def calculateIntegrals(self):
""" Calculate and store primal and dual integral values
... for every problem under 'PrimalIntegral' and 'DualIntegral'
"""
dualargs = dict(historytouse = Key.DualBoundHistory, boundkey = Key.DualBound)
for testrun in self.getTestRuns():
# go through problems and calculate both primal and dual integrals
for problemid in testrun.getProblemIds():
processplotdata = getProcessPlotData(testrun, problemid)
# check for well defined data (may not exist sometimes)
if processplotdata:
try:
testrun.addDataById(Key.PrimalIntegral, calcIntegralValue(processplotdata), problemid)
logging.debug("Computed primal integral %.1f for problem %s, data %s" % (testrun.getProblemDataById(problemid, 'PrimalIntegral'), problemid, repr(processplotdata)))
except AssertionError as e:
logging.error("Error for primal bound on problem %s, list: %s" % (problemid, processplotdata))
processplotdata = getProcessPlotData(testrun, problemid, **dualargs)
# check for well defined data (may not exist sometimes)
if processplotdata:
try:
testrun.addDataById(Key.DualIntegral, calcIntegralValue(processplotdata, pwlinear = True), problemid)
logging.debug("Computed dual integral %.1f for problem %s, data %s" % (testrun.getProblemDataById(problemid, 'DualIntegral'), problemid, repr(processplotdata)))
except AssertionError as e:
logging.error("Error for dual bound on problem %s, list: %s " % (problemid, processplotdata))
def calculateIntegrals(self,scale=False, lim=(None,None)):
""" Calculate and store primal and dual integral values
... for every problem under 'PrimalIntegral' and 'DualIntegral'
"""
dualargs = dict(historytouse = Key.DualBoundHistory, xaftersolvekey = Key.DualBound)
if self.validation is None:
return
for testrun in self.getTestRuns():
# go through problems and calculate both primal and dual integrals
for problemid in testrun.getProblemIds():
problemname = testrun.getProblemDataById(problemid, Key.ProblemName)
processplotdata = getProcessPlotData(testrun, problemid, reference = self.validation.getReferencePb(problemname),scale=scale, lim=lim)
# check for well defined data (may not exist sometimes)
if processplotdata:
try:
testrun.addDataById(Key.PrimalIntegral, calcIntegralValue(processplotdata), problemid)
logger.debug("Computed primal integral %.1f for problem %s, data %s" % (testrun.getProblemDataById(problemid, 'PrimalIntegral'), problemid, repr(processplotdata)))
except AssertionError:
logger.error("Error for primal bound on problem %s, list: %s" % (problemid, processplotdata))
processplotdata = getProcessPlotData(testrun, problemid, reference = self.validation.getReferenceDb(problemname), scale=scale, lim=lim, **dualargs)
# check for well defined data (may not exist sometimes)
if processplotdata:
try:
testrun.addDataById(Key.DualIntegral, calcIntegralValue(processplotdata, pwlinear = True), problemid)
logger.debug("Computed dual integral %.1f for problem %s, data %s" % (testrun.getProblemDataById(problemid, 'DualIntegral'), problemid, repr(processplotdata)))
except AssertionError:
logger.error("Error for dual bound on problem %s, list: %s " % (problemid, processplotdata))
def update_Axis(self, probnames, testruns):
"""
update method called every time a new instance was selected
"""
#self.resetAxis()
# make up data for plotting method
x = {}
y = {}
z = {}
zx = {}
kws = {}
duallinekws = {}
dualbarkws = {}
baseline = 0
if len(probnames) == 1:
self.updateStatus("Showing problem %s on %d test runs" %
(probnames[0], len(testruns)))
else:
self.updateStatus("Showing mean over %d problems on %d test runs" %
(len(probnames), len(testruns)))
self.resetAxis()
usenormalization = True
showdualbound = True
xmax = xmin = ymax = ymin = 0
labelorder = []
for testrun in testruns:
testrunname = self.getTestrunName(testrun)
if len(probnames) == 1:
x[testrunname], y[testrunname] = getProcessPlotData(
testrun, probnames[0], usenormalization, access="name")
else:
x[testrunname], y[testrunname] = getMeanIntegral(testrun,
probnames,
access="name")
if not usenormalization and len(probnames) == 1:
baseline = testrun.problemGetOptimalSolution(probnames[0])
y[testrunname] -= baseline
xmax = max(xmax, max(x[testrunname]))
ymax = max(ymax, max(y[testrunname]))
ymin = min(ymin, min(y[testrunname]))
print(y[testrunname])
print(y[testrunname][1:] - y[testrunname][:-1] > 0)
if numpy.any(y[testrunname][1:] - y[testrunname][:-1] > 0):
logging.warn(
"Error: Increasing primal gap function on problems {}".
format(probnames))
if showdualbound:
arguments = {
"historytouse": Key.DualBoundHistory,
"boundkey": Key.DualBound
}
if len(probnames) == 1:
zx[testrunname], z[testrunname] = getProcessPlotData(
testrun,
probnames[0],
usenormalization,
access="name",
**arguments)
else:
zx[testrunname], z[testrunname] = getMeanIntegral(
testrun, probnames, access="name", **arguments)
# normalization requires negative dual gap
if usenormalization:
z[testrunname] = -numpy.array(z[testrunname])
ymin = min(ymin, min(z[testrunname]))
duallinekws[testrunname] = dict(linestyle='dashed')
dualbarkws = dict(alpha=0.1)
# set special key words for the testrun
kws[testrunname] = dict(alpha=0.1)
labelorder.append(testrunname)
# for now, only one color cycle exists
#colormap = cm.get_cmap(name='spectral', lut=128)
#self.axes.set_color_cycle([colormap(i) for i in numpy.linspace(0.1, 0.9, len(self.gui.getTestrunList()))])
# call the plot on the collected data
self.primalpatches, self.primallines, _ = self.axisPlotForTestrunData(
x,
y,
baseline=baseline,
legend=False,
labelsuffix=" (primal)",
plotkw=kws,
barkw=kws,
labelorder=labelorder)
if showdualbound:
__, self.duallines, _ = self.axisPlotForTestrunData(
zx,
z,
step=False,
baseline=0,
legend=False,
labelsuffix=" (dual)",
plotkw=duallinekws,
barkw=dualbarkws,
labelorder=labelorder)
# set a legend and limits
self.sc.axes.legend(fontsize=8)
self.sc.axes.set_xlim(
(xmin - 0.05 * (xmax - xmin), xmax + 0.05 * (xmax - xmin)))
self.sc.axes.set_ylim(
(ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)),
emit=True)
self.sc.draw()