topic_modelling / funcs /clean_funcs.py
Sonnyjim's picture
Reduce outliers now more efficient and relabels with correct vectoriser. Default topic labels now tidier. Hiearchical topics outputs more useful for joining to df afterwards. Switched low resource reduction algorithm to UMAP as default is not good.
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import re
import string
import polars as pl
import gradio as gr
# Adding custom words to the stopwords
custom_words = []
my_stop_words = custom_words
# #### Some of my cleaning functions
email_start_pattern_regex = r'.*(?i)importance:|.*(?i)subject:'
email_end_pattern_regex = r'(?i)kind regards.*|(?i)many thanks.*|(?i)sincerely.*'
html_pattern_regex = r'<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});|\xa0|&nbsp;'
email_pattern_regex = r'\S*@\S*\s?'
num_pattern_regex = r'[0-9]+'
nums_two_more_regex = r'\b[0-9]{2,}\b|\b[0-9]+\s[0-9]+\b'
postcode_pattern_regex = r'(\b(?:[A-Z][A-HJ-Y]?[0-9][0-9A-Z]? ?[0-9][A-Z]{2})|((GIR ?0A{2})\b$)|(?:[A-Z][A-HJ-Y]?[0-9][0-9A-Z]? ?[0-9]{1}?)$)|(\b(?:[A-Z][A-HJ-Y]?[0-9][0-9A-Z]?)\b$)'
warning_pattern_regex = r'(?i)caution: this email originated from outside of the organization. do not click links or open attachments unless you recognize the sender and know the content is safe.'
egress_pattern_regex = r'(?i)has been securely delivered by egress switch and was securely decoded on'
nbsp_pattern_regex = r'&nbsp;'
multiple_spaces_regex = r'\s{2,}'
# Pre-compiling the regular expressions for efficiency (not actually used)
# email_start_pattern = re.compile(email_start_pattern_regex)
# email_end_pattern = re.compile(email_end_pattern_regex)
# html_pattern = re.compile(html_pattern_regex)
# email_pattern = re.compile(email_end_pattern_regex)
# num_pattern = re.compile(num_pattern_regex)
# nums_two_more_regex_pattern = re.compile(nums_two_more_regex)
# postcode_pattern = re.compile(postcode_pattern_regex)
# warning_pattern = re.compile(warning_pattern_regex)
# nbsp_pattern = re.compile(nbsp_pattern_regex)
def initial_clean(texts, custom_regex, progress=gr.Progress()):
texts = pl.Series(texts).str.strip_chars()
text = texts.str.replace_all(html_pattern_regex, ' ')
text = text.str.replace_all(email_pattern_regex, ' ')
text = text.str.replace_all(nums_two_more_regex, ' ')
text = text.str.replace_all(postcode_pattern_regex, ' ')
# Allow for custom regex patterns to be removed
if len(custom_regex) > 0:
for pattern in custom_regex:
raw_string_pattern = r'{}'.format(pattern)
print("Removing regex pattern: ", raw_string_pattern)
text = text.str.replace_all(raw_string_pattern, ' ')
text = text.str.replace_all(multiple_spaces_regex, ' ')
text = text.to_list()
return text
def remove_hyphens(text_text):
return re.sub(r'(\w+)-(\w+)-?(\w)?', r'\1 \2 \3', text_text)
def remove_characters_after_tokenization(tokens):
pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))
filtered_tokens = filter(None, [pattern.sub('', token) for token in tokens])
return filtered_tokens
def convert_to_lowercase(tokens):
return [token.lower() for token in tokens if token.isalpha()]
def remove_short_tokens(tokens):
return [token for token in tokens if len(token) > 3]
def remove_dups_text(data_samples_ready, data_samples_clean, data_samples):
# Identify duplicates in the data: https://stackoverflow.com/questions/44191465/efficiently-identify-duplicates-in-large-list-500-000
# Only identifies the second duplicate
seen = set()
dups = []
for i, doi in enumerate(data_samples_ready):
if doi not in seen:
seen.add(doi)
else:
dups.append(i)
#data_samples_ready[dupes[0:]]
# To see a specific duplicated value you know the position of
#matching = [s for s in data_samples_ready if data_samples_ready[83] in s]
#matching
# Remove duplicates only (keep first instance)
#data_samples_ready = list( dict.fromkeys(data_samples_ready) ) # This way would keep one version of the duplicates
### Remove all duplicates including original instance
# Identify ALL duplicates including initial values
# https://stackoverflow.com/questions/11236006/identify-duplicate-values-in-a-list-in-python
from collections import defaultdict
D = defaultdict(list)
for i,item in enumerate(data_samples_ready):
D[item].append(i)
D = {k:v for k,v in D.items() if len(v)>1}
# https://stackoverflow.com/questions/952914/how-to-make-a-flat-list-out-of-a-list-of-lists
L = list(D.values())
flat_list_dups = [item for sublist in L for item in sublist]
# https://stackoverflow.com/questions/11303225/how-to-remove-multiple-indexes-from-a-list-at-the-same-time
for index in sorted(flat_list_dups, reverse=True):
del data_samples_ready[index]
del data_samples_clean[index]
del data_samples[index]
# Remove blanks
data_samples_ready = [i for i in data_samples_ready if i]
data_samples_clean = [i for i in data_samples_clean if i]
data_samples = [i for i in data_samples if i]
return data_samples_ready, data_samples_clean, flat_list_dups, data_samples