class_docstr: A method for determining frequent itemsets in a transactional database and
  also for generating rules for those itemsets. 
  
  Parameters:
  -----------
  min_sup: float
      The minimum fraction of transactions an itemets needs to
      occur in to be deemed frequent
  min_conf: float:
      The minimum fraction of times the antecedent needs to imply
      the concequent to justify rule
class_docstr_tok: ["A","method","for","determining","frequent","itemsets","in","a","transactional","database","and","also","for","generating","rules","for","those","itemsets",".","Parameters",":","--","--","--","--","--","-","min_sup",":","float","The","minimum","fraction","of","transactions","an","itemets","needs","to","occur","in","to","be","deemed","frequent","min_conf",":","float",":","The","minimum","fraction","of","times","the","antecedent","needs","to","imply","the","concequent","to","justify","rule"]
docstr: True or false depending on each item in the itemset is
  in the transaction 
docstr_tok: ["True","or","false","depending","on","each","item","in","the","itemset","is","in","the","transaction"]
code: def _transaction_contains_items(self, transaction, items):
      """True or false depending on each item in the itemset is
      in the transaction"""
      # If items is in fact only one item
      if isinstance(items, int):
          return items in transaction
      # Iterate through list of items and make sure that
      # all items are in the transaction
      for item in items:
          if not item in transaction:
              return False
      return True
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/apriori.py
imports: from __future__ import division, print_function
  import numpy
  import itertools
label_desc: Use image node_id 6 for calling the Apriori obj's underlying member method code with example usage: obj._transaction_contains_items(transaction, items) and returns: True, unknown, False