Issue with using Sparse Data Frame in Mlxtend Apriori function

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 have created 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 (pdf version) , condalist.txt

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.