Learning Apriori Algorithm - in Python

"Machine Learning in Action" is a good book. I've learnt Apriori algorithm successfully. Here is a working Python3 code piece:

#   Load Data
def loadDataSet(path):
    return [[1, 3, 4],
            [2, 3, 5],
            [1, 2, 3, 5],
            [2, 5]]
    

'''
    ======== Frequent Set Searching ========
'''

#   Create size1 sets
def createC1(dataSet):
    C1 = []
    #   TODO: list to set maybe good enough
    for transaction in dataSet:
        for item in transaction:
            if not [item] in C1:
                C1.append([item])
    C1.sort()
    return map(frozenset, C1)

# Pruning out all set with support < minSupport
#   D - dataset
#   Ck - candidate sets
#   minSupport - threshold
def scanD(D, Ck, minSupport):
    ssCnt = {}
    for tid in D:
        for can in Ck:
            if can.issubset(tid):
                if not can in ssCnt: ssCnt[can] = 1
                else: ssCnt[can] += 1
    numItems = float(len(D))
    retList = []
    supportData = {}
    # Measure support and prune
    for key in ssCnt:
        support = ssCnt[key] / numItems
        if support >= minSupport:
            retList.insert(0, key)
        supportData[key] = support
    return retList, supportData


def aprioriGen(Lk, k): # creates Ck
    retList = []
    lenLk = len(Lk)
    for i in range(lenLk):
        for j in range(i + 1, lenLk):
            L1 = list(Lk[i])[:k-2]  # [0,1] | [0,2] -> [0,1,2]
            L2 = list(Lk[j])[:k-2]
            if L1 == L2:
                retList.append(Lk[i] | Lk[j])
    return retList


def apriori(dataSet, minSupport = 0.5):
    # start from size 1
    C1 = list(createC1(dataSet))
    D = list(map(set, dataSet))
    L1, supportData = scanD(D, C1, minSupport)
    #
    L = [L1]
    k = 2
    while(len(L[k-2]) > 0):
        print ('=Debug= Apriori Size of Last Level', len(L[k-2]))
        Ck = aprioriGen(L[k-2], k)
        Lk, supK = scanD(D, Ck, minSupport)
        supportData.update(supK)
        L.append(Lk)
        k += 1

    return L, supportData

'''
    ======== Association Rule Searching ========
    H: a list of items that could be on the right-hand side of a rule
'''

def calcConf(freqSet, H, supportData, brl, minConf=0.7):
    prunedH = []
    for conseq in H:
        conf = supportData[freqSet] / supportData[freqSet - conseq]
        if conf >= minConf:
            print (set(freqSet - conseq), '-->', set(conseq), 'conf:', conf * 100, '%')
            brl.append((freqSet - conseq, conseq, conf))
            prunedH.append(conseq)
    return prunedH

def rulesFromConseq(freqSet, H, supportData, brl, minConf = 0.7):
    m = len(H[0])
    if (len(freqSet) > (m + 1)):
        Hmp1 = aprioriGen(H, m + 1) # Gen list of next iteration
        Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf) # pruning. pick qualified rules.
        if (len(Hmp1) > 1):
            rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf) # Continue\Iterate to next level

#   L: a set of freqent itemset; sorted by length
def generateRules(L, supportData, minConf = 0.7):
    bigRuleList = []
    for i in range(1, len(L)): # from length 2
        #print ('Apriori Rule ', i)
        for freqSet in L[i]:
            H1 = [frozenset([item]) for item in freqSet] # {0,1,2} -> [{0},{1},{2}].
                                                         # Build from size 1 on right-hand side
            if (i > 1): # length > 2, go level by level
                rulesFromConseq(freqSet, H1, supportData, bigRuleList, minConf)
            else: # if only 2 items, just prune - the base
                calcConf(freqSet, H1, supportData, bigRuleList, minConf)
    return bigRuleList