How to use outcome_to_color method in Lemoncheesecake
Best Python code snippet using lemoncheesecake
bankProblem.py
Source:bankProblem.py 
...722 page.th("%0.2f" % y)723 for x in xValues:724 o = xy_to_outcome[(x,y)]725 page.td.open()726 #page.font(markup.oneliner.a("*", href="index.html#%d" % i), color=outcome_to_color(o))727 page.a(markup.oneliner.font("*", color=outcome_to_color(o)), href="index.html#%d" % xy_to_i[(x,y)])728 page.td.close() 729 page.tr.close()730 page.table.close()731 # color graph732 fig = plt.figure()733 ax = fig.add_subplot(111) 734 ax.set_xlabel(xlabel if (xlabel != None) else xName)735 ax.set_ylabel(ylabel if (ylabel != None) else yName)736 colors = [outcome_to_color(o) for o in outcomeList]737 ax.scatter(xList, yList, color=colors, edgecolor=colors)738 page.br()739 imgFilename = "2d_summary.png"740 imgPath = os.path.join(dirname, imgFilename)741 try: 742 os.remove(imgPath)743 except OSError: 744 pass 745 plt.savefig(imgPath, format='png', dpi=120) 746 page.img(src=imgFilename) 747 # non-color graph748 fig = plt.figure()749 ax = fig.add_subplot(111) 750 ax.set_xlabel(xlabel if (xlabel != None) else xName)751 ax.set_ylabel(ylabel if (ylabel != None) else yName)752 (markers_labels) = [outcome_to_marker(o) for o in outcomeList]753 by_marker = defaultdict(list)754 [by_marker[marker_label].append((x,y)) for (x,y,marker_label) in zip(xList, yList, markers_labels)]755 for ((marker, label), xyList) in by_marker.items():756 (x_list, y_list) = zip(*xyList)757 ax.scatter(x_list, y_list, marker=marker, label=label, facecolor='none')758 (handles, labels) = ax.get_legend_handles_labels()759 hl = sorted(zip(handles, labels), key=operator.itemgetter(1))760 (handles2, labels2) = zip(*hl) 761 fig.legend(handles2, labels2, 'upper center') 762 if (addLines1): 763 ax.axvline((1.0/beta)-1.0, color='gray')764 ax.plot([xList[0], xList[-1]], [yList[0], yList[-1]], color='gray')765 page.br()766 imgFilename = "2d_summary2.png"767 imgPath = os.path.join(dirname, imgFilename)768 try: 769 os.remove(imgPath)770 except OSError: 771 pass 772 plt.savefig(imgPath, format='png', dpi=120) 773 page.img(src=imgFilename) 774 775 filename = os.path.join(dirname, "2d_summary.html")776 f = open(filename, 'w') 777 f.write(str(page))778 f.close()779 780 return (xList, yList, outcomeList)781 782def outcome_to_color(outcome):783 if (outcome == SOLUTION_CATEGORY_NOINVEST): return 'black'784 if (outcome == SOLUTION_CATEGORY_INVEST): return 'green'785 if (outcome == SOLUTION_CATEGORY_NONCONVERGENCE): return 'red'786 assert(false)787def outcome_to_marker(outcome):788 if (outcome == SOLUTION_CATEGORY_NOINVEST): return ('o', "no loans made")789 if (outcome == SOLUTION_CATEGORY_INVEST): return ('+', "loans made")790 if (outcome == SOLUTION_CATEGORY_NONCONVERGENCE): return ('s', "nonconvergence")791 assert(false)792 793def run_all(dirname, testGenObj):794 result1 = run_test_cases(dirname, testGenObj)795 result2 = generate_plots(dirname, testGenObj)796 result3 = write_test_summary_2d(dirname, testGenObj)797 result4 = plot_test_summary_2d(dirname, testGenObj)798 (filename, xName, yName, xList, yList, outcomes) = simulate_summary(dirname, testGenObj)799 result6 = plot_simulation_summary(dirname, testGenObj, filename)800 801def run_test_cases(dirname, testGenObj, skipWrite=False, skipIfExists=True, usePrevVArray=True, nMaxIters=g.MAX_VITERS, maxTime=g.MAX_TIME, maxV=g.MAX_V): 802 currentVArray = None803 prevRunConverged = False804 for (testName, overrideParams) in testGenObj.getOverrideParamsList(): 805 newParams = dict(testGenObj.getDefaultParamsDict())806 newParams.update(overrideParams)807 prefix = testGenObj.getFilenamePrefix(testName, newParams)808 path = os.path.join(dirname, prefix) + ".out"809 print(path)810 if (skipIfExists and os.path.exists(path)):811 print("%s exists, skipping" % path)812 continue813 g.reset()814 if ((not usePrevVArray) or (prevRunConverged == False)): currentVArray = None; # if the previous optimization didn't converge, start from scratch, otherwise, start from previous optimized value815 (iterCode, prevNIter, currentVArray, newVArray, optControls) = test_bank2(plotResult=False, initialVArray=currentVArray, nMaxIters=nMaxIters, maxTime=g.MAX_TIME, maxV=g.MAX_V, overrideParamsDict=newParams)816 prevRunConverged = True if (iterCode == bellman.ITER_RESULT_CONVERGENCE) else False817 if (not skipWrite):818 saveRun(path)819def generate_one_plot(arg):820 (dirname, outPath, prefix, caption, skipIfExists) = arg821 if (not os.path.exists(outPath)):822 print ("not found: %s" % outPath)823 return None824 try:825 plt.ioff()826 loadRun(outPath)827 #G = createGraph(-1)828 # save plots 829 suffixes = ["-V", "-optD", "-inF"]830 plotFns = [plotV, plotOptD, plotFracIn]831 imgList = []832 for (suffix, plotFn) in zip(suffixes, plotFns):833 plotFn(-1, len(g.Grid_P)/2)834 imgFilename = prefix + suffix + ".png"835 imgPath = os.path.join(dirname, imgFilename)836 if (not skipIfExists):837 try: 838 os.remove(imgPath)839 except OSError: 840 pass 841 print("writing %s" % imgPath) 842 plt.savefig(imgPath, format='png', dpi=120) 843 imgList.append(imgFilename) 844 plt.close('all')845 plt.ion()846 return (caption, imgList)847 except AssertionError as err:848 print("exception on %s: " % outPath, err)849 return None850 851def generate_plots(dirname, testGenObj, skipIfExists=False, multiprocess=True, nProcesses=6):852 argList = []853 for (testName, overrideParams) in testGenObj.getOverrideParamsList(): 854 newParams = dict(testGenObj.getDefaultParamsDict())855 newParams.update(overrideParams)856 prefix = testGenObj.getFilenamePrefix(testName, newParams)857 outPath = os.path.join(dirname, prefix) + ".out"858 caption = "%s: beta=%f, rSlow=%f, rFast=%f, diff=%f, probSpace=[%f, %f], fastOutFraction=[%f, %f]" % (testName, newParams['beta'], newParams['rSlow'], newParams['rFast'], newParams['rSlow']-newParams['rFast'], newParams['probSpace'][0], newParams['probSpace'][1], newParams['fastOut'][0], newParams['fastOut'][1])859 argList.append((dirname, outPath, prefix, caption, skipIfExists))860 861 if (multiprocess):862 p = multiprocessing.Pool(nProcesses) 863 plotList = p.map(generate_one_plot, argList)864 p.close()865 else: 866 plotList = map(generate_one_plot, argList)867 868 # write html index869 page = markup.page()870 page.init(title="bankProblem")871 page.br( )872 873 for (i, plot) in enumerate(plotList):874 (prefix, imgList) = plot875 page.p(prefix, id="%d" % i)876 for imgFile in imgList:877 page.img(src=imgFile) 878 filename = os.path.join(dirname, "index.html")879 f = open(filename, 'w') 880 f.write(str(page))881 f.close()882 883# run with current parameters884def test_bank0(**kwargs):885 overrideParamsDict = dict(g.ParamSettings)886 del overrideParamsDict['grid_M']887 del overrideParamsDict['grid_S']888 del overrideParamsDict['grid_P']889 return test_bank2(overrideParamsDict=overrideParamsDict, **kwargs)890 891def test_bank1(beta=0.9, rSlow=0.15, rFast=0.02, probSpace=scipy.array([0.5, 0.5]), fastOut=scipy.array([0.7, 0.9]), 892 slowOut=scipy.array([0.1, 0.1]), fastIn=scipy.array([0.8, 0.8]), bankruptcyPenalty=scipy.array([0.0, 0.0, 0.0]), popGrowth=1.0, gridSizeDict=None, **kwargs):893 overrideParamsDict = {'beta':beta, 'rSlow':rSlow, 'rFast':rFast, 'probSpace':probSpace, 'fastOut':fastOut, 'slowOut':slowOut, 'fastIn':fastIn, 'bankruptcyPenalty':bankruptcyPenalty, 'popGrowth':popGrowth, 'gridSizeDict':gridSizeDict}894 return test_bank2(overrideParamsDict=overrideParamsDict, **kwargs)895 896def test_bank2(useValueIter=True, plotResult=True, nMaxIters=g.MAX_VITERS, initialVArray=None, nMultiGrid=2, overrideParamsDict=None, **kwargs):897 time1 = time.time()898 localvars = {}899 900 def postVIterCallbackFn(nIter, currentVArray, newVArray, optControls, stoppingResult): 901 (stoppingDecision, diff) = stoppingResult902 print("iter %d, diff %f" % (nIter, diff))903 localvars[0] = nIter904 # append iteration results to g.IterList905 g.IterList.append({'V': currentVArray, 'd': optControls[0], 'fracIn': optControls[1]})906 g.NIters += 1907 def postPIterCallbackFn(nIter, newVArray, currentPolicyArrayList, greedyPolicyList, stoppingResult): 908 (stoppingDecision, diff) = stoppingResult909 print("iter %d, diff %f" % (nIter, diff))910 localvars[0] = nIter 911 g.setDefaultGridSize()912 if ('gridSizeDict' in overrideParamsDict and overrideParamsDict['gridSizeDict'] != None):913 g.setGridSize(**overrideParamsDict['gridSizeDict']) 914 # TODO: fix this915 (grid_M, grid_S, grid_P) = (g.Grid_M, g.Grid_S, g.Grid_P)916 917 defaultParamsDict = {918 'beta': 0.9,919 'rSlow': 0.15,920 'rFast': 0.10,921 'probSpace': scipy.array([0.5, 0.5]),922 'fastOut': scipy.array([0.7, 0.9]),923 'slowOut': scipy.array([0.1, 0.1]),924 'fastIn': scipy.array([0.8, 0.8]),925 'bankruptcyPenalty': scipy.array([0.0, 0.0, 0.0]),926 'popGrowth': 1.0927 }928 paramsDict = dict(defaultParamsDict)929 paramsDict.update(overrideParamsDict)930 [beta, rSlow, rFast, probSpace, fastOut, slowOut, fastIn, bankruptcyPenalty, popGrowth] = [paramsDict[x] for x in ['beta', 'rSlow', 'rFast', 'probSpace', 'fastOut', 'slowOut', 'fastIn', 'bankruptcyPenalty', 'popGrowth']]931 print("using params: beta=%f rFast=%f rSlow=%f" % (beta, rFast, rSlow))932 print("probSpace: ", probSpace, " slowOut: ", slowOut, " fastOut: ", fastOut, " fastIn: ", fastIn, " bp: ", bankruptcyPenalty, " popGrowth: ", popGrowth)933 print("M grid: ", (grid_M[-1], len(grid_M)), " S grid: ", (grid_S[-1], len(grid_S)), " P grid: ", (grid_P[-1], len(grid_P)), "d grid size: %d inFrac grid: " % g.D_GRID_SIZE, (g.SLOW_IN_FRAC_MAX, g.SLOW_IN_GRID_SIZE))934 g.ParamSettings = {'beta':beta, 'grid_M':grid_M, 'grid_S':grid_S, 'grid_P':grid_P, 'rFast':rFast, 'rSlow':rSlow, 'slowOut':slowOut, 935 'fastOut':fastOut, 'fastIn':fastIn, 'probSpace':probSpace, 'bankruptcyPenalty':bankruptcyPenalty, 'popGrowth':popGrowth}936 #beta, rFast, rSlow, slowOut, fastOut, fastIn, probSpace937 params = BankParams(**g.ParamSettings)938 g.Params = params939 940 # initial guess for V: V = M941 if (initialVArray == None):942 initialVArray = scipy.zeros((len(grid_M), len(grid_S), len(grid_P)))943 for (iM, M) in enumerate(grid_M):944 for (iS, S) in enumerate(grid_S):945 for (iP, P) in enumerate(grid_P):946 initialVArray[iM, iS, iP] = M947 #initialVArray[iM,:,:] = M 948 949 g.IterList.append({'V':initialVArray, 'd':None, 'fracIn':None})950 if (useValueIter == True):951 if (nMultiGrid == None): 952 result = bellman.grid_valueIteration([grid_M, grid_S, grid_P], initialVArray, params, postIterCallbackFn=postVIterCallbackFn, parallel=True, nMaxIters=nMaxIters, **kwargs)953 else:954 # start with coarse grids and progressively get finer. 955 gridList = [grid_M, grid_S, grid_P]956 coarseGridList = []957 for n in reversed(range(nMultiGrid)):958 coarseGridList.append( [scipy.linspace(grid[0], grid[-1], len(grid)/(2**n)) for grid in gridList] )959 result = bellman.grid_valueIteration2(coarseGridList, gridList, initialVArray, params, postIterCallbackFn=postVIterCallbackFn, parallel=True, nMaxIters=nMaxIters, **kwargs)960 (iterCode, nIter, currentVArray, newVArray, optControls) = result961 g.IterResult = iterCode962 else:963 initialPolicyArrayList = bellman.getGreedyPolicy([grid_M, grid_S, grid_P], initialVArray, params, parallel=True)964 result = bellman.grid_policyIteration([grid_M, grid_S, grid_P], initialPolicyArrayList, initialVArray, params, postIterCallbackFn=postPIterCallbackFn, parallel=True)965 (iterCode, nIter, currentVArray, currentPolicyArrayList, greedyPolicyList) = result966 newVArray = currentVArray967 optControls = currentPolicyArrayList968 time2 = time.time()969 nIters = localvars[0]970 print("total time: %f, avg time: %f iter code: %s" % (time2-time1, (time2-time1)/(1+nIters), bellman.iterResultString(iterCode)))971 972 if (plotResult):973 plotV(-1, 1)974 plotOptD(-1, 1)975 plotFracIn(-1, 1) 976 return result977def plotV(nIter, iP):978 setupData = setupGetNextState(nIter)979 colorFn = lambda x: getColor(setupData, x[0], x[1], g.Grid_P[iP])980 currentVArray = g.IterList[nIter]['V']981 fnObj_V = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S, g.Grid_P], currentVArray)982 fnObj_V2 = lambda x: fnObj_V([x[0], x[1], g.Grid_P[iP]])983 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_V2, xlabel="M", ylabel="L", zlabel="V", colorFn=colorFn) 984def plotOptD(nIter, iP):985 setupData = setupGetNextState(nIter)986 colorFn = lambda x: getColor(setupData, x[0], x[1], g.Grid_P[iP])987 optControl_d = g.IterList[nIter]['d']988 fnObj_optD = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S, g.Grid_P], optControl_d)989 fnObj_optD2 = lambda x: fnObj_optD([x[0], x[1], g.Grid_P[iP]])990 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_optD2, xlabel="M", ylabel="L", zlabel="opt d", colorFn=colorFn) 991def plotFracIn(nIter, iP):992 setupData = setupGetNextState(nIter)993 colorFn = lambda x: getColor(setupData, x[0], x[1], g.Grid_P[iP])994 optControl_inFrac = g.IterList[nIter]['fracIn'] 995 fnObj_optInFrac = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S, g.Grid_P], optControl_inFrac)996 fnObj_optInFrac2 = lambda x: fnObj_optInFrac([x[0], x[1], g.Grid_P[iP]])997 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_optInFrac2, xlabel="M", ylabel="L", zlabel="opt frac in", colorFn=colorFn) 998# outcome can be 0 (low) or 1 (high)999# returns (M, S)1000def setupGetNextState(n, currentVArray=None, optControl_d=None, optControl_inFrac=None):1001 if (n != None):1002 currentVArray = g.IterList[n]['V']1003 optControl_d = g.IterList[n]['d']1004 optControl_inFrac = g.IterList[n]['fracIn']1005 fnObj_optD = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S, g.Grid_P], optControl_d)1006 fnObj_optInFrac = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S, g.Grid_P], optControl_inFrac) 1007 params = BankParams(**g.ParamSettings)1008 params.setPrevIteration([g.Grid_M, g.Grid_S, g.Grid_P], currentVArray)1009 return (fnObj_optD, fnObj_optInFrac, params)1010def getNextStateVar(setupData, M, S, P, outcome):1011 (fnObj_optD, fnObj_optInFrac, params) = setupData1012 d = fnObj_optD([M, S, P])1013 inFrac = fnObj_optInFrac([M, S, P])1014 params.setStateVars([M, S, P]) 1015 (ev, nextMList, nextSList, nextPList) = params.calc_EV(d, inFrac)1016 return ((nextMList[outcome], nextSList[outcome], nextPList[outcome]), (d, inFrac))1017# coloring states:1018def getColor(setupData, M, S, P):1019 (fnObj_optD, fnObj_optInFrac, params) = setupData1020 d = fnObj_optD([M, S, P])1021 inFrac = fnObj_optInFrac([M, S, P])1022 params.setStateVars([M, S, P]) 1023 (ev, nextMList, nextSList, nextPList) = params.calc_EV(d, inFrac)1024 state = None1025 # bankrupt next period1026 if (nextMList[0] <= 0.0 and nextMList[1] <= 0.0):1027 state = g.STATE_BANKRUPT1028 elif (nextMList[0] > 0.0 and nextMList[1] <= 0.0):1029 state = g.STATE_RISKY1030 else:1031 assert(nextMList[0] > 0.0 and nextMList[1] > 0.0)1032 state = g.STATE_SAFE1033 return g.StateToColor[state]1034def plotNextM(n, outcome):1035 setupData = setupGetNextState(n)1036 colorFn = lambda x: getColor(setupData, x[0], x[1], x[2])1037 fnObj_nextM = lambda x: getNextStateVar(setupData, x[0], x[1], x[2], outcome)[0][0]1038 #plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_nextM, xlabel="M", ylabel="S", zlabel="next M | outcome %d" % outcome, colorFn=colorFn) 1039def plotNextS(n, outcome):1040 setupData = setupGetNextState(n)1041 colorFn = lambda x: getColor(setupData, x[0], x[1], x[2])1042 fnObj_nextS = lambda x: getNextStateVar(setupData, x[0], x[1], x[2], outcome)[0][1]1043 #plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_nextS, xlabel="M", ylabel="S", zlabel="next S | outcome %d" % outcome, colorFn=colorFn) 1044 1045def plotObjectiveFn(params, nIter, state1, state2, state3):1046 prevIterVArray = g.IterList[nIter][0]1047 params.setPrevIteration([g.Grid_M, g.Grid_S, g.Grid_P], prevIterVArray)1048 params.setStateVars([state1, state2, state3])1049 controlGrids = params.getControlGridList([state1, state2, state3])1050 objFn = lambda x: params.objectiveFunction([x[0], x[1], x[2]])1051 #plot3d.plotSurface(controlGrids[0], controlGrids[1], objFn, xlabel="d", ylabel="frac in", zlabel="objFn") 1052# def createGraph(n): 1053 # setupData = setupGetNextState(n)1054 # def fnObj_nextM(x, shock_i):1055 # (next_x, controls) = getNextStateVar(setupData, x[0], x[1], shock_i)1056 # return (next_x[0], controls) 1057 # def fnObj_nextS(x, shock_i): 1058 # (next_x, controls) = getNextStateVar(setupData, x[0], x[1], shock_i)1059 # return (next_x[1], controls) 1060 # gridList = [g.Grid_M, g.Grid_S, g.Grid_P]1061 # coffinState = g.STATE_BANKRUPT1062 # shockList = range(len(g.ParamSettings['probSpace'])); 1063 # def nextStateFn(x, shock_i):1064 # return getNextStateVar(setupData, x[0], x[1], shock_i)1065 # def isBankrupt(x):1066 # (M, S) = (x[0], x[1])1067 # return (M <= 0.0)1068 # (isAbsorbedFn, nextIListFn) = markovChain.policyFn_to_transitionFns(gridList, nextStateFn, isBankrupt)1069 # G = markovChain.createGraphFromPolicyFn(gridList, coffinState, shockList, isAbsorbedFn, nextIListFn)1070 # return G1071# plot locus of nodes that are destinations, conditional on shock1072# def plotLocus(G, shock):1073 # def isSuccessor(G, node):1074 # if (node == g.STATE_BANKRUPT):1075 # return False 1076 # predecessors = G.predecessors(node)1077 # if (len(predecessors) == 0):1078 # return False1079 # #print(node, predecessors)1080 # for p in predecessors:1081 # s = G.edge[p][node]['shock']1082 # if (s == shock):1083 # return True1084 # return False1085 # markovChain.plotGraphNodes2D(G, isSuccessor, title="locus | %d" % shock, xlabel="M", ylabel="S")1086# def plotPrunedNodes(G):1087 # S = markovChain.pruneZeroIndegree(G)1088 # def in_S(G, node):1089 # return (node in S)1090 # markovChain.plotGraphNodes2D(G, in_S, title="after pruning zero indegree", xlabel="M", ylabel="S")1091 1092class MarkovChain:1093 def __init__(self, initialM, initialS, initialP, VArray, optDArray, optInFracArray):1094 (self.initialM, self.initialS, self.initialP) = (initialM, initialS, initialP)1095 (self.currentM, self.currentS, self.currentP) = (initialM, initialS, initialP)1096 self.VArray = VArray1097 self.optDArray = optDArray1098 self.optInFracArray = optInFracArray1099 self.setupData = setupGetNextState(n=None, currentVArray=VArray, optControl_d=optDArray, optControl_inFrac=optInFracArray)1100 self.fnObj_V = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S, g.Grid_P], VArray)1101 #self.fnObj_nextM = lambda x, shock_i: getNextStateVar(self.setupData, x[0], x[1], shock_i)[0]1102 #self.fnObj_nextS = lambda x, shock_i: getNextStateVar(self.setupData, x[0], x[1], shock_i)[1]1103 1104 def applyShock(self, shock_i): 1105 ((nextM, nextS, nextP), (d, inFrac)) = getNextStateVar(self.setupData, self.currentM, self.currentS, self.currentP, shock_i)1106 (self.currentM, self.currentS, self.currentP) = (nextM, nextS, nextP)1107 return ((nextM, nextS, nextP), (d, inFrac))1108 def reset(self):1109 (self.currentM, self.currentS, self.currentP) = (self.initialM, self.initialS, self.initialP)1110 1111# probSpace is an array that sums to 1.01112# x is a random number drawn from Uniform[0,1]1113# return the index of probSpace corresponding to x1114def drawFromProbSpace(probSpace, x):1115 c = scipy.cumsum(probSpace)1116 return scipy.sum(c < x) 1117M_ERROR_MAX = 0.0001; # if M is within this much of 0, truncate to 01118def simulate(n, initialM, initialS, initialP, nSteps=100, bPrint=True, markovChainObj=None):1119 def myprint(*args, **kwargs):1120 if (bPrint): print(*args, **kwargs) 1121 (currentM, currentS, currentP) = (initialM, initialS, initialP) 1122 # simulate directly using policies and transition equations1123 if (markovChainObj == None):1124 currentVArray = g.IterList[n]['V'] 1125 optControl_d = g.IterList[n]['d']1126 optControl_inFrac = g.IterList[n]['fracIn'] 1127 markovChainObj = MarkovChain(initialM, initialS, initialP, currentVArray, optControl_d, optControl_inFrac)1128 # shocks1129 rvs = scipy.stats.uniform.rvs(size=nSteps)1130 [beta, grid_M, grid_S, grid_P, rFast, rSlow, popGrowth, slowOut, fastOut, fastIn, probSpace] = [g.ParamSettings[x] for x in 1131 ['beta', 'grid_M', 'grid_S', 'grid_P', 'rFast', 'rSlow', 'popGrowth', 'slowOut', 'fastOut', 'fastIn', 'probSpace']]1132 shockList = [drawFromProbSpace(probSpace, rv) for rv in rvs]1133 # initialize stats list1134 stats = defaultdict(list)1135 stats['M_outside']=[False]1136 stats['S_outside']=[False]1137 stats['P_outside']=[False]1138 1139 for (i, shock) in enumerate(shockList):1140 myprint("initial: M=%.4f, S=%.4f, P=%.4f" % (currentM, currentS, currentP), end="") 1141 # apply shock to initial state 1142 myprint(" -> shock: %d, " % shock, end="")1143 ((nextM, nextS, nextP), (d, slow_in_frac)) = markovChainObj.applyShock(shock)1144 myprint("controls: d=%.4f, frac=%.4f" % (d, slow_in_frac), end="")1145 # record statistics1146 stats['M'].append(currentM)1147 stats['S'].append(currentS)1148 stats['P'].append(currentP)1149 stats['shock'].append(shock)1150 stats['d'].append(d)1151 stats['in_frac'].append(slow_in_frac)1152 1153 # calculate next period's state variables1154 (fast_in_frac, fast_out_frac, slow_out_frac, pop_growth) = (fastIn[shock], fastOut[shock], slowOut[shock], popGrowth)1155 (M, S, P) = (currentM, currentS, currentP)1156 fast_growth = (1.0 + fast_in_frac*P - fast_out_frac) * (1.0 + rFast);1157 nextM_2 = (M - d + slow_out_frac*S - slow_in_frac*S - fast_out_frac + fast_in_frac*P)/fast_growth;1158 nextS_2 = (1.0 + slow_in_frac - slow_out_frac) * S * (1.0 + rSlow) / fast_growth; 1159 nextP_2 = (pop_growth * P) / fast_growth;1160 1161 myprint(" fast_growth = (1.0 + %f*%f - %f) * (%0.3f) = %0.4f" % (fast_in_frac, P, fast_out_frac, 1.0+rFast, fast_growth))1162 myprint(" nextM = %f/%f = %f" % ((M - d + slow_out_frac*S - slow_in_frac*S - fast_out_frac + fast_in_frac*P), fast_growth, nextM_2))1163 myprint(" nextS = %0.4f/%0.4f = %0.4f" % ((1.0 + slow_in_frac - slow_out_frac) * S * (1.0 + rSlow), fast_growth, nextS_2))1164 myprint(" nextP = %0.4f/%0.4f = %0.4f" % (pop_growth*P, fast_growth, nextS_2))1165 myprint(" next: M=%0.4f, S=%0.4f, P=%0.4f" % (nextM, nextS, nextP)) 1166 # check if absorbed or outside grid1167 if (nextM <= 0.0):1168 myprint(" -> absorbed")1169 break1170 (M_outside, S_outside, P_outside) = (False, False, False)1171 if (nextM > g.Grid_M[-1]):1172 myprint(" -> M outside grid, set to %0.4f" % g.Grid_M[-1])1173 nextM = g.Grid_M[-1]1174 M_outside = True1175 if (nextS > g.Grid_S[-1]):1176 myprint(" -> S outside grid, set to %0.4f" % g.Grid_S[-1])1177 nextS = g.Grid_S[-1]1178 S_outside = True1179 if (nextP > g.Grid_P[-1]):1180 myprint(" -> P outside grid, set to %0.4f" % g.Grid_P[-1])1181 nextP = g.Grid_P[-1]1182 P_outside = True 1183 # record stats that are conditional on bankruptcy1184 stats['M_outside'].append(M_outside)1185 stats['S_outside'].append(S_outside)1186 stats['P_outside'].append(P_outside)1187 # update state1188 (currentM, currentS, currentP) = (nextM, nextS, nextP)1189 # print statistics1190 stats['nSteps'] = i+11191 stats['V'] = markovChainObj.fnObj_V([initialM, initialS, initialP])1192 myprint ("number of steps: %d" % i)1193 myprint ("average M: %0.4f" % scipy.mean(stats['M']))1194 myprint ("average S: %0.4f" % scipy.mean(stats['S']))1195 myprint ("average P: %0.4f" % scipy.mean(stats['P']))1196 myprint ("average shock: %0.4f" % scipy.mean(stats['shock']))1197 myprint ("M outside grid: %d/%d = %f" % (scipy.sum(stats['M_outside']), i, scipy.mean(stats['M_outside'])))1198 myprint ("S outside grid: %d/%d = %f" % (scipy.sum(stats['S_outside']), i, scipy.mean(stats['S_outside'])))1199 myprint ("P outside grid: %d/%d = %f" % (scipy.sum(stats['P_outside']), i, scipy.mean(stats['P_outside'])))1200 myprint ("average d: %0.4f" % scipy.mean(stats['d']))1201 myprint ("average in_frac: %0.4f" % scipy.mean(stats['in_frac']))1202 myprint ("V: %0.4f" % scipy.mean(stats['V']))1203 return (i, stats)1204def plot_one_sim(initialM, initialS, initialP, nSteps=100):1205 (i, stats) = simulate(-1, initialM, initialS, initialP, nSteps=nSteps, bPrint=False)1206 fig = plt.figure()1207 ax = fig.add_subplot(111)1208 M_series = stats['M']1209 S_series = stats['S']1210 in_frac_series=stats['in_frac']1211 lines = ax.plot(range(len(S_series)), S_series, range(len(M_series)), M_series, 'r--', range(len(in_frac_series)), in_frac_series, 'g-.')1212 ax.set_xlabel("time")1213 ax.legend(lines, (r'$\bar{L}$', r'$\bar{M}$', r'$\gamma^L_t$'))1214 1215g_outcome_to_label = {'avg_M': r'mean $m$', 'avg_S': r'mean $l$', 'V': 'V', 'nSteps': 'mean lifetime', 'avg_in_frac': r'mean $\gamma^L_t$'}1216def simulate_multiple(initialM, initialS, initialP, nSteps=1000, nRuns=1000):1217 t1 = time.time()1218 nIter = -11219 runStats = defaultdict(list)1220 currentVArray = g.IterList[nIter]['V'] 1221 optControl_d = g.IterList[nIter]['d']1222 optControl_inFrac = g.IterList[nIter]['fracIn'] 1223 markovChainObj = MarkovChain(initialM, initialS, initialP, currentVArray, optControl_d, optControl_inFrac) 1224 for i in range(nRuns):1225 markovChainObj.reset()1226 (n, stats) = simulate(nIter, initialM, initialS, initialP, nSteps=nSteps, bPrint=False, markovChainObj=markovChainObj)1227 # mean1228 avg_M = scipy.mean(stats['M'])1229 avg_S = scipy.mean(stats['S'])1230 avg_P = scipy.mean(stats['P'])1231 avg_shock = scipy.mean(stats['shock'])1232 avg_M_outside = scipy.mean(stats['M_outside'])1233 avg_S_outside = scipy.mean(stats['S_outside'])1234 avg_P_outside = scipy.mean(stats['P_outside'])1235 avg_d = scipy.mean(stats['d'])1236 avg_in_frac = scipy.mean(stats['in_frac']) 1237 V = scipy.mean(stats['V'])1238 1239 runStats['nSteps'].append(n)1240 runStats['V'].append(V)1241 runStats['avg_M'].append(avg_M)1242 runStats['avg_S'].append(avg_S)1243 runStats['avg_P'].append(avg_P)1244 runStats['avg_shock'].append(avg_shock)1245 runStats['avg_M_outside'].append(avg_M_outside)1246 runStats['avg_S_outside'].append(avg_S_outside) 1247 runStats['avg_P_outside'].append(avg_P_outside) 1248 runStats['avg_d'].append(avg_d)1249 runStats['avg_in_frac'].append(avg_in_frac) 1250 1251 print("runs=%d, steps=%d" % (nRuns, nSteps))1252 for key in ['nSteps', 'avg_M', 'avg_S', 'avg_P', 'avg_shock', 'avg_M_outside', 'avg_S_outside', 'avg_P_outside', 'avg_d', 'avg_in_frac', 'V']: 1253 print("mean %s: %f (%f)" % (key, scipy.mean(runStats[key]), scipy.std(runStats[key])))1254 t2 = time.time()1255 print("total time for %d runs: %f avg: %f" % (nRuns, t2-t1, (t2-t1)/nRuns))1256 return runStats1257def simulateTestCase(arg):1258 (test, newParams, outPath, initialM, initialS, initialP, nSteps, nRuns, xName, yName, x, y, recreate) = arg1259 (testName, overrideParams) = test1260 if (not os.path.exists(outPath)):1261 print ("not found: %s" % outPath)1262 return (None, None, None)1263 try:1264 loadRun(outPath)1265 print("%s=%f, %s=%f" % (xName, x, yName, y))1266 simFilename = outPath + ".sim"1267 if (recreate or not os.path.exists(simFilename)):1268 runStats = simulate_multiple(initialM, initialS, initialP, nSteps=nSteps, nRuns=nRuns)1269 f = gzip.open(simFilename, 'wb')1270 pickle.dump((x, y, runStats), f)1271 f.close()1272 else:1273 f = gzip.open(simFilename, 'rb')1274 (x, y, runStats) = pickle.load(f)1275 f.close()1276 except AssertionError as err:1277 print("exception on %s: " % outPath, err)1278 return (x, y, runStats)1279 1280def simulate_summary(dirname, testGenObj, recreate=True, initialM=1, initialS=1, initialP=1, nSteps=1000, nRuns=1000, multiprocess=True, nProcesses=6): 1281 (xList, yList) = ([], []) 1282 outcomes = defaultdict(list)1283 args = []1284 for (testName, overrideParams) in testGenObj.getOverrideParamsList(): 1285 newParams = dict(testGenObj.getDefaultParamsDict())1286 newParams.update(overrideParams)1287 prefix = testGenObj.getFilenamePrefix(testName, newParams)1288 outPath = os.path.join(dirname, prefix) + ".out"1289 (xName, yName) = testGenObj.getXYName()1290 (x, y) = testGenObj.getXY(newParams)1291 args.append(((testName, overrideParams), newParams, outPath, initialM, initialS, initialP, nSteps, nRuns, xName, yName, x, y, recreate))1292 if (multiprocess):1293 p = multiprocessing.Pool(nProcesses)1294 # run simulations in parallel1295 result = p.map(simulateTestCase, args)1296 p.close()1297 else:1298 # run simulations1299 result = map(simulateTestCase, args)1300 for (x, y, runStats) in result:1301 xList.append(x)1302 yList.append(y)1303 # store sample averages from simulations as outcomes1304 for (key, value) in runStats.items():1305 mean_val_across_runs = scipy.mean(value)1306 outcomes[key].append(mean_val_across_runs)1307 # store 1308 sum_dict = {} 1309 g_dict = g.to_dict()1310 sum_dict['g'] = g_dict1311 sum_dict['initialState'] = (initialM, initialS)1312 sum_dict['xName'] = xName1313 sum_dict['yName'] = yName1314 sum_dict['xList'] = xList1315 sum_dict['yList'] = yList1316 sum_dict['outcomes'] = outcomes1317 filename = "sim_M_%0.2f_S_%0.2f_summary.out" % (initialM, initialS)1318 path = os.path.join(dirname, filename)1319 f = gzip.open(path, 'wb')1320 pickle.dump(sum_dict, f)1321 f.close()1322 1323 return (filename, xName, yName, xList, yList, outcomes)1324import fnmatch 1325def convert_to_gzip():1326 files = os.listdir(".")1327 for file in files:1328 if fnmatch.fnmatch(file, '*.sim'):1329 print(file)1330 f = open(file, 'r')1331 (x, y, runStats) = pickle.load(f) 1332 f.close() 1333 f = gzip.open(file, 'wb')1334 pickle.dump((x, y, runStats), f) 1335 f.close()1336def test_sim1(dirname, initialM=1, initialS=1, recreate=False, beta=0.9, multiprocess=True):1337 x = simulate_summary(dirname, "rSlow", "rFast", initialM=initialM, initialS=initialS, recreate=recreate, generateTestListFn=generateTestList_test_r, generateFnArgs={'beta':beta}, multiprocess=multiprocess)1338def test_sim2(dirname, initialM=1, initialS=1, recreate=False, beta=0.9, multiprocess=True, nProcesses=2):1339 x = simulate_summary(dirname, initialM=initialM, initialS=initialS, recreate=recreate, multiprocess=multiprocess, nProcesses=nProcesses, **test_var_args())1340def test_delta_sim(dirname, **kwargs):1341 x = simulate_summary(dirname, g_TestDelta, initialM=1.1, initialS=1.1, **kwargs)1342def test_delta_sim2(dirname, **kwargs):1343 x = simulate_summary(dirname, g_TestDelta2, initialM=1.1, initialS=1.1, **kwargs)1344 1345def plot_simulation_summary(dirname, testGenObj, filename="sim_M_1.00_S_1.00_summary.out"):1346 (xlabel, ylabel) = testGenObj.getXYGraphLabel()1347 simFilename = os.path.join(dirname, filename)1348 f = gzip.open(simFilename, 'rb')1349 sim_dict = pickle.load(f)1350 f.close()1351 [sim_xName, sim_yName, sim_xList, sim_yList, sim_outcomes, sim_initialState] = [sim_dict[x] for x in ['xName', 'yName', 'xList', 'yList', 'outcomes', 'initialState']]1352 # load summary of solutions1353 solutionFilename = os.path.join(dirname, "solutions.out")1354 f = gzip.open(solutionFilename, 'rb')1355 solution_dict = pickle.load(f)1356 f.close()1357 [sol_xName, sol_yName, sol_xList, sol_yList, sol_outcomeList] = [solution_dict[x] for x in ['xName', 'yName', 'xList', 'yList', 'outcomeList']] 1358 # keep track of points where iteration did not converge1359 nonconverge_points = {}1360 for (x, y, outcome) in zip(sol_xList, sol_yList, sol_outcomeList):1361 if (outcome == SOLUTION_CATEGORY_NONCONVERGENCE): # did not converge1362 nonconverge_points[(x,y)] = 11363 sol_colors = [outcome_to_color(o) for o in sol_outcomeList]1364 sol_markers_labels = [outcome_to_marker(o) for o in sol_outcomeList]1365 # return (xList, yList, valList) with nonconvergence points removed1366 def filter_nonconverge(*args): 1367 result = [item for item in zip(*args) if (not (item[0],item[1]) in nonconverge_points)]1368 if (len(result) > 0):1369 return zip(*result)1370 else:1371 return [] * len(args)1372 1373 page = markup.page()1374 page.init(title="simulation summary, initial state=%f,%f" % sim_initialState)1375 page.p("initial state=%f,%f" % sim_initialState)1376 page.br( ) 1377 # text summary of simulations...
experiment.py
Source:experiment.py
...765 smoothed_lengths = pd.Series(lengths).rolling(window=window, center=True, min_periods=1).sum()766 highest_points[outcome] = max(smoothed_lengths / self.total_reads * 100)767 return highest_points768 @memoized_property769 def outcome_to_color(self):770 # To minimize the chance that a color will be used more than once in the same panel in 771 # outcome_stratified_lengths plots, sort color order by highest point.772 # Factored out here so that same colors can be used in svgs.773 color_order = sorted(self.categories_by_frequency, key=self.outcome_highest_points.get, reverse=True)774 return {outcome: f'C{i % 10}' for i, outcome in enumerate(color_order)}775 @memoized_property776 def expected_lengths(self):777 ti = self.target_info778 expected_lengths = {779 'expected\nWT': ti.amplicon_length,780 }781 if ti.clean_HDR_length is not None:782 expected_lengths['expected\nHDR'] = ti.clean_HDR_length783 return expected_lengths...
bankProblem_orig.py
Source:bankProblem_orig.py
...518 page.th("%0.2f" % y)519 for x in xValues:520 o = xy_to_outcome[(x,y)]521 page.td.open()522 #page.font(markup.oneliner.a("*", href="index.html#%d" % i), color=outcome_to_color(o))523 page.a(markup.oneliner.font("*", color=outcome_to_color(o)), href="index.html#%d" % xy_to_i[(x,y)])524 page.td.close() 525 page.tr.close()526 page.table.close()527 # color graph528 fig = plt.figure()529 ax = fig.add_subplot(111) 530 ax.set_xlabel(xlabel if (xlabel != None) else xName)531 ax.set_ylabel(ylabel if (ylabel != None) else yName)532 colors = [outcome_to_color(o) for o in outcomeList]533 ax.scatter(xList, yList, color=colors, edgecolor=colors)534 page.br()535 imgFilename = "2d_summary.png"536 imgPath = os.path.join(dirname, imgFilename)537 try: 538 os.remove(imgPath)539 except OSError: 540 pass 541 plt.savefig(imgPath, format='png', dpi=100) 542 page.img(src=imgFilename) 543 # non-color graph544 fig = plt.figure()545 ax = fig.add_subplot(111) 546 ax.set_xlabel(xlabel if (xlabel != None) else xName)547 ax.set_ylabel(ylabel if (ylabel != None) else yName)548 (markers_labels) = [outcome_to_marker(o) for o in outcomeList]549 by_marker = defaultdict(list)550 [by_marker[marker_label].append((x,y)) for (x,y,marker_label) in zip(xList, yList, markers_labels)]551 for ((marker, label), xyList) in by_marker.items():552 (x_list, y_list) = zip(*xyList)553 ax.scatter(x_list, y_list, marker=marker, label=label, facecolor='none')554 (handles, labels) = ax.get_legend_handles_labels()555 hl = sorted(zip(handles, labels), key=operator.itemgetter(1))556 (handles2, labels2) = zip(*hl) 557 fig.legend(handles2, labels2, 'upper center') 558 if (addLines1): 559 ax.axvline((1.0/beta)-1.0, color='gray')560 ax.plot([xList[0], xList[-1]], [yList[0], yList[-1]], color='gray')561 page.br()562 imgFilename = "2d_summary2.png"563 imgPath = os.path.join(dirname, imgFilename)564 try: 565 os.remove(imgPath)566 except OSError: 567 pass 568 plt.savefig(imgPath, format='png', dpi=100) 569 page.img(src=imgFilename) 570 571 filename = os.path.join(dirname, "2d_summary.html")572 f = open(filename, 'w') 573 f.write(str(page))574 f.close()575 576 return (xList, yList, outcomeList)577 578def outcome_to_color(outcome):579 if (outcome == SOLUTION_CATEGORY_NOINVEST): return 'black'580 if (outcome == SOLUTION_CATEGORY_INVEST): return 'green'581 if (outcome == SOLUTION_CATEGORY_NONCONVERGENCE): return 'red'582 assert(false)583def outcome_to_marker(outcome):584 if (outcome == SOLUTION_CATEGORY_NOINVEST): return ('o', "no loans made")585 if (outcome == SOLUTION_CATEGORY_INVEST): return ('+', "loans made")586 if (outcome == SOLUTION_CATEGORY_NONCONVERGENCE): return ('s', "nonconvergence")587 assert(false)588 589# vary pHigh and out frac variance590# def generateTestList_test_var_pHigh(beta=0.9, pHighRange=scipy.linspace(0., 1., 20), outFracLowRange=scipy.linspace(0.3, 0.9, 20)):591 # defaultParams = {592 # 'beta': beta,593 # 'rSlow': 0.15,594 # 'rFast': 0.10,595 # 'probSpace': scipy.array([0.5, 0.5]),596 # 'fastOut': scipy.array([0.7, 0.9]),597 # 'slowOut': scipy.array([0.1, 0.1]),598 # 'fastIn': scipy.array([0.8, 0.8]),599 # 'bankruptcyPenalty': scipy.array([0.0, 0.0, 0.0])600 # }601 # tests = []602 # beta = defaultParams['beta']603 # for pHigh in pHighRange:604 # for outFracLow in outFracLowRange:605 # probSpace = scipy.array([1.0-pHigh, pHigh])606 # fastOut = scipy.array([outFracLow, 0.9])607 # tests.append( ("test_var_pHigh", {'probSpace':probSpace, 'fastOut':fastOut}) )608 # # filename generating function609 # def filenameFn(testName, newParams):610 # filename = "%s_pHigh%.2f_fastOut%.2f" % (testName, newParams['probSpace'][1], newParams['fastOut'][0])611 # return filename612 613 # return (defaultParams, tests, filenameFn)614# def test_var_args():615 # dict = {}616 # xName = 'outFracLow'617 # yName = 'pHigh'618 # def getXFn(params):619 # return params['fastOut'][0]620 # def getYFn(params):621 # return params['probSpace'][1]622 # return {'xName':xName, 'yName':yName, 'getXFn':getXFn, 'getYFn':getYFn, 'generateTestListFn':generateTestList_test_var_pHigh}623# # param range 2 (narrower): pHigh=linspace(0.3, 0.8, 20), outFrac=linspace(0.5, 0.9, 20)624# def run_test_var_cases(dirname, pHighMin=0., pHighMax=1., pHighLen=20, outFracLowMin=0.3, outFracLowMax=0.9, outFracLowLen=20, **kwargs):625 # return run_test_cases(dirname, generateTestListFn=generateTestList_test_var_pHigh, 626 # generateFnArgs={'pHighRange': scipy.linspace(pHighMin, pHighMax, pHighLen), 'outFracLowRange': scipy.linspace(outFracLowMin, outFracLowMax, outFracLowLen)}) 627def run_all(dirname, testGenObj):628 result1 = run_test_cases(dirname, testGenObj)629 result2 = generate_plots(dirname, testGenObj)630 result3 = write_test_summary_2d(dirname, testGenObj)631 (xlabel, ylabel) = testGenObj.getXYGraphLabel()632 result4 = plot_test_summary_2d(dirname, xlabel=xlabel, ylabel=ylabel)633 634def run_test_cases(dirname, testGenObj, skipWrite=False, skipIfExists=True, usePrevVArray=True, nMaxIters=g.MAX_VITERS, maxTime=g.MAX_TIME, maxV=g.MAX_V): 635 currentVArray = None636 prevRunConverged = False637 for (testName, overrideParams) in testGenObj.getOverrideParamsList(): 638 newParams = dict(testGenObj.getDefaultParamsDict())639 newParams.update(overrideParams)640 prefix = testGenObj.getFilenamePrefix(testName, newParams)641 path = os.path.join(dirname, prefix) + ".out"642 print(path)643 if (skipIfExists and os.path.exists(path)):644 print("%s exists, skipping" % path)645 continue646 g.reset()647 if ((not usePrevVArray) or (prevRunConverged == False)): currentVArray = None; # if the previous optimization didn't converge, start from scratch, otherwise, start from previous optimized value648 (iterCode, prevNIter, currentVArray, newVArray, optControls) = test_bank2(plotResult=False, initialVArray=currentVArray, nMaxIters=nMaxIters, maxTime=g.MAX_TIME, maxV=g.MAX_V, overrideParamsDict=newParams)649 prevRunConverged = True if (iterCode == bellman.ITER_RESULT_CONVERGENCE) else False650 if (not skipWrite):651 saveRun(path)652def generate_one_plot(arg):653 (dirname, outPath, prefix, caption, skipIfExists) = arg654 if (not os.path.exists(outPath)):655 print ("not found: %s" % outPath)656 return None657 try:658 plt.ioff()659 loadRun(outPath)660 G = createGraph(-1)661 # save plots 662 suffixes = ["-V", "-optD", "-inF", "-nextM_1", "-nextS_1", "-locus_0", "-locus_1", "-pruned"]663 plotFns = [plotV, plotOptD, plotFracIn, lambda x: plotNextM(-1, 1), lambda x: plotNextS(-1, 1), lambda x: plotLocus(G, 0), lambda x: plotLocus(G, 1), lambda x: plotPrunedNodes(G)]664 imgList = []665 for (suffix, plotFn) in zip(suffixes, plotFns):666 plotFn(-1)667 imgFilename = prefix + suffix + ".png"668 imgPath = os.path.join(dirname, imgFilename)669 if (not skipIfExists):670 try: 671 os.remove(imgPath)672 except OSError: 673 pass 674 print("writing %s" % imgPath) 675 plt.savefig(imgPath, format='png', dpi=60) 676 imgList.append(imgFilename) 677 plt.close('all')678 plt.ion()679 return (caption, imgList)680 except AssertionError as err:681 print("exception on %s: " % outPath, err)682 return None683 684def generate_plots(dirname, testGenObj, skipIfExists=False, multiprocess=True, nProcesses=6):685 argList = []686 for (testName, overrideParams) in testGenObj.getOverrideParamsList(): 687 newParams = dict(testGenObj.getDefaultParamsDict())688 newParams.update(overrideParams)689 prefix = testGenObj.getFilenamePrefix(testName, newParams)690 outPath = os.path.join(dirname, prefix) + ".out"691 caption = "%s: beta=%f, rSlow=%f, rFast=%f, diff=%f, probSpace=[%f, %f], fastOutFraction=[%f, %f]" % (testName, newParams['beta'], newParams['rSlow'], newParams['rFast'], newParams['rSlow']-newParams['rFast'], newParams['probSpace'][0], newParams['probSpace'][1], newParams['fastOut'][0], newParams['fastOut'][1])692 argList.append((dirname, outPath, prefix, caption, skipIfExists))693 694 if (multiprocess):695 p = multiprocessing.Pool(nProcesses) 696 plotList = p.map(generate_one_plot, argList)697 p.close()698 else: 699 plotList = map(generate_one_plot, argList)700 701 # write html index702 page = markup.page()703 page.init(title="bankProblem")704 page.br( )705 706 for (i, plot) in enumerate(plotList):707 (prefix, imgList) = plot708 page.p(prefix, id="%d" % i)709 for imgFile in imgList:710 page.img(src=imgFile) 711 filename = os.path.join(dirname, "index.html")712 f = open(filename, 'w') 713 f.write(str(page))714 f.close()715 716def test_bank1(beta=0.9, rSlow=0.15, rFast=0.02, probSpace=scipy.array([0.5, 0.5]), fastOut=scipy.array([0.7, 0.9]), 717 slowOut=scipy.array([0.1, 0.1]), fastIn=scipy.array([0.8, 0.8]), bankruptcyPenalty=scipy.array([0.0, 0.0, 0.0]), **kwargs):718 overrideParamsDict = {'beta':beta, 'rSlow':rSlow, 'rFast':rFast, 'probSpace':probSpace, 'fastOut':fastOut, 'slowOut':slowOut, 'fastIn':fastIn, 'bankruptcyPenalty':bankruptcyPenalty}719 return test_bank2(overrideParamsDict=overrideParamsDict, **kwargs)720 721def test_bank2(useValueIter=True, plotResult=True, nMaxIters=g.MAX_VITERS, initialVArray=None, nMultiGrid=3, overrideParamsDict=None, **kwargs):722 time1 = time.time()723 localvars = {}724 725 def postVIterCallbackFn(nIter, currentVArray, newVArray, optControls, stoppingResult): 726 (stoppingDecision, diff) = stoppingResult727 print("iter %d, diff %f" % (nIter, diff))728 localvars[0] = nIter729 # append iteration results to g.IterList730 g.IterList.append({'V': currentVArray, 'd': optControls[0], 'fracIn': optControls[1]})731 g.NIters += 1732 def postPIterCallbackFn(nIter, newVArray, currentPolicyArrayList, greedyPolicyList, stoppingResult): 733 (stoppingDecision, diff) = stoppingResult734 print("iter %d, diff %f" % (nIter, diff))735 localvars[0] = nIter 736 grid_M = scipy.linspace(0, g.M_MAX, g.M_GRID_SIZE)737 grid_S = scipy.linspace(0, g.S_MAX, g.S_GRID_SIZE) 738 (g.Grid_M, g.Grid_S) = (grid_M, grid_S)739 740 defaultParamsDict = {741 'beta': 0.9,742 'rSlow': 0.15,743 'rFast': 0.10,744 'probSpace': scipy.array([0.5, 0.5]),745 'fastOut': scipy.array([0.7, 0.9]),746 'slowOut': scipy.array([0.1, 0.1]),747 'fastIn': scipy.array([0.8, 0.8]),748 'bankruptcyPenalty': scipy.array([0.0, 0.0, 0.0])749 }750 paramsDict = dict(defaultParamsDict)751 paramsDict.update(overrideParamsDict)752 [beta, rSlow, rFast, probSpace, fastOut, slowOut, fastIn, bankruptcyPenalty] = [paramsDict[x] for x in ['beta', 'rSlow', 'rFast', 'probSpace', 'fastOut', 'slowOut', 'fastIn', 'bankruptcyPenalty']]753 print("using params: beta=%f rFast=%f rSlow=%f" % (beta, rFast, rSlow))754 print("probSpace: ", probSpace, " slowOut: ", slowOut, " fastOut: ", fastOut, " fastIn: ", fastIn, " bp: ", bankruptcyPenalty)755 g.ParamSettings = {'beta':beta, 'grid_M':grid_M, 'grid_S':grid_S, 'rFast':rFast, 'rSlow':rSlow, 'slowOut':slowOut, 756 'fastOut':fastOut, 'fastIn':fastIn, 'probSpace':probSpace, 'bankruptcyPenalty':bankruptcyPenalty}757 #beta, rFast, rSlow, slowOut, fastOut, fastIn, probSpace758 params = BankParams(**g.ParamSettings)759 g.Params = params760 761 # initial guess for V: V = M762 if (initialVArray == None): 763 initialVArray = scipy.zeros((g.M_GRID_SIZE, g.S_GRID_SIZE)); 764 for (iM, M) in enumerate(grid_M):765 for (iS, S) in enumerate(grid_S):766 initialVArray[iM, iS] = M767 initialPolicyArrayList = bellman.getGreedyPolicy([grid_M, grid_S], initialVArray, params, parallel=True)768 769 g.IterList.append({'V':initialVArray, 'd':None, 'fracIn':None})770 if (useValueIter == True):771 if (nMultiGrid == None): 772 result = bellman.grid_valueIteration([grid_M, grid_S], initialVArray, params, postIterCallbackFn=postVIterCallbackFn, parallel=True, nMaxIters=nMaxIters, **kwargs)773 else:774 # start with coarse grids and progressively get finer. 775 gridList = [grid_M, grid_S]776 coarseGridList = []777 for n in reversed(range(nMultiGrid)):778 coarseGridList.append( [scipy.linspace(grid[0], grid[-1], len(grid)/(2**n)) for grid in gridList] )779 result = bellman.grid_valueIteration2(coarseGridList, gridList, initialVArray, params, postIterCallbackFn=postVIterCallbackFn, parallel=True, nMaxIters=nMaxIters, **kwargs)780 (iterCode, nIter, currentVArray, newVArray, optControls) = result781 g.IterResult = iterCode782 else:783 result = bellman.grid_policyIteration([grid_M, grid_S], initialPolicyArrayList, initialVArray, params, postIterCallbackFn=postPIterCallbackFn, parallel=True)784 (iterCode, nIter, currentVArray, currentPolicyArrayList, greedyPolicyList) = result785 newVArray = currentVArray786 optControls = currentPolicyArrayList787 time2 = time.time()788 nIters = localvars[0]789 print("total time: %f, avg time: %f iter code: %s" % (time2-time1, (time2-time1)/(1+nIters), bellman.iterResultString(iterCode)))790 791 if (plotResult):792 plotV(-1)793 plotOptD(-1)794 plotFracIn(-1) 795 return result796def plotV(n):797 setupData = setupGetNextState(n)798 colorFn = lambda x: getColor(setupData, x[0], x[1])799 currentVArray = g.IterList[n]['V']800 fnObj_V = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S], currentVArray) 801 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_V, xlabel="M", ylabel="L", zlabel="V", colorFn=colorFn) 802def plotOptD(n):803 setupData = setupGetNextState(n)804 colorFn = lambda x: getColor(setupData, x[0], x[1])805 optControl_d = g.IterList[n]['d']806 fnObj_optD = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S], optControl_d)807 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_optD, xlabel="M", ylabel="L", zlabel="opt d", colorFn=colorFn) 808def plotFracIn(n):809 setupData = setupGetNextState(n)810 colorFn = lambda x: getColor(setupData, x[0], x[1])811 optControl_inFrac = g.IterList[n]['fracIn'] 812 fnObj_optInFrac = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S], optControl_inFrac)813 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_optInFrac, xlabel="M", ylabel="L", zlabel="opt frac in", colorFn=colorFn) 814# outcome can be 0 (low) or 1 (high)815# returns (M, S)816def setupGetNextState(n, currentVArray=None, optControl_d=None, optControl_inFrac=None):817 if (n != None):818 currentVArray = g.IterList[n]['V']819 optControl_d = g.IterList[n]['d']820 optControl_inFrac = g.IterList[n]['fracIn']821 fnObj_optD = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S], optControl_d)822 fnObj_optInFrac = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S], optControl_inFrac) 823 params = BankParams(**g.ParamSettings)824 params.setPrevIteration([g.Grid_M, g.Grid_S], currentVArray)825 return (fnObj_optD, fnObj_optInFrac, params)826def getNextStateVar(setupData, M, S, outcome):827 (fnObj_optD, fnObj_optInFrac, params) = setupData828 d = fnObj_optD([M, S])829 inFrac = fnObj_optInFrac([M, S])830 params.setStateVars([M, S]) 831 (ev, nextMList, nextSList) = params.calc_EV(d, inFrac)832 return ((nextMList[outcome], nextSList[outcome]), (d, inFrac))833# coloring states:834def getColor(setupData, M, S):835 (fnObj_optD, fnObj_optInFrac, params) = setupData836 d = fnObj_optD([M, S])837 inFrac = fnObj_optInFrac([M, S])838 params.setStateVars([M, S]) 839 (ev, nextMList, nextSList) = params.calc_EV(d, inFrac)840 state = None841 # bankrupt next period842 if (nextMList[0] <= 0.0 and nextMList[1] <= 0.0):843 state = g.STATE_BANKRUPT844 elif (nextMList[0] > 0.0 and nextMList[1] <= 0.0):845 state = g.STATE_RISKY846 else:847 assert(nextMList[0] > 0.0 and nextMList[1] > 0.0)848 state = g.STATE_SAFE849 return g.StateToColor[state]850def plotNextM(n, outcome):851 setupData = setupGetNextState(n)852 colorFn = lambda x: getColor(setupData, x[0], x[1])853 fnObj_nextM = lambda x: getNextStateVar(setupData, x[0], x[1], outcome)[0][0]854 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_nextM, xlabel="M", ylabel="S", zlabel="next M | outcome %d" % outcome, colorFn=colorFn) 855def plotNextS(n, outcome):856 setupData = setupGetNextState(n)857 colorFn = lambda x: getColor(setupData, x[0], x[1])858 fnObj_nextS = lambda x: getNextStateVar(setupData, x[0], x[1], outcome)[0][1]859 plot3d.plotSurface(g.Grid_M, g.Grid_S, fnObj_nextS, xlabel="M", ylabel="S", zlabel="next S | outcome %d" % outcome, colorFn=colorFn) 860 861def plotObjectiveFn(params, nIter, state1, state2):862 prevIterVArray = g.IterList[nIter][0]863 params.setPrevIteration([g.Grid_M, g.Grid_S], prevIterVArray)864 params.setStateVars([state1, state2])865 controlGrids = params.getControlGridList([state1, state2])866 objFn = lambda x: params.objectiveFunction([x[0], x[1]])867 plot3d.plotSurface(controlGrids[0], controlGrids[1], objFn, xlabel="d", ylabel="frac in", zlabel="objFn") 868def createGraph(n): 869 setupData = setupGetNextState(n)870 def fnObj_nextM(x, shock_i):871 (next_x, controls) = getNextStateVar(setupData, x[0], x[1], shock_i)872 return (next_x[0], controls) 873 def fnObj_nextS(x, shock_i): 874 (next_x, controls) = getNextStateVar(setupData, x[0], x[1], shock_i)875 return (next_x[1], controls) 876 gridList = [g.Grid_M, g.Grid_S]877 coffinState = g.STATE_BANKRUPT878 shockList = range(len(g.ParamSettings['probSpace'])); 879 def nextStateFn(x, shock_i):880 return getNextStateVar(setupData, x[0], x[1], shock_i)881 def isBankrupt(x):882 (M, S) = (x[0], x[1])883 return (M <= 0.0)884 (isAbsorbedFn, nextIListFn) = markovChain.policyFn_to_transitionFns(gridList, nextStateFn, isBankrupt)885 G = markovChain.createGraphFromPolicyFn(gridList, coffinState, shockList, isAbsorbedFn, nextIListFn)886 return G887# plot locus of nodes that are destinations, conditional on shock888def plotLocus(G, shock):889 def isSuccessor(G, node):890 if (node == g.STATE_BANKRUPT):891 return False 892 predecessors = G.predecessors(node)893 if (len(predecessors) == 0):894 return False895 #print(node, predecessors)896 for p in predecessors:897 s = G.edge[p][node]['shock']898 if (s == shock):899 return True900 return False901 markovChain.plotGraphNodes2D(G, isSuccessor, title="locus | %d" % shock, xlabel="M", ylabel="S")902def plotPrunedNodes(G):903 S = markovChain.pruneZeroIndegree(G)904 def in_S(G, node):905 return (node in S)906 markovChain.plotGraphNodes2D(G, in_S, title="after pruning zero indegree", xlabel="M", ylabel="S")907 908class MarkovChain:909 def __init__(self, initialM, initialS, VArray, optDArray, optInFracArray):910 (self.initialM, self.initialS) = (initialM, initialS)911 (self.currentM, self.currentS) = (initialM, initialS)912 self.VArray = VArray913 self.optDArray = optDArray914 self.optInFracArray = optInFracArray915 self.setupData = setupGetNextState(n=None, currentVArray=VArray, optControl_d=optDArray, optControl_inFrac=optInFracArray)916 self.fnObj_V = linterp.GetLinterpFnObj([g.Grid_M, g.Grid_S], VArray)917 #self.fnObj_nextM = lambda x, shock_i: getNextStateVar(self.setupData, x[0], x[1], shock_i)[0]918 #self.fnObj_nextS = lambda x, shock_i: getNextStateVar(self.setupData, x[0], x[1], shock_i)[1]919 920 def applyShock(self, shock_i): 921 ((nextM, nextS), (d, inFrac)) = getNextStateVar(self.setupData, self.currentM, self.currentS, shock_i)922 (self.currentM, self.currentS) = (nextM, nextS)923 return ((nextM, nextS), (d, inFrac))924 def reset(self):925 self.currentM = self.initialM926 self.currentS = self.initialS927 928 929# probSpace is an array that sums to 1.0930# x is a random number drawn from Uniform[0,1]931# return the index of probSpace corresponding to x932def drawFromProbSpace(probSpace, x):933 c = scipy.cumsum(probSpace)934 return scipy.sum(c < x)935 936def testSim(n, initialNode, nSteps=100):937 G = createGraph(n)938 (iM, iS) = initialNode939 # simulate using networkx graph940 sim = markovChain.Simulation(G, initialNode)941 # simulate directly using policies and transition equations942 currentVArray = g.IterList[n]['V']943 optControl_d = g.IterList[n]['d']944 optControl_inFrac = g.IterList[n]['fracIn']945 currentM = g.Grid_M[iM]946 currentS = g.Grid_S[iS]947 mc = MarkovChain(currentM, currentS, currentVArray, optControl_d, optControl_inFrac)948 # shocks949 rvs = scipy.stats.uniform.rvs(size=nSteps)950 [beta, grid_M, grid_S, rFast, rSlow, slowOut, fastOut, fastIn, probSpace] = [g.ParamSettings[x] for x in 951 ['beta', 'grid_M', 'grid_S', 'rFast', 'rSlow', 'slowOut', 'fastOut', 'fastIn', 'probSpace',]]952 shockList = [drawFromProbSpace(probSpace, rv) for rv in rvs]953 coffinState = G.graph['coffinState']954 for (i, shock) in enumerate(shockList):955 print ("initial: M=%.4f, S=%.4f" % (currentM, currentS), end="")956 # apply shock to initial state 957 print (" -> shock: %d, " % shock, end="")958 print ("controls: d=%.4f, frac=%.4f" % (optControl_d[iM, iS], optControl_inFrac[iM, iS]))959 sim.applyShock(shock)960 mc.applyShock(shock)961 962 # record next state963 (currentNode, controls) = (sim.currentNode, sim.currentControls)964 965 if (currentNode == coffinState):966 print(" -> absorbed")967 return i968 M = G.graph['gridList'][0][currentNode[0]]969 S = G.graph['gridList'][1][currentNode[1]]970 print(" -> M=%0.4f, S=%0.4f" % (mc.currentM, mc.currentS))971 (iM, iS) = currentNode972 currentM = g.Grid_M[iM]973 currentS = g.Grid_S[iS] 974 return i975M_ERROR_MAX = 0.0001; # if M is within this much of 0, truncate to 0976def simulate(n, initialM, initialS, nSteps=100, bPrint=True, markovChainObj=None):977 def myprint(*args, **kwargs):978 if (bPrint): print(*args, **kwargs) 979 (currentM, currentS) = (initialM, initialS) 980 # simulate directly using policies and transition equations981 if (markovChainObj == None):982 currentVArray = g.IterList[n]['V'] 983 optControl_d = g.IterList[n]['d']984 optControl_inFrac = g.IterList[n]['fracIn'] 985 markovChainObj = MarkovChain(initialM, initialS, currentVArray, optControl_d, optControl_inFrac)986 # shocks987 rvs = scipy.stats.uniform.rvs(size=nSteps)988 [beta, grid_M, grid_S, rFast, rSlow, slowOut, fastOut, fastIn, probSpace] = [g.ParamSettings[x] for x in 989 ['beta', 'grid_M', 'grid_S', 'rFast', 'rSlow', 'slowOut', 'fastOut', 'fastIn', 'probSpace']]990 shockList = [drawFromProbSpace(probSpace, rv) for rv in rvs]991 # initialize stats list992 stats = defaultdict(list)993 stats['M_outside']=[False]994 stats['S_outside']=[False]995 996 for (i, shock) in enumerate(shockList):997 myprint("initial: M=%.4f, S=%.4f" % (currentM, currentS), end="") 998 # apply shock to initial state 999 myprint(" -> shock: %d, " % shock, end="")1000 ((nextM, nextS), (d, slow_in_frac)) = markovChainObj.applyShock(shock)1001 myprint("controls: d=%.4f, frac=%.4f" % (d, slow_in_frac), end="")1002 # record statistics1003 stats['M'].append(currentM)1004 stats['S'].append(currentS)1005 stats['shock'].append(shock)1006 stats['d'].append(d)1007 stats['in_frac'].append(slow_in_frac)1008 1009 # calculate next period's state variables1010 (fast_in_frac, fast_out_frac, slow_out_frac) = (fastIn[shock], fastOut[shock], slowOut[shock])1011 (M, S) = (currentM, currentS)1012 fast_growth = (1.0 + fast_in_frac - fast_out_frac) * (1.0 + rFast);1013 nextM_2 = (M - d + slow_out_frac*S - slow_in_frac*S - fast_out_frac + fast_in_frac)/fast_growth;1014 nextS_2 = (1.0 + slow_in_frac - slow_out_frac) * S * (1.0 + rSlow) / fast_growth; 1015 1016 myprint(" fast_growth = (1.0 + %f - %f) * (%0.3f) = %0.4f" % (fast_in_frac, fast_out_frac, 1.0+rFast, fast_growth))1017 myprint(" nextM = %f/%f = %f" % ((M - d + slow_out_frac*S - slow_in_frac*S - fast_out_frac + fast_in_frac), fast_growth, nextM_2))1018 myprint(" nextS = %0.4f/%0.4f = %0.4f" % ((1.0 + slow_in_frac - slow_out_frac) * S * (1.0 + rSlow), fast_growth, nextS_2))1019 myprint(" next: M=%0.4f, S=%0.4f" % (nextM, nextS)) 1020 # check if absorbed or outside grid1021 if (nextM <= 0.0):1022 myprint(" -> absorbed")1023 break1024 (M_outside, S_outside) = (False, False)1025 if (nextM > g.Grid_M[-1]):1026 myprint(" -> M outside grid, set to %0.4f" % g.Grid_M[1])1027 nextM = g.Grid_M[-1]1028 M_outside = True1029 if (nextS > g.Grid_S[-1]):1030 myprint(" -> S outside grid, set to %0.4f" % g.Grid_S[1])1031 nextS = g.Grid_S[-1]1032 S_outside = True1033 # record stats that are conditional on bankruptcy1034 stats['M_outside'].append(M_outside)1035 stats['S_outside'].append(S_outside)1036 # update state1037 (currentM, currentS) = (nextM, nextS)1038 # print statistics1039 stats['nSteps'] = i+11040 stats['V'] = markovChainObj.fnObj_V((initialM, initialS))1041 myprint ("number of steps: %d" % i)1042 myprint ("average M: %0.4f" % scipy.mean(stats['M']))1043 myprint ("average S: %0.4f" % scipy.mean(stats['S']))1044 myprint ("average shock: %0.4f" % scipy.mean(stats['shock']))1045 myprint ("M outside grid: %d/%d = %f" % (scipy.sum(stats['M_outside']), i, scipy.mean(stats['M_outside'])))1046 myprint ("S outside grid: %d/%d = %f" % (scipy.sum(stats['S_outside']), i, scipy.mean(stats['S_outside'])))1047 myprint ("average d: %0.4f" % scipy.mean(stats['d']))1048 myprint ("average in_frac: %0.4f" % scipy.mean(stats['in_frac']))1049 return (i, stats)1050 1051def simulate_multiple(initialM, initialS, nSteps=1000, nRuns=1000):1052 t1 = time.time()1053 nIter = -11054 runStats = defaultdict(list)1055 currentVArray = g.IterList[nIter]['V'] 1056 optControl_d = g.IterList[nIter]['d']1057 optControl_inFrac = g.IterList[nIter]['fracIn'] 1058 markovChainObj = MarkovChain(initialM, initialS, currentVArray, optControl_d, optControl_inFrac) 1059 for i in range(nRuns):1060 markovChainObj.reset()1061 (n, stats) = simulate(nIter, initialM, initialS, nSteps=nSteps, bPrint=False, markovChainObj=markovChainObj)1062 # mean1063 avg_M = scipy.mean(stats['M'])1064 avg_S = scipy.mean(stats['S'])1065 avg_shock = scipy.mean(stats['shock'])1066 avg_M_outside = scipy.mean(stats['M_outside'])1067 avg_S_outside = scipy.mean(stats['S_outside'])1068 avg_d = scipy.mean(stats['d'])1069 avg_in_frac = scipy.mean(stats['in_frac']) 1070 V = scipy.mean(stats['V'])1071 1072 runStats['nSteps'].append(n)1073 runStats['V'].append(V)1074 runStats['avg_M'].append(avg_M)1075 runStats['avg_S'].append(avg_S)1076 runStats['avg_shock'].append(avg_shock)1077 runStats['avg_M_outside'].append(avg_M_outside)1078 runStats['avg_S_outside'].append(avg_S_outside) 1079 runStats['avg_d'].append(avg_d)1080 runStats['avg_in_frac'].append(avg_in_frac) 1081 1082 print("runs=%d, steps=%d" % (nRuns, nSteps))1083 for key in ['nSteps', 'avg_M', 'avg_S', 'avg_shock', 'avg_M_outside', 'avg_S_outside', 'avg_d', 'avg_in_frac']: 1084 print("mean %s: %f (%f)" % (key, scipy.mean(runStats[key]), scipy.std(runStats[key])))1085 t2 = time.time()1086 print("total time for %d runs: %f avg: %f" % (nRuns, t2-t1, (t2-t1)/nRuns))1087 return runStats1088def simulateTestCase(arg):1089 (test, newParams, outPath, initialM, initialS, nSteps, nRuns, xName, yName, x, y, recreate) = arg1090 (testName, overrideParams) = test1091 if (not os.path.exists(outPath)):1092 print ("not found: %s" % outPath)1093 return (None, None, None)1094 try:1095 loadRun(outPath)1096 print("%s=%f, %s=%f" % (xName, x, yName, y))1097 simFilename = outPath + ".sim"1098 if (recreate or not os.path.exists(simFilename)):1099 runStats = simulate_multiple(initialM, initialS, nSteps=nSteps, nRuns=nRuns)1100 f = gzip.open(simFilename, 'wb')1101 pickle.dump((x, y, runStats), f)1102 f.close()1103 else:1104 f = gzip.open(simFilename, 'rb')1105 (x, y, runStats) = pickle.load(f)1106 f.close()1107 except AssertionError as err:1108 print("exception on %s: " % outPath, err)1109 return (x, y, runStats)1110 1111def simulate_summary(dirname, testGenObj, recreate=True, initialM=1, initialS=1, nSteps=1000, nRuns=1000, multiprocess=True, nProcesses=6): 1112 (xList, yList) = ([], []) 1113 outcomes = defaultdict(list)1114 args = []1115 for (testName, overrideParams) in testGenObj.getOverrideParamsList(): 1116 newParams = dict(testGenObj.getDefaultParamsDict())1117 newParams.update(overrideParams)1118 prefix = testGenObj.getFilenamePrefix(testName, newParams)1119 outPath = os.path.join(dirname, prefix) + ".out"1120 (xName, yName) = testGenObj.getXYName()1121 (x, y) = testGenObj.getXY(newParams)1122 args.append(((testName, overrideParams), newParams, outPath, initialM, initialS, nSteps, nRuns, xName, yName, x, y, recreate))1123 if (multiprocess):1124 p = multiprocessing.Pool(nProcesses)1125 # run simulations in parallel1126 result = p.map(simulateTestCase, args)1127 p.close()1128 else:1129 # run simulations1130 result = map(simulateTestCase, args)1131 for (x, y, runStats) in result:1132 xList.append(x)1133 yList.append(y)1134 # store sample averages from simulations as outcomes1135 for (key, value) in runStats.items():1136 mean_val_across_runs = scipy.mean(value)1137 outcomes[key].append(mean_val_across_runs)1138 # store 1139 sum_dict = {} 1140 g_dict = g.to_dict()1141 sum_dict['g'] = g_dict1142 sum_dict['initialState'] = (initialM, initialS)1143 sum_dict['xName'] = xName1144 sum_dict['yName'] = yName1145 sum_dict['xList'] = xList1146 sum_dict['yList'] = yList1147 sum_dict['outcomes'] = outcomes1148 summaryFilename = os.path.join(dirname, "sim_M_%0.2f_S_%0.2f_summary.out" % (initialM, initialS))1149 f = gzip.open(summaryFilename, 'wb')1150 pickle.dump(sum_dict, f)1151 f.close()1152 1153 return (xName, yName, xList, yList, outcomes)1154import fnmatch 1155def convert_to_gzip():1156 files = os.listdir(".")1157 for file in files:1158 if fnmatch.fnmatch(file, '*.sim'):1159 print(file)1160 f = open(file, 'r')1161 (x, y, runStats) = pickle.load(f) 1162 f.close() 1163 f = gzip.open(file, 'wb')1164 pickle.dump((x, y, runStats), f) 1165 f.close()1166def test_sim1(dirname, initialM=1, initialS=1, recreate=False, beta=0.9, multiprocess=True):1167 x = simulate_summary(dirname, "rSlow", "rFast", initialM=initialM, initialS=initialS, recreate=recreate, generateTestListFn=generateTestList_test_r, generateFnArgs={'beta':beta}, multiprocess=multiprocess)1168def test_sim2(dirname, initialM=1, initialS=1, recreate=False, beta=0.9, multiprocess=True, nProcesses=2):1169 x = simulate_summary(dirname, initialM=initialM, initialS=initialS, recreate=recreate, multiprocess=multiprocess, nProcesses=nProcesses, **test_var_args())1170def test_delta_sim(dirname, **kwargs):1171 x = simulate_summary(dirname, g_TestDelta, initialM=1.1, initialS=1.1, **kwargs)1172def test_delta_sim2(dirname, **kwargs):1173 x = simulate_summary(dirname, g_TestDelta2, initialM=1.1, initialS=1.1, **kwargs)1174 1175def plot_simulation_summary(dirname, prefix="sim_summary", xlabel=None, ylabel=None):1176 simFilename = os.path.join(dirname, prefix + ".out")1177 f = gzip.open(simFilename, 'rb')1178 sim_dict = pickle.load(f)1179 f.close()1180 [sim_xName, sim_yName, sim_xList, sim_yList, sim_outcomes, sim_initialState] = [sim_dict[x] for x in ['xName', 'yName', 'xList', 'yList', 'outcomes', 'initialState']]1181 # load summary of solutions1182 solutionFilename = os.path.join(dirname, "solutions.out")1183 f = gzip.open(solutionFilename, 'rb')1184 solution_dict = pickle.load(f)1185 f.close()1186 [sol_xName, sol_yName, sol_xList, sol_yList, sol_outcomeList] = [solution_dict[x] for x in ['xName', 'yName', 'xList', 'yList', 'outcomeList']] 1187 # keep track of points where iteration did not converge1188 nonconverge_points = {}1189 for (x, y, outcome) in zip(sol_xList, sol_yList, sol_outcomeList):1190 if (outcome == SOLUTION_CATEGORY_NONCONVERGENCE): # did not converge1191 nonconverge_points[(x,y)] = 11192 sol_colors = [outcome_to_color(o) for o in sol_outcomeList]1193 sol_markers_labels = [outcome_to_marker(o) for o in sol_outcomeList]1194 # return (xList, yList, valList) with nonconvergence points removed1195 def filter_nonconverge(*args): 1196 result = [item for item in zip(*args) if (not (item[0],item[1]) in nonconverge_points)]1197 if (len(result) > 0):1198 return zip(*result)1199 else:1200 return [] * len(args)1201 1202 page = markup.page()1203 page.init(title="simulation summary, initial state=%f,%f" % sim_initialState)1204 page.p("initial state=%f,%f" % sim_initialState)1205 page.br( ) 1206 # text summary of simulations...
calc_fig5_clustering.py
Source:calc_fig5_clustering.py
1# imports and definitions2import pandas as pd3import numpy as np4from scipy.stats import ttest_ind, mannwhitneyu5import matplotlib as mpl6import matplotlib.pyplot as plt7import seaborn as sns8from sklearn import preprocessing9import os10import cPickle as pickle11from ShaniBA.GeneralFeaturePhenotypeInteractions.Feature_phenotype_functions import add_corrected_pValues,\12 compare_TCR_between_groups, calc_PCA13GENIE_BASE_DIR = '/net/mraid08/export/genie/Lab/Personal/ShaniBAF/'14PRED_RESULTS_DIR='/net/mraid08/export/jafar/Microbiome/Analyses/ShaniBAF/predictions2/'15DATA_DIR = '/net/mraid08/export/jafar/Microbiome/Analyses/TCR/'16CARDIO_PHEN_DIR='/net/mraid08/export/genie/Lab/Personal/ShaniBAF/TCR_real_data/CardioSamples/phenotypicData/'17FIGURE_DIR = GENIE_BASE_DIR + 'Presentations and Manuscripts/CardioTCR paper/\18June2019/fig5_outcomes/calc_fig5/'19with open('/net/mraid08/export/genie/Lab/Personal/ShaniBAF/Sample files/BD lists/Cardio126', 'rb') as fp:20 Cardio126 = pickle.load(fp)21def get_suffix(only_productive,only_annotation,only_calc_features,get_suffix):22 d = {'only_productive': only_productive,23 'only_annotation': only_annotation,24 'only_calc_features': only_calc_features,25 'filter_negatives_only': filter_negatives_only}26 suffix = ''27 for k,v in d.items():28 if v: suffix = suffix + '_' + k29 return suffix30def get_productive_only(df):31 col_list = [col for col in df.columns if col.endswith('_1')]32 return df[col_list]33def get_annotation_only(df):34 col_list = [col for col in df.columns if 'cum_freq' in col]35 return df[col_list]36def get_calc_features_only(df):37 col_list=df.columns.tolist()38 for prefix in ['V','D','J']:39 col_list = [col for col in col_list if not col.startswith(prefix)]40 col_list = [col for col in col_list if 'rel' not in col]41 return df[col_list]42def filter_by_followup_length(df,outcome_df,filter_negatives_only=True,length_limit=547,follow_up_end="2019-05-13"):43 #length_limit = 1.5 years44 outcome = outcome_df.columns.tolist()[0]45 follow_up_end_str = follow_up_end.replace('_', '')46 fu = pd.read_excel(CARDIO_PHEN_DIR + 'follow_up_period_length_%s.xlsx' % follow_up_end_str).set_index('BD')47 df = pd.merge(df,fu,how='left',left_index=True,right_index=True)48 df = pd.merge(df,outcome_df,how='left',left_index=True,right_index=True)49 if filter_negatives_only:50 df_negatives = df[(df['follow_up_period_length'] > length_limit) & \51 (df[outcome] ==0)]52 df_positives = df[df[outcome] == 1]53 df = pd.concat([df_negatives,df_positives])54 else:55 df = df[df['follow_up_period_length']> length_limit]56 return df57def get_patients_feature_df(patient_list,only_productive=False,only_annotation=False,only_calc_features=False):58 feature_df=pd.read_excel(DATA_DIR +59 'TCRfeatures/TCRfeaturesNoT_PCA10percShared_withRels_VDJ\60_noCorr0-999_nanFilled_noConsts_Cardio126.xlsx').set_index('BD')61 feature_df = feature_df.loc[patient_list,:]62 if only_productive: feature_df = get_productive_only(feature_df)63 elif only_annotation: feature_df = get_annotation_only(feature_df)64 elif only_calc_features: feature_df = get_calc_features_only(feature_df)65 return feature_df66def compare_TCR_features(feature_df,outcome_df,filter_negatives_only,filter_by_fu_length=None):67 if filter_by_fu_length is not None:68 feature_df = filter_by_followup_length(feature_df, outcome_df, filter_negatives_only=filter_negatives_only,69 length_limit=filter_by_fu_length)70 outcome = outcome_df.columns.tolist()[0]71 groupName1 = outcome + '_0'; groupName2 = outcome + '_1'72 TCRdf_binary=pd.DataFrame()73 negatives = outcome_df[outcome_df[outcome]==0].index.tolist()74 positives = outcome_df[outcome_df[outcome] == 1].index.tolist()75 sample_list1 = [BD for BD in feature_df.index if BD in negatives]76 sample_list2 = [BD for BD in feature_df.index if BD in positives]77 print ('# patients in %s is %s ' %(groupName1,len(sample_list1)))78 print ('# patients in %s is %s ' % (groupName2, len(sample_list2)))79 feature_df = feature_df.drop(outcome, axis=1)80 feature_comparison_df,TCR_comparison_df = compare_TCR_between_groups(sample_list1,groupName1,81 sample_list2,groupName2,feature_df,TCRdf_binary,82 compare_features=True,compare_seqs=False)83 return feature_comparison_df84def normalize_df(df):85 return pd.DataFrame(index=df.index, columns=df.columns, data=preprocessing.scale(df,copy=False))86def get_outcome(outcome):87 outcome_df=pd.read_excel(CARDIO_PHEN_DIR +'outcomeDF.xlsx').set_index('BD')88 return pd.DataFrame(outcome_df[outcome])89def get_patients_clusters(patient_list, outcome_df,do_binary = True,filter_by_fu_length=None,filter_negatives_only=False):90 print ('loading TCR cluster file...')91 cluster_df = pd.read_pickle(DATA_DIR + 'TCR_seqs_clusters/\92newX_onlySeqs_040_085_noNans.dat')93 cluster_df = cluster_df.loc[patient_list, :]94 if filter_by_fu_length is not None:95 cluster_df = filter_by_followup_length(cluster_df, outcome_df, filter_negatives_only,length_limit=filter_by_fu_length)96 if do_binary:97 print ('binarizing file...')98 cluster_df = (cluster_df>0).astype(int)99 print ('Done!')100 return cluster_df101def gen_clustermap(df,only_partial=False,filter_by_fu_length=None,102 do_cluster=True, filter_negatives_only=False,103 figsize_frac=None, outcome_to_color=None):104 row_cluster = True; col_cluster=True105 if not do_cluster:106 row_cluster = False107 col_cluster = True108 outcome = outcome_to_color.columns.tolist()[0]109 df = pd.merge(df, outcome_to_color, how='inner', left_index=True, right_index=True)110 color_map = {0: 'b', 1: 'r', np.nan: 'g'}111 row_colors = df.iloc[:, -1].map(color_map)112 if only_partial:113 df = df.iloc[:10,:10]114 print ('df shape before filtering by fu period:' ,df.shape)115 if filter_by_fu_length is not None:116 df = filter_by_followup_length(df, outcome_to_color, filter_negatives_only,length_limit=filter_by_fu_length)117 print ('df shape after filtering by fu period:', df.shape)118 if figsize_frac is not None:119 height = np.ceil(df.shape[0] * figsize_frac)120 width = np.ceil(df.shape[1] * figsize_frac)121 else:122 height = 10; width = 10123 # if outcome_to_color is not None:124 # df = pd.merge(df,outcome_to_color,how='inner',left_index=True,right_index=True)125 # color_map = {0:'b',1:'r',np.nan:'g'}126 # row_colors = df.iloc[:,-1].map(color_map)127 # outcome=outcome_to_color.columns.tolist()[0]128 print ('df shape after filtering by fu period:', df.shape)129 df = df.sort_values(by=outcome, ascending=False)130 if filter_by_fu_length is not None: df = df.drop('follow_up_period_length',axis=1)131 g = sns.clustermap(df.drop(outcome,axis=1),xticklabels=True,yticklabels=True,132 figsize=(width,height),row_colors=row_colors,133 row_cluster=row_cluster, col_cluster=col_cluster)134 plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), fontsize='xx-small')135 plt.setp(g.ax_heatmap.xaxis.get_majorticklabels(), fontsize='xx-small')136 plt.subplots_adjust(bottom=0.3)137 # g.ax_heatmap.set_title('Subplot Title', y=1.25)138 return g139def gen_heatmap(df,only_partial=False,figsize_frac=None,outcome_to_color=None):140 outcome = outcome_to_color.columns.tolist()[0]141 df = pd.merge(df, outcome_to_color, how='inner', left_index=True, right_index=True)142 color_map = {0: 'b', 1: 'r', np.nan: 'g'}143 row_colors = df.iloc[:, -1].map(color_map)144 df = df.sort_values(by=outcome,ascending=False)145 if only_partial:146 df = df.iloc[:10,:10]147 if figsize_frac is not None:148 height = np.ceil(df.shape[0] * figsize_frac)149 width = np.ceil(df.shape[1] * figsize_frac)150 else:151 height = 10; width = 10152 g = sns.clustermap(df.drop(outcome,axis=1),xticklabels=True,yticklabels=True,153 figsize=(width,height),row_colors=row_colors,154 row_cluster=False, col_cluster=False)155 plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), fontsize='xx-small')156 plt.setp(g.ax_heatmap.xaxis.get_majorticklabels(), fontsize='xx-small')157 plt.subplots_adjust(bottom=0.3)158 return g159def plot_PCA(feature_df,outcome_df,suffix,to_scale=True):160 outcome = outcome_df.columns[0]161 sample_list1 = (outcome_df[outcome_df[outcome]==0]).index.tolist()162 sample_list2 = (outcome_df[outcome_df[outcome] == 1]).index.tolist()163 print ('outcome: ', outcome)164 print ('sample list 1:', sample_list1)165 print ('sample list 2:', sample_list2)166 sample_list_list=[(outcome+'_0', sample_list1),(outcome+'_1', sample_list2)]167 color_list=['blue','red']168 PCAdf,fig,ax = calc_PCA(feature_df,isSparse=False,n_comp=10,sample_list_list=sample_list_list,169 color_list=color_list,pca_n1_toplot=0,pca_n2_toplot=1,toScale=to_scale,170 toPlot=True, toAnnotate=True,fig=None,ax=None,calculateSeperation=True)171 ax.set_title('PCA_%s%s' %(outcome,suffix))172 plt.subplots_adjust(right=0.8)173 return PCAdf174if __name__ == "__main__":175 outcome = 'CV hospitalization including chest pain'176 filter_by_fu_length= None177 filter_negatives_only=False178 only_productive=False179 only_annotation=False180 only_calc_features=True181 feature_for_pca='clusters' #'clusters' / 'features'182 suffix = get_suffix(only_productive,only_annotation,only_calc_features,filter_negatives_only)183 print ('loading feature df...')184 feature_df = get_patients_feature_df(patient_list=Cardio126,only_productive=only_productive,only_annotation=only_annotation,185 only_calc_features=only_calc_features)186 print ('loading outcome df...')187 outcome_df = get_outcome(outcome)188 print ('loading cluster df...')189 cluster_df = get_patients_clusters(patient_list=Cardio126, outcome_df=outcome_df, do_binary=True,190 filter_by_fu_length=filter_by_fu_length,191 filter_negatives_only=filter_negatives_only)192 # print ('comparing TCR features...')193 # feature_comparison_df = compare_TCR_features(feature_df,outcome_df,filter_negatives_only,filter_by_fu_length)194 # feature_comparison_df.to_excel(os.path.join(FIGURE_DIR+'feature_comparison/',195 # 'feature_comparison_df%s_%s_%s.xlsx' %(suffix,outcome,str(filter_by_fu_length).replace('.',''))))196 #197 # print feature_comparison_df.head()198 # print ('normalizing df...')199 # feature_df_scaled = normalize_df(feature_df)200 print ('calculating PCA...')201 if feature_for_pca == 'clusters':202 suffix = suffix + '_clusters'203 PCAdf = plot_PCA(cluster_df,outcome_df,suffix,to_scale=False)204 else:205 PCAdf = plot_PCA(feature_df, outcome_df, suffix)206 # print('generating heatmapmap...')207 # do_cluster=False208 # g = gen_clustermap(feature_df_scaled,only_partial=False,filter_by_fu_length=filter_by_fu_length,209 # do_cluster=do_cluster, filter_negatives_only=filter_negatives_only,210 # figsize_frac=0.25,outcome_to_color=outcome_df)211 #212 #213 # print('generating clustermap...')214 # do_cluster=True215 # g = gen_clustermap(feature_df_scaled,only_partial=False,filter_by_fu_length=filter_by_fu_length,216 # do_cluster=do_cluster,217 # figsize_frac=0.25,outcome_to_color=outcome_df)218 # df = pd.DataFrame(np.random.randint(0,99,(4,10)))219 # g2 = sns.clustermap(df)220 # plt.setp(g2.ax_heatmap.yaxis.get_majorticklabels(), fontsize='xx-large',rotation=0)221 # g2.ax_heatmap.set_title('Subplot Title',y=1.25)222 # iris = sns.load_dataset("iris")223 # species = iris.pop("species")224 # lut = dict(zip(species.unique(), "rbg"))225 # row_colors = species.map(lut)226 # >>> g = sns.clustermap(iris, row_colors=row_colors)...
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