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: Makes sure that the first item in itemset is a child of node 
  and that every following item in itemset is reachable via that path 
docstr_tok: ["Makes","sure","that","the","first","item","in","itemset","is","a","child","of","node","and","that","every","following","item","in","itemset","is","reachable","via","that","path"]
code: def _is_prefix(self, itemset, node):
      """Makes sure that the first item in itemset is a child of node
      and that every following item in itemset is reachable via that path
      """
      for item in itemset:
          if not item in node.children:
              return False
          node = node.children[item]
      return True
  
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 7 for calling the FPGrowth obj's underlying member method code with example usage: obj._is_prefix(itemset, node) and returns: True, False