mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-15 22:27:12 +03:00
657af5f91f
* 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
368 lines
12 KiB
Python
368 lines
12 KiB
Python
import pytest
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import numpy
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from numpy.testing import assert_array_equal, assert_almost_equal
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from thinc.api import get_current_ops
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from spacy import util
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.tokens import SpanGroup
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from spacy.tokens._dict_proxies import SpanGroups
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from spacy.training import Example
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from spacy.util import fix_random_seed, registry, make_tempdir
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OPS = get_current_ops()
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SPAN_KEY = "labeled_spans"
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TRAIN_DATA = [
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("Who is Shaka Khan?", {"spans": {SPAN_KEY: [(7, 17, "PERSON")]}}),
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(
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"I like London and Berlin.",
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{"spans": {SPAN_KEY: [(7, 13, "LOC"), (18, 24, "LOC")]}},
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),
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]
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TRAIN_DATA_OVERLAPPING = [
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("Who is Shaka Khan?", {"spans": {SPAN_KEY: [(7, 17, "PERSON")]}}),
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(
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"I like London and Berlin",
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{"spans": {SPAN_KEY: [(7, 13, "LOC"), (18, 24, "LOC"), (7, 24, "DOUBLE_LOC")]}},
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),
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]
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def make_examples(nlp, data=TRAIN_DATA):
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train_examples = []
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for t in data:
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eg = Example.from_dict(nlp.make_doc(t[0]), t[1])
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train_examples.append(eg)
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return train_examples
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def test_no_label():
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nlp = Language()
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nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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with pytest.raises(ValueError):
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nlp.initialize()
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def test_no_resize():
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nlp = Language()
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spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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spancat.add_label("Thing")
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spancat.add_label("Phrase")
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assert spancat.labels == ("Thing", "Phrase")
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nlp.initialize()
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assert spancat.model.get_dim("nO") == 2
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# this throws an error because the spancat can't be resized after initialization
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with pytest.raises(ValueError):
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spancat.add_label("Stuff")
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def test_implicit_labels():
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nlp = Language()
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spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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assert len(spancat.labels) == 0
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train_examples = make_examples(nlp)
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nlp.initialize(get_examples=lambda: train_examples)
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assert spancat.labels == ("PERSON", "LOC")
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def test_explicit_labels():
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nlp = Language()
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spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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assert len(spancat.labels) == 0
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spancat.add_label("PERSON")
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spancat.add_label("LOC")
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nlp.initialize()
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assert spancat.labels == ("PERSON", "LOC")
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def test_doc_gc():
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# If the Doc object is garbage collected, the spans won't be functional afterwards
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nlp = Language()
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spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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spancat.add_label("PERSON")
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nlp.initialize()
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texts = [
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"Just a sentence.",
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"I like London and Berlin",
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"I like Berlin",
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"I eat ham.",
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]
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all_spans = [doc.spans for doc in nlp.pipe(texts)]
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for text, spangroups in zip(texts, all_spans):
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assert isinstance(spangroups, SpanGroups)
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for key, spangroup in spangroups.items():
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assert isinstance(spangroup, SpanGroup)
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assert len(spangroup) > 0
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with pytest.raises(RuntimeError):
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span = spangroup[0]
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@pytest.mark.parametrize(
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"max_positive,nr_results", [(None, 4), (1, 2), (2, 3), (3, 4), (4, 4)]
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)
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def test_make_spangroup(max_positive, nr_results):
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fix_random_seed(0)
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nlp = Language()
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spancat = nlp.add_pipe(
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"spancat",
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config={"spans_key": SPAN_KEY, "threshold": 0.5, "max_positive": max_positive},
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)
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doc = nlp.make_doc("Greater London")
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ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2])
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indices = ngram_suggester([doc])[0].dataXd
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assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
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labels = ["Thing", "City", "Person", "GreatCity"]
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scores = numpy.asarray(
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[[0.2, 0.4, 0.3, 0.1], [0.1, 0.6, 0.2, 0.4], [0.8, 0.7, 0.3, 0.9]], dtype="f"
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)
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spangroup = spancat._make_span_group(doc, indices, scores, labels)
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assert len(spangroup) == nr_results
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# first span is always the second token "London"
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assert spangroup[0].text == "London"
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assert spangroup[0].label_ == "City"
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assert_almost_equal(0.6, spangroup.attrs["scores"][0], 5)
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# second span depends on the number of positives that were allowed
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assert spangroup[1].text == "Greater London"
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if max_positive == 1:
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assert spangroup[1].label_ == "GreatCity"
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assert_almost_equal(0.9, spangroup.attrs["scores"][1], 5)
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else:
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assert spangroup[1].label_ == "Thing"
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assert_almost_equal(0.8, spangroup.attrs["scores"][1], 5)
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if nr_results > 2:
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assert spangroup[2].text == "Greater London"
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if max_positive == 2:
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assert spangroup[2].label_ == "GreatCity"
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assert_almost_equal(0.9, spangroup.attrs["scores"][2], 5)
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else:
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assert spangroup[2].label_ == "City"
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assert_almost_equal(0.7, spangroup.attrs["scores"][2], 5)
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assert spangroup[-1].text == "Greater London"
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assert spangroup[-1].label_ == "GreatCity"
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assert_almost_equal(0.9, spangroup.attrs["scores"][-1], 5)
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def test_ngram_suggester(en_tokenizer):
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# test different n-gram lengths
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for size in [1, 2, 3]:
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ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[size])
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docs = [
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en_tokenizer(text)
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for text in [
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"a",
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"a b",
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"a b c",
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"a b c d",
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"a b c d e",
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"a " * 100,
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]
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]
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ngrams = ngram_suggester(docs)
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# span sizes are correct
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for s in ngrams.data:
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assert s[1] - s[0] == size
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# spans are within docs
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offset = 0
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for i, doc in enumerate(docs):
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spans = ngrams.dataXd[offset : offset + ngrams.lengths[i]]
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spans_set = set()
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for span in spans:
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assert 0 <= span[0] < len(doc)
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assert 0 < span[1] <= len(doc)
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spans_set.add((int(span[0]), int(span[1])))
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# spans are unique
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assert spans.shape[0] == len(spans_set)
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offset += ngrams.lengths[i]
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# the number of spans is correct
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assert_array_equal(
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OPS.to_numpy(ngrams.lengths),
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[max(0, len(doc) - (size - 1)) for doc in docs],
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)
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# test 1-3-gram suggestions
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ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2, 3])
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docs = [
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en_tokenizer(text) for text in ["a", "a b", "a b c", "a b c d", "a b c d e"]
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]
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ngrams = ngram_suggester(docs)
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assert_array_equal(OPS.to_numpy(ngrams.lengths), [1, 3, 6, 9, 12])
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assert_array_equal(
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OPS.to_numpy(ngrams.data),
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[
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# doc 0
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[0, 1],
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# doc 1
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[0, 1],
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[1, 2],
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[0, 2],
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# doc 2
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[0, 1],
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[1, 2],
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[2, 3],
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[0, 2],
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[1, 3],
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[0, 3],
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# doc 3
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[0, 1],
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[1, 2],
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[2, 3],
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[3, 4],
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[0, 2],
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[1, 3],
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[2, 4],
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[0, 3],
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[1, 4],
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# doc 4
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[0, 1],
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[1, 2],
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[2, 3],
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[3, 4],
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[4, 5],
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[0, 2],
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[1, 3],
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[2, 4],
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[3, 5],
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[0, 3],
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[1, 4],
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[2, 5],
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],
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)
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# test some empty docs
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ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1])
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docs = [en_tokenizer(text) for text in ["", "a", ""]]
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ngrams = ngram_suggester(docs)
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assert_array_equal(OPS.to_numpy(ngrams.lengths), [len(doc) for doc in docs])
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# test all empty docs
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ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1])
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docs = [en_tokenizer(text) for text in ["", "", ""]]
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ngrams = ngram_suggester(docs)
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assert_array_equal(OPS.to_numpy(ngrams.lengths), [len(doc) for doc in docs])
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def test_ngram_sizes(en_tokenizer):
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# test that the range suggester works well
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size_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2, 3])
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suggester_factory = registry.misc.get("spacy.ngram_range_suggester.v1")
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range_suggester = suggester_factory(min_size=1, max_size=3)
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docs = [
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en_tokenizer(text) for text in ["a", "a b", "a b c", "a b c d", "a b c d e"]
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]
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ngrams_1 = size_suggester(docs)
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ngrams_2 = range_suggester(docs)
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assert_array_equal(OPS.to_numpy(ngrams_1.lengths), [1, 3, 6, 9, 12])
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assert_array_equal(OPS.to_numpy(ngrams_1.lengths), OPS.to_numpy(ngrams_2.lengths))
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assert_array_equal(OPS.to_numpy(ngrams_1.data), OPS.to_numpy(ngrams_2.data))
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# one more variation
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suggester_factory = registry.misc.get("spacy.ngram_range_suggester.v1")
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range_suggester = suggester_factory(min_size=2, max_size=4)
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ngrams_3 = range_suggester(docs)
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assert_array_equal(OPS.to_numpy(ngrams_3.lengths), [0, 1, 3, 6, 9])
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the spancat component - ensuring the ML models work correctly
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fix_random_seed(0)
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nlp = English()
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spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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train_examples = make_examples(nlp)
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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assert spancat.model.get_dim("nO") == 2
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assert set(spancat.labels) == {"LOC", "PERSON"}
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["spancat"] < 0.01
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# test the trained model
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test_text = "I like London and Berlin"
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doc = nlp(test_text)
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assert doc.spans[spancat.key] == doc.spans[SPAN_KEY]
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spans = doc.spans[SPAN_KEY]
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assert len(spans) == 2
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assert len(spans.attrs["scores"]) == 2
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assert min(spans.attrs["scores"]) > 0.9
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assert set([span.text for span in spans]) == {"London", "Berlin"}
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assert set([span.label_ for span in spans]) == {"LOC"}
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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spans2 = doc2.spans[SPAN_KEY]
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assert len(spans2) == 2
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assert len(spans2.attrs["scores"]) == 2
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assert min(spans2.attrs["scores"]) > 0.9
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assert set([span.text for span in spans2]) == {"London", "Berlin"}
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assert set([span.label_ for span in spans2]) == {"LOC"}
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# Test scoring
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scores = nlp.evaluate(train_examples)
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assert f"spans_{SPAN_KEY}_f" in scores
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assert scores[f"spans_{SPAN_KEY}_p"] == 1.0
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assert scores[f"spans_{SPAN_KEY}_r"] == 1.0
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assert scores[f"spans_{SPAN_KEY}_f"] == 1.0
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# also test that the spancat works for just a single entity in a sentence
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doc = nlp("London")
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assert len(doc.spans[spancat.key]) == 1
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def test_overfitting_IO_overlapping():
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# Test for overfitting on overlapping entities
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fix_random_seed(0)
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nlp = English()
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spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY})
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train_examples = make_examples(nlp, data=TRAIN_DATA_OVERLAPPING)
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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assert spancat.model.get_dim("nO") == 3
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assert set(spancat.labels) == {"PERSON", "LOC", "DOUBLE_LOC"}
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["spancat"] < 0.01
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# test the trained model
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test_text = "I like London and Berlin"
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doc = nlp(test_text)
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spans = doc.spans[SPAN_KEY]
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assert len(spans) == 3
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assert len(spans.attrs["scores"]) == 3
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assert min(spans.attrs["scores"]) > 0.9
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assert set([span.text for span in spans]) == {
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"London",
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"Berlin",
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"London and Berlin",
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}
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assert set([span.label_ for span in spans]) == {"LOC", "DOUBLE_LOC"}
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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spans2 = doc2.spans[SPAN_KEY]
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|
assert len(spans2) == 3
|
|
assert len(spans2.attrs["scores"]) == 3
|
|
assert min(spans2.attrs["scores"]) > 0.9
|
|
assert set([span.text for span in spans2]) == {
|
|
"London",
|
|
"Berlin",
|
|
"London and Berlin",
|
|
}
|
|
assert set([span.label_ for span in spans2]) == {"LOC", "DOUBLE_LOC"}
|