abhijeet-talaulikar

def _draw_bootstrap_sample(rng, X, y, frac = 1.0):
    """
    Function to draw a single bootstrap sub-sample from given data.
    Use frac to adjust the size of the sample as a fraction of data. Defaults to size of given data.
    """
    sample_indices = np.arange(X.shape[0])
    bootstrap_indices = rng.choice(sample_indices,
                                   size=int(sample_indices.shape[0] * frac),
                                   replace=True)
    return X[bootstrap_indices], y[bootstrap_indices]  

abhijeet-talaulikar

def fp_growth(transaction_db, min_sup, fp_list, prefix = []):
  
  # Step A code to be filled here
  
  # Step B code to be filled here
  
  # Step C code to be filled here
  
  if cpb_db.shape[0] > 0:
    fp_growth(cpb_db, min_sup, fp_list, prefix + [item])

abhijeet-talaulikar

def get_one_itemsets(transaction_db, min_sup):
    
    items = {}
    
    for t_tup in transaction_db.list_items:
        
        for i in new_t:
            if i in items.keys():
                items[i] += t_tup[1]
            else:

abhijeet-talaulikar

def fp_growth(transaction_db, min_sup, fp_list, prefix = []):
    
    ####### STEP A: CONCATENATE #######
    # Get one itemsets which meet minimum support
    one_itemset_dict = get_one_itemsets(transaction_db, min_sup)
    
    # Concanenate frequent patterns with prefix
    freq_patterns = []
    for key,val in one_itemset_dict.items():
        pattern = prefix + [key]

abhijeet-talaulikar

def fp_growth(transaction_db, min_sup, fp_list, prefix = []):
    
    ####### STEP A: CONCATENATE #######
    # Get one itemsets which meet minimum support
    one_itemset_dict = get_one_itemsets(transaction_db, min_sup)
    
    # Concanenate frequent patterns with prefix
    freq_patterns = []
    for key,val in one_itemset_dict.items():
        pattern = prefix + [key]

abhijeet-talaulikar

def sort_transaction(t, one_itemset_dict):
    sorted_t = sorted(one_itemset_dict.keys(), key=one_itemset_dict.get, reverse=True)
    return [i for i in sorted_t if i in t]

abhijeet-talaulikar

def tree_contains_node(name, parent, fp_tree):
    for node in fp_tree:
        if (node['name'] == name) and (node['parent'] is parent):
            return node
    
    return False

abhijeet-talaulikar

def insert_tree(prefix, suffix, current_root, fp_tree, node_link_dict, transaction_count):
    
    # Check if route to the prefix node exists in tree
    # If exists, get the node
    new_root = tree_contains_node(prefix, current_root, fp_tree)
    
    if new_root == False:
        # If route doesn't exist, create new child node to current root
        new_root = {'name':prefix, 'count':transaction_count, 'parent': current_root}
        

abhijeet-talaulikar

def fp_growth(transaction_db, min_sup, fp_list, prefix = []):
    
    ####### STEP A: CONCATENATE #######
    # Get one itemsets which meet minimum support
    one_itemset_dict = get_one_itemsets(transaction_db, min_sup)
    
    # Concanenate frequent patterns with prefix
    freq_patterns = []
    for key,val in one_itemset_dict.items():
        pattern = prefix + [key]

abhijeet-talaulikar

lag = {
    "CTV": 2,
    "DISPLAY": 2,
    "DIRECT_MAIL": 2,
    "EMAIL": 2,
    "PAID_SOCIAL": 2,
    "PAID_SEARCH": 1,
    "TV": 3
}