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import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
import pickle
def sort_coo(coo_matrix):
tuples = zip(coo_matrix.col, coo_matrix.data)
return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
def extract_topn_from_vector(feature_names, sorted_items, topn=10):
"""get the feature names and tf-idf score of top n items"""
#use only topn items from vector
sorted_items = sorted_items[:topn]
score_vals = []
feature_vals = []
# word index and corresponding tf-idf score
for idx, score in sorted_items:
#keep track of feature name and its corresponding score
score_vals.append(round(score, 3))
feature_vals.append(feature_names[idx])
#create a tuples of feature,score
#results = zip(feature_vals,score_vals)
results= {}
for idx in range(len(feature_vals)):
results[feature_vals[idx]]=score_vals[idx]
return results
def keyword_extraction(sdg:int,sdgdata):
model_path = "docStore/sdg{}/".format(sdg)
vectorizer = pickle.load(open(model_path+'vectorizer.pkl', 'rb'))
tfidfmodel = pickle.load(open(model_path+'tfidfmodel.pkl', 'rb'))
features = vectorizer.get_feature_names_out()
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