def runAnalysis(obj, ds, myspecs, gsTarget, lsgstStringsToUse, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None, distributeMethod="gatestrings"): #Run LGST to get starting gate set assertGatesetsInSync(gsTarget, comm) gs_lgst = pygsti.do_lgst(ds, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) assertGatesetsInSync(gs_lgst, comm) gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst, gsTarget) assertGatesetsInSync(gs_lgst_go, comm) #Run full iterative LSGST tStart = time.time() if obj == "chi2": all_gs_lsgst = pygsti.do_iterative_mc2gst( ds, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5, 1e5), verbosity=1, memLimit=3 * (1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) elif obj == "logl": all_gs_lsgst = pygsti.do_iterative_mlgst( ds, gs_lgst_go, lsgstStringsToUse, minProbClip=minProbClipForWeighting, probClipInterval=(-1e5, 1e5), verbosity=1, memLimit=3 * (1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) tEnd = time.time() print("Time = ", (tEnd - tStart) / 3600.0, "hours") return all_gs_lsgst
def runAnalysis(obj, myspecs, mygerms, gsTarget, seed, maxLs = [1,2,4,8], nSamples=1000, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None, distributeMethod="gatestrings"): rhoStrs, EStrs = pygsti.construction.get_spam_strs(myspecs) lgstStrings = pygsti.construction.list_lgst_gatestrings( myspecs, gsTarget.gates.keys()) lsgstStrings = pygsti.construction.make_lsgst_lists( gsTarget.gates.keys(), rhoStrs, EStrs, mygerms, maxLs, fidPairList ) print len(myspecs[0]), " rho specifiers" print len(myspecs[1]), " effect specifiers" print len(mygerms), " germs" print len(lgstStrings), " total LGST gate strings" print len(lsgstStrings[-1]), " LSGST strings before thinning" lsgstStringsToUse = lsgstStrings allRequiredStrs = pygsti.remove_duplicates(lgstStrings + lsgstStrings[-1]) gs_dataGen = gsTarget.depolarize(gate_noise=0.1) dsFake = pygsti.construction.generate_fake_data( gs_dataGen, allRequiredStrs, nSamples, sampleError="multinomial", seed=seed) #Run LGST to get starting gate set gs_lgst = pygsti.do_lgst(dsFake, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target", targetGateset=gs_dataGen) #Run full iterative LSGST tStart = time.time() if obj == "chi2": all_gs_lsgst = pygsti.do_iterative_mc2gst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) elif obj == "logl": all_gs_lsgst = pygsti.do_iterative_mlgst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClip=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) tEnd = time.time() print "Time = ",(tEnd-tStart)/3600.0,"hours" return all_gs_lsgst, gs_dataGen
def runAnalysis(obj, ds, myspecs, gsTarget, lsgstStringsToUse, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None, distributeMethod="gatestrings"): #Run LGST to get starting gate set assertGatesetsInSync(gsTarget, comm) gs_lgst = pygsti.do_lgst(ds, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) assertGatesetsInSync(gs_lgst, comm) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target", targetGateset=gsTarget) assertGatesetsInSync(gs_lgst_go, comm) #Run full iterative LSGST tStart = time.time() if obj == "chi2": all_gs_lsgst = pygsti.do_iterative_mc2gst( ds, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) elif obj == "logl": all_gs_lsgst = pygsti.do_iterative_mlgst( ds, gs_lgst_go, lsgstStringsToUse, minProbClip=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) tEnd = time.time() print("Time = ",(tEnd-tStart)/3600.0,"hours") return all_gs_lsgst
def test_MLGST(self): ds = self.ds #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1], # nSamples=1000, sampleError='binomial', seed=100) gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_mlgst = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=0) gs_mlegst = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings, verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2,1e2), memLimit=1000*1024**2) maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst( ds, gs_clgst, [ [gs.tup for gs in gsList] for gsList in self.lsgstStrings], minProbClip=1e-6, probClipInterval=(-1e2,1e2), returnAll=True, returnMaxLogL=True) gs_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, gs_clgst, self.lsgstStrings, verbosity=10, minProbClip=1e-6, probClipInterval=(-1e2,1e2), memLimit=10*1024**2) self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb),0) self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),0) #Run internal checks on less max-L values (so it doesn't take forever) gs_mlegst_chk = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2,1e2), check=True) #Other option variations - just make sure they run at this point gs_mlegst_chk_opts = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2,1e2), gateStringSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True ) aliased_list = [ pygsti.obj.GateString( [ (x if x != "Gx" else "GA1") for x in gs]) for gs in self.lsgstStrings[0] ] gs_withA1 = gs_clgst.copy(); gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"] gs_mlegst_chk_opts2 = pygsti.do_mlgst(ds, gs_withA1, aliased_list, minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=0, gateLabelAliases={ 'GA1': ('Gx',) }) #Other option variations - just make sure they run at this point gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), useFreqWeightedChiSq=True, gateStringSetLabels=["Set1","Set2"], gatestringWeightsDict={ ('Gx',): 2.0 } ) self.runSilent(pygsti.do_mlgst, ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=4, memLimit=300000) #invoke memory control pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False) #non-Poisson picture - should use (-1,-1) gateset for consistency? #Check errors: with self.assertRaises(MemoryError): pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2),verbosity=0, memLimit=1) # RUN BELOW LINES TO SEED SAVED GATESET FILES #pygsti.io.write_gateset(gs_mlegst,compare_files + "/mle_gst.gateset", "Saved MLE-GST Gateset") gs_mle_compare = pygsti.io.load_gateset(compare_files + "/mle_gst.gateset") gs_mlegst_go = pygsti.optimize_gauge(gs_mlegst, 'target', targetGateset=gs_mle_compare, spamWeight=1.0) self.assertAlmostEqual( gs_mlegst_go.frobeniusdist(gs_mle_compare), 0, places=5)
def test_MLGST(self): ds = self.ds mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.model, svdTruncateTo=4, verbosity=0) mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst, self.model, { 'spam': 1.0, 'gates': 1.0 }, checkJac=True) mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP") mdl_clgst = mdl_clgst.depolarize( op_noise=0.02, spam_noise=0.02 ) # just to avoid infinity objective funct & jacs below CM = profiler._get_mem_usage() mdl_single_mlgst = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), verbosity=0) #this test often gives an assetion error "finite Jacobian has inf norm!" on Travis CI Python 3 case try: mdl_single_mlgst_cpsp = pygsti.do_mlgst( ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), cptp_penalty_factor=1.0, spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity > 0 except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... try: mdl_single_mlgst_cp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), cptp_penalty_factor=1.0, verbosity=10) except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... try: mdl_single_mlgst_sp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), spam_penalty_factor=1.0, verbosity=10) except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... mdl_mlegst = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2, 1e2), memLimit=CM + 1024**3) maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst( ds, mdl_clgst, [[mdl.tup for mdl in gsList] for gsList in self.lsgstStrings], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), returnAll=True, returnMaxLogL=True) mdl_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, mdl_clgst, self.lsgstStrings, verbosity=10, minProbClip=1e-4, probClipInterval=(-1e2, 1e2), memLimit=CM + 1024**3) self.assertAlmostEqual(mdl_mlegst.frobeniusdist(mdl_mlegst_verb), 0, places=5) self.assertAlmostEqual(mdl_mlegst.frobeniusdist( all_gs_mlegst_tups[-1]), 0, places=5) #Run internal checks on less max-L values (so it doesn't take forever) mdl_mlegst_chk = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2, 1e2), check=True) #Forcing function used by linear response error bars forcingfn_grad = np.ones((1, mdl_clgst.num_params()), 'd') mdl_lsgst_chk_opts3 = pygsti.algorithms.core._do_mlgst_base( ds, mdl_clgst, self.lsgstStrings[0], verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2, 1e2), forcefn_grad=forcingfn_grad) with self.assertRaises(NotImplementedError): # Non-poisson picture needs support for a non-leastsq solver (not impl yet) mdl_lsgst_chk_opts4 = pygsti.algorithms.core._do_mlgst_base( ds, mdl_clgst, self.lsgstStrings[0], verbosity=0, poissonPicture=False, minProbClip=1e-4, probClipInterval=(-1e2, 1e2), forcefn_grad=forcingfn_grad) # non-poisson picture #Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust #self.runSilent(pygsti.do_mlgst, ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-6, # probClipInterval=(-1e2,1e2), verbosity=4, memLimit=curMem+8500000) #invoke memory control #non-Poisson picture - should use (-1,-1) model for consistency? with self.assertRaises(NotImplementedError): # Non-poisson picture needs support for a non-leastsq solver (not impl yet) pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), verbosity=0, poissonPicture=False) try: pygsti.do_mlgst( ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-1, # 1e-1 b/c get inf Jacobians... probClipInterval=(-1e2, 1e2), verbosity=0, poissonPicture=False, spam_penalty_factor=1.0, cptp_penalty_factor=1.0) except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... #Check errors: with self.assertRaises(MemoryError): pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2, 1e2), verbosity=0, memLimit=1) # RUN BELOW LINES TO SEED SAVED GATESET FILES if regenerate_references(): pygsti.io.write_model(mdl_mlegst, compare_files + "/mle_gst.model", "Saved MLE-GST Model") mdl_mle_compare = pygsti.io.load_model(compare_files + "/mle_gst.model") mdl_mlegst_go = pygsti.gaugeopt_to_target(mdl_mlegst, mdl_mle_compare, {'spam': 1.0}, checkJac=True) self.assertAlmostEqual(mdl_mlegst_go.frobeniusdist(mdl_mle_compare), 0, places=4)
def setUp(self): super(ReportBaseCase, self).setUp() self.targetGateset = std.gs_target datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05, spam_noise=0.1) self.fiducials = std.fiducials self.germs = std.germs self.specs = pygsti.construction.build_spam_specs( self.fiducials, effect_labels=['E0']) #only use the first EVec self.gateLabels = list( self.targetGateset.gates.keys()) # also == std.gates self.lgstStrings = pygsti.construction.list_lgst_gatestrings( self.specs, self.gateLabels) self.maxLengthList = [0, 1, 2, 4, 8] self.lsgstStrings = pygsti.construction.make_lsgst_lists( self.gateLabels, self.fiducials, self.fiducials, self.germs, self.maxLengthList) self.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/reportgen.dataset") # RUN BELOW LINES TO GENERATE ANALYSIS DATASET #ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000, # sampleError='binomial', seed=100) #ds.save(compare_files + "/reportgen.dataset") gs_lgst = pygsti.do_lgst(self.ds, self.specs, self.targetGateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.targetGateset, gateWeight=1.0, spamWeight=0.0) self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM") self.gs_clgst_tp.set_all_parameterizations("TP") try: import pptx self.have_python_pptx = True except ImportError: warnings.warn( "**** IMPORT: Cannot import pptx (python-pptx), and so" + " Powerpoint slide generation tests have been disabled.") self.have_python_pptx = False #Compute results for MC2GST lsgst_gatesets_prego = pygsti.do_iterative_mc2gst( self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6, 1e6), returnAll=True) lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, gateWeight=1, spamWeight=0.001) for gs in lsgst_gatesets_prego ] self.results = pygsti.report.Results() self.results.init_Ls_and_germs( "chi2", self.targetGateset, self.ds, self.gs_clgst, self.maxLengthList, self.germs, lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego) self.results.parameters.update({ 'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4, 'probClipInterval': (-1e6, 1e6), 'radius': 1e-4, 'weights': None, 'defaultDirectory': temp_files + "", 'defaultBasename': "MyDefaultReportName" }) self.results.options.precision = 4 self.results.options.polar_precision = 3 #Compute results for MLGST with TP constraint lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e6, 1e6), returnAll=True) lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True, gateWeight=1, spamWeight=0.001) for gs in lsgst_gatesets_TP ] self.results_logL = pygsti.report.Results() self.results_logL.init_Ls_and_germs( "logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs, lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, True) self.results_logL.options.precision = 4 self.results_logL.options.polar_precision = 3 try: basestring #Only defined in Python 2 self.versionsuffix = "" #Python 2 except NameError: self.versionsuffix = "v3" #Python 3
def test_reports_logL_TP(self): lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_TP] self.results_logL = pygsti.report.Results() self.results_logL.init_Ls_and_germs("logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs, lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, True) #db = lsgst_gatesets_TP[-1] #firstElIdentityVec = np.zeros( (db.dim,1) ) #firstElIdentityVec[0] = db.dim**0.25 #db.povm_identity = firstElIdentityVec # #print "Target Identity = ", np.asarray(self.targetGateset.povm_identity) #print "Identity = ", np.asarray(db.povm_identity) #print "rho0 = ", np.asarray(db.preps['rho0']) #for plbl in db.get_prep_labels(): # for elbl in db.get_effect_labels(): # print "DB: dot(%s,%s) = " % (plbl,elbl), np.dot(np.transpose(db.effects[elbl]),db.preps[plbl]) #return #Run a few tests to generate tables & figures we don't use in reports self.results_logL.tables["chi2ProgressTable"] self.results_logL.tables["logLProgressTable"] self.results_logL.figures["bestEstimateSummedColorBoxPlot"] self.results_logL.figures["blankBoxPlot"] self.results_logL.figures["blankSummedBoxPlot"] self.results_logL.figures["directLGSTColorBoxPlot"] self.results_logL.figures["directLGSTDeviationColorBoxPlot"] with self.assertRaises(KeyError): self.results_logL.figures["FooBar"] with self.assertRaises(KeyError): self.results_logL._specials['FooBar'] #Run tests to generate tables we don't use in reports self.results_logL.tables["bestGatesetVsTargetAnglesTable"] self.results_logL.create_full_report_pdf(filename="temp_test_files/full_reportC.pdf", confidenceLevel=None, debugAidsAppendix=False, gaugeOptAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False, verbosity=2) self.results_logL.create_brief_report_pdf(filename="temp_test_files/brief_reportC.pdf", confidenceLevel=None, verbosity=2) self.results_logL.create_presentation_pdf(filename="temp_test_files/slidesC.pdf", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False, verbosity=2) if self.have_python_pptx: self.results_logL.create_presentation_ppt(filename="temp_test_files/slidesC.ppt", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False, verbosity=2) ##Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportC.tex") self.checkFile("brief_reportC.tex") self.checkFile("slidesC.tex") self.results_logL.create_full_report_pdf(filename="temp_test_files/full_reportD.pdf", confidenceLevel=95, debugAidsAppendix=True, gaugeOptAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) self.results_logL.create_brief_report_pdf(filename="temp_test_files/brief_reportD.pdf", confidenceLevel=95, verbosity=2) self.results_logL.create_presentation_pdf(filename="temp_test_files/slidesD.pdf", confidenceLevel=95, debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) if self.have_python_pptx: self.results_logL.create_presentation_ppt(filename="temp_test_files/slidesD.ppt", confidenceLevel=95, debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) ##Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportD.tex") self.checkFile("full_reportD_appendices.tex") self.checkFile("brief_reportD.tex") self.checkFile("slidesD.tex")
def setUp(self): #Set GateSet objects to "strict" mode for testing pygsti.objects.GateSet._strict = True self.targetGateset = std.gs_target datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05, spam_noise=0.1) self.fiducials = std.fiducials self.germs = std.germs self.specs = pygsti.construction.build_spam_specs(self.fiducials, effect_labels=['E0']) #only use the first EVec self.gateLabels = self.targetGateset.gates.keys() # also == std.gates self.lgstStrings = pygsti.construction.list_lgst_gatestrings(self.specs, self.gateLabels) self.maxLengthList = [0,1,2,4,8] self.lsgstStrings = pygsti.construction.make_lsgst_lists( self.gateLabels, self.fiducials, self.fiducials, self.germs, self.maxLengthList) self.ds = pygsti.objects.DataSet(fileToLoadFrom="cmp_chk_files/reportgen.dataset") # RUN BELOW LINES TO GENERATE ANALYSIS DATASET #ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000, # sampleError='binomial', seed=100) #ds.save("cmp_chk_files/reportgen.dataset") gs_lgst = pygsti.do_lgst(self.ds, self.specs, self.targetGateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.targetGateset) self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM") self.gs_clgst_tp.set_all_parameterizations("TP") try: import pptx self.have_python_pptx = True except ImportError: warnings.warn("**** IMPORT: Cannot import pptx (python-pptx), and so" + " Powerpoint slide generation tests have been disabled.") self.have_python_pptx = False #Compute results for MC2GST lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_prego] self.results = pygsti.report.Results() self.results.init_Ls_and_germs("chi2", self.targetGateset, self.ds, self.gs_clgst, self.maxLengthList, self.germs, lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego) self.results.parameters.update({'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4, 'probClipInterval': (-1e6,1e6), 'radius': 1e-4, 'weights': None, 'defaultDirectory': "temp_test_files", 'defaultBasename': "MyDefaultReportName" } ) #Compute results for MLGST with TP constraint lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_TP] self.results_logL = pygsti.report.Results() self.results_logL.init_Ls_and_germs("logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs, lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, True)
def test_MLGST(self): ds = self.ds #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1], # nSamples=1000, sampleError='binomial', seed=100) mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.model, svdTruncateTo=4, verbosity=0) mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst,self.model, {'spam':1.0, 'gates': 1.0}, checkJac=True) mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP") mdl_clgst = mdl_clgst.depolarize(op_noise=0.02, spam_noise=0.02) # just to avoid infinity objective funct & jacs below CM = pygsti.baseobjs.profiler._get_mem_usage() mdl_single_mlgst = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2,1e2), verbosity=0) #this test often gives an assetion error "finite Jacobian has inf norm!" on Travis CI Python 3 case try: mdl_single_mlgst_cpsp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2,1e2), cptp_penalty_factor=1.0, spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity > 0 except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... try: mdl_single_mlgst_cp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2,1e2), cptp_penalty_factor=1.0, verbosity=10) except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... try: mdl_single_mlgst_sp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2,1e2), spam_penalty_factor=1.0, verbosity=10) except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... mdl_mlegst = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2,1e2), memLimit=CM + 1024**3) maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst( ds, mdl_clgst, [ [mdl.tup for mdl in gsList] for gsList in self.lsgstStrings], minProbClip=1e-4, probClipInterval=(-1e2,1e2), returnAll=True, returnMaxLogL=True) mdl_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, mdl_clgst, self.lsgstStrings, verbosity=10, minProbClip=1e-4, probClipInterval=(-1e2,1e2), memLimit=CM + 1024**3) self.assertAlmostEqual(mdl_mlegst.frobeniusdist(mdl_mlegst_verb),0, places=5) self.assertAlmostEqual(mdl_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),0,places=5) #Run internal checks on less max-L values (so it doesn't take forever) mdl_mlegst_chk = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2,1e2), check=True) #Other option variations - just make sure they run at this point mdl_mlegst_chk_opts = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2,1e2), circuitSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True, circuitWeightsDict={ (L('Gx'),): 2.0 } ) aliased_list = [ pygsti.obj.Circuit( [ (x if x != L("Gx") else L("GA1")) for x in mdl]) for mdl in self.lsgstStrings[0] ] mdl_withA1 = mdl_clgst.copy(); mdl_withA1.operations["GA1"] = mdl_clgst.operations["Gx"] del mdl_withA1.operations["Gx"] # otherwise mdl_withA1 will have Gx params that we have no knowledge of! mdl_mlegst_chk_opts2 = pygsti.do_mlgst(ds, mdl_withA1, aliased_list, minProbClip=1e-4, probClipInterval=(-1e2,1e2), verbosity=10, opLabelAliases={ L('GA1'): (L('Gx'),) }) #Other option variations - just make sure they run at this point mdl_mlegst_chk_opts3 = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2,1e2), circuitSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True, circuitWeightsDict={ (L('Gx'),): 2.0 }, alwaysPerformMLE=True ) #Forcing function used by linear response error bars forcingfn_grad = np.ones((1,mdl_clgst.num_params()), 'd') mdl_lsgst_chk_opts3 = pygsti.algorithms.core._do_mlgst_base( ds, mdl_clgst, self.lsgstStrings[0], verbosity=0, minProbClip=1e-4, probClipInterval=(-1e2,1e2), forcefn_grad=forcingfn_grad) with self.assertRaises(NotImplementedError): # Non-poisson picture needs support for a non-leastsq solver (not impl yet) mdl_lsgst_chk_opts4 = pygsti.algorithms.core._do_mlgst_base( ds, mdl_clgst, self.lsgstStrings[0], verbosity=0, poissonPicture=False, minProbClip=1e-4, probClipInterval=(-1e2,1e2), forcefn_grad=forcingfn_grad) # non-poisson picture #Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust #self.runSilent(pygsti.do_mlgst, ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-6, # probClipInterval=(-1e2,1e2), verbosity=4, memLimit=curMem+8500000) #invoke memory control #non-Poisson picture - should use (-1,-1) model for consistency? with self.assertRaises(NotImplementedError): # Non-poisson picture needs support for a non-leastsq solver (not impl yet) pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False) try: pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-1, # 1e-1 b/c get inf Jacobians... probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False, spam_penalty_factor=1.0, cptp_penalty_factor=1.0) except ValueError: pass # ignore when assertions in customlm.py are disabled except AssertionError: pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians... #Check errors: with self.assertRaises(MemoryError): pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4, probClipInterval=(-1e2,1e2),verbosity=0, memLimit=1) # RUN BELOW LINES TO SEED SAVED GATESET FILES if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"): pygsti.io.write_model(mdl_mlegst,compare_files + "/mle_gst.model", "Saved MLE-GST Model") mdl_mle_compare = pygsti.io.load_model(compare_files + "/mle_gst.model") mdl_mlegst_go = pygsti.gaugeopt_to_target(mdl_mlegst, mdl_mle_compare, {'spam':1.0}, checkJac=True) self.assertAlmostEqual( mdl_mlegst_go.frobeniusdist(mdl_mle_compare), 0, places=4)
def test_MLGST(self): ds = self.ds #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1], # nSamples=1000, sampleError='binomial', seed=100) gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_mlgst = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0) gs_mlegst = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings, verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), memLimit=1000 * 1024**2) maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst( ds, gs_clgst, [[gs.tup for gs in gsList] for gsList in self.lsgstStrings], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), returnAll=True, returnMaxLogL=True) gs_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, gs_clgst, self.lsgstStrings, verbosity=10, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), memLimit=10 * 1024**2) self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb), 0) self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]), 0) #Run internal checks on less max-L values (so it doesn't take forever) gs_mlegst_chk = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), check=True) #Other option variations - just make sure they run at this point gs_mlegst_chk_opts = pygsti.do_iterative_mlgst( ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), gateStringSetLabels=["Set1", "Set2"], useFreqWeightedChiSq=True) aliased_list = [ pygsti.obj.GateString([(x if x != "Gx" else "GA1") for x in gs]) for gs in self.lsgstStrings[0] ] gs_withA1 = gs_clgst.copy() gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"] gs_mlegst_chk_opts2 = pygsti.do_mlgst( ds, gs_withA1, aliased_list, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0, gateLabelAliases={'GA1': ('Gx', )}) #Other option variations - just make sure they run at this point gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst( ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6, 1e6), useFreqWeightedChiSq=True, gateStringSetLabels=["Set1", "Set2"], gatestringWeightsDict={('Gx', ): 2.0}) self.runSilent(pygsti.do_mlgst, ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=4, memLimit=300000) #invoke memory control pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0, poissonPicture=False) #non-Poisson picture - should use (-1,-1) gateset for consistency? #Check errors: with self.assertRaises(MemoryError): pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0, memLimit=1) # RUN BELOW LINES TO SEED SAVED GATESET FILES #pygsti.io.write_gateset(gs_mlegst,compare_files + "/mle_gst.gateset", "Saved MLE-GST Gateset") gs_mle_compare = pygsti.io.load_gateset(compare_files + "/mle_gst.gateset") gs_mlegst_go = pygsti.optimize_gauge(gs_mlegst, 'target', targetGateset=gs_mle_compare, spamWeight=1.0) self.assertAlmostEqual(gs_mlegst_go.frobeniusdist(gs_mle_compare), 0, places=5)
def runAnalysis(obj, myspecs, mygerms, gsTarget, seed, maxLs=[1, 2, 4, 8], nSamples=1000, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None, distributeMethod="gatestrings"): rhoStrs, EStrs = pygsti.construction.get_spam_strs(myspecs) lgstStrings = pygsti.construction.list_lgst_gatestrings( myspecs, gsTarget.gates.keys()) lsgstStrings = pygsti.construction.make_lsgst_lists( gsTarget.gates.keys(), rhoStrs, EStrs, mygerms, maxLs, fidPairList) print len(myspecs[0]), " rho specifiers" print len(myspecs[1]), " effect specifiers" print len(mygerms), " germs" print len(lgstStrings), " total LGST gate strings" print len(lsgstStrings[-1]), " LSGST strings before thinning" lsgstStringsToUse = lsgstStrings allRequiredStrs = pygsti.remove_duplicates(lgstStrings + lsgstStrings[-1]) gs_dataGen = gsTarget.depolarize(gate_noise=0.1) dsFake = pygsti.construction.generate_fake_data(gs_dataGen, allRequiredStrs, nSamples, sampleError="multinomial", seed=seed) #Run LGST to get starting gate set gs_lgst = pygsti.do_lgst(dsFake, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=gs_dataGen) #Run full iterative LSGST tStart = time.time() if obj == "chi2": all_gs_lsgst = pygsti.do_iterative_mc2gst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5, 1e5), verbosity=1, memLimit=3 * (1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) elif obj == "logl": all_gs_lsgst = pygsti.do_iterative_mlgst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClip=minProbClipForWeighting, probClipInterval=(-1e5, 1e5), verbosity=1, memLimit=3 * (1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) tEnd = time.time() print "Time = ", (tEnd - tStart) / 3600.0, "hours" return all_gs_lsgst, gs_dataGen