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SDT Blockchain.py
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SDT Blockchain.py
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'''This program is still a working progress'''
from collections import defaultdict
import os
import json
import sys
import numpy as np
import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt
import math
import time
import datetime
from dateutil.parser import parse
from binary_heap import MaxHeap
import copy
''' Item class that represent each transaction'''
class Item:
def __init__(self,key,value,weight, alfa):
self.key = key
self.value = float(value)
self.weight = float(weight)
self.offset = alfa * 1
self.density = (self.value- self.offset)/self.weight
def __str__(self):
return str(self.density)
def __repr__(self):
return (self.density)
def __eq__(self, other):
return ((self.density) == (other.density))
def __lt__(self, other):
return ((self.density) < (other.density))
def __le__(self, other):
return ((self.density) <= (other.density))
def __gt__(self, other):
return ((self.density) > (other.density))
def __ge__(self, other):
return ((self.density) >= (other.density))
''' Block class represents a block with a set of transactions, total fee, size and number of transactions.'''
class Block:
def __init__(self):
self.transactions = []
self.fee = 0
self.count = 0
self.size = 0
def add(self, item):
result = False
if isinstance(item, Item):
self.transactions.append(item)
self.fee += item.value # in order to converter the value to dollars
self.count +=1
self.size += item.weight
result = True
return result
def addBulk(self, listItems):
#self.transactions.extend(listItems)
#option to improve performance
for item in listItems:
if not self.add(item):
print("error")
def print(self):
print("Size = ", self.size)
print("Value = ", self.fee)
print("Total = ", self.count)
"""
Class Knapsack has method that simulate the algorithm used in bitcore.
"""
class Knapsack:
def __init__(self,):
self.max_heap = MaxHeap()
def add(self, item):
self.max_heap.add_element(item)
#function to fill the knapsack(block) with transactions based on thier density value
def fillold(self, items, capacity):
block = Block()
cap = capacity - block.size()
items = sorted(items, key=lambda item: (item.value/item.weight), reverse=True)
for item in items:
if item.weight <= cap:
block.add(item)
cap -= item.weight
return block
def fill (self, capacity):
#add a function that limit the algorithm to check the whole list.
#max_heap = MaxHeap(items)
block = Block()
cap = capacity - block.size
lastFewTxs = 0
for x in range(len(self.max_heap.elements())):
item = self.max_heap.get_root_value()
if item.weight <= cap:
block.add(item)
self.max_heap.extract_root()
cap -= item.weight
elif block.size > capacity -100 or lastFewTxs > 50:
break
if block.size > capacity - 1000:
lastFewTxs += 1
return block
"""Algorithm to fill the blocks with transactions. It is based on a matrix of size by densite. Each classe is represented by one index of this matrix."""
class SDT:
'''
Method to initialize the class with upperbounds and
'''
def __init__(self,sizeClass, densityClass,sizeUpper,DensitUpper):
# have different inputs for X and Y (2 dif classes)
self.sizeClassLimit = sizeClass
self.densityClassLimit = densityClass
self.sizeUpper = sizeUpper
self.densityUpper = DensitUpper
self.table = [[[] for x in range(self.densityClassLimit)]for y in range(self.sizeClassLimit)]
self.sizeTable = [[0 for x in range(self.densityClassLimit)]for y in range(self.sizeClassLimit)]
self.count = 0
'''
Method responsible for add transaction into the right classes.
'''
def add(self, item):
self.count +=1
density = item.value/item.weight
densityScaled = density/self.densityUpper
sizeScaled = item.weight/self.sizeUpper
if densityScaled >= 1:
densityClass = self.densityClassLimit -1
else:
densityClass = math.floor(densityScaled*(self.densityClassLimit-1))
if sizeScaled >=1 :
sizeClass = self.sizeClassLimit -1
else:
sizeClass = math.floor((item.weight/self.sizeUpper)*(self.sizeClassLimit-1))
self.table[sizeClass][densityClass].append(item)
self.sizeTable[sizeClass][densityClass] += item.weight
return
def addLog(self,item,logbase):
self.count +=1
density = item.value/item.weight
if density >0:
densityScaledReversed = self.densityUpper/density
else:
densityScaledReversed = 1000000
sizeScaledReversed = self.sizeLimit/item.weight
if sizeScaledReversed <=1:
sizeClass = self.sizeClassLimit -1
else:
#print(sizeScaledReversed)
#print(math.log(sizeScaledReversed,logbase))
sizeClass = self.sizeClassLimit - math.floor(math.log(sizeScaledReversed,logbase)) -1
if sizeClass <0 :
sizeClass = 0
#print(sizeClass)
if densityScaledReversed <= 1:
self.table[sizeClass][self.densityClassLimit-1].append(item)
self.sizeTable[sizeClass][self.densityClassLimit-1] += item.weight
else:
densityClass = self.densityClassLimit - math.floor(math.log(densityScaledReversed,logbase))-2
if densityClass < 0 :
densityClass = 0
#print(sizeClass)
#print(densityClass)
self.table[sizeClass][densityClass].append(item)
self.sizeTable[sizeClass][densityClass] += item.weight
return
'''
Method responsible for create a block with the better profit possible.
'''
def fill(self, blockLimit):
#print(self.count)
block = Block()
cap = blockLimit- block.size
terminated = False
j = self.densityClassLimit -1
while not terminated and j >= 0:
index = cap/self.densityUpper #empty fraction of the block
if index >=1:
si = self.sizeClassLimit -1
else:
si = math.floor(index * (self.sizeClassLimit-1)) # class which cap belongs
selected = False
i = si -1 # any transaction at class I for sizes fits in the remaining capacity. While transaction in SI might fit or might not.
# check if all the transactions in this class fit in the remaning portion of the block
while i >= 0 and not selected:
if len(self.table[i][j]) > 0:
x = self.table[i][j].pop()
block.add(x)
selected = True
else:
i -= 1
#classSi = self.table[si][j]
#sorted(classSi, key=lambda item: (item.value/item.weight), reverse=True)
if not selected:
# for item in self.table[si][j]:
# if item.weight <= cap:
# block.add(item)
# self.table[si][j].remove(item)
# selected = True
# break
for item in range(len(self.table[si][j])):
if self.table[si][j][item].weight <= cap:
block.add(self.table[si][j][item])
self.table[si][j].pop(item)
selected = True
break
if not selected:
j -= 1
else:
cap = blockLimit - block.size
if cap < 100 :
terminated = True
return block
def fillOpt(self, blockLimit):
#print(self.count)
block = Block()
cap = blockLimit- block.size
terminated = False
j = self.densityClassLimit -1
#lastFewTxs = 0
while not terminated and j >= 0:
index = cap/blockLimit #empty fraction of the block
si = math.floor(index * (self.sizeClassLimit-1)) # class which cap belongs
selected = False
i = si -1 # any transaction at class I for sizes fits in the remaining capacity. While transaction in SI might fit or might not.
# check if all the transactions in this class fit in the remaning portion of the block
if self.sizeTable[si][j] < cap and self.sizeTable[si][j] != 0:
block.addBulk(self.table[si][j])
selected = True
self.table[si][j].clear()
self.sizeTable[si][j] = 0 # clean the list with transaction I just added in the block
elif self.sizeTable[i][j] < cap and self.sizeTable[i][j] != 0:
block.addBulk(self.table[i][j])
selected = True
self.table[i][j].clear()
self.sizeTable[i][j] = 0 # clean the list with transaction I just added in the block
# some of them fits, but some of them not. So check the size of each one of them.
if not selected:
classSi = self.table[si][j][:]
if len(classSi) > 0:
#print(*classSi)
#classSi.sort(key=lambda item: (item.value/item.weight), reverse=True)
#print(*classSi)
#input()
for item in classSi: #self.table[si][j]:
if item.weight <= cap:
block.add(item)
self.table[si][j].remove(item)
cap = blockLimit - block.size
selected = True
break
# maybe sorting by density can help
# add one by one transactions because any of them fits, but not all of them as whole.
while i >= 0 and not selected:
if len(self.table[i][j]) > 0:
x = self.table[i][j].pop()
block.add(x)
selected = True
else:
i -= 1
if not selected:
j -= 1
else:
cap = blockLimit - block.size
if cap <= 100 :
terminated = True
return block
block = Block()
cap = blockLimit - block.size
terminated = False
j = self.densityClassLimit -1
si = self.sizeClassLimit -1
while not terminated and j >=0:
classSi = self.table[si][j][:]
classSi.sort(key=lambda item: (item.value/item.weight), reverse=True)
for item in classSi:
if item.weight <= cap:
block.add(item)
self.table[si][j].remove(item)
cap = blockLimit - block.size
if cap == 0:
break
if si == 0:
si = self.sizeClassLimit -1
j -=1
else:
si -= 1
cap = blockLimit - block.size
if cap == 0:
terminated =True
return block
"""print the table with the amount of transactions in each class"""
def print(self,):
for x in reversed(range(self.sizeClassLimit)):
for y in range(self.densityClassLimit):
print("<",len(self.table[x][y]),">",end=" ")
print()
class Greed:
def __init__(self,):
self.memPool = list()
def add(self, item):
self.memPool.append(item)
def fill(self,capacity):
block = Block()
cap = capacity - block.size
lastFewTxs = 0
index = 0
for item in self.memPool:
if item.weight <= cap:
block.add(item)
cap -= item.weight
self.memPool.pop(index)
if block.size > capacity -100 or lastFewTxs > 50:
break
elif block.size > capacity - 1000:
lastFewTxs += 1
index +=1
return block
class GreedAdvice:
def __init__(self,advice):
self.memPool = list()
self.advice = advice
self.memPollRejected = list()
def add(self, item):
if item.density >= self.advice:
self.memPool.append(item)
else:
self.memPollRejected.append(item)
def fill(self,capacity):
block = Block()
cap = capacity - block.size
lastFewTxs = 0
index = 0
for item in self.memPool:
if item.weight <= cap:
block.add(item)
cap -= item.weight
self.memPool.pop(index)
if block.size > capacity -100 or lastFewTxs > 50:
break
elif block.size > capacity - 1000:
lastFewTxs += 1
index += 1
return block
def main (opt,SDTParam,transactionFile,fineTune):
Tdatetime = "2019-10-20 23:52:45"
capacity = 1000000
#initial = datetime.datetime.strptime(Tdatetime,'%Y-%m-%d %H:%M:%S')
nextblock = datetime.timedelta(minutes=10)
delay = datetime.timedelta(minutes=0)
blocksMinded = []
rounds = 3 # numebr of simulations
totalTime = 0
totalFee = 0
totalTransactions = 0
simulationTTime = 0
broadcastTransactions = 0
startSimulationTime = time.time()
container = None
infos = {}
minedInfo = {'addExtre':[], 'BlocksFee':[]}
for i in range(rounds):
delay = datetime.timedelta(minutes=i)
initial = parse(Tdatetime) + nextblock
if opt == 0 :
container = Greed()
#greedonly
elif opt == 1:
container = GreedAdvice(0.00037)
elif opt == 2:
container = Knapsack()
elif opt == 3:
container = SDT(SDTParam[0],SDTParam[1],SDTParam[2],SDTParam[3])
else:
print("Invalid Argument")
extraTrans = 0
extraTransList = []
for line in transactionFile:
fields = line.split(',')
timeStamp = parse(fields[1])
if timeStamp < (initial + delay):
extraTrans += 1
#sdt.addLog(Item(line.split(',')[0],line.split(',')[1],line.split(',')[2]),1.2)
broadcastTransactions += 1
container.add(Item(fields[0],fields[2],fields[3],fineTune))
else:
#print(extraTrans)
extraTransList.append(extraTrans)
extraTrans = 0
initial += nextblock
#print(count)
begin = time.time()
mined = container.fill(capacity)
blocksMinded.append(mined)
totalTime += time.time() - begin
#mined.print()
totalFee += mined.fee
totalTransactions += mined.count
#print(totalFee, end=',')
#sdt.print()
values = []
print(extraTransList)
extraTransList.clear()
for block in blocksMinded:
values.append(block.fee)
print(values)
print()
values.clear()
blocksMinded.clear()
simulationTTime += time.time() - startSimulationTime
#mined.print()
print('Total time = {}, Opcao = {} TransactionsReceived = {}, avgBlock = {}'.format(simulationTTime, opt,broadcastTransactions,totalTime/rounds), end=' ')
print("Total fee = {} Total transactions = {}".format(totalFee/rounds,totalTransactions))
return totalFee/rounds,totalTransactions/rounds, totalTime/rounds, simulationTTime
def simulation (capacity):
#SDT(100,100,1000.0,0.01)
sizeClass = 100
densitClass = 100
sizeUpper = 95000
densityUpper = 0.0015
result = None
best = Block()
simulationTime = 0
SDTParam = [sizeClass,densitClass,sizeUpper,densityUpper]
result, cumulativeTime, simulationTime = main(3, SDTParam)
print(simulationTime, cumulativeTime, SDTParam, result.fee, result.count, result.size)
'''
for x in range(100):
SDTParam = [sizeClass,densitClass,sizeUpper,densityUpper]
result, cumulativeTime = main(3, SDTParam)
print(cumulativeTime/size, SDTParam, result.fee, result.count, result.size)
cumulativeTime = 0
if result.fee > best.fee:
best = copy.deepcopy(result)
best.print()
sizeUpper +=1000
'''
return result
def simulationGreed (capacity):
block = Block()
best = 0
count = 0
advice = 0.00036
Tdatetime = "2019-10-20 23:52:45"
capacity = 1000000
#initial = datetime.datetime.strptime(Tdatetime,'%Y-%m-%d %H:%M:%S')
nextblock = datetime.timedelta(minutes=10)
initial = parse(Tdatetime) + nextblock
blocksMinded = []
begin = time.time()
rounds = 1 # numebr of simulations
totalTime = 0
totalFee = 0
totalTransactions = 0
simulationTTime = 0
broadcastTransactions = 0
startSimulationTime = time.time()
container = Greed()
oldTotalFee = 0
for i in range(10):
with open('../Data/parsedData.csv','r') as f:
for line in f:
fields = line.split(',')
timeStamp = parse(fields[1])
if timeStamp < initial:
#sdt.addLog(Item(line.split(',')[0],line.split(',')[1],line.split(',')[2]),1.2)
broadcastTransactions += 1
container.add(Item(fields[0],fields[2],fields[3],0))
else:
initial += nextblock
#print(rounds)
mined = container.fillAdvice(capacity,advice)
totalFee += mined.fee
totalTransactions += mined.count
rounds +=1
#sdt.print()
print(rounds)
rounds = 0
count += 1
if totalFee > oldTotalFee:
oldTotalFee = totalFee
best = advice
print("Total={} - Advice={}".format(oldTotalFee,best))
totalFee = 0
totalTransactions = 0
initial = parse(Tdatetime) + nextblock
f.close()
print(count)
advice -= 0.00001
block.print()
print('advice =', best)
if __name__ == "__main__":
#simulationGreed(1000000)
best = 0
transactionFile = open('/Users/dossants/Desktop/knapsack problem /Data/parsedData.csv','r').readlines()
fineTune = 0.0000
for option in range (2,4):
totalFee,totalTransactions, cumulativeTime , simulationTime= main(option,[100,100,60000,0.00069],transactionFile,fineTune)
#result = simulation(1000000)
# time for the algorithm is computed and printed
#result.print()
#print(x)
# if totalFee > best:
# best = totalFee
# print("Total ={}, upperboud = {}".format(best,upperbound))
#fineTune += 0.001
print(simulationTime,cumulativeTime, [], totalFee, totalTransactions)
#simulation(1000000)