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I'm trying to calculate cosine similarity scores between all possible combinations of text documents from a corpus. I'm using scikit-learn's cosinesimilarity function to do this. Since my corpus is huge (30 million documents), the number of possible combinations between the documents in the corpus is just too many to store as a dataframe. So, I'd like to filter the similarity scores using a threshold, as they're being created, before storing them in a dataframe for future use. While I do that, I also want to assign the corresponding IDs of each of these documents to the index and column names of the dataframe. So, for a data value in the dataframe, each value should have index(row) and column names which are the document IDs for which the value is a cosine similarity score. Similarityvalues = pd.DataFrame(cosinesimilarity(tfidfmatrix), index = IDs, columns= IDs)This piece of code works well without the filtering part.
Another TextBlob release (0.6.1, changelog), another quick tutorial.This one's on using the TF-IDF algorithm to find the most important words in a text document. It's simpler than you think. What is TF-IDF? TF-IDF stands for 'Term Frequency, Inverse Document Frequency.' It's a way to score the importance of words (or 'terms') in a document based on how frequently they appear across multiple. Join GitHub today. GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together.
![Find A Similar Document Tf-idf Python Dataframe Find A Similar Document Tf-idf Python Dataframe](/uploads/1/2/5/6/125625092/832189031.jpg)
IDs is a list variable that has all document IDs sorted corresponding to the tfidf matrix. Similarityvalues = pd.DataFrame(cosinesimilarity(tfidfmatrix)0.65, index = IDs, columns= IDs)This modification helps with the filtering but the similarity scores are turned into boolean (True/False) values. How can I keep the actual cosine similarity scores here instead of the boolean True/False values.