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Ignore what CAZy families were retrieved when getting the CAZy class consensus annotation for dbCAN. Only compare the class annotations. Otherwise, HMMER: GH1, DIAMOND: GH2, Hotpep: GH3 would result in no consensus dbCAN CAZy class annotation when GH is the consensus CAZy class annotation, although the tools disagree upon the specific CAZy family
don't hardcode in the name of the tools to be parsed. Make this a default param that can be overwritten if needed, i.e. during the evaluation of recombining tools
also add writing out new classifications to csv files and writing out the stats to a csv file
add the recombined tools cazy class classifications to the existing df
same reason for application in binary classification evaluation
write out dfs containing recombined tool outputs
create one new row per protein in the original df. Get the CAZy class consensus classification, create a new row. Store all new rows then add one row at a time to the original df
create one new row per protein in the original df. Get the CAZy family consensus classification, create a new row. Store all new rows then add one row at a time to the original df
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