Merge e7c4170cc2 into c4b3512df7
				
					
				
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		| @@ -147,6 +147,7 @@ class Indexer(): | ||||
| 		#word the word we finding the score for | ||||
| 		#return the score | ||||
| 		try: | ||||
| 			''' | ||||
| 			tfidf = TfidfVectorizer() | ||||
| 			tfidf_matrix = tfidf.fit_transform(words) | ||||
| 			df = pd.DataFrame(tfidf_matrix.toarray(), columns = tfidf.get_feature_names_out()) | ||||
| @@ -166,12 +167,14 @@ class Indexer(): | ||||
| 			#print(df) | ||||
| 		except KeyError:  | ||||
| 			return -1 | ||||
| 			''' | ||||
| 		try:	 | ||||
| 			tfidf = TfidfVectorizer(ngram_range=(1,3)) # ngram_range is range of n-values for different n-grams to be extracted (1,3) gets unigrams, bigrams, trigrams | ||||
| 			tfidf_matrix = tfidf.fit_transform(words)  # fit trains the model, transform creates matrix | ||||
| 			df = pd.DataFrame(tfidf_matrix.toarray(), columns = tfidf.get_feature_names_out()) # store value of matrix to associated word/n-gram | ||||
| 			#return(df.iloc[0][''.join(word)]) #used for finding single word in dataset | ||||
| 			data = df.to_dict() # transform dataframe to dict *could be expensive the larger the data gets, tested on ~1000 word doc and took 0.002 secs to run | ||||
| 			return data			# returns the dict of words/n-grams with tf-idf | ||||
| 			tfidf_dict = df.to_dict() # transform dataframe to dict *could be expensive the larger the data gets, tested on ~1000 word doc and took 0.002 secs to run | ||||
| 			return tfidf_dict			# returns the dict of words/n-grams with tf-idf as value | ||||
| 			#print(df)			# debugging  | ||||
| 		except: 		 | ||||
| 			print("Error in tf_idf!") | ||||
| @@ -229,4 +232,4 @@ def main(): | ||||
| 	indexer.get_data() | ||||
|  | ||||
| if __name__ == "__main__": | ||||
| 	main() | ||||
| 	main() | ||||
|   | ||||
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