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#!/Users/pranab/Tools/anaconda/bin/python
# Package imports
import os
import sys
import numpy as np
import sklearn as sk
import random
import jprops
import abc
import math
import random
sys.path.append(os.path.abspath("../lib"))
from util import *
#base parameter search
class BaseParameterSearch(object):
__metaclass__ = abc.ABCMeta
def __init__(self, verbose):
self.verbose = verbose
self.parameters = []
self.paramData = {}
self.currentParams = []
self.curIter = 0
self.bestSolution = None
# add param name and type
def addParam(self, param):
self.parameters.append(param)
# add param data
def addParamVaues(self, paramName, paramData):
self.paramData[paramName] = paramData
# max iterations
def setMaxIter(self, maxIter):
self.maxIter = maxIter
@abc.abstractmethod
def prepare(self):
pass
@abc.abstractmethod
def nextParamValues(self):
pass
@abc.abstractmethod
def setCost(self, cost):
pass
# get best solution
def getBestSolution(self):
return self.bestSolution
#enumerate through provided list of param values
class GuidedParameterSearch:
def __init__(self, verbose=False):
self.verbose = verbose
self.parameters = []
self.paramData = {}
self.paramIndexes = []
self.numParamValues = []
self.currentParams = []
self.bestSolution = None
# max iterations
def setMaxIter(self,maxIter):
self.maxIter = maxIter
# add param name and type
def addParam(self, param):
self.parameters.append(param)
# add param data
def addParamVaues(self, paramName, paramData):
self.paramData[paramName] = paramData
# prepare
def prepare(self):
self.numParams = len(self.parameters)
for i in range(self.numParams):
self.paramIndexes.append(0)
#number of values for each parameter
paramName = self.parameters[i][0]
self.numParamValues.append(len(self.paramData[paramName]))
self.curParamIndex = 0
paramValueCombList = []
paramValueComb = []
paramValueCombList.append(paramValueComb)
# all params
for i in range(self.numParams):
paramValueCombListTemp = []
for paramValueComb in paramValueCombList:
# all param values
for j in range(self.numParamValues[i]):
paramValueCombTemp = paramValueComb[:]
paramValueCombTemp.append(j)
paramValueCombListTemp.append(paramValueCombTemp)
paramValueCombList = paramValueCombListTemp
self.paramValueCombList = paramValueCombList
self.numParamValueComb = len(self.paramValueCombList)
self.curParamValueCombIndx = 0;
# next param combination
def nextParamValues(self):
retParamNameValue = None
if self.curParamValueCombIndx < len(self.paramValueCombList):
retParamNameValue = []
curParams = self.paramValueCombList[self.curParamValueCombIndx]
print (curParams)
for i in range(len(curParams)):
paramName = self.parameters[i][0]
paramValue = self.paramData[paramName][curParams[i]]
retParamNameValue.append((paramName, paramValue))
self.curParamValueCombIndx = self.curParamValueCombIndx + 1
self.currentParams = retParamNameValue
return retParamNameValue
# set cost of current parameter set
def setCost(self, cost):
if self.bestSolution is not None:
if cost < self.bestSolution[1]:
self.bestSolution = (self.currentParams, cost)
else:
self.bestSolution = (self.currentParams, cost)
# get best solution
def getBestSolution(self):
return self.bestSolution
#random search through provided list of parameter values
class RandomParameterSearch(BaseParameterSearch):
def __init__(self, verbose=False):
super(RandomParameterSearch, self).__init__(verbose)
# prepare
def prepare(self):
pass
# next param combination
def nextParamValues(self):
retParamNameValue = None
if (self.curIter < self.maxIter):
retParamNameValue = []
for pName, pValues in self.paramData.iteritems():
pValue = selectRandomFromList(pValues)
retParamNameValue.append((pName, pValue))
self.curIter = self.curIter + 1
self.currentParams = retParamNameValue
return retParamNameValue
# set cost of current parameter set
def setCost(self, cost):
if self.bestSolution is not None:
if cost < self.bestSolution[1]:
self.bestSolution = (self.currentParams, cost)
else:
self.bestSolution = (self.currentParams, cost)
#random search through provided list of parameter values
class SimulatedAnnealingParameterSearch(BaseParameterSearch):
def __init__(self, verbose=False):
self.curSolution = None
self.nextSolution = None
super(SimulatedAnnealingParameterSearch, self).__init__(verbose)
# prepare
def prepare(self):
pass
def setTemp(self, temp):
self.temp = temp
def setTempReductionRate(self, tempRedRate):
self.tempRedRate = tempRedRate
# next param combination
def nextParamValues(self):
retParamNameValue = None
if (self.curIter == 0):
#initial random solution
retParamNameValue = []
for pName, pValues in self.paramData.iteritems():
pValue = selectRandomFromList(pValues)
retParamNameValue.append((pName, pValue))
self.curIter = self.curIter + 1
self.currentParams = retParamNameValue
elif (self.curIter < self.maxIter):
#perturb current solution
retParamNameValue = []
#randomly mutate one parameter value
(pNameSel, pValue) = selectRandomFromList(self.currentParams)
pValueNext = selectRandomFromList(self.paramData[pNameSel])
while (pValueNext == pValue):
pValueNext = selectRandomFromList(self.paramData[pNameSel])
#copy
for (pName, pValue) in self.currentParams:
if (pName == pNameSel):
pValueNew = pValueNext
else:
pValueNew = pValue
retParamNameValue.append((pName, pValueNew))
self.curIter = self.curIter + 1
self.currentParams = retParamNameValue
return retParamNameValue
# set cost of current parameter set
def setCost(self, cost):
if self.curSolution is None:
self.curSolution = (self.currentParams, cost)
self.bestSolution = (self.currentParams, cost)
else:
self.nextSolution = (self.currentParams, cost)
if (self.nextSolution[1] < self.curSolution[1]):
if (self.verbose):
print ("next soln better")
self.curSolution = self.nextSolution
if (self.nextSolution[1] < self.bestSolution[1]):
if (self.verbose):
print ("next soln better than best")
self.bestSolution = self.nextSolution
else:
if (self.verbose):
print ("next soln worst")
pr = math.exp((self.curSolution[1] - self.nextSolution[1]) / self.temp)
if (pr > random.random()):
self.curSolution = self.nextSolution
if (self.verbose):
print ("next soln worst but accepted")
else:
if (self.verbose):
print ("next soln worst and rejected")
self.temp = self.temp * self.tempRedRate
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