50 100644 --- a/spacy/lang/fa/syntax_iterators.py +++ b/spacy/lang/fa/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -20,7 +20,7 @@ def noun_chunks(obj): "attr", "ROOT", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -29,7 +29,7 @@ def noun_chunks(obj): conj = doc.vocab.strings.add("conj") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/fr/syntax_iterators.py b/spacy/lang/fr/syntax_iterators.py index b38be57fc..3523e2f02 100644 --- a/spacy/lang/fr/syntax_iterators.py +++ b/spacy/lang/fr/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -19,7 +19,7 @@ def noun_chunks(obj): "nmod", "nmod:poss", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -28,7 +28,7 @@ def noun_chunks(obj): conj = doc.vocab.strings.add("conj") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/fr/tokenizer_exceptions.py b/spacy/lang/fr/tokenizer_exceptions.py index cb1702300..4eb4c1568 100644 --- a/spacy/lang/fr/tokenizer_exceptions.py +++ b/spacy/lang/fr/tokenizer_exceptions.py @@ -461,5 +461,5 @@ _regular_exp.append(URL_PATTERN) TOKENIZER_EXCEPTIONS = _exc TOKEN_MATCH = re.compile( - "(?iu)" + "|".join("(?:{})".format(m) for m in _regular_exp) + "(?iu)" + "|".join("(?:{})".format(m) for m in _regular_exp) ).match diff --git a/spacy/lang/gu/stop_words.py b/spacy/lang/gu/stop_words.py index f641b5720..85d33763d 100644 --- a/spacy/lang/gu/stop_words.py +++ b/spacy/lang/gu/stop_words.py @@ -3,7 +3,7 @@ from __future__ import unicode_literals STOP_WORDS = set( """ -એમ +એમ આ એ રહી @@ -24,7 +24,7 @@ STOP_WORDS = set( તેમને તેમના તેમણે -તેમનું +તેમનું તેમાં અને અહીં @@ -33,12 +33,12 @@ STOP_WORDS = set( થાય જે ને -કે +કે ના ની નો ને -નું +નું શું માં પણ @@ -69,12 +69,12 @@ STOP_WORDS = set( કોઈ કેમ કર્યો -કર્યુ +કર્યુ કરે સૌથી -ત્યારબાદ +ત્યારબાદ તથા -દ્વારા +દ્વારા જુઓ જાઓ જ્યારે diff --git a/spacy/lang/hy/__init__.py b/spacy/lang/hy/__init__.py index 3320edb6c..6aaa965bb 100644 --- a/spacy/lang/hy/__init__.py +++ b/spacy/lang/hy/__init__.py @@ -1,11 +1,12 @@ +# coding: utf8 +from __future__ import unicode_literals + from .stop_words import STOP_WORDS from .lex_attrs import LEX_ATTRS from .tag_map import TAG_MAP - from ...attrs import LANG from ...language import Language -from ...tokens import Doc class ArmenianDefaults(Language.Defaults): diff --git a/spacy/lang/hy/examples.py b/spacy/lang/hy/examples.py index b0df31aae..323f77b1c 100644 --- a/spacy/lang/hy/examples.py +++ b/spacy/lang/hy/examples.py @@ -1,6 +1,6 @@ +# coding: utf8 from __future__ import unicode_literals - """ Example sentences to test spaCy and its language models. >>> from spacy.lang.hy.examples import sentences diff --git a/spacy/lang/hy/lex_attrs.py b/spacy/lang/hy/lex_attrs.py index 7c1b9592f..910625fb8 100644 --- a/spacy/lang/hy/lex_attrs.py +++ b/spacy/lang/hy/lex_attrs.py @@ -1,3 +1,4 @@ +# coding: utf8 from __future__ import unicode_literals from ...attrs import LIKE_NUM diff --git a/spacy/lang/hy/stop_words.py b/spacy/lang/hy/stop_words.py index c671956a4..d75aad6e2 100644 --- a/spacy/lang/hy/stop_words.py +++ b/spacy/lang/hy/stop_words.py @@ -1,6 +1,6 @@ +# coding: utf8 from __future__ import unicode_literals - STOP_WORDS = set( """ նա @@ -105,6 +105,6 @@ STOP_WORDS = set( յուրաքանչյուր այս մեջ -թ +թ """.split() ) diff --git a/spacy/lang/hy/tag_map.py b/spacy/lang/hy/tag_map.py index 90690c22e..722270110 100644 --- a/spacy/lang/hy/tag_map.py +++ b/spacy/lang/hy/tag_map.py @@ -1,7 +1,7 @@ # coding: utf8 from __future__ import unicode_literals -from ...symbols import POS, SYM, ADJ, NUM, DET, ADV, ADP, X, VERB, NOUN +from ...symbols import POS, ADJ, NUM, DET, ADV, ADP, X, VERB, NOUN from ...symbols import PROPN, PART, INTJ, PRON, SCONJ, AUX, CCONJ TAG_MAP = { @@ -716,7 +716,7 @@ TAG_MAP = { POS: NOUN, "Animacy": "Nhum", "Case": "Dat", - "Number": "Coll", + # "Number": "Coll", "Number": "Sing", "Person": "1", }, @@ -815,7 +815,7 @@ TAG_MAP = { "Animacy": "Nhum", "Case": "Nom", "Definite": "Def", - "Number": "Plur", + # "Number": "Plur", "Number": "Sing", "Poss": "Yes", }, @@ -880,7 +880,7 @@ TAG_MAP = { POS: NOUN, "Animacy": "Nhum", "Case": "Nom", - "Number": "Plur", + # "Number": "Plur", "Number": "Sing", "Person": "2", }, @@ -1223,9 +1223,9 @@ TAG_MAP = { "PRON_Case=Nom|Number=Sing|Number=Plur|Person=3|Person=1|PronType=Emp": { POS: PRON, "Case": "Nom", - "Number": "Sing", + # "Number": "Sing", "Number": "Plur", - "Person": "3", + # "Person": "3", "Person": "1", "PronType": "Emp", }, diff --git a/spacy/lang/id/syntax_iterators.py b/spacy/lang/id/syntax_iterators.py index b38be57fc..3523e2f02 100644 --- a/spacy/lang/id/syntax_iterators.py +++ b/spacy/lang/id/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -19,7 +19,7 @@ def noun_chunks(obj): "nmod", "nmod:poss", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -28,7 +28,7 @@ def noun_chunks(obj): conj = doc.vocab.strings.add("conj") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/ml/lex_attrs.py b/spacy/lang/ml/lex_attrs.py index 345da8126..468ad88f8 100644 --- a/spacy/lang/ml/lex_attrs.py +++ b/spacy/lang/ml/lex_attrs.py @@ -55,7 +55,7 @@ _num_words = [ "തൊണ്ണൂറ് ", "നുറ് ", "ആയിരം ", - "പത്തുലക്ഷം" + "പത്തുലക്ഷം", ] diff --git a/spacy/lang/ml/stop_words.py b/spacy/lang/ml/stop_words.py index 4012571bc..8bd6a7e02 100644 --- a/spacy/lang/ml/stop_words.py +++ b/spacy/lang/ml/stop_words.py @@ -3,7 +3,6 @@ from __future__ import unicode_literals STOP_WORDS = set( - """ അത് ഇത് diff --git a/spacy/lang/nb/syntax_iterators.py b/spacy/lang/nb/syntax_iterators.py index b38be57fc..3523e2f02 100644 --- a/spacy/lang/nb/syntax_iterators.py +++ b/spacy/lang/nb/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -19,7 +19,7 @@ def noun_chunks(obj): "nmod", "nmod:poss", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -28,7 +28,7 @@ def noun_chunks(obj): conj = doc.vocab.strings.add("conj") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/pl/__init__.py b/spacy/lang/pl/__init__.py index 61608a3d9..52b662a90 100644 --- a/spacy/lang/pl/__init__.py +++ b/spacy/lang/pl/__init__.py @@ -12,7 +12,7 @@ from ..tokenizer_exceptions import BASE_EXCEPTIONS from ..norm_exceptions import BASE_NORMS from ...language import Language from ...attrs import LANG, NORM -from ...util import update_exc, add_lookups +from ...util import add_lookups from ...lookups import Lookups diff --git a/spacy/lang/pl/lemmatizer.py b/spacy/lang/pl/lemmatizer.py index 2be4b0fb7..cd555b9c2 100644 --- a/spacy/lang/pl/lemmatizer.py +++ b/spacy/lang/pl/lemmatizer.py @@ -3,7 +3,6 @@ from __future__ import unicode_literals from ...lemmatizer import Lemmatizer from ...parts_of_speech import NAMES -from ...errors import Errors class PolishLemmatizer(Lemmatizer): diff --git a/spacy/lang/pl/punctuation.py b/spacy/lang/pl/punctuation.py index aa8adac29..c87464b1b 100644 --- a/spacy/lang/pl/punctuation.py +++ b/spacy/lang/pl/punctuation.py @@ -8,7 +8,9 @@ from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES _quotes = CONCAT_QUOTES.replace("'", "") -_prefixes = _prefixes = [r"(długo|krótko|jedno|dwu|trzy|cztero)-"] + BASE_TOKENIZER_PREFIXES +_prefixes = _prefixes = [ + r"(długo|krótko|jedno|dwu|trzy|cztero)-" +] + BASE_TOKENIZER_PREFIXES _infixes = ( LIST_ELLIPSES diff --git a/spacy/lang/sv/lex_attrs.py b/spacy/lang/sv/lex_attrs.py index 4b5278c7b..24d06a97a 100644 --- a/spacy/lang/sv/lex_attrs.py +++ b/spacy/lang/sv/lex_attrs.py @@ -40,7 +40,7 @@ _num_words = [ "miljard", "biljon", "biljard", - "kvadriljon" + "kvadriljon", ] diff --git a/spacy/lang/sv/syntax_iterators.py b/spacy/lang/sv/syntax_iterators.py index 12d351148..99621e6a9 100644 --- a/spacy/lang/sv/syntax_iterators.py +++ b/spacy/lang/sv/syntax_iterators.py @@ -5,7 +5,7 @@ from ...symbols import NOUN, PROPN, PRON from ...errors import Errors -def noun_chunks(obj): +def noun_chunks(doclike): """ Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ @@ -20,7 +20,7 @@ def noun_chunks(obj): "nmod", "nmod:poss", ] - doc = obj.doc # Ensure works on both Doc and Span. + doc = doclike.doc # Ensure works on both Doc and Span. if not doc.is_parsed: raise ValueError(Errors.E029) @@ -29,7 +29,7 @@ def noun_chunks(obj): conj = doc.vocab.strings.add("conj") np_label = doc.vocab.strings.add("NP") prev_end = -1 - for i, word in enumerate(obj): + for i, word in enumerate(doclike): if word.pos not in (NOUN, PROPN, PRON): continue # Prevent nested chunks from being produced diff --git a/spacy/lang/ur/tag_map.py b/spacy/lang/ur/tag_map.py index eebd3a14a..aad548e9b 100644 --- a/spacy/lang/ur/tag_map.py +++ b/spacy/lang/ur/tag_map.py @@ -38,7 +38,6 @@ TAG_MAP = { "NNPC": {POS: PROPN}, "NNC": {POS: NOUN}, "PSP": {POS: ADP}, - ".": {POS: PUNCT}, ",": {POS: PUNCT}, "-LRB-": {POS: PUNCT}, diff --git a/spacy/lang/zh/__init__.py b/spacy/lang/zh/__init__.py index ed0b3eb74..9d1cb71a7 100644 --- a/spacy/lang/zh/__init__.py +++ b/spacy/lang/zh/__init__.py @@ -109,6 +109,7 @@ class ChineseTokenizer(DummyTokenizer): if reset: try: import pkuseg + self.pkuseg_seg.preprocesser = pkuseg.Preprocesser(None) except ImportError: if self.use_pkuseg: @@ -118,7 +119,7 @@ class ChineseTokenizer(DummyTokenizer): ) raise ImportError(msg) for word in words: - self.pkuseg_seg.preprocesser.insert(word.strip(), '') + self.pkuseg_seg.preprocesser.insert(word.strip(), "") def _get_config(self): config = OrderedDict( @@ -168,21 +169,16 @@ class ChineseTokenizer(DummyTokenizer): return util.to_bytes(serializers, []) def from_bytes(self, data, **kwargs): - pkuseg_features_b = b"" - pkuseg_weights_b = b"" - pkuseg_processors_data = None + pkuseg_data = {"features_b": b"", "weights_b": b"", "processors_data": None} def deserialize_pkuseg_features(b): - nonlocal pkuseg_features_b - pkuseg_features_b = b + pkuseg_data["features_b"] = b def deserialize_pkuseg_weights(b): - nonlocal pkuseg_weights_b - pkuseg_weights_b = b + pkuseg_data["weights_b"] = b def deserialize_pkuseg_processors(b): - nonlocal pkuseg_processors_data - pkuseg_processors_data = srsly.msgpack_loads(b) + pkuseg_data["processors_data"] = srsly.msgpack_loads(b) deserializers = OrderedDict( ( @@ -194,13 +190,13 @@ class ChineseTokenizer(DummyTokenizer): ) util.from_bytes(data, deserializers, []) - if pkuseg_features_b and pkuseg_weights_b: + if pkuseg_data["features_b"] and pkuseg_data["weights_b"]: with tempfile.TemporaryDirectory() as tempdir: tempdir = Path(tempdir) with open(tempdir / "features.pkl", "wb") as fileh: - fileh.write(pkuseg_features_b) + fileh.write(pkuseg_data["features_b"]) with open(tempdir / "weights.npz", "wb") as fileh: - fileh.write(pkuseg_weights_b) + fileh.write(pkuseg_data["weights_b"]) try: import pkuseg except ImportError: @@ -209,13 +205,9 @@ class ChineseTokenizer(DummyTokenizer): + _PKUSEG_INSTALL_MSG ) self.pkuseg_seg = pkuseg.pkuseg(str(tempdir)) - if pkuseg_processors_data: - ( - user_dict, - do_process, - common_words, - other_words, - ) = pkuseg_processors_data + if pkuseg_data["processors_data"]: + processors_data = pkuseg_data["processors_data"] + (user_dict, do_process, common_words, other_words) = processors_data self.pkuseg_seg.preprocesser = pkuseg.Preprocesser(user_dict) self.pkuseg_seg.postprocesser.do_process = do_process self.pkuseg_seg.postprocesser.common_words = set(common_words) diff --git a/spacy/language.py b/spacy/language.py index c4eb26bad..ba11e2371 100644 --- a/spacy/language.py +++ b/spacy/language.py @@ -79,7 +79,9 @@ class BaseDefaults(object): lookups=lookups, ) vocab.lex_attr_getters[NORM] = util.add_lookups( - vocab.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), BASE_NORMS, vocab.lookups.get_table("lexeme_norm") + vocab.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), + BASE_NORMS, + vocab.lookups.get_table("lexeme_norm"), ) for tag_str, exc in cls.morph_rules.items(): for orth_str, attrs in exc.items(): @@ -974,7 +976,9 @@ class Language(object): serializers = OrderedDict() serializers["vocab"] = lambda: self.vocab.to_bytes() serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"]) - serializers["meta.json"] = lambda: srsly.json_dumps(OrderedDict(sorted(self.meta.items()))) + serializers["meta.json"] = lambda: srsly.json_dumps( + OrderedDict(sorted(self.meta.items())) + ) for name, proc in self.pipeline: if name in exclude: continue diff --git a/spacy/lemmatizer.py b/spacy/lemmatizer.py index 33908eecf..1f0f0da3f 100644 --- a/spacy/lemmatizer.py +++ b/spacy/lemmatizer.py @@ -6,6 +6,7 @@ from collections import OrderedDict from .symbols import NOUN, VERB, ADJ, PUNCT, PROPN from .errors import Errors from .lookups import Lookups +from .parts_of_speech import NAMES as UPOS_NAMES class Lemmatizer(object): @@ -43,17 +44,11 @@ class Lemmatizer(object): lookup_table = self.lookups.get_table("lemma_lookup", {}) if "lemma_rules" not in self.lookups: return [lookup_table.get(string, string)] - if univ_pos in (NOUN, "NOUN", "noun"): - univ_pos = "noun" - elif univ_pos in (VERB, "VERB", "verb"): - univ_pos = "verb" - elif univ_pos in (ADJ, "ADJ", "adj"): - univ_pos = "adj" - elif univ_pos in (PUNCT, "PUNCT", "punct"): - univ_pos = "punct" - elif univ_pos in (PROPN, "PROPN"): - return [string] - else: + if isinstance(univ_pos, int): + univ_pos = UPOS_NAMES.get(univ_pos, "X") + univ_pos = univ_pos.lower() + + if univ_pos in ("", "eol", "space"): return [string.lower()] # See Issue #435 for example of where this logic is requied. if self.is_base_form(univ_pos, morphology): @@ -61,6 +56,11 @@ class Lemmatizer(object): index_table = self.lookups.get_table("lemma_index", {}) exc_table = self.lookups.get_table("lemma_exc", {}) rules_table = self.lookups.get_table("lemma_rules", {}) + if not any((index_table.get(univ_pos), exc_table.get(univ_pos), rules_table.get(univ_pos))): + if univ_pos == "propn": + return [string] + else: + return [string.lower()] lemmas = self.lemmatize( string, index_table.get(univ_pos, {}), diff --git a/spacy/matcher/matcher.pyx b/spacy/matcher/matcher.pyx index 4cfab915f..0c1a56187 100644 --- a/spacy/matcher/matcher.pyx +++ b/spacy/matcher/matcher.pyx @@ -213,28 +213,28 @@ cdef class Matcher: else: yield doc - def __call__(self, object doc_or_span): + def __call__(self, object doclike): """Find all token sequences matching the supplied pattern. - doc_or_span (Doc or Span): The document to match over. + doclike (Doc or Span): The document to match over. RETURNS (list): A list of `(key, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `label_id` and `key` are both integers. """ - if isinstance(doc_or_span, Doc): - doc = doc_or_span + if isinstance(doclike, Doc): + doc = doclike length = len(doc) - elif isinstance(doc_or_span, Span): - doc = doc_or_span.doc - length = doc_or_span.end - doc_or_span.start + elif isinstance(doclike, Span): + doc = doclike.doc + length = doclike.end - doclike.start else: - raise ValueError(Errors.E195.format(good="Doc or Span", got=type(doc_or_span).__name__)) + raise ValueError(Errors.E195.format(good="Doc or Span", got=type(doclike).__name__)) if len(set([LEMMA, POS, TAG]) & self._seen_attrs) > 0 \ and not doc.is_tagged: raise ValueError(Errors.E155.format()) if DEP in self._seen_attrs and not doc.is_parsed: raise ValueError(Errors.E156.format()) - matches = find_matches(&self.patterns[0], self.patterns.size(), doc_or_span, length, + matches = find_matches(&self.patterns[0], self.patterns.size(), doclike, length, extensions=self._extensions, predicates=self._extra_predicates) for i, (key, start, end) in enumerate(matches): on_match = self._callbacks.get(key, None) @@ -257,7 +257,7 @@ def unpickle_matcher(vocab, patterns, callbacks): return matcher -cdef find_matches(TokenPatternC** patterns, int n, object doc_or_span, int length, extensions=None, predicates=tuple()): +cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, extensions=None, predicates=tuple()): """Find matches in a doc, with a compiled array of patterns. Matches are returned as a list of (id, start, end) tuples. @@ -286,7 +286,7 @@ cdef find_matches(TokenPatternC** patterns, int n, object doc_or_span, int lengt else: nr_extra_attr = 0 extra_attr_values = mem.alloc(length, sizeof(attr_t)) - for i, token in enumerate(doc_or_span): + for i, token in enumerate(doclike): for name, index in extensions.items(): value = token._.get(name) if isinstance(value, basestring): @@ -298,7 +298,7 @@ cdef find_matches(TokenPatternC** patterns, int n, object doc_or_span, int lengt for j in range(n): states.push_back(PatternStateC(patterns[j], i, 0)) transition_states(states, matches, predicate_cache, - doc_or_span[i], extra_attr_values, predicates) + doclike[i], extra_attr_values, predicates) extra_attr_values += nr_extra_attr predicate_cache += len(predicates) # Handle matches that end in 0-width patterns diff --git a/spacy/tests/conftest.py b/spacy/tests/conftest.py index d26f0ce5c..63bbf2e0a 100644 --- a/spacy/tests/conftest.py +++ b/spacy/tests/conftest.py @@ -112,6 +112,7 @@ def ga_tokenizer(): def gu_tokenizer(): return get_lang_class("gu").Defaults.create_tokenizer() + @pytest.fixture(scope="session") def he_tokenizer(): return get_lang_class("he").Defaults.create_tokenizer() @@ -246,7 +247,9 @@ def yo_tokenizer(): @pytest.fixture(scope="session") def zh_tokenizer_char(): - return get_lang_class("zh").Defaults.create_tokenizer(config={"use_jieba": False, "use_pkuseg": False}) + return get_lang_class("zh").Defaults.create_tokenizer( + config={"use_jieba": False, "use_pkuseg": False} + ) @pytest.fixture(scope="session") @@ -258,7 +261,9 @@ def zh_tokenizer_jieba(): @pytest.fixture(scope="session") def zh_tokenizer_pkuseg(): pytest.importorskip("pkuseg") - return get_lang_class("zh").Defaults.create_tokenizer(config={"pkuseg_model": "default", "use_jieba": False, "use_pkuseg": True}) + return get_lang_class("zh").Defaults.create_tokenizer( + config={"pkuseg_model": "default", "use_jieba": False, "use_pkuseg": True} + ) @pytest.fixture(scope="session") diff --git a/spacy/tests/doc/test_creation.py b/spacy/tests/doc/test_creation.py index 8f543e86a..863a7c210 100644 --- a/spacy/tests/doc/test_creation.py +++ b/spacy/tests/doc/test_creation.py @@ -50,7 +50,9 @@ def test_create_from_words_and_text(vocab): assert [t.text for t in doc] == [" ", "'", "dogs", "'", "\n\n", "run", " "] assert [t.whitespace_ for t in doc] == ["", "", "", "", "", " ", ""] assert doc.text == text - assert [t.text for t in doc if not t.text.isspace()] == [word for word in words if not word.isspace()] + assert [t.text for t in doc if not t.text.isspace()] == [ + word for word in words if not word.isspace() + ] # partial whitespace in words words = [" ", "'", "dogs", "'", "\n\n", "run", " "] @@ -60,7 +62,9 @@ def test_create_from_words_and_text(vocab): assert [t.text for t in doc] == [" ", "'", "dogs", "'", "\n\n", "run", " "] assert [t.whitespace_ for t in doc] == ["", "", "", "", "", " ", ""] assert doc.text == text - assert [t.text for t in doc if not t.text.isspace()] == [word for word in words if not word.isspace()] + assert [t.text for t in doc if not t.text.isspace()] == [ + word for word in words if not word.isspace() + ] # non-standard whitespace tokens words = [" ", " ", "'", "dogs", "'", "\n\n", "run"] @@ -70,7 +74,9 @@ def test_create_from_words_and_text(vocab): assert [t.text for t in doc] == [" ", "'", "dogs", "'", "\n\n", "run", " "] assert [t.whitespace_ for t in doc] == ["", "", "", "", "", " ", ""] assert doc.text == text - assert [t.text for t in doc if not t.text.isspace()] == [word for word in words if not word.isspace()] + assert [t.text for t in doc if not t.text.isspace()] == [ + word for word in words if not word.isspace() + ] # mismatch between words and text with pytest.raises(ValueError): diff --git a/spacy/tests/doc/test_token_api.py b/spacy/tests/doc/test_token_api.py index 1c2253dfa..4dcd07ad9 100644 --- a/spacy/tests/doc/test_token_api.py +++ b/spacy/tests/doc/test_token_api.py @@ -181,6 +181,7 @@ def test_is_sent_start(en_tokenizer): doc.is_parsed = True assert len(list(doc.sents)) == 2 + def test_is_sent_end(en_tokenizer): doc = en_tokenizer("This is a sentence. This is another.") assert doc[4].is_sent_end is None @@ -213,6 +214,7 @@ def test_token0_has_sent_start_true(): assert doc[1].is_sent_start is None assert not doc.is_sentenced + def test_tokenlast_has_sent_end_true(): doc = Doc(Vocab(), words=["hello", "world"]) assert doc[0].is_sent_end is None diff --git a/spacy/tests/lang/de/test_noun_chunks.py b/spacy/tests/lang/de/test_noun_chunks.py index 12ece84b5..8d76ddd79 100644 --- a/spacy/tests/lang/de/test_noun_chunks.py +++ b/spacy/tests/lang/de/test_noun_chunks.py @@ -5,9 +5,9 @@ import pytest def test_noun_chunks_is_parsed_de(de_tokenizer): - """Test that noun_chunks raises Value Error for 'de' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'de' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = de_tokenizer("Er lag auf seinem") diff --git a/spacy/tests/lang/el/test_noun_chunks.py b/spacy/tests/lang/el/test_noun_chunks.py index be14acc81..4f24865d0 100644 --- a/spacy/tests/lang/el/test_noun_chunks.py +++ b/spacy/tests/lang/el/test_noun_chunks.py @@ -5,9 +5,9 @@ import pytest def test_noun_chunks_is_parsed_el(el_tokenizer): - """Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = el_tokenizer("είναι χώρα της νοτιοανατολικής") diff --git a/spacy/tests/lang/en/test_noun_chunks.py b/spacy/tests/lang/en/test_noun_chunks.py index 1109af150..ff67986a5 100644 --- a/spacy/tests/lang/en/test_noun_chunks.py +++ b/spacy/tests/lang/en/test_noun_chunks.py @@ -13,9 +13,9 @@ from ...util import get_doc def test_noun_chunks_is_parsed(en_tokenizer): - """Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = en_tokenizer("This is a sentence") diff --git a/spacy/tests/lang/es/test_noun_chunks.py b/spacy/tests/lang/es/test_noun_chunks.py index 71069d313..66bbd8c3a 100644 --- a/spacy/tests/lang/es/test_noun_chunks.py +++ b/spacy/tests/lang/es/test_noun_chunks.py @@ -5,9 +5,9 @@ import pytest def test_noun_chunks_is_parsed_es(es_tokenizer): - """Test that noun_chunks raises Value Error for 'es' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'es' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = es_tokenizer("en Oxford este verano") diff --git a/spacy/tests/lang/es/test_text.py b/spacy/tests/lang/es/test_text.py index e237f922d..999e788dd 100644 --- a/spacy/tests/lang/es/test_text.py +++ b/spacy/tests/lang/es/test_text.py @@ -62,4 +62,4 @@ def test_lex_attrs_like_number(es_tokenizer, text, match): @pytest.mark.parametrize("word", ["once"]) def test_es_lex_attrs_capitals(word): assert like_num(word) - assert like_num(word.upper()) \ No newline at end of file + assert like_num(word.upper()) diff --git a/spacy/tests/lang/fr/test_noun_chunks.py b/spacy/tests/lang/fr/test_noun_chunks.py index 876bc0ea4..ea93a5a35 100644 --- a/spacy/tests/lang/fr/test_noun_chunks.py +++ b/spacy/tests/lang/fr/test_noun_chunks.py @@ -5,9 +5,9 @@ import pytest def test_noun_chunks_is_parsed_fr(fr_tokenizer): - """Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = fr_tokenizer("trouver des travaux antérieurs") diff --git a/spacy/tests/lang/gu/test_text.py b/spacy/tests/lang/gu/test_text.py index 9f3ae45a4..aa8d442a2 100644 --- a/spacy/tests/lang/gu/test_text.py +++ b/spacy/tests/lang/gu/test_text.py @@ -3,17 +3,16 @@ from __future__ import unicode_literals import pytest + def test_gu_tokenizer_handlers_long_text(gu_tokenizer): text = """પશ્ચિમ ભારતમાં આવેલું ગુજરાત રાજ્ય જે વ્યક્તિઓની માતૃભૂમિ છે""" tokens = gu_tokenizer(text) assert len(tokens) == 9 + @pytest.mark.parametrize( "text,length", - [ - ("ગુજરાતીઓ ખાવાના શોખીન માનવામાં આવે છે", 6), - ("ખેતરની ખેડ કરવામાં આવે છે.", 5), - ], + [("ગુજરાતીઓ ખાવાના શોખીન માનવામાં આવે છે", 6), ("ખેતરની ખેડ કરવામાં આવે છે.", 5)], ) def test_gu_tokenizer_handles_cnts(gu_tokenizer, text, length): tokens = gu_tokenizer(text) diff --git a/spacy/tests/lang/hy/test_text.py b/spacy/tests/lang/hy/test_text.py index 6b785bdfc..cbdb77e4e 100644 --- a/spacy/tests/lang/hy/test_text.py +++ b/spacy/tests/lang/hy/test_text.py @@ -1,3 +1,4 @@ +# coding: utf8 from __future__ import unicode_literals import pytest diff --git a/spacy/tests/lang/hy/test_tokenizer.py b/spacy/tests/lang/hy/test_tokenizer.py index 424fb886f..3eeb8b54e 100644 --- a/spacy/tests/lang/hy/test_tokenizer.py +++ b/spacy/tests/lang/hy/test_tokenizer.py @@ -1,3 +1,4 @@ +# coding: utf8 from __future__ import unicode_literals import pytest diff --git a/spacy/tests/lang/id/test_noun_chunks.py b/spacy/tests/lang/id/test_noun_chunks.py index 7bac808b3..add76f9b9 100644 --- a/spacy/tests/lang/id/test_noun_chunks.py +++ b/spacy/tests/lang/id/test_noun_chunks.py @@ -5,9 +5,9 @@ import pytest def test_noun_chunks_is_parsed_id(id_tokenizer): - """Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = id_tokenizer("sebelas") diff --git a/spacy/tests/lang/ml/test_text.py b/spacy/tests/lang/ml/test_text.py index 92eca6b21..2883cf5bb 100644 --- a/spacy/tests/lang/ml/test_text.py +++ b/spacy/tests/lang/ml/test_text.py @@ -10,7 +10,16 @@ def test_ml_tokenizer_handles_long_text(ml_tokenizer): assert len(tokens) == 5 -@pytest.mark.parametrize("text,length", [("എന്നാൽ അച്ചടിയുടെ ആവിർഭാവം ലിപിയിൽ കാര്യമായ മാറ്റങ്ങൾ വരുത്തിയത് കൂട്ടക്ഷരങ്ങളെ അണുഅക്ഷരങ്ങളായി പിരിച്ചുകൊണ്ടായിരുന്നു", 10), ("പരമ്പരാഗതമായി മലയാളം ഇടത്തുനിന്ന് വലത്തോട്ടാണ് എഴുതുന്നത്", 5)]) +@pytest.mark.parametrize( + "text,length", + [ + ( + "എന്നാൽ അച്ചടിയുടെ ആവിർഭാവം ലിപിയിൽ കാര്യമായ മാറ്റങ്ങൾ വരുത്തിയത് കൂട്ടക്ഷരങ്ങളെ അണുഅക്ഷരങ്ങളായി പിരിച്ചുകൊണ്ടായിരുന്നു", + 10, + ), + ("പരമ്പരാഗതമായി മലയാളം ഇടത്തുനിന്ന് വലത്തോട്ടാണ് എഴുതുന്നത്", 5), + ], +) def test_ml_tokenizer_handles_cnts(ml_tokenizer, text, length): tokens = ml_tokenizer(text) assert len(tokens) == length diff --git a/spacy/tests/lang/nb/test_noun_chunks.py b/spacy/tests/lang/nb/test_noun_chunks.py index 17ec6cfda..653491a64 100644 --- a/spacy/tests/lang/nb/test_noun_chunks.py +++ b/spacy/tests/lang/nb/test_noun_chunks.py @@ -5,9 +5,9 @@ import pytest def test_noun_chunks_is_parsed_nb(nb_tokenizer): - """Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = nb_tokenizer("Smørsausen brukes bl.a. til") diff --git a/spacy/tests/lang/sv/test_noun_chunks.py b/spacy/tests/lang/sv/test_noun_chunks.py index 38086c255..a6283b65e 100644 --- a/spacy/tests/lang/sv/test_noun_chunks.py +++ b/spacy/tests/lang/sv/test_noun_chunks.py @@ -7,9 +7,9 @@ from ...util import get_doc def test_noun_chunks_is_parsed_sv(sv_tokenizer): - """Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed. + """Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed. To check this test, we're constructing a Doc - with a new Vocab here and forcing is_parsed to 'False' + with a new Vocab here and forcing is_parsed to 'False' to make sure the noun chunks don't run. """ doc = sv_tokenizer("Studenten läste den bästa boken") diff --git a/spacy/tests/lang/zh/test_serialize.py b/spacy/tests/lang/zh/test_serialize.py index 58133a88e..56f092ed8 100644 --- a/spacy/tests/lang/zh/test_serialize.py +++ b/spacy/tests/lang/zh/test_serialize.py @@ -34,5 +34,15 @@ def test_zh_tokenizer_serialize_pkuseg(zh_tokenizer_pkuseg): @pytest.mark.slow def test_zh_tokenizer_serialize_pkuseg_with_processors(zh_tokenizer_pkuseg): - nlp = Chinese(meta={"tokenizer": {"config": {"use_jieba": False, "use_pkuseg": True, "pkuseg_model": "medicine"}}}) + nlp = Chinese( + meta={ + "tokenizer": { + "config": { + "use_jieba": False, + "use_pkuseg": True, + "pkuseg_model": "medicine", + } + } + } + ) zh_tokenizer_serialize(nlp.tokenizer) diff --git a/spacy/tests/lang/zh/test_tokenizer.py b/spacy/tests/lang/zh/test_tokenizer.py index 035798aa1..28240b6a9 100644 --- a/spacy/tests/lang/zh/test_tokenizer.py +++ b/spacy/tests/lang/zh/test_tokenizer.py @@ -43,12 +43,16 @@ def test_zh_tokenizer_pkuseg(zh_tokenizer_pkuseg, text, expected_tokens): def test_zh_tokenizer_pkuseg_user_dict(zh_tokenizer_pkuseg): user_dict = _get_pkuseg_trie_data(zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie) zh_tokenizer_pkuseg.pkuseg_update_user_dict(["nonsense_asdf"]) - updated_user_dict = _get_pkuseg_trie_data(zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie) + updated_user_dict = _get_pkuseg_trie_data( + zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie + ) assert len(user_dict) == len(updated_user_dict) - 1 # reset user dict zh_tokenizer_pkuseg.pkuseg_update_user_dict([], reset=True) - reset_user_dict = _get_pkuseg_trie_data(zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie) + reset_user_dict = _get_pkuseg_trie_data( + zh_tokenizer_pkuseg.pkuseg_seg.preprocesser.trie + ) assert len(reset_user_dict) == 0 diff --git a/spacy/tests/matcher/test_matcher_api.py b/spacy/tests/matcher/test_matcher_api.py index 0295ada82..1112195da 100644 --- a/spacy/tests/matcher/test_matcher_api.py +++ b/spacy/tests/matcher/test_matcher_api.py @@ -265,15 +265,15 @@ def test_matcher_regex_shape(en_vocab): @pytest.mark.parametrize( - "cmp, bad", + "cmp, bad", [ ("==", ["a", "aaa"]), ("!=", ["aa"]), (">=", ["a"]), ("<=", ["aaa"]), (">", ["a", "aa"]), - ("<", ["aa", "aaa"]) - ] + ("<", ["aa", "aaa"]), + ], ) def test_matcher_compare_length(en_vocab, cmp, bad): matcher = Matcher(en_vocab) diff --git a/spacy/tests/pipeline/test_sentencizer.py b/spacy/tests/pipeline/test_sentencizer.py index 7e58b3e98..ee9220a29 100644 --- a/spacy/tests/pipeline/test_sentencizer.py +++ b/spacy/tests/pipeline/test_sentencizer.py @@ -106,7 +106,9 @@ def test_sentencizer_complex(en_vocab, words, sent_starts, sent_ends, n_sents): ), ], ) -def test_sentencizer_custom_punct(en_vocab, punct_chars, words, sent_starts, sent_ends, n_sents): +def test_sentencizer_custom_punct( + en_vocab, punct_chars, words, sent_starts, sent_ends, n_sents +): doc = Doc(en_vocab, words=words) sentencizer = Sentencizer(punct_chars=punct_chars) doc = sentencizer(doc) diff --git a/spacy/tests/serialize/test_serialize_vocab_strings.py b/spacy/tests/serialize/test_serialize_vocab_strings.py index 63faf44fc..3be0a75b3 100644 --- a/spacy/tests/serialize/test_serialize_vocab_strings.py +++ b/spacy/tests/serialize/test_serialize_vocab_strings.py @@ -37,7 +37,7 @@ def test_serialize_vocab_roundtrip_bytes(strings1, strings2): assert vocab1.to_bytes() == vocab1_b new_vocab1 = Vocab().from_bytes(vocab1_b) assert new_vocab1.to_bytes() == vocab1_b - assert len(new_vocab1.strings) == len(strings1) + 1 # adds _SP + assert len(new_vocab1.strings) == len(strings1) + 1 # adds _SP assert sorted([s for s in new_vocab1.strings]) == sorted(strings1 + ["_SP"]) @@ -56,9 +56,13 @@ def test_serialize_vocab_roundtrip_disk(strings1, strings2): assert strings1 == [s for s in vocab1_d.strings if s != "_SP"] assert strings2 == [s for s in vocab2_d.strings if s != "_SP"] if strings1 == strings2: - assert [s for s in vocab1_d.strings if s != "_SP"] == [s for s in vocab2_d.strings if s != "_SP"] + assert [s for s in vocab1_d.strings if s != "_SP"] == [ + s for s in vocab2_d.strings if s != "_SP" + ] else: - assert [s for s in vocab1_d.strings if s != "_SP"] != [s for s in vocab2_d.strings if s != "_SP"] + assert [s for s in vocab1_d.strings if s != "_SP"] != [ + s for s in vocab2_d.strings if s != "_SP" + ] @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs) @@ -76,9 +80,8 @@ def test_serialize_vocab_lex_attrs_bytes(strings, lex_attr): def test_deserialize_vocab_seen_entries(strings, lex_attr): # Reported in #2153 vocab = Vocab(strings=strings) - length = len(vocab) vocab.from_bytes(vocab.to_bytes()) - assert len(vocab.strings) == len(strings) + 1 # adds _SP + assert len(vocab.strings) == len(strings) + 1 # adds _SP @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs) @@ -130,6 +133,7 @@ def test_serialize_stringstore_roundtrip_disk(strings1, strings2): else: assert list(sstore1_d) != list(sstore2_d) + @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs) def test_pickle_vocab(strings, lex_attr): vocab = Vocab(strings=strings) diff --git a/spacy/tests/test_gold.py b/spacy/tests/test_gold.py index 37b877561..53665d852 100644 --- a/spacy/tests/test_gold.py +++ b/spacy/tests/test_gold.py @@ -112,7 +112,7 @@ def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer): data = ( "I'll return the ₹54 amount", { - "words": ["I", "'ll", "return", "the", "₹", "54", "amount",], + "words": ["I", "'ll", "return", "the", "₹", "54", "amount"], "entities": [(16, 19, "MONEY")], }, ) @@ -122,7 +122,7 @@ def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer): data = ( "I'll return the $54 amount", { - "words": ["I", "'ll", "return", "the", "$", "54", "amount",], + "words": ["I", "'ll", "return", "the", "$", "54", "amount"], "entities": [(16, 19, "MONEY")], }, ) diff --git a/spacy/tests/vocab_vectors/test_vectors.py b/spacy/tests/vocab_vectors/test_vectors.py index 24eb3a1af..1821f8abc 100644 --- a/spacy/tests/vocab_vectors/test_vectors.py +++ b/spacy/tests/vocab_vectors/test_vectors.py @@ -366,6 +366,7 @@ def test_vectors_serialize(): assert row == row_r assert_equal(v.data, v_r.data) + def test_vector_is_oov(): vocab = Vocab(vectors_name="test_vocab_is_oov") data = numpy.ndarray((5, 3), dtype="f") @@ -375,4 +376,4 @@ def test_vector_is_oov(): vocab.set_vector("dog", data[1]) assert vocab["cat"].is_oov is True assert vocab["dog"].is_oov is True - assert vocab["hamster"].is_oov is False \ No newline at end of file + assert vocab["hamster"].is_oov is False diff --git a/spacy/util.py b/spacy/util.py index d4cdca4e0..419c99bc0 100644 --- a/spacy/util.py +++ b/spacy/util.py @@ -774,7 +774,7 @@ def get_words_and_spaces(words, text): except ValueError: raise ValueError(Errors.E194.format(text=text, words=words)) if word_start > 0: - text_words.append(text[text_pos:text_pos+word_start]) + text_words.append(text[text_pos : text_pos + word_start]) text_spaces.append(False) text_pos += word_start text_words.append(word) diff --git a/website/meta/universe.json b/website/meta/universe.json index cf587f5f0..857e26813 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -2172,6 +2172,43 @@ "model_uri = f'runs:/{my_run_id}/model'", "nlp2 = mlflow.spacy.load_model(model_uri=model_uri)" ] + }, + { + "id": "pyate", + "title": "PyATE", + "slogan": "Python Automated Term Extraction", + "description": "PyATE is a term extraction library written in Python using Spacy POS tagging with Basic, Combo Basic, C-Value, TermExtractor, and Weirdness.", + "github": "kevinlu1248/pyate", + "pip": "pyate", + "code_example": [ + "import spacy", + "from pyate.term_extraction_pipeline import TermExtractionPipeline", + "", + "nlp = spacy.load('en_core_web_sm')", + "nlp.add_pipe(TermExtractionPipeline())", + "# source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994795/", + "string = 'Central to the development of cancer are genetic changes that endow these “cancer cells” with many of the hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors, are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the connection between the inflammatory response and cancer.'", + "", + "doc = nlp(string)", + "print(doc._.combo_basic.sort_values(ascending=False).head(5))", + "\"\"\"\"\"\"", + "dysfunctional tumor 1.443147", + "tumor suppressors 1.443147", + "genetic changes 1.386294", + "cancer cells 1.386294", + "dysfunctional tumor suppressors 1.298612", + "\"\"\"\"\"\"" + ], + "code_language": "python", + "url": "https://github.com/kevinlu1248/pyate", + "author": "Kevin Lu", + "author_links": { + "twitter": "kevinlu1248", + "github": "kevinlu1248", + "website": "https://github.com/kevinlu1248/pyate" + }, + "category": ["pipeline", "research"], + "tags": ["term_extraction"] } ],