spaCy/spacy/pipeline/textcat.py
Connor Brinton 657af5f91f
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167)
* 🚨 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>
2021-10-14 15:21:40 +02:00

381 lines
14 KiB
Python

from itertools import islice
from typing import Iterable, Tuple, Optional, Dict, List, Callable, Any
from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
from thinc.types import Floats2d
import numpy
from .trainable_pipe import TrainablePipe
from ..language import Language
from ..training import Example, validate_examples, validate_get_examples
from ..errors import Errors
from ..scorer import Scorer
from ..tokens import Doc
from ..vocab import Vocab
single_label_default_config = """
[model]
@architectures = "spacy.TextCatEnsemble.v2"
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
depth = 2
[model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
"""
DEFAULT_SINGLE_TEXTCAT_MODEL = Config().from_str(single_label_default_config)["model"]
single_label_bow_config = """
[model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
"""
single_label_cnn_config = """
[model]
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = true
[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v2"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
"""
@Language.factory(
"textcat",
assigns=["doc.cats"],
default_config={"threshold": 0.5, "model": DEFAULT_SINGLE_TEXTCAT_MODEL},
default_score_weights={
"cats_score": 1.0,
"cats_score_desc": None,
"cats_micro_p": None,
"cats_micro_r": None,
"cats_micro_f": None,
"cats_macro_p": None,
"cats_macro_r": None,
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
"cats_macro_auc_per_type": None,
},
)
def make_textcat(
nlp: Language, name: str, model: Model[List[Doc], List[Floats2d]], threshold: float
) -> "TextCategorizer":
"""Create a TextCategorizer component. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels are considered
to be mutually exclusive (i.e. one true label per doc).
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category.
threshold (float): Cutoff to consider a prediction "positive".
"""
return TextCategorizer(nlp.vocab, model, name, threshold=threshold)
class TextCategorizer(TrainablePipe):
"""Pipeline component for single-label text classification.
DOCS: https://spacy.io/api/textcategorizer
"""
def __init__(
self, vocab: Vocab, model: Model, name: str = "textcat", *, threshold: float
) -> None:
"""Initialize a text categorizer for single-label classification.
vocab (Vocab): The shared vocabulary.
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name, used to add entries to the
losses during training.
threshold (float): Cutoff to consider a prediction "positive".
DOCS: https://spacy.io/api/textcategorizer#init
"""
self.vocab = vocab
self.model = model
self.name = name
self._rehearsal_model = None
cfg = {"labels": [], "threshold": threshold, "positive_label": None}
self.cfg = dict(cfg)
@property
def labels(self) -> Tuple[str]:
"""RETURNS (Tuple[str]): The labels currently added to the component.
DOCS: https://spacy.io/api/textcategorizer#labels
"""
return tuple(self.cfg["labels"]) # type: ignore[arg-type, return-value]
@property
def label_data(self) -> List[str]:
"""RETURNS (List[str]): Information about the component's labels.
DOCS: https://spacy.io/api/textcategorizer#label_data
"""
return self.labels # type: ignore[return-value]
def predict(self, docs: Iterable[Doc]):
"""Apply the pipeline's model to a batch of docs, without modifying them.
docs (Iterable[Doc]): The documents to predict.
RETURNS: The models prediction for each document.
DOCS: https://spacy.io/api/textcategorizer#predict
"""
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
tensors = [doc.tensor for doc in docs]
xp = get_array_module(tensors)
scores = xp.zeros((len(list(docs)), len(self.labels)))
return scores
scores = self.model.predict(docs)
scores = self.model.ops.asarray(scores)
return scores
def set_annotations(self, docs: Iterable[Doc], scores) -> None:
"""Modify a batch of Doc objects, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by TextCategorizer.predict.
DOCS: https://spacy.io/api/textcategorizer#set_annotations
"""
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
doc.cats[label] = float(scores[i, j])
def update(
self,
examples: Iterable[Example],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]:
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model. Delegates to predict and get_loss.
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
sgd (thinc.api.Optimizer): The optimizer.
losses (Dict[str, float]): Optional record of the loss during training.
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/textcategorizer#update
"""
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
validate_examples(examples, "TextCategorizer.update")
self._validate_categories(examples)
if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
# Handle cases where there are no tokens in any docs.
return losses
set_dropout_rate(self.model, drop)
scores, bp_scores = self.model.begin_update([eg.predicted for eg in examples])
loss, d_scores = self.get_loss(examples, scores)
bp_scores(d_scores)
if sgd is not None:
self.finish_update(sgd)
losses[self.name] += loss
return losses
def rehearse(
self,
examples: Iterable[Example],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]:
"""Perform a "rehearsal" update from a batch of data. Rehearsal updates
teach the current model to make predictions similar to an initial model,
to try to address the "catastrophic forgetting" problem. This feature is
experimental.
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
sgd (thinc.api.Optimizer): The optimizer.
losses (Dict[str, float]): Optional record of the loss during training.
Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/textcategorizer#rehearse
"""
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
if self._rehearsal_model is None:
return losses
validate_examples(examples, "TextCategorizer.rehearse")
self._validate_categories(examples)
docs = [eg.predicted for eg in examples]
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
return losses
set_dropout_rate(self.model, drop)
scores, bp_scores = self.model.begin_update(docs)
target = self._rehearsal_model(examples)
gradient = scores - target
bp_scores(gradient)
if sgd is not None:
self.finish_update(sgd)
losses[self.name] += (gradient ** 2).sum()
return losses
def _examples_to_truth(
self, examples: Iterable[Example]
) -> Tuple[numpy.ndarray, numpy.ndarray]:
nr_examples = len(list(examples))
truths = numpy.zeros((nr_examples, len(self.labels)), dtype="f")
not_missing = numpy.ones((nr_examples, len(self.labels)), dtype="f")
for i, eg in enumerate(examples):
for j, label in enumerate(self.labels):
if label in eg.reference.cats:
truths[i, j] = eg.reference.cats[label]
else:
not_missing[i, j] = 0.0
truths = self.model.ops.asarray(truths) # type: ignore
return truths, not_missing # type: ignore
def get_loss(self, examples: Iterable[Example], scores) -> Tuple[float, float]:
"""Find the loss and gradient of loss for the batch of documents and
their predicted scores.
examples (Iterable[Examples]): The batch of examples.
scores: Scores representing the model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/textcategorizer#get_loss
"""
validate_examples(examples, "TextCategorizer.get_loss")
self._validate_categories(examples)
truths, not_missing = self._examples_to_truth(examples)
not_missing = self.model.ops.asarray(not_missing) # type: ignore
d_scores = (scores - truths) / scores.shape[0]
d_scores *= not_missing
mean_square_error = (d_scores ** 2).sum(axis=1).mean()
return float(mean_square_error), d_scores
def add_label(self, label: str) -> int:
"""Add a new label to the pipe.
label (str): The label to add.
RETURNS (int): 0 if label is already present, otherwise 1.
DOCS: https://spacy.io/api/textcategorizer#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
if label in self.labels:
return 0
self._allow_extra_label()
self.cfg["labels"].append(label) # type: ignore[attr-defined]
if self.model and "resize_output" in self.model.attrs:
self.model = self.model.attrs["resize_output"](self.model, len(self.labels))
self.vocab.strings.add(label)
return 1
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
nlp: Optional[Language] = None,
labels: Optional[Iterable[str]] = None,
positive_label: Optional[str] = None,
) -> None:
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Language): The current nlp object the component is part of.
labels (Optional[Iterable[str]]): The labels to add to the component, typically generated by the
`init labels` command. If no labels are provided, the get_examples
callback is used to extract the labels from the data.
positive_label (Optional[str]): The positive label for a binary task with exclusive classes,
`None` otherwise and by default.
DOCS: https://spacy.io/api/textcategorizer#initialize
"""
validate_get_examples(get_examples, "TextCategorizer.initialize")
self._validate_categories(get_examples())
if labels is None:
for example in get_examples():
for cat in example.y.cats:
self.add_label(cat)
else:
for label in labels:
self.add_label(label)
if len(self.labels) < 2:
raise ValueError(Errors.E867)
if positive_label is not None:
if positive_label not in self.labels:
err = Errors.E920.format(pos_label=positive_label, labels=self.labels)
raise ValueError(err)
if len(self.labels) != 2:
err = Errors.E919.format(pos_label=positive_label, labels=self.labels)
raise ValueError(err)
self.cfg["positive_label"] = positive_label
subbatch = list(islice(get_examples(), 10))
doc_sample = [eg.reference for eg in subbatch]
label_sample, _ = self._examples_to_truth(subbatch)
self._require_labels()
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_cats.
DOCS: https://spacy.io/api/textcategorizer#score
"""
validate_examples(examples, "TextCategorizer.score")
self._validate_categories(examples)
kwargs.setdefault("threshold", self.cfg["threshold"])
kwargs.setdefault("positive_label", self.cfg["positive_label"])
return Scorer.score_cats(
examples,
"cats",
labels=self.labels,
multi_label=False,
**kwargs,
)
def _validate_categories(self, examples: Iterable[Example]):
"""Check whether the provided examples all have single-label cats annotations."""
for ex in examples:
if list(ex.reference.cats.values()).count(1.0) > 1:
raise ValueError(Errors.E895.format(value=ex.reference.cats))