机器学习12—FP-growth学习笔记

test12.py

#-*- coding：utf-8
import sys
sys.path.append("fpGrowth.py")

import fpGrowth
from numpy import *

# rootNode = fpGrowth.treeNode('pyramid', 9, None)
# rootNode.children['eye'] = fpGrowth.treeNode('eye', 13, None)

# rootNode.children['phoenix'] = fpGrowth.treeNode('phoenix', 3, None)
# rootNode.disp()

simpDat = fpGrowth.loadSimpDat()
# print(simpDat)

initSet = fpGrowth.createInitSet(simpDat)
# print(initSet)

myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 3)
# myFPtree.disp()

# resX = fpGrowth.findPrefixPath('x', myHeaderTab['x'][1])
# print(resX)
# resZ = fpGrowth.findPrefixPath('z', myHeaderTab['z'][1])
# print(resZ)
# resR = fpGrowth.findPrefixPath('r', myHeaderTab['r'][1])
# print(resR)

freqItems = []
fpGrowth.mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems)

print("freqItems:")
print(freqItems)

print("over!!!")

fpGrowth.py

'''
Created on Jun 14, 2011
FP-Growth FP means frequent pattern
the FP-Growth algorithm needs:
1. FP-tree (class treeNode)
2. header table (use dict)

This finds frequent itemsets similar to apriori but does not
find association rules.  

@author: Peter
'''
class treeNode:
    def __init__(self, nameValue, numOccur, parentNode):
        self.name = nameValue
        self.count = numOccur
        self.nodeLink = None
        self.parent = parentNode      #needs to be updated
        self.children = {} 

    def inc(self, numOccur):
        self.count += numOccur

    def disp(self, ind=1):
        print('  '*ind, self.name, ' ', self.count)
        for child in self.children.values():
            child.disp(ind+1)

def createTree(dataSet, minSup=1): #create FP-tree from dataset but don't mine
    headerTable = {}
    #go over dataSet twice
    for trans in dataSet:#first pass counts frequency of occurance
        for item in trans:
            # test0 = headerTable.get(item, 0)
            # test1 = dataSet[trans]
            headerTable[item] = headerTable.get(item, 0) + dataSet[trans]
    for k in list(headerTable):  #remove items not meeting minSup
        if headerTable[k] < minSup:
            del(headerTable[k])
    freqItemSet = set(headerTable.keys())
    #print 'freqItemSet: ',freqItemSet
    if len(freqItemSet) == 0: return None, None  #if no items meet min support -->get out
    for k in headerTable:
        headerTable[k] = [headerTable[k], None] #reformat headerTable to use Node link
    #print('headerTable: ',headerTable)
    retTree = treeNode('Null Set', 1, None) #create tree
    for tranSet, count in dataSet.items():     #go through dataset 2nd time
        localD = {}
        for item in tranSet:  #put transaction items in order
            if item in freqItemSet:
                localD[item] = headerTable[item][0]
        if len(localD) > 0:
            orderedItems = [v[0] for v in sorted(localD.items(), key=lambda p: p[1], reverse=True)]
            updateTree(orderedItems, retTree, headerTable, count)#populate tree with ordered freq itemset
    return retTree, headerTable #return tree and header table

def updateTree(items, inTree, headerTable, count):
    if items[0] in inTree.children:#check if orderedItems[0] in retTree.children
        inTree.children[items[0]].inc(count) #incrament count
    else:   #add items[0] to inTree.children
        inTree.children[items[0]] = treeNode(items[0], count, inTree)
        if headerTable[items[0]][1] == None: #update header table
            headerTable[items[0]][1] = inTree.children[items[0]]
        else:
            updateHeader(headerTable[items[0]][1], inTree.children[items[0]])
    if len(items) > 1:#call updateTree() with remaining ordered items
        updateTree(items[1::], inTree.children[items[0]], headerTable, count)

def updateHeader(nodeToTest, targetNode):   #this version does not use recursion
    while (nodeToTest.nodeLink != None):    #Do not use recursion to traverse a linked list!
        nodeToTest = nodeToTest.nodeLink
    nodeToTest.nodeLink = targetNode

def ascendTree(leafNode, prefixPath): #ascends from leaf node to root
    if leafNode.parent != None:
        prefixPath.append(leafNode.name)
        ascendTree(leafNode.parent, prefixPath)

def findPrefixPath(basePat, treeNode): #treeNode comes from header table
    condPats = {}
    while treeNode != None:
        prefixPath = []
        ascendTree(treeNode, prefixPath)
        if len(prefixPath) > 1:
            condPats[frozenset(prefixPath[1:])] = treeNode.count
        treeNode = treeNode.nodeLink
    return condPats

def mineTree(inTree, headerTable, minSup, preFix, freqItemList):
    bigL = [v[0] for v in sorted(headerTable.items(), key=lambda p: p[1][0])]#(sort header table)
    for basePat in bigL:  #start from bottom of header table
        newFreqSet = preFix.copy()
        newFreqSet.add(basePat)
        #print('finalFrequent Item: ',newFreqSet)    #append to set
        freqItemList.append(newFreqSet)
        condPattBases = findPrefixPath(basePat, headerTable[basePat][1])
        #print('condPattBases :',basePat, condPattBases)
        #2. construct cond FP-tree from cond. pattern base
        myCondTree, myHead = createTree(condPattBases, minSup)
        #print('head from conditional tree: ', myHead)
        if myHead != None: #3. mine cond. FP-tree
            print('conditional tree for: ',newFreqSet)
            myCondTree.disp(1)
            mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList)

def loadSimpDat():
    simpDat = [['r', 'z', 'h', 'j', 'p'],
               ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'],
               ['z'],
               ['r', 'x', 'n', 'o', 's'],
               ['y', 'r', 'x', 'z', 'q', 't', 'p'],
               ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']]
    return simpDat

def createInitSet(dataSet):
    retDict = {}
    for trans in dataSet:
        retDict[frozenset(trans)] = 1
    return retDict

import twitter
from time import sleep
import re

def textParse(bigString):
    urlsRemoved = re.sub('(http:[/][/]|www.)([a-z]|[A-Z]|[0-9]|[/.]|[~])*', '', bigString)
    listOfTokens = re.split(r'\W*', urlsRemoved)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2]

def getLotsOfTweets(searchStr):
    CONSUMER_KEY = ''
    CONSUMER_SECRET = ''
    ACCESS_TOKEN_KEY = ''
    ACCESS_TOKEN_SECRET = ''
    api = twitter.Api(consumer_key=CONSUMER_KEY, consumer_secret=CONSUMER_SECRET,
                      access_token_key=ACCESS_TOKEN_KEY,
                      access_token_secret=ACCESS_TOKEN_SECRET)
    #you can get 1500 results 15 pages * 100 per page
    resultsPages = []
    for i in range(1,15):
        print("fetching page %d" % i)
        searchResults = api.GetSearch(searchStr, per_page=100, page=i)
        resultsPages.append(searchResults)
        sleep(6)
    return resultsPages

def mineTweets(tweetArr, minSup=5):
    parsedList = []
    for i in range(14):
        for j in range(100):
            parsedList.append(textParse(tweetArr[i][j].text))
    initSet = createInitSet(parsedList)
    myFPtree, myHeaderTab = createTree(initSet, minSup)
    myFreqList = []
    mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
    return myFreqList

#minSup = 3
#simpDat = loadSimpDat()
#initSet = createInitSet(simpDat)
#myFPtree, myHeaderTab = createTree(initSet, minSup)
#myFPtree.disp()
#myFreqList = []
#mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)