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ords_demo_tfidf.py
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ords_demo_tfidf.py
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#!/usr/bin/env python3
# Demonstration of extracting vocabulary, features and "bag of words" using TfidfVectorizer.
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
import polars as pl
from funcs import *
# Using English language records (assumed by country).
def get_data():
return ordsfuncs.get_data(cfg.get_envvar("ORDS_DATA")).filter(
pl.col("country").is_in(["USA"]),
pl.col("problem").str.len_chars() > 24,
)
def get_problem_text(data, category):
return list(
data.filter(
pl.col("product_category") == category,
).select(
pl.col(
"problem",
)
)["problem"]
)
# Split the partner_product_category string.
def get_products(data, category):
return list(
ordsfuncs.extract_products(
data.filter(
pl.col("product_category") == category,
)
)
.select(pl.col("product"))
.drop_nulls()
.unique()["product"]
)
# Using derived `item_type` values.
def fit_products(data):
logger.debug("*** PRODUCT TfidfVectorizer ***")
categories = ordsfuncs.get_categories(cfg.get_envvar("ORDS_CATS"))
for id, category in categories.iter_rows():
logger.debug(f"**** {category} ****")
strings = get_products(data, category)
if len(strings) == 0:
continue
tv = TfidfVectorizer()
cv = CountVectorizer()
tv_fit = tv.fit_transform(strings).toarray()
cv_fit = cv.fit_transform(strings).toarray()
logger.debug("** TV vocabulary **")
logger.debug(tv.vocabulary_)
logger.debug("** CV vocabulary **")
logger.debug(cv.vocabulary_)
logger.debug("** TV feature names **")
logger.debug(tv.get_feature_names_out())
logger.debug("** CV feature names **")
logger.debug(cv.get_feature_names_out())
# logger.debug('** TV bag of words **')
# logger.debug(tv_fit)
# logger.debug('** CV bag of words **')
# logger.debug(cv_fit)
def fit_problem_text(data):
logger.debug("*** PROBLEM ***")
categories = ordsfuncs.get_categories(cfg.get_envvar("ORDS_CATS"))
logger.debug("*** TfidfVectorizer ***")
for id, category in categories.iter_rows():
logger.debug(f"**** {category} ****")
# strings = get_problem_text(data, category)
strings = list(
data.filter(
pl.col("product_category") == category,
).select(
pl.col(
"problem",
)
)["problem"]
)
if len(strings) == 0:
continue
tv = TfidfVectorizer()
cv = CountVectorizer()
tv_fit = tv.fit_transform(strings).toarray()
cv_fit = cv.fit_transform(strings).toarray()
foo = tv.vocabulary_
bar = cv.vocabulary_
if foo != bar:
logger.debug("** DIFF vocabulary **")
logger.debug("** TV vocabulary **")
logger.debug(tv.vocabulary_)
logger.debug("** CV vocabulary **")
logger.debug(cv.vocabulary_)
logger.debug("** TV stop words **")
logger.debug(tv.get_stop_words())
logger.debug("** CV stop words **")
logger.debug(cv.get_stop_words())
foo = tv.get_feature_names_out()
bar = cv.get_feature_names_out()
if (foo != bar).all():
logger.debug("** DIFF feature names **")
logger.debug("** TV feature names **")
logger.debug(tv.get_feature_names_out())
logger.debug("** CV feature names **")
logger.debug(cv.get_feature_names_out())
# logger.debug('** TV bag of words **')
# logger.debug(tv_fit)
# logger.debug('** CV bag of words **')
# logger.debug(cv_fit)
if __name__ == "__main__":
logger = cfg.init_logger(__file__)
data = get_data()
fit_products(data)
fit_problem_text(data)