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"""
A Vocabulary maps strings to integers, allowing for strings to be mapped to an
out-of-vocabulary token.
"""
import codecs
import copy
import logging
import os
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Union, TYPE_CHECKING
from allennlp.common.util import namespace_match
from allennlp.common import Registrable
from allennlp.common.checks import ConfigurationError
from allennlp.common.tqdm import Tqdm
if TYPE_CHECKING:
from allennlp.data import instance as adi # noqa
logger = logging.getLogger(__name__)
DEFAULT_NON_PADDED_NAMESPACES = ("*tags", "*labels")
DEFAULT_PADDING_TOKEN = "@@PADDING@@"
DEFAULT_OOV_TOKEN = "@@UNKNOWN@@"
NAMESPACE_PADDING_FILE = "non_padded_namespaces.txt"
class _NamespaceDependentDefaultDict(defaultdict):
"""
This is a [defaultdict]
(https://docs.python.org/2/library/collections.html#collections.defaultdict) where the
default value is dependent on the key that is passed.
We use "namespaces" in the :class:`Vocabulary` object to keep track of several different
mappings from strings to integers, so that we have a consistent API for mapping words, tags,
labels, characters, or whatever else you want, into integers. The issue is that some of those
namespaces (words and characters) should have integers reserved for padding and
out-of-vocabulary tokens, while others (labels and tags) shouldn't. This class allows you to
specify filters on the namespace (the key used in the `defaultdict`), and use different
default values depending on whether the namespace passes the filter.
To do filtering, we take a set of `non_padded_namespaces`. This is a set of strings
that are either matched exactly against the keys, or treated as suffixes, if the
string starts with `*`. In other words, if `*tags` is in `non_padded_namespaces` then
`passage_tags`, `question_tags`, etc. (anything that ends with `tags`) will have the
`non_padded` default value.
# Parameters
non_padded_namespaces : `Iterable[str]`
A set / list / tuple of strings describing which namespaces are not padded. If a namespace
(key) is missing from this dictionary, we will use :func:`namespace_match` to see whether
the namespace should be padded. If the given namespace matches any of the strings in this
list, we will use `non_padded_function` to initialize the value for that namespace, and
we will use `padded_function` otherwise.
padded_function : `Callable[[], Any]`
A zero-argument function to call to initialize a value for a namespace that `should` be
padded.
non_padded_function : `Callable[[], Any]`
A zero-argument function to call to initialize a value for a namespace that should `not` be
padded.
"""
def __init__(
self,
non_padded_namespaces: Iterable[str],
padded_function: Callable[[], Any],
non_padded_function: Callable[[], Any],
) -> None:
self._non_padded_namespaces = set(non_padded_namespaces)
self._padded_function = padded_function
self._non_padded_function = non_padded_function
super().__init__()
def __missing__(self, key: str):
if any(namespace_match(pattern, key) for pattern in self._non_padded_namespaces):
value = self._non_padded_function()
else:
value = self._padded_function()
dict.__setitem__(self, key, value)
return value
def add_non_padded_namespaces(self, non_padded_namespaces: Set[str]):
# add non_padded_namespaces which weren't already present
self._non_padded_namespaces.update(non_padded_namespaces)
class _TokenToIndexDefaultDict(_NamespaceDependentDefaultDict):
def __init__(self, non_padded_namespaces: Set[str], padding_token: str, oov_token: str) -> None:
super().__init__(
non_padded_namespaces, lambda: {padding_token: 0, oov_token: 1}, lambda: {}
)
class _IndexToTokenDefaultDict(_NamespaceDependentDefaultDict):
def __init__(self, non_padded_namespaces: Set[str], padding_token: str, oov_token: str) -> None:
super().__init__(
non_padded_namespaces, lambda: {0: padding_token, 1: oov_token}, lambda: {}
)
def _read_pretrained_tokens(embeddings_file_uri: str) -> List[str]:
# Moving this import to the top breaks everything (cycling import, I guess)
from allennlp.modules.token_embedders.embedding import EmbeddingsTextFile
logger.info("Reading pretrained tokens from: %s", embeddings_file_uri)
tokens: List[str] = []
with EmbeddingsTextFile(embeddings_file_uri) as embeddings_file:
for line_number, line in enumerate(Tqdm.tqdm(embeddings_file), start=1):
token_end = line.find(" ")
if token_end >= 0:
token = line[:token_end]
tokens.append(token)
else:
line_begin = line[:20] + "..." if len(line) > 20 else line
logger.warning(f"Skipping line number %d: %s", line_number, line_begin)
return tokens
class Vocabulary(Registrable):
"""
A Vocabulary maps strings to integers, allowing for strings to be mapped to an
out-of-vocabulary token.
Vocabularies are fit to a particular dataset, which we use to decide which tokens are
in-vocabulary.
Vocabularies also allow for several different namespaces, so you can have separate indices for
'a' as a word, and 'a' as a character, for instance, and so we can use this object to also map
tag and label strings to indices, for a unified :class:`~.fields.field.Field` API. Most of the
methods on this class allow you to pass in a namespace; by default we use the 'tokens'
namespace, and you can omit the namespace argument everywhere and just use the default.
# Parameters
counter : `Dict[str, Dict[str, int]]`, optional (default=`None`)
A collection of counts from which to initialize this vocabulary. We will examine the
counts and, together with the other parameters to this class, use them to decide which
words are in-vocabulary. If this is `None`, we just won't initialize the vocabulary with
anything.
min_count : `Dict[str, int]`, optional (default=None)
When initializing the vocab from a counter, you can specify a minimum count, and every
token with a count less than this will not be added to the dictionary. These minimum
counts are `namespace-specific`, so you can specify different minimums for labels versus
words tokens, for example. If a namespace does not have a key in the given dictionary, we
will add all seen tokens to that namespace.
max_vocab_size : `Union[int, Dict[str, int]]`, optional (default=`None`)
If you want to cap the number of tokens in your vocabulary, you can do so with this
parameter. If you specify a single integer, every namespace will have its vocabulary fixed
to be no larger than this. If you specify a dictionary, then each namespace in the
`counter` can have a separate maximum vocabulary size. Any missing key will have a value
of `None`, which means no cap on the vocabulary size.
non_padded_namespaces : `Iterable[str]`, optional
By default, we assume you are mapping word / character tokens to integers, and so you want
to reserve word indices for padding and out-of-vocabulary tokens. However, if you are
mapping NER or SRL tags, or class labels, to integers, you probably do not want to reserve
indices for padding and out-of-vocabulary tokens. Use this field to specify which
namespaces should `not` have padding and OOV tokens added.
The format of each element of this is either a string, which must match field names
exactly, or `*` followed by a string, which we match as a suffix against field names.
We try to make the default here reasonable, so that you don't have to think about this.
The default is `("*tags", "*labels")`, so as long as your namespace ends in "tags" or
"labels" (which is true by default for all tag and label fields in this code), you don't
have to specify anything here.
pretrained_files : `Dict[str, str]`, optional
If provided, this map specifies the path to optional pretrained embedding files for each
namespace. This can be used to either restrict the vocabulary to only words which appear
in this file, or to ensure that any words in this file are included in the vocabulary
regardless of their count, depending on the value of `only_include_pretrained_words`.
Words which appear in the pretrained embedding file but not in the data are NOT included
in the Vocabulary.
min_pretrained_embeddings : `Dict[str, int]`, optional
If provided, specifies for each namespace a minimum number of lines (typically the
most common words) to keep from pretrained embedding files, even for words not
appearing in the data.
only_include_pretrained_words : `bool`, optional (default=False)
This defines the strategy for using any pretrained embedding files which may have been
specified in `pretrained_files`. If False, an inclusive strategy is used: and words
which are in the `counter` and in the pretrained file are added to the `Vocabulary`,
regardless of whether their count exceeds `min_count` or not. If True, we use an
exclusive strategy: words are only included in the Vocabulary if they are in the pretrained
embedding file (their count must still be at least `min_count`).
tokens_to_add : `Dict[str, List[str]]`, optional (default=None)
If given, this is a list of tokens to add to the vocabulary, keyed by the namespace to add
the tokens to. This is a way to be sure that certain items appear in your vocabulary,
regardless of any other vocabulary computation.
padding_token : `str`, optional (default=DEFAULT_PADDING_TOKEN)
If given, this the string used for padding.
oov_token : `str`, optional (default=DEFAULT_OOV_TOKEN)
If given, this the string used for the out of vocabulary (OOVs) tokens.
"""
default_implementation = "from_instances"
def __init__(
self,
counter: Dict[str, Dict[str, int]] = None,
min_count: Dict[str, int] = None,
max_vocab_size: Union[int, Dict[str, int]] = None,
non_padded_namespaces: Iterable[str] = DEFAULT_NON_PADDED_NAMESPACES,
pretrained_files: Optional[Dict[str, str]] = None,
only_include_pretrained_words: bool = False,
tokens_to_add: Dict[str, List[str]] = None,
min_pretrained_embeddings: Dict[str, int] = None,
padding_token: Optional[str] = DEFAULT_PADDING_TOKEN,
oov_token: Optional[str] = DEFAULT_OOV_TOKEN,
) -> None:
self._padding_token = padding_token if padding_token is not None else DEFAULT_PADDING_TOKEN
self._oov_token = oov_token if oov_token is not None else DEFAULT_OOV_TOKEN
self._non_padded_namespaces = set(non_padded_namespaces)
self._token_to_index = _TokenToIndexDefaultDict(
self._non_padded_namespaces, self._padding_token, self._oov_token
)
self._index_to_token = _IndexToTokenDefaultDict(
self._non_padded_namespaces, self._padding_token, self._oov_token
)
self._retained_counter: Optional[Dict[str, Dict[str, int]]] = None
# Made an empty vocabulary, now extend it.
self._extend(
counter,
min_count,
max_vocab_size,
non_padded_namespaces,
pretrained_files,
only_include_pretrained_words,
tokens_to_add,
min_pretrained_embeddings,
)
@classmethod
def from_instances(
cls,
instances: Iterable["adi.Instance"],
min_count: Dict[str, int] = None,
max_vocab_size: Union[int, Dict[str, int]] = None,
non_padded_namespaces: Iterable[str] = DEFAULT_NON_PADDED_NAMESPACES,
pretrained_files: Optional[Dict[str, str]] = None,
only_include_pretrained_words: bool = False,
tokens_to_add: Dict[str, List[str]] = None,
min_pretrained_embeddings: Dict[str, int] = None,
padding_token: Optional[str] = DEFAULT_PADDING_TOKEN,
oov_token: Optional[str] = DEFAULT_OOV_TOKEN,
) -> "Vocabulary":
"""
Constructs a vocabulary given a collection of `Instances` and some parameters.
We count all of the vocabulary items in the instances, then pass those counts
and the other parameters, to :func:`__init__`. See that method for a description
of what the other parameters do.
"""
logger.info("Fitting token dictionary from dataset.")
padding_token = padding_token if padding_token is not None else DEFAULT_PADDING_TOKEN
oov_token = oov_token if oov_token is not None else DEFAULT_OOV_TOKEN
namespace_token_counts: Dict[str, Dict[str, int]] = defaultdict(lambda: defaultdict(int))
for instance in Tqdm.tqdm(instances):
instance.count_vocab_items(namespace_token_counts)
return cls(
counter=namespace_token_counts,
min_count=min_count,
max_vocab_size=max_vocab_size,
non_padded_namespaces=non_padded_namespaces,
pretrained_files=pretrained_files,
only_include_pretrained_words=only_include_pretrained_words,
tokens_to_add=tokens_to_add,
min_pretrained_embeddings=min_pretrained_embeddings,
padding_token=padding_token,
oov_token=oov_token,
)
@classmethod
def from_files(
cls,
directory: str,
padding_token: Optional[str] = DEFAULT_PADDING_TOKEN,
oov_token: Optional[str] = DEFAULT_OOV_TOKEN,
) -> "Vocabulary":
"""
Loads a `Vocabulary` that was serialized using `save_to_files`.
# Parameters
directory : `str`
The directory containing the serialized vocabulary.
"""
logger.info("Loading token dictionary from %s.", directory)
padding_token = padding_token if padding_token is not None else DEFAULT_PADDING_TOKEN
oov_token = oov_token if oov_token is not None else DEFAULT_OOV_TOKEN
with codecs.open(
os.path.join(directory, NAMESPACE_PADDING_FILE), "r", "utf-8"
) as namespace_file:
non_padded_namespaces = [namespace_str.strip() for namespace_str in namespace_file]
vocab = cls(
non_padded_namespaces=non_padded_namespaces,
padding_token=padding_token,
oov_token=oov_token,
)
# Check every file in the directory.
for namespace_filename in os.listdir(directory):
if namespace_filename == NAMESPACE_PADDING_FILE:
continue
if namespace_filename.startswith("."):
continue
namespace = namespace_filename.replace(".txt", "")
if any(namespace_match(pattern, namespace) for pattern in non_padded_namespaces):
is_padded = False
else:
is_padded = True
filename = os.path.join(directory, namespace_filename)
vocab.set_from_file(filename, is_padded, namespace=namespace, oov_token=oov_token)
return vocab
@classmethod
def from_files_and_instances(
cls,
instances: Iterable["adi.Instance"],
directory: str,
padding_token: Optional[str] = DEFAULT_PADDING_TOKEN,
oov_token: Optional[str] = DEFAULT_OOV_TOKEN,
min_count: Dict[str, int] = None,
max_vocab_size: Union[int, Dict[str, int]] = None,
non_padded_namespaces: Iterable[str] = DEFAULT_NON_PADDED_NAMESPACES,
pretrained_files: Optional[Dict[str, str]] = None,
only_include_pretrained_words: bool = False,
tokens_to_add: Dict[str, List[str]] = None,
min_pretrained_embeddings: Dict[str, int] = None,
) -> "Vocabulary":
"""
Extends an already generated vocabulary using a collection of instances.
"""
vocab = cls.from_files(directory, padding_token, oov_token)
logger.info("Fitting token dictionary from dataset.")
namespace_token_counts: Dict[str, Dict[str, int]] = defaultdict(lambda: defaultdict(int))
for instance in Tqdm.tqdm(instances):
instance.count_vocab_items(namespace_token_counts)
vocab._extend(
counter=namespace_token_counts,
min_count=min_count,
max_vocab_size=max_vocab_size,
non_padded_namespaces=non_padded_namespaces,
pretrained_files=pretrained_files,
only_include_pretrained_words=only_include_pretrained_words,
tokens_to_add=tokens_to_add,
min_pretrained_embeddings=min_pretrained_embeddings,
)
return vocab
@classmethod
def empty(cls) -> "Vocabulary":
"""
This method returns a bare vocabulary instantiated with `cls()` (so, `Vocabulary()` if you
haven't made a subclass of this object). The only reason to call `Vocabulary.empty()`
instead of `Vocabulary()` is if you are instantiating this object from a config file. We
register this constructor with the key "empty", so if you know that you don't need to
compute a vocabulary (either because you're loading a pre-trained model from an archive
file, you're using a pre-trained transformer that has its own vocabulary, or something
else), you can use this to avoid having the default vocabulary construction code iterate
through the data.
"""
return cls()
def set_from_file(
self,
filename: str,
is_padded: bool = True,
oov_token: str = DEFAULT_OOV_TOKEN,
namespace: str = "tokens",
):
"""
If you already have a vocabulary file for a trained model somewhere, and you really want to
use that vocabulary file instead of just setting the vocabulary from a dataset, for
whatever reason, you can do that with this method. You must specify the namespace to use,
and we assume that you want to use padding and OOV tokens for this.
# Parameters
filename : `str`
The file containing the vocabulary to load. It should be formatted as one token per
line, with nothing else in the line. The index we assign to the token is the line
number in the file (1-indexed if `is_padded`, 0-indexed otherwise). Note that this
file should contain the OOV token string!
is_padded : `bool`, optional (default=True)
Is this vocabulary padded? For token / word / character vocabularies, this should be
`True`; while for tag or label vocabularies, this should typically be `False`. If
`True`, we add a padding token with index 0, and we enforce that the `oov_token` is
present in the file.
oov_token : `str`, optional (default=DEFAULT_OOV_TOKEN)
What token does this vocabulary use to represent out-of-vocabulary characters? This
must show up as a line in the vocabulary file. When we find it, we replace
`oov_token` with `self._oov_token`, because we only use one OOV token across
namespaces.
namespace : `str`, optional (default="tokens")
What namespace should we overwrite with this vocab file?
"""
if is_padded:
self._token_to_index[namespace] = {self._padding_token: 0}
self._index_to_token[namespace] = {0: self._padding_token}
else:
self._token_to_index[namespace] = {}
self._index_to_token[namespace] = {}
with codecs.open(filename, "r", "utf-8") as input_file:
lines = input_file.read().split("\n")
# Be flexible about having final newline or not
if lines and lines[-1] == "":
lines = lines[:-1]
for i, line in enumerate(lines):
index = i + 1 if is_padded else i
token = line.replace("@@NEWLINE@@", "\n")
if token == oov_token:
token = self._oov_token
self._token_to_index[namespace][token] = index
self._index_to_token[namespace][index] = token
if is_padded:
assert self._oov_token in self._token_to_index[namespace], "OOV token not found!"
def extend_from_instances(self, instances: Iterable["adi.Instance"]) -> None:
logger.info("Fitting token dictionary from dataset.")
namespace_token_counts: Dict[str, Dict[str, int]] = defaultdict(lambda: defaultdict(int))
for instance in Tqdm.tqdm(instances):
instance.count_vocab_items(namespace_token_counts)
self._extend(counter=namespace_token_counts)
def extend_from_vocab(self, vocab: "Vocabulary") -> None:
"""
Adds all vocabulary items from all namespaces in the given vocabulary to this vocabulary.
Useful if you want to load a model and extends its vocabulary from new instances.
We also add all non-padded namespaces from the given vocabulary to this vocabulary.
"""
self._non_padded_namespaces.update(vocab._non_padded_namespaces)
self._token_to_index._non_padded_namespaces.update(vocab._non_padded_namespaces)
self._index_to_token._non_padded_namespaces.update(vocab._non_padded_namespaces)
for namespace in vocab.get_namespaces():
for token in vocab.get_token_to_index_vocabulary(namespace):
self.add_token_to_namespace(token, namespace)
def _extend(
self,
counter: Dict[str, Dict[str, int]] = None,
min_count: Dict[str, int] = None,
max_vocab_size: Union[int, Dict[str, int]] = None,
non_padded_namespaces: Iterable[str] = DEFAULT_NON_PADDED_NAMESPACES,
pretrained_files: Optional[Dict[str, str]] = None,
only_include_pretrained_words: bool = False,
tokens_to_add: Dict[str, List[str]] = None,
min_pretrained_embeddings: Dict[str, int] = None,
) -> None:
"""
This method can be used for extending already generated vocabulary. It takes same
parameters as Vocabulary initializer. The `_token_to_index` and `_index_to_token`
mappings of calling vocabulary will be retained. It is an inplace operation so None will be
returned.
"""
if not isinstance(max_vocab_size, dict):
int_max_vocab_size = max_vocab_size
max_vocab_size = defaultdict(lambda: int_max_vocab_size) # type: ignore
min_count = min_count or {}
pretrained_files = pretrained_files or {}
min_pretrained_embeddings = min_pretrained_embeddings or {}
non_padded_namespaces = set(non_padded_namespaces)
counter = counter or {}
tokens_to_add = tokens_to_add or {}
self._retained_counter = counter
# Make sure vocabulary extension is safe.
current_namespaces = {*self._token_to_index}
extension_namespaces = {*counter, *tokens_to_add}
for namespace in current_namespaces & extension_namespaces:
# if new namespace was already present
# Either both should be padded or none should be.
original_padded = not any(
namespace_match(pattern, namespace) for pattern in self._non_padded_namespaces
)
extension_padded = not any(
namespace_match(pattern, namespace) for pattern in non_padded_namespaces
)
if original_padded != extension_padded:
raise ConfigurationError(
"Common namespace {} has conflicting ".format(namespace)
+ "setting of padded = True/False. "
+ "Hence extension cannot be done."
)
# Add new non-padded namespaces for extension
self._token_to_index.add_non_padded_namespaces(non_padded_namespaces)
self._index_to_token.add_non_padded_namespaces(non_padded_namespaces)
self._non_padded_namespaces.update(non_padded_namespaces)
for namespace in counter:
if namespace in pretrained_files:
pretrained_list = _read_pretrained_tokens(pretrained_files[namespace])
min_embeddings = min_pretrained_embeddings.get(namespace, 0)
if min_embeddings > 0:
tokens_old = tokens_to_add.get(namespace, [])
tokens_new = pretrained_list[:min_embeddings]
tokens_to_add[namespace] = tokens_old + tokens_new
pretrained_set = set(pretrained_list)
else:
pretrained_set = None
token_counts = list(counter[namespace].items())
token_counts.sort(key=lambda x: x[1], reverse=True)
try:
max_vocab = max_vocab_size[namespace]
except KeyError:
max_vocab = None
if max_vocab:
token_counts = token_counts[:max_vocab]
for token, count in token_counts:
if pretrained_set is not None:
if only_include_pretrained_words:
if token in pretrained_set and count >= min_count.get(namespace, 1):
self.add_token_to_namespace(token, namespace)
elif token in pretrained_set or count >= min_count.get(namespace, 1):
self.add_token_to_namespace(token, namespace)
elif count >= min_count.get(namespace, 1):
self.add_token_to_namespace(token, namespace)
for namespace, tokens in tokens_to_add.items():
for token in tokens:
self.add_token_to_namespace(token, namespace)
def __getstate__(self):
"""
Need to sanitize defaultdict and defaultdict-like objects
by converting them to vanilla dicts when we pickle the vocabulary.
"""
state = copy.copy(self.__dict__)
state["_token_to_index"] = dict(state["_token_to_index"])
state["_index_to_token"] = dict(state["_index_to_token"])
if "_retained_counter" in state:
state["_retained_counter"] = {
key: dict(value) for key, value in state["_retained_counter"].items()
}
return state
def __setstate__(self, state):
"""
Conversely, when we unpickle, we need to reload the plain dicts
into our special DefaultDict subclasses.
"""
self.__dict__ = copy.copy(state)
self._token_to_index = _TokenToIndexDefaultDict(
self._non_padded_namespaces, self._padding_token, self._oov_token
)
self._token_to_index.update(state["_token_to_index"])
self._index_to_token = _IndexToTokenDefaultDict(
self._non_padded_namespaces, self._padding_token, self._oov_token
)
self._index_to_token.update(state["_index_to_token"])
def save_to_files(self, directory: str) -> None:
"""
Persist this Vocabulary to files so it can be reloaded later.
Each namespace corresponds to one file.
# Parameters
directory : `str`
The directory where we save the serialized vocabulary.
"""
os.makedirs(directory, exist_ok=True)
if os.listdir(directory):
logging.warning("vocabulary serialization directory %s is not empty", directory)
with codecs.open(
os.path.join(directory, NAMESPACE_PADDING_FILE), "w", "utf-8"
) as namespace_file:
for namespace_str in self._non_padded_namespaces:
print(namespace_str, file=namespace_file)
for namespace, mapping in self._index_to_token.items():
# Each namespace gets written to its own file, in index order.
with codecs.open(
os.path.join(directory, namespace + ".txt"), "w", "utf-8"
) as token_file:
num_tokens = len(mapping)
start_index = 1 if mapping[0] == self._padding_token else 0
for i in range(start_index, num_tokens):
print(mapping[i].replace("\n", "@@NEWLINE@@"), file=token_file)
def is_padded(self, namespace: str) -> bool:
"""
Returns whether or not there are padding and OOV tokens added to the given namespace.
"""
return self._index_to_token[namespace][0] == self._padding_token
def add_token_to_namespace(self, token: str, namespace: str = "tokens") -> int:
"""
Adds `token` to the index, if it is not already present. Either way, we return the index of
the token.
"""
if not isinstance(token, str):
raise ValueError(
"Vocabulary tokens must be strings, or saving and loading will break."
" Got %s (with type %s)" % (repr(token), type(token))
)
if token not in self._token_to_index[namespace]:
index = len(self._token_to_index[namespace])
self._token_to_index[namespace][token] = index
self._index_to_token[namespace][index] = token
return index
else:
return self._token_to_index[namespace][token]
def add_tokens_to_namespace(self, tokens: List[str], namespace: str = "tokens") -> List[int]:
"""
Adds `tokens` to the index, if they are not already present. Either way, we return the
indices of the tokens in the order that they were given.
"""
return [self.add_token_to_namespace(token, namespace) for token in tokens]
def get_index_to_token_vocabulary(self, namespace: str = "tokens") -> Dict[int, str]:
return self._index_to_token[namespace]
def get_token_to_index_vocabulary(self, namespace: str = "tokens") -> Dict[str, int]:
return self._token_to_index[namespace]
def get_token_index(self, token: str, namespace: str = "tokens") -> int:
if token in self._token_to_index[namespace]:
return self._token_to_index[namespace][token]
else:
try:
return self._token_to_index[namespace][self._oov_token]
except KeyError:
logger.error("Namespace: %s", namespace)
logger.error("Token: %s", token)
raise
def get_token_from_index(self, index: int, namespace: str = "tokens") -> str:
return self._index_to_token[namespace][index]
def get_vocab_size(self, namespace: str = "tokens") -> int:
return len(self._token_to_index[namespace])
def get_namespaces(self) -> Set[str]:
return set(self._index_to_token.keys())
def __eq__(self, other):
if isinstance(self, other.__class__):
return self.__dict__ == other.__dict__
return False
def __str__(self) -> str:
base_string = f"Vocabulary with namespaces:\n"
non_padded_namespaces = f"\tNon Padded Namespaces: {self._non_padded_namespaces}\n"
namespaces = [
f"\tNamespace: {name}, Size: {self.get_vocab_size(name)} \n"
for name in self._index_to_token
]
return " ".join([base_string, non_padded_namespaces] + namespaces)
def __repr__(self) -> str:
# This is essentially the same as __str__, but with no newlines
base_string = f"Vocabulary with namespaces: "
namespaces = [
f"{name}, Size: {self.get_vocab_size(name)} ||" for name in self._index_to_token
]
non_padded_namespaces = f"Non Padded Namespaces: {self._non_padded_namespaces}"
return " ".join([base_string] + namespaces + [non_padded_namespaces])
def print_statistics(self) -> None:
if self._retained_counter:
logger.info(
"Printed vocabulary statistics are only for the part of the vocabulary generated "
"from instances. If vocabulary is constructed by extending saved vocabulary with "
"dataset instances, the directly loaded portion won't be considered here."
)
print("\n\n----Vocabulary Statistics----\n")
# Since we don't saved counter info, it is impossible to consider pre-saved portion.
for namespace in self._retained_counter:
tokens_with_counts = list(self._retained_counter[namespace].items())
tokens_with_counts.sort(key=lambda x: x[1], reverse=True)
print(f"\nTop 10 most frequent tokens in namespace '{namespace}':")
for token, freq in tokens_with_counts[:10]:
print(f"\tToken: {token}\t\tFrequency: {freq}")
# Now sort by token length, not frequency
tokens_with_counts.sort(key=lambda x: len(x[0]), reverse=True)
print(f"\nTop 10 longest tokens in namespace '{namespace}':")
for token, freq in tokens_with_counts[:10]:
print(f"\tToken: {token}\t\tlength: {len(token)}\tFrequency: {freq}")
print(f"\nTop 10 shortest tokens in namespace '{namespace}':")
for token, freq in reversed(tokens_with_counts[-10:]):
print(f"\tToken: {token}\t\tlength: {len(token)}\tFrequency: {freq}")
else:
# _retained_counter would be set only if instances were used for vocabulary construction.
logger.info(
"Vocabulary statistics cannot be printed since "
"dataset instances were not used for its construction."
)
# We can't decorate `Vocabulary` with `Vocabulary.register()`, because `Vocabulary` hasn't been
# defined yet. So we put these down here.
Vocabulary.register("from_instances", constructor="from_instances")(Vocabulary)
Vocabulary.register("from_files", constructor="from_files")(Vocabulary)
Vocabulary.register("extend", constructor="from_files_and_instances")(Vocabulary)
Vocabulary.register("empty", constructor="empty")(Vocabulary)
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