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import string
import re
import codecs
from collections import Counter
from bs4 import BeautifulSoup
from nltk.corpus import wordnet
from langchain.text_splitter import RecursiveCharacterTextSplitter
def compact_text(text):
"""
Compact the text by removing unnecessary spaces and punctuation issues.
Args:
text (str): Input text to be compacted.
Returns:
str: Compacted text.
"""
text = text.replace("\n", ". ").replace("\r", "")
text = text.replace("- ", "")
text = text.replace(": .", ":").replace(":.", ":")
text = re.sub(r"\s{2,}", " ", text)
text = text.replace(".. ", ". ")
return text
def remove_punctuation(text):
"""
Remove all punctuation from the given text.
Args:
text (str): Input text from which punctuation will be removed.
Returns:
str: Text without punctuation.
"""
for punctuation in string.punctuation:
text = text.replace(punctuation, '')
return text
def clean_data(item):
"""
Clean the text data.
Args:
item (Union[str, list, dict]): An object that contains text data which is cleaned iteratively.
Returns:
The cleaned data in the same format as item.
"""
if isinstance(item, str):
item = ' '.join(BeautifulSoup(item, "lxml").text.split())
elif isinstance(item, list):
item = [clean_data(i) for i in item]
elif isinstance(item, dict):
item = {remove_punctuation(clean_data(k).lower()).replace(' ', '_'): clean_data(i) for k, i in item.items()}
return item
def chunk_text(text, chunk_size):
"""
Split text into chunks of specified size.
Args:
text (str): Input text to be chunked.
chunk_size (int): Size of each chunk.
Returns:
list: List of text chunks.
"""
custom_text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_size // 5,
length_function=len
)
texts = custom_text_splitter.create_documents([text])
chunks = [text.page_content for text in texts]
return chunks
def clean_dict(dictionary, remove_values=['', 'nan']):
"""
Clean the dictionary by removing specific values.
Args:
dictionary (dict): A dictionary to be cleaned.
remove_values (list): List of values to remove from the dictionary.
Returns:
dict: Cleaned dictionary.
"""
new_dict = {}
for k, v in dictionary.items():
if isinstance(v, dict):
new_dict[k] = clean_dict(v, remove_values)
elif str(v) in remove_values:
pass
else:
new_dict[k] = v
return new_dict
def normalize_answer(s):
"""
Normalize text by removing punctuation, articles and extra whitespace, and lowercasing the text.
Args:
s (str): Input text to be normalized.
Returns:
str: Normalized text.
"""
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def recall_score(prediction, ground_truth):
"""
Calculate the recall score between prediction and ground truth.
Args:
prediction (str): Predicted text.
ground_truth (str): Ground truth text.
Returns:
float: Recall score.
"""
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
recall = 1.0 * num_same / len(ground_truth_tokens)
return recall
def f1_score(prediction, ground_truth):
"""
Calculate the F1 score between prediction and ground truth.
Args:
prediction (str): Predicted text.
ground_truth (str): Ground truth text.
Returns:
float: F1 score.
"""
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
"""
Calculate the exact match score between prediction and ground truth.
Args:
prediction (str): Predicted text.
ground_truth (str): Ground truth text.
Returns:
float: Exact match score.
"""
return float(normalize_answer(prediction) == normalize_answer(ground_truth))
# Pluralization and Synonym extraction
ABERRANT_PLURAL_MAP = {
'appendix': 'appendices',
'barracks': 'barracks',
'cactus': 'cacti',
'child': 'children',
'criterion': 'criteria',
'deer': 'deer',
'echo': 'echoes',
'elf': 'elves',
'embargo': 'embargoes',
'focus': 'foci',
'fungus': 'fungi',
'goose': 'geese',
'hero': 'heroes',
'hoof': 'hooves',
'index': 'indices',
'knife': 'knives',
'leaf': 'leaves',
'life': 'lives',
'man': 'men',
'mouse': 'mice',
'nucleus': 'nuclei',
'person': 'people',
'phenomenon': 'phenomena',
'potato': 'potatoes',
'self': 'selves',
'syllabus': 'syllabi',
'tomato': 'tomatoes',
'torpedo': 'torpedoes',
'veto': 'vetoes',
'woman': 'women',
}
VOWELS = set('aeiou')
def synonym_extractor(phrase):
"""
Extract synonyms for a given phrase using WordNet.
Args:
phrase (str): Input phrase to find synonyms for.
Returns:
list: List of synonyms.
"""
synonyms = []
for syn in wordnet.synsets(phrase):
if '.n.' in syn.name():
for l in syn.lemmas():
synonyms.append(l.name())
return list(set(synonyms))
def pluralize(singular):
"""
Return the plural form of a given lowercase singular word (English only).
Args:
singular (str): Singular word.
Returns:
str: Plural form of the word.
"""
if not singular:
return ''
plural = ABERRANT_PLURAL_MAP.get(singular)
if plural:
return plural
root = singular
try:
if singular[-1] == 'y' and singular[-2] not in VOWELS:
root = singular[:-1]
suffix = 'ies'
elif singular[-1] == 's':
if singular[-2] in VOWELS:
if singular[-3:] == 'ius':
root = singular[:-2]
suffix = 'i'
else:
root = singular[:-1]
suffix = 'ses'
else:
suffix = 'es'
elif singular[-2:] in ('ch', 'sh'):
suffix = 'es'
else:
suffix = 's'
except IndexError:
suffix = 's'
plural = root + suffix
return plural
def decode_escapes(s):
"""
Decode escape sequences in a string.
Args:
s (str): Input string with escape sequences.
Returns:
str: Decoded string.
"""
ESCAPE_SEQUENCE_RE = re.compile(r'''
( \\U........ # 8-digit hex escapes
| \\u.... # 4-digit hex escapes
| \\x.. # 2-digit hex escapes
| \\[0-7]{1,3} # Octal escapes
| \\N\{[^}]+\} # Unicode characters by name
| \\[\\'"abfnrtv] # Single-character escapes
)''', re.UNICODE | re.VERBOSE)
def decode_match(match):
return codecs.decode(match.group(0), 'unicode-escape')
return ESCAPE_SEQUENCE_RE.sub(decode_match, s)
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