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* 🚨 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>
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| title | teaser | tag | source |
|---|---|---|---|
| Vocab | A storage class for vocabulary and other data shared across a language | class | spacy/vocab.pyx |
The Vocab object provides a lookup table that allows you to access
Lexeme objects, as well as the
StringStore. It also owns underlying C-data that is shared
between Doc objects.
Vocab.__init__
Create the vocabulary.
Example
from spacy.vocab import Vocab vocab = Vocab(strings=["hello", "world"])
| Name | Description |
|---|---|
lex_attr_getters |
A dictionary mapping attribute IDs to functions to compute them. Defaults to None. |
strings |
A StringStore that maps strings to hash values, and vice versa, or a list of strings. |
lookups |
A Lookups that stores the lexeme_norm and other large lookup tables. Defaults to None. |
oov_prob |
The default OOV probability. Defaults to -20.0. |
vectors_name 2.2 |
A name to identify the vectors table. |
writing_system |
A dictionary describing the language's writing system. Typically provided by Language.Defaults. |
get_noun_chunks |
A function that yields base noun phrases used for Doc.noun_chunks. |
Vocab.__len__
Get the current number of lexemes in the vocabulary.
Example
doc = nlp("This is a sentence.") assert len(nlp.vocab) > 0
| Name | Description |
|---|---|
| RETURNS | The number of lexemes in the vocabulary. |
Vocab.__getitem__
Retrieve a lexeme, given an int ID or a string. If a previously unseen string is given, a new lexeme is created and stored.
Example
apple = nlp.vocab.strings["apple"] assert nlp.vocab[apple] == nlp.vocab["apple"]
| Name | Description |
|---|---|
id_or_string |
The hash value of a word, or its string. |
| RETURNS | The lexeme indicated by the given ID. |
Vocab.__iter__
Iterate over the lexemes in the vocabulary.
Example
stop_words = (lex for lex in nlp.vocab if lex.is_stop)
| Name | Description |
|---|---|
| YIELDS | An entry in the vocabulary. |
Vocab.__contains__
Check whether the string has an entry in the vocabulary. To get the ID for a
given string, you need to look it up in
vocab.strings.
Example
apple = nlp.vocab.strings["apple"] oov = nlp.vocab.strings["dskfodkfos"] assert apple in nlp.vocab assert oov not in nlp.vocab
| Name | Description |
|---|---|
string |
The ID string. |
| RETURNS | Whether the string has an entry in the vocabulary. |
Vocab.add_flag
Set a new boolean flag to words in the vocabulary. The flag_getter function
will be called over the words currently in the vocab, and then applied to new
words as they occur. You'll then be able to access the flag value on each token,
using token.check_flag(flag_id).
Example
def is_my_product(text): products = ["spaCy", "Thinc", "displaCy"] return text in products MY_PRODUCT = nlp.vocab.add_flag(is_my_product) doc = nlp("I like spaCy") assert doc[2].check_flag(MY_PRODUCT) == True
| Name | Description |
|---|---|
flag_getter |
A function that takes the lexeme text and returns the boolean flag value. |
flag_id |
An integer between 1 and 63 (inclusive), specifying the bit at which the flag will be stored. If -1, the lowest available bit will be chosen. |
| RETURNS | The integer ID by which the flag value can be checked. |
Vocab.reset_vectors
Drop the current vector table. Because all vectors must be the same width, you
have to call this to change the size of the vectors. Only one of the width and
shape keyword arguments can be specified.
Example
nlp.vocab.reset_vectors(width=300)
| Name | Description |
|---|---|
| keyword-only | |
width |
The new width. |
shape |
The new shape. |
Vocab.prune_vectors
Reduce the current vector table to nr_row unique entries. Words mapped to the
discarded vectors will be remapped to the closest vector among those remaining.
For example, suppose the original table had vectors for the words:
['sat', 'cat', 'feline', 'reclined']. If we prune the vector table to, two
rows, we would discard the vectors for "feline" and "reclined". These words
would then be remapped to the closest remaining vector – so "feline" would have
the same vector as "cat", and "reclined" would have the same vector as "sat".
The similarities are judged by cosine. The original vectors may be large, so the
cosines are calculated in minibatches to reduce memory usage.
Example
nlp.vocab.prune_vectors(10000) assert len(nlp.vocab.vectors) <= 1000
| Name | Description |
|---|---|
nr_row |
The number of rows to keep in the vector table. |
batch_size |
Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory. |
| RETURNS | A dictionary keyed by removed words mapped to (string, score) tuples, where string is the entry the removed word was mapped to, and score the similarity score between the two words. |
Vocab.get_vector
Retrieve a vector for a word in the vocabulary. Words can be looked up by string
or hash value. If no vectors data is loaded, a ValueError is raised. If minn
is defined, then the resulting vector uses FastText's
subword features by average over n-grams of orth (introduced in spaCy v2.1).
Example
nlp.vocab.get_vector("apple") nlp.vocab.get_vector("apple", minn=1, maxn=5)
| Name | Description |
|---|---|
orth |
The hash value of a word, or its unicode string. |
minn 2.1 |
Minimum n-gram length used for FastText's n-gram computation. Defaults to the length of orth. |
maxn 2.1 |
Maximum n-gram length used for FastText's n-gram computation. Defaults to the length of orth. |
| RETURNS | A word vector. Size and shape are determined by the Vocab.vectors instance. |
Vocab.set_vector
Set a vector for a word in the vocabulary. Words can be referenced by string or hash value.
Example
nlp.vocab.set_vector("apple", array([...]))
| Name | Description |
|---|---|
orth |
The hash value of a word, or its unicode string. |
vector |
The vector to set. |
Vocab.has_vector
Check whether a word has a vector. Returns False if no vectors are loaded.
Words can be looked up by string or hash value.
Example
if nlp.vocab.has_vector("apple"): vector = nlp.vocab.get_vector("apple")
| Name | Description |
|---|---|
orth |
The hash value of a word, or its unicode string. |
| RETURNS | Whether the word has a vector. |
Vocab.to_disk
Save the current state to a directory.
Example
nlp.vocab.to_disk("/path/to/vocab")
| Name | Description |
|---|---|
path |
A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects. |
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
Vocab.from_disk
Loads state from a directory. Modifies the object in place and returns it.
Example
from spacy.vocab import Vocab vocab = Vocab().from_disk("/path/to/vocab")
| Name | Description |
|---|---|
path |
A path to a directory. Paths may be either strings or Path-like objects. |
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
| RETURNS | The modified Vocab object. |
Vocab.to_bytes
Serialize the current state to a binary string.
Example
vocab_bytes = nlp.vocab.to_bytes()
| Name | Description |
|---|---|
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
| RETURNS | The serialized form of the Vocab object. |
Vocab.from_bytes
Load state from a binary string.
Example
from spacy.vocab import Vocab vocab_bytes = nlp.vocab.to_bytes() vocab = Vocab() vocab.from_bytes(vocab_bytes)
| Name | Description |
|---|---|
bytes_data |
The data to load from. |
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
| RETURNS | The Vocab object. |
Attributes
Example
apple_id = nlp.vocab.strings["apple"] assert type(apple_id) == int PERSON = nlp.vocab.strings["PERSON"] assert type(PERSON) == int
| Name | Description |
|---|---|
strings |
A table managing the string-to-int mapping. |
vectors 2 |
A table associating word IDs to word vectors. |
vectors_length |
Number of dimensions for each word vector. |
lookups |
The available lookup tables in this vocab. |
writing_system 2.1 |
A dict with information about the language's writing system. |
get_noun_chunks 3.0 |
A function that yields base noun phrases used for Doc.noun_chunks. |
Serialization fields
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
serialization by passing in the string names via the exclude argument.
Example
data = vocab.to_bytes(exclude=["strings", "vectors"]) vocab.from_disk("./vocab", exclude=["strings"])
| Name | Description |
|---|---|
strings |
The strings in the StringStore. |
vectors |
The word vectors, if available. |
lookups |
The lookup tables, if available. |