Issue with using sparse data frame with mlxtend apriori.
I am running python 2.7 in anaconda and have installed mlxtend. Based on the latest version of mlxtend, the aprioir class supports sparse dataframe as its input. I have over 500k products that I want to run a market basket analysis on. I am trying to create a onehot encoded sparse dataframe using a small dataset to test but I am running into df.to_coo() issue on the sparse data frame inside the mlextend apriori function.
Please find the code, the input data file and the errors I get here -
https://github.com/nshahHome/pycode
Click on the view code to see the files.
code = code2.py input data file= mbatest.txt errors = code2-error.html (you may need to download this file and open it in a browser)
I expect the code to not throw errors and try to create frequent_itemsets. The set could be empy if there are no sets > min_support.
