def allocate(self, avoid_conflicts=True):
p = PanelMaker()
panels = p.form_panels(self.adjudicators, len(self.debates))
assert len(self.debates) <= len(panels)
self.debates.sort(key=lambda d: self.get_debate_energy(d), reverse=True)
panels.sort(key=lambda p:p.get_energy(), reverse=True)
self.pairings = zip(self.debates, panels)
if avoid_conflicts:
for i, (debate, panel) in enumerate(self.pairings):
if panel.conflicts(debate):
j = self.search_swap(i, range(i, 0, -1))
if j is None:
j = self.search_swap(i, range(i+1, len(panels)))
from debate.models import AdjudicatorAllocation
allocation = []
for debate, panel in self.pairings:
a = AdjudicatorAllocation(debate)
a.chair = panel[0]
a.panel = panel[1:]
allocation.append(a)
return allocation
def allocate(self, initial=None):
from debate.models import AdjudicatorAllocation
if initial is None:
initial = StabAllocator(self.debates, self.adjudicators).allocate()
pairs = [(aa.debate, tuple(a[1] for a in aa)) for aa in initial]
top_bracket = pairs[0][0].bracket
bot_bracket = pairs[-1][0].bracket
# 4-0 - 5 brackets, needs 6 gaps
# 5-2
gaps = (top_bracket - bot_bracket) + 2
div = 3.0 / gaps
for debate, panel in pairs:
setattr(debate, 'target_panel',
2 + (debate.bracket - bot_bracket + 1) * div)
print[d.target_panel for d, p in pairs]
self.state = dict(pairs)
self.anneal(800, 1, 1e4, self.state)
#i = 0
#while self.best_energy > 0 and i < self.MAX_TRIES:
# self.anneal(100, 1, 1e3, self.best_state)
# i += 1
result = []
for debate, panel in self.best_state.items():
aa = AdjudicatorAllocation(debate)
panel = list(panel)
panel.sort(key=lambda x: x.score, reverse=True)
aa.chair = panel.pop(0)
aa.panel = panel
result.append(aa)
return result
def allocate(self):
from debate.models import AdjudicatorAllocation
debates = self.debates
adjs = self.adjudicators
result = []
for debate in debates:
alloc = AdjudicatorAllocation(debate)
alloc.chair = adjs.pop(0)
result.append(alloc)
while len(adjs) >= 2:
for alloc in reversed(result):
if len(adjs) >= 2:
alloc.panel.append(adjs.pop(0))
alloc.panel.append(adjs.pop(0))
return result
cost = 0
for r, c in indexes:
cost += cost_matrix[r][c]
print 'total cost for panellists', cost
# transfer the indices to the debates
# the debate corresponding to row r is floor(r/3) (i.e. r // 3)
p = [[] for i in range(n)]
for r, c in indexes[:n*3]:
p[r // 3].append(panellists[c])
# create the corresponding adjudicator allocations, making sure
# that the chair is the highest-ranked adjudicator in the panel
for i, d in enumerate(panel_debates):
a = AdjudicatorAllocation(d)
p[i].sort(key=lambda a: a.score, reverse=True)
a.chair = p[i].pop(0)
a.panel = p[i]
alloc.append(a)
print [(a.debate, a.chair, a.panel) for a in alloc[len(chairs):]]
return alloc
def test():
from debate.models import Round
r = Round.objects.get(pk=4)
debates = r.debates()
adjs = list(r.active_adjudicators.all())
def allocate(self):
from debate.models import AdjudicatorAllocation
# remove trainees
self.adjudicators = filter(lambda a: a.score > self.MIN_SCORE,
self.adjudicators)
# sort adjudicators and debates in descending score/importance
self.adjudicators_sorted = list(self.adjudicators)
self.adjudicators_sorted.sort(key=lambda a: a.score, reverse=True)
self.debates_sorted = list(self.debates)
self.debates_sorted.sort(key=lambda a: a.importance, reverse=True)
n_adjudicators = len(self.adjudicators)
n_debates = len(self.debates)
n_solos = n_debates - (n_adjudicators - n_debates) / 2
# get adjudicators that can adjudicate solo
chairs = self.adjudicators_sorted[:n_solos]
#chairs = [a for a in self.adjudicators_sorted if a.score >
# self.CHAIR_CUTOFF]
# get debates that will be judged by solo adjudicators
chair_debates = self.debates_sorted[:len(chairs)]
panel_debates = self.debates_sorted[len(chairs):]
panellists = [a for a in self.adjudicators_sorted if a not in chairs]
assert len(panel_debates) * 3 <= len(panellists)
print "costing chairs"
n = len(chairs)
cost_matrix = [[0] * n for i in range(n)]
for i, debate in enumerate(chair_debates):
for j, adj in enumerate(chairs):
cost_matrix[i][j] = self.calc_cost(debate, adj)
print "optimizing"
m = Munkres()
indexes = m.compute(cost_matrix)
total_cost = 0
for r, c in indexes:
total_cost += cost_matrix[r][c]
print 'total cost for solos', total_cost
print 'number of solo debates', n
result = ((chair_debates[i], chairs[j]) for i, j in indexes
if i < len(chair_debates))
alloc = [AdjudicatorAllocation(d, c) for d, c in result]
print[(a.debate, a.chair) for a in alloc]
# do panels
n = len(panel_debates)
npan = len(panellists)
if npan:
print "costing panellists"
# matrix is square, dummy debates have cost 0
cost_matrix = [[0] * npan for i in range(npan)]
for i, debate in enumerate(panel_debates):
for j in range(3):
# for the top half of these debates, the final panellist
# can be of lower quality than the other 2
if i < npan / 2 and j == 2:
adjustment = -1.0
else:
adjustment = 0
for k, adj in enumerate(panellists):
cost_matrix[3 * i + j][k] = self.calc_cost(
debate, adj, adjustment)
print "optimizing"
indexes = m.compute(cost_matrix)
cost = 0
for r, c in indexes:
cost += cost_matrix[r][c]
print 'total cost for panellists', cost
# transfer the indices to the debates
# the debate corresponding to row r is floor(r/3) (i.e. r // 3)
p = [[] for i in range(n)]
for r, c in indexes[:n * 3]:
p[r // 3].append(panellists[c])
# create the corresponding adjudicator allocations, making sure
# that the chair is the highest-ranked adjudicator in the panel
for i, d in enumerate(panel_debates):
a = AdjudicatorAllocation(d)
p[i].sort(key=lambda a: a.score, reverse=True)
a.chair = p[i].pop(0)
a.panel = p[i]
alloc.append(a)
print[(a.debate, a.chair, a.panel) for a in alloc[len(chairs):]]
return alloc
1) * div)
print [d.target_panel for d, p in pairs]
self.state = dict(pairs)
self.anneal(800, 1, 1e4, self.state)
#i = 0
#while self.best_energy > 0 and i < self.MAX_TRIES:
# self.anneal(100, 1, 1e3, self.best_state)
# i += 1
result = []
for debate, panel in self.best_state.items():
aa = AdjudicatorAllocation(debate)
panel = list(panel)
panel.sort(key=lambda x: x.score, reverse=True)
aa.chair = panel.pop(0)
aa.panel = panel
result.append(aa)
return result
def save_best(self):
self.best_energy = self.energy
self.best_state = dict(self.state)
def anneal(self, steps, min_temp, max_temp, state):
def allocate(self):
from debate.models import AdjudicatorAllocation
# remove trainees
self.adjudicators = filter(lambda a: a.score >= self.MIN_SCORE, self.adjudicators)
# sort adjudicators and debates in descending score/importance
self.adjudicators_sorted = list(self.adjudicators)
shuffle(self.adjudicators_sorted) # randomize equally-ranked judges
self.adjudicators_sorted.sort(key=lambda a: a.score, reverse=True)
self.debates_sorted = list(self.debates)
self.debates_sorted.sort(key=lambda a: a.importance, reverse=True)
n_adjudicators = len(self.adjudicators)
n_debates = len(self.debates)
n_solos = n_debates - (n_adjudicators - n_debates)/2
# get adjudicators that can adjudicate solo
chairs = self.adjudicators_sorted[:n_solos]
#chairs = [a for a in self.adjudicators_sorted if a.score >
# self.CHAIR_CUTOFF]
# get debates that will be judged by solo adjudicators
chair_debates = self.debates_sorted[:len(chairs)]
panel_debates = self.debates_sorted[len(chairs):]
panellists = [a for a in self.adjudicators_sorted if a not in chairs]
assert len(panel_debates) * 3 <= len(panellists)
m = Munkres()
# TODO I think "chairs" actually means "solos", rename variables if correct
if len(chairs) > 0:
print "costing chairs"
n = len(chairs)
cost_matrix = [[0] * n for i in range(n)]
for i, debate in enumerate(chair_debates):
for j, adj in enumerate(chairs):
cost_matrix[i][j] = self.calc_cost(debate, adj)
print "optimizing"
indexes = m.compute(cost_matrix)
total_cost = 0
for r, c in indexes:
total_cost += cost_matrix[r][c]
print 'total cost for solos', total_cost
print 'number of solo debates', n
result = ((chair_debates[i], chairs[j]) for i, j in indexes if i <
len(chair_debates))
alloc = [AdjudicatorAllocation(d, c) for d, c in result]
print [(a.debate, a.chair) for a in alloc]
else:
print "No solo adjudicators."
alloc = []
# do panels
n = len(panel_debates)
npan = len(panellists)
if npan:
print "costing panellists"
# matrix is square, dummy debates have cost 0
cost_matrix = [[0] * npan for i in range(npan)]
for i, debate in enumerate(panel_debates):
for j in range(3):
# for the top half of these debates, the final panellist
# can be of lower quality than the other 2
if i < npan/2 and j==2:
adjustment = -1.0
else:
adjustment = 0
for k, adj in enumerate(panellists):
cost_matrix[3*i+j][k] = self.calc_cost(debate, adj,
adjustment)
print "optimizing"
indexes = m.compute(cost_matrix)
cost = 0
for r, c in indexes:
cost += cost_matrix[r][c]
print 'total cost for panellists', cost
# transfer the indices to the debates
# the debate corresponding to row r is floor(r/3) (i.e. r // 3)
p = [[] for i in range(n)]
for r, c in indexes[:n*3]:
p[r // 3].append(panellists[c])
# create the corresponding adjudicator allocations, making sure
# that the chair is the highest-ranked adjudicator in the panel
for i, d in enumerate(panel_debates):
a = AdjudicatorAllocation(d)
p[i].sort(key=lambda a: a.score, reverse=True)
a.chair = p[i].pop(0)
a.panel = p[i]
alloc.append(a)
print [(a.debate, a.chair, a.panel) for a in alloc[len(chairs):]]
