class_docstr: A method for determining frequent itemsets in a transactional database. 
  This is done by building a so called FP Growth tree, which can then be mined
  to collect the frequent itemsets. More effective than Apriori for large transactional
  databases.
  
  Parameters:
  -----------
  min_sup: float
      The minimum fraction of transactions an itemets needs to
      occur in to be deemed frequent
class_docstr_tok: ["A","method","for","determining","frequent","itemsets","in","a","transactional","database",".","This","is","done","by","building","a","so","called","FP","Growth","tree",",","which","can","then","be","mined","to","collect","the","frequent","itemsets",".","More","effective","than","Apriori","for","large","transactional","databases",".","Parameters",":","--","--","--","--","--","-","min_sup",":","float","The","minimum","fraction","of","transactions","an","itemets","needs","to","occur","in","to","be","deemed","frequent"]
docstr: Determines the look of the hashmap key for self.prefixes
  List of more strings than one gets joined by '-' 
docstr_tok: ["Determines","the","look","of","the","hashmap","key","for","self.prefixes","List","of","more","strings","than","one","gets","joined","by","'-","'"]
code: def _get_itemset_key(self, itemset):
      """Determines the look of the hashmap key for self.prefixes
      List of more strings than one gets joined by '-'"""
      if len(itemset) > 1:
          itemset_key = "-".join(itemset)
      else:
          itemset_key = str(itemset[0])
      return itemset_key
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/fp_growth.py
imports: from __future__ import division, print_function
  import numpy
  import itertools
label_desc: Use image node_id 9 for calling the FPGrowth obj's underlying member method code with example usage: obj._get_itemset_key(itemset) and returns: itemset_key