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"""Compute text statistics for a document."""
from typing import TYPE_CHECKING, Iterable, Optional, cast
from typing_extensions import override
from ..schema import Field, Item, RichData, field
from ..signal import TextSignal
from ..utils import chunks
SPACY_LANG_MODEL = 'en_core_web_sm'
SPACY_BATCH_SIZE = 128
NUM_CHARS = 'num_characters'
READABILITY = 'readability'
TYPE_TOKEN_RATIO = 'log(type_token_ratio)'
FRAC_NON_ASCII = 'frac_non_ascii'
if TYPE_CHECKING:
from spacy import Language
from spacy.tokens import Doc
class TextStatisticsSignal(TextSignal):
"""Compute text statistics for a document such as readability scores, type-token-ratio, etc.."""
name = 'text_statistics'
display_name = 'Text Statistics'
_lang: Optional['Language'] = None
@override
def fields(self) -> Field:
return field(
fields={
NUM_CHARS: 'int32',
READABILITY: 'float32',
TYPE_TOKEN_RATIO: 'float32',
FRAC_NON_ASCII: field(
'float32', bins=[('Low', None, 0.15), ('Medium', 0.15, 0.3), ('High', 0.3, None)])
})
@override
def setup(self) -> None:
try:
import spacy
import spacy.cli
import spacy.util
except ImportError:
raise ImportError('Could not import the "spacy" python package. '
'Please install it with `pip install spacy`.')
if not spacy.util.is_package(SPACY_LANG_MODEL):
spacy.cli.download(SPACY_LANG_MODEL)
self._lang = spacy.load(
SPACY_LANG_MODEL,
disable=[
'parser', 'tagger', 'ner', 'lemmatizer', 'textcat', 'custom', 'tok2vec', 'attribute_ruler'
])
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
try:
import textacy.corpus
from textacy import text_stats
except ImportError:
raise ImportError('Could not import the "textacy" python package. '
'Please install it with `pip install textacy`.')
if not self._lang:
raise RuntimeError('Language model was not loaded.')
data = cast(Iterable[str], data)
for batch in chunks(data, SPACY_BATCH_SIZE):
# Replace None with empty strings to avoid spacy errors.
batch = [x or '' for x in batch]
# See https://textacy.readthedocs.io/en/0.11.0/api_reference/text_stats.html for a list of
# available statistics.
corpus = textacy.corpus.Corpus(lang=self._lang, data=batch)
for doc in cast(Iterable['Doc'], corpus):
if not doc or not doc.text.strip():
yield None
continue
try:
readability = text_stats.readability.automated_readability_index(doc)
except ZeroDivisionError:
readability = None
try:
ttr = text_stats.diversity.log_ttr(doc)
except ValueError:
ttr = None
num_chars = len(doc.text)
num_non_ascii = 0
for c in doc.text:
if ord(c) >= 128:
num_non_ascii += 1
frac_non_ascii = num_non_ascii / num_chars if num_chars else 0
yield {
NUM_CHARS: num_chars,
READABILITY: readability,
TYPE_TOKEN_RATIO: ttr,
FRAC_NON_ASCII: frac_non_ascii
}
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