def solve(dat):
"""
core solving routine
:param dat: a good ticdat for the input_schema
:return: a good ticdat for the solution_schema, or None
"""
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
mdl = Model(_model_type, "netflow")
flow = {(h, i, j): mdl.add_var(name='flow_%s_%s_%s' % (h, i, j))
for h, i, j in dat.cost if (i,j) in dat.arcs}
flowslice = Slicer(flow)
# Arc Capacity constraints
for i_,j_ in dat.arcs:
mdl.add_constraint(mdl.sum(flow[h,i,j] for h,i,j in flowslice.slice('*',i_, j_))
<= dat.arcs[i_,j_]["Capacity"],
name='cap_%s_%s' % (i_, j_))
# Flow conservation constraints. Constraints are generated only for relevant pairs.
# So we generate a conservation of flow constraint if there is negative or positive inflow
# quantity, or at least one inbound flow variable, or at least one outbound flow variable.
for h,j in set(k for k,v in dat.inflow.items() if abs(v["Quantity"]) > 0)\
.union({(h,i) for h,i,j in flow}, {(h,j) for h,i,j in flow}):
mdl.add_constraint(
mdl.sum(flow[h_,i_,j_] for h_,i_,j_ in flowslice.slice(h,'*',j)) +
dat.inflow.get((h,j), {"Quantity":0})["Quantity"] ==
mdl.sum(flow[h_,j_,i_] for h_,j_,i_ in flowslice.slice(h, j, '*')),
name='node_%s_%s' % (h, j))
mdl.set_objective(mdl.sum(flow * dat.cost[h, i, j]["Cost"]
for (h, i, j), flow in flow.items()))
# Compute optimal solution
if mdl.optimize():
rtn = solution_schema.TicDat()
for (h, i, j),var in flow.items():
if mdl.get_solution_value(var) > 0:
rtn.flow[h, i, j] = mdl.get_solution_value(var)
rtn.parameters["Total Cost"] = sum(dat.cost[h, i, j]["Cost"] * r["Quantity"]
for (h, i, j), r in rtn.flow.items())
return rtn
def dietSolver(modelType):
tdf = TicDatFactory(**dietSchema())
addDietForeignKeys(tdf)
addDietDataTypes(tdf)
dat = tdf.copy_tic_dat(dietData())
assert not tdf.find_data_type_failures(dat) and not tdf.find_foreign_key_failures(dat)
mdl = Model(modelType, "diet")
nutrition = {}
for c,n in dat.categories.items() :
nutrition[c] = mdl.add_var(lb=n["minNutrition"], ub=n["maxNutrition"], name=c)
# Create decision variables for the foods to buy
buy = {}
for f in dat.foods:
buy[f] = mdl.add_var(name=f)
# Nutrition constraints
for c in dat.categories:
mdl.add_constraint(mdl.sum(dat.nutritionQuantities[f,c]["qty"] * buy[f]
for f in dat.foods)
== nutrition[c],
name = c)
mdl.set_objective(mdl.sum(buy[f] * c["cost"] for f,c in dat.foods.items()))
if mdl.optimize():
solutionFactory = TicDatFactory(
parameters = [[],["totalCost"]],
buyFood = [["food"],["qty"]],
consumeNutrition = [["category"],["qty"]])
sln = solutionFactory.TicDat()
for f,x in buy.items():
if mdl.get_solution_value(x) > 0.0001:
sln.buyFood[f] = mdl.get_solution_value(x)
for c,x in nutrition.items():
sln.consumeNutrition[c] = mdl.get_solution_value(x)
return sln, sum(dat.foods[f]["cost"] * r["qty"] for f,r in sln.buyFood.items())
def netflowSolver(modelType):
tdf = TicDatFactory(**netflowSchema())
addNetflowForeignKeys(tdf)
addNetflowDataTypes(tdf)
dat = tdf.copy_tic_dat(netflowData())
assert not tdf.find_data_type_failures(dat) and not tdf.find_foreign_key_failures(dat)
mdl = Model(modelType, "netflow")
flow = {}
for h, i, j in dat.cost:
if (i,j) in dat.arcs:
flow[h,i,j] = mdl.add_var(name='flow_%s_%s_%s' % (h, i, j))
flowslice = Slicer(flow)
for i_,j_ in dat.arcs:
mdl.add_constraint(mdl.sum(flow[h,i,j] for h,i,j in flowslice.slice('*',i_, j_))
<= dat.arcs[i_,j_]["capacity"],
name = 'cap_%s_%s' % (i_, j_))
for h_,j_ in set(k for k,v in dat.inflow.items() if abs(v["quantity"]) > 0).union(
{(h,i) for h,i,j in flow}, {(h,j) for h,i,j in flow}) :
mdl.add_constraint(
mdl.sum(flow[h,i,j] for h,i,j in flowslice.slice(h_,'*',j_)) +
dat.inflow.get((h_,j_), {"quantity":0})["quantity"] ==
mdl.sum(flow[h,i,j] for h,i,j in flowslice.slice(h_, j_, '*')),
name = 'node_%s_%s' % (h_, j_))
mdl.set_objective(mdl.sum(flow * dat.cost[h, i, j]["cost"] for (h, i, j),flow in flow.items()))
if mdl.optimize():
solutionFactory = TicDatFactory(
flow = [["commodity", "source", "destination"], ["quantity"]])
if mdl.optimize():
rtn = solutionFactory.TicDat()
for (h, i, j),var in flow.items():
if mdl.get_solution_value(var) > 0:
rtn.flow[h,i,j] = mdl.get_solution_value(var)
return rtn, sum(dat.cost[h,i,j]["cost"] * r["quantity"] for (h,i,j),r in rtn.flow.items())
def solve(dat):
"""
core solving routine
:param dat: a good ticdat for the input_schema
:return: a good ticdat for the solution_schema, or None
"""
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
assert not input_schema.find_data_row_failures(dat)
mdl = Model(_model_type, "diet")
nutrition = {
c: mdl.add_var(lb=n["Min Nutrition"], ub=n["Max Nutrition"], name=c)
for c, n in dat.categories.items()
}
# Create decision variables for the foods to buy
buy = {f: mdl.add_var(name=f) for f in dat.foods}
# Nutrition constraints
for c in dat.categories:
mdl.add_constraint(mdl.sum(dat.nutrition_quantities[f, c]["Quantity"] *
buy[f] for f in dat.foods) == nutrition[c],
name=c)
mdl.set_objective(mdl.sum(buy[f] * c["Cost"]
for f, c in dat.foods.items()))
if mdl.optimize():
sln = solution_schema.TicDat()
for f, x in buy.items():
if mdl.get_solution_value(x) > 0:
sln.buy_food[f] = mdl.get_solution_value(x)
for c, x in nutrition.items():
sln.consume_nutrition[c] = mdl.get_solution_value(x)
sln.parameters['Total Cost'] = sum(dat.foods[f]["Cost"] * r["Quantity"]
for f, r in sln.buy_food.items())
return sln
def solve(dat):
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
expected_draft_position = {}
# for our purposes, its fine to assume all those drafted by someone else are drafted
# prior to any players drafted by me
for player_name in sorted(
dat.players,
key=lambda _p: {
"Un-drafted": dat.players[_p]["Average Draft Position"],
"Drafted By Me": -1,
"Drafted By Someone Else": -2
}[dat.players[_p]["Draft Status"]]):
expected_draft_position[player_name] = len(expected_draft_position) + 1
assert max(expected_draft_position.values()) == len(
set(expected_draft_position.values())) == len(dat.players)
assert min(expected_draft_position.values()) == 1
already_drafted_by_me = {
player_name
for player_name, row in dat.players.items()
if row["Draft Status"] == "Drafted By Me"
}
can_be_drafted_by_me = {
player_name
for player_name, row in dat.players.items()
if row["Draft Status"] != "Drafted By Someone Else"
}
m = Model(_model_type, 'fantop')
my_starters = {
player_name: m.add_var(type="binary", name="starter_%s" % player_name)
for player_name in can_be_drafted_by_me
}
my_reserves = {
player_name: m.add_var(type="binary", name="reserve_%s" % player_name)
for player_name in can_be_drafted_by_me
}
for player_name in can_be_drafted_by_me:
if player_name in already_drafted_by_me:
m.add_constraint(my_starters[player_name] +
my_reserves[player_name] == 1,
name="already_drafted_%s" % player_name)
else:
m.add_constraint(
my_starters[player_name] + my_reserves[player_name] <= 1,
name="cant_draft_twice_%s" % player_name)
for i, draft_position in enumerate(sorted(dat.my_draft_positions)):
m.add_constraint(m.sum(
my_starters[player_name] + my_reserves[player_name]
for player_name in can_be_drafted_by_me
if expected_draft_position[player_name] < draft_position) <= i,
name="at_most_%s_can_be_ahead_of_%s" %
(i, draft_position))
my_draft_size = m.sum(my_starters[player_name] + my_reserves[player_name]
for player_name in can_be_drafted_by_me)
m.add_constraint(my_draft_size >= len(already_drafted_by_me) + 1,
name="need_to_extend_by_at_least_one")
m.add_constraint(my_draft_size <= len(dat.my_draft_positions),
name="cant_exceed_draft_total")
for position, row in dat.roster_requirements.items():
players = {
player_name
for player_name in can_be_drafted_by_me
if dat.players[player_name]["Position"] == position
}
starters = m.sum(my_starters[player_name] for player_name in players)
reserves = m.sum(my_reserves[player_name] for player_name in players)
m.add_constraint(starters >= row["Min Num Starters"],
name="min_starters_%s" % position)
m.add_constraint(starters <= row["Max Num Starters"],
name="max_starters_%s" % position)
m.add_constraint(reserves >= row["Min Num Reserve"],
name="min_reserve_%s" % position)
m.add_constraint(reserves <= row["Max Num Reserve"],
name="max_reserve_%s" % position)
if "Maximum Number of Flex Starters" in dat.parameters:
players = {
player_name
for player_name in can_be_drafted_by_me
if dat.roster_requirements[dat.players[player_name]["Position"]]
["Flex Status"] == "Flex Eligible"
}
m.add_constraint(
m.sum(my_starters[player_name] for player_name in players) <=
dat.parameters["Maximum Number of Flex Starters"]["Value"],
name="max_flex")
starter_weight = dat.parameters["Starter Weight"][
"Value"] if "Starter Weight" in dat.parameters else 1
reserve_weight = dat.parameters["Reserve Weight"][
"Value"] if "Reserve Weight" in dat.parameters else 1
m.set_objective(m.sum(dat.players[player_name]["Expected Points"] *
(my_starters[player_name] * starter_weight +
my_reserves[player_name] * reserve_weight)
for player_name in can_be_drafted_by_me),
sense="maximize")
if not m.optimize():
print("No draft at all is possible!")
return
sln = solution_schema.TicDat()
def almostone(x):
return abs(m.get_solution_value(x) - 1) < 0.0001
picked = sorted([
player_name for player_name in can_be_drafted_by_me
if almostone(my_starters[player_name])
or almostone(my_reserves[player_name])
],
key=lambda _p: expected_draft_position[_p])
assert len(picked) <= len(dat.my_draft_positions)
if len(picked) < len(dat.my_draft_positions):
print(
"Your model is over-constrained, and thus only a partial draft was possible"
)
draft_yield = 0
for player_name, draft_position in zip(picked,
sorted(dat.my_draft_positions)):
draft_yield += dat.players[player_name]["Expected Points"] * \
(starter_weight if almostone(my_starters[player_name]) else reserve_weight)
assert draft_position <= expected_draft_position[player_name]
sln.my_draft[player_name]["Draft Position"] = draft_position
sln.my_draft[player_name]["Position"] = dat.players[player_name][
"Position"]
sln.my_draft[player_name][
"Planned Or Actual"] = "Actual" if player_name in already_drafted_by_me else "Planned"
sln.my_draft[player_name]["Starter Or Reserve"] = \
"Starter" if almostone(my_starters[player_name]) else "Reserve"
sln.parameters["Total Yield"] = draft_yield
sln.parameters["Feasible"] = len(sln.my_draft) == len(
dat.my_draft_positions)
return sln
def _testFantop(modelType, sqlFile):
dataFactory = TicDatFactory(parameters=[["Key"], ["Value"]],
players=[['Player Name'],
[
'Position',
'Average Draft Position',
'Expected Points', 'Draft Status'
]],
roster_requirements=[['Position'],
[
'Min Num Starters',
'Max Num Starters',
'Min Num Reserve',
'Max Num Reserve',
'Flex Status'
]],
my_draft_positions=[['Draft Position'], []])
# add foreign key constraints (optional, but helps with preventing garbage-in, garbage-out)
dataFactory.add_foreign_key("players", "roster_requirements",
['Position', 'Position'])
# set data types (optional, but helps with preventing garbage-in, garbage-out)
dataFactory.set_data_type("parameters",
"Key",
number_allowed=False,
strings_allowed=[
"Starter Weight", "Reserve Weight",
"Maximum Number of Flex Starters"
])
dataFactory.set_data_type("parameters",
"Value",
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=False)
dataFactory.set_data_type("players",
"Average Draft Position",
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=False)
dataFactory.set_data_type("players",
"Expected Points",
min=-float("inf"),
max=float("inf"),
inclusive_min=False,
inclusive_max=False)
dataFactory.set_data_type("players",
"Draft Status",
strings_allowed=[
"Un-drafted", "Drafted By Me",
"Drafted By Someone Else"
])
for fld in ("Min Num Starters", "Min Num Reserve", "Max Num Reserve"):
dataFactory.set_data_type("roster_requirements",
fld,
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=False,
must_be_int=True)
dataFactory.set_data_type("roster_requirements",
"Max Num Starters",
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=True,
must_be_int=True)
dataFactory.set_data_type(
"roster_requirements",
"Flex Status",
number_allowed=False,
strings_allowed=["Flex Eligible", "Flex Ineligible"])
dataFactory.set_data_type("my_draft_positions",
"Draft Position",
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=False,
must_be_int=True)
solutionFactory = TicDatFactory(my_draft=[[
'Player Name'
], [
'Draft Position', 'Position', 'Planned Or Actual', 'Starter Or Reserve'
]])
dat = dataFactory.sql.create_tic_dat_from_sql(os.path.join(
_codeDir(), sqlFile),
freeze_it=True)
assert dataFactory.good_tic_dat_object(dat)
assert not dataFactory.find_foreign_key_failures(dat)
assert not dataFactory.find_data_type_failures(dat)
expected_draft_position = {}
# for our purposes, its fine to assume all those drafted by someone else are drafted
# prior to any players drafted by me
for player_name in sorted(
dat.players,
key=lambda _p: {
"Un-drafted": dat.players[_p]["Average Draft Position"],
"Drafted By Me": -1,
"Drafted By Someone Else": -2
}[dat.players[_p]["Draft Status"]]):
expected_draft_position[player_name] = len(expected_draft_position) + 1
assert max(expected_draft_position.values()) == len(
set(expected_draft_position.values())) == len(dat.players)
assert min(expected_draft_position.values()) == 1
already_drafted_by_me = {
player_name
for player_name, row in dat.players.items()
if row["Draft Status"] == "Drafted By Me"
}
can_be_drafted_by_me = {
player_name
for player_name, row in dat.players.items()
if row["Draft Status"] != "Drafted By Someone Else"
}
m = Model(modelType, 'fantop')
my_starters = {
player_name: m.add_var(type="binary", name="starter_%s" % player_name)
for player_name in can_be_drafted_by_me
}
my_reserves = {
player_name: m.add_var(type="binary", name="reserve_%s" % player_name)
for player_name in can_be_drafted_by_me
}
for player_name in can_be_drafted_by_me:
if player_name in already_drafted_by_me:
m.add_constraint(my_starters[player_name] +
my_reserves[player_name] == 1,
name="already_drafted_%s" % player_name)
else:
m.add_constraint(
my_starters[player_name] + my_reserves[player_name] <= 1,
name="cant_draft_twice_%s" % player_name)
for i, draft_position in enumerate(sorted(dat.my_draft_positions)):
m.add_constraint(m.sum(
my_starters[player_name] + my_reserves[player_name]
for player_name in can_be_drafted_by_me
if expected_draft_position[player_name] < draft_position) <= i,
name="at_most_%s_can_be_ahead_of_%s" %
(i, draft_position))
my_draft_size = m.sum(my_starters[player_name] + my_reserves[player_name]
for player_name in can_be_drafted_by_me)
m.add_constraint(my_draft_size >= len(already_drafted_by_me) + 1,
name="need_to_extend_by_at_least_one")
m.add_constraint(my_draft_size <= len(dat.my_draft_positions),
name="cant_exceed_draft_total")
for position, row in dat.roster_requirements.items():
players = {
player_name
for player_name in can_be_drafted_by_me
if dat.players[player_name]["Position"] == position
}
starters = m.sum(my_starters[player_name] for player_name in players)
reserves = m.sum(my_reserves[player_name] for player_name in players)
m.add_constraint(starters >= row["Min Num Starters"],
name="min_starters_%s" % position)
m.add_constraint(starters <= row["Max Num Starters"],
name="max_starters_%s" % position)
m.add_constraint(reserves >= row["Min Num Reserve"],
name="min_reserve_%s" % position)
m.add_constraint(reserves <= row["Max Num Reserve"],
name="max_reserve_%s" % position)
if "Maximum Number of Flex Starters" in dat.parameters:
players = {
player_name
for player_name in can_be_drafted_by_me
if dat.roster_requirements[dat.players[player_name]["Position"]]
["Flex Status"] == "Flex Eligible"
}
m.add_constraint(
m.sum(my_starters[player_name] for player_name in players) <=
dat.parameters["Maximum Number of Flex Starters"]["Value"],
name="max_flex")
starter_weight = dat.parameters["Starter Weight"][
"Value"] if "Starter Weight" in dat.parameters else 1
reserve_weight = dat.parameters["Reserve Weight"][
"Value"] if "Reserve Weight" in dat.parameters else 1
m.set_objective(m.sum(dat.players[player_name]["Expected Points"] *
(my_starters[player_name] * starter_weight +
my_reserves[player_name] * reserve_weight)
for player_name in can_be_drafted_by_me),
sense="maximize")
if not m.optimize():
return
sln = solutionFactory.TicDat()
def almostone(x):
return abs(m.get_solution_value(x) - 1) < 0.0001
picked = sorted([
player_name for player_name in can_be_drafted_by_me
if almostone(my_starters[player_name])
or almostone(my_reserves[player_name])
],
key=lambda _p: expected_draft_position[_p])
assert len(picked) <= len(dat.my_draft_positions)
if len(picked) < len(dat.my_draft_positions):
print(
"Your model is over-constrained, and thus only a partial draft was possible"
)
draft_yield = 0
for player_name, draft_position in zip(picked,
sorted(dat.my_draft_positions)):
draft_yield += dat.players[player_name]["Expected Points"] * \
(starter_weight if almostone(my_starters[player_name]) else reserve_weight)
assert draft_position <= expected_draft_position[player_name]
sln.my_draft[player_name]["Draft Position"] = draft_position
sln.my_draft[player_name]["Position"] = dat.players[player_name][
"Position"]
sln.my_draft[player_name][
"Planned Or Actual"] = "Actual" if player_name in already_drafted_by_me else "Planned"
sln.my_draft[player_name]["Starter Or Reserve"] = \
"Starter" if almostone(my_starters[player_name]) else "Reserve"
return sln, draft_yield
def _testParameters(self, modelType):
mdl = Model(modelType, "parameters")
mdl.set_parameters(MIP_Gap=0.01)
def solve(dat, progress):
assert isinstance(progress, Progress)
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
full_parameters = input_schema.create_full_parameters_dict(dat)
def get_distance(x, y):
if (x, y) in dat.distance:
return dat.distance[x, y]["Distance"]
if (y, x) in dat.distance:
return dat.distance[y, x]["Distance"]
return float("inf")
def can_assign(x, y):
return dat.sites[y]["Center Status"] == "Can Be Center" \
and get_distance(x,y)<float("inf")
m = Model(model_name="cog", model_type=full_parameters["Core Model Type"])
assign_vars = {(n, assigned_to): m.add_var(type="binary",
name="%s_%s" % (n, assigned_to))
for n in dat.sites for assigned_to in dat.sites
if can_assign(n, assigned_to)}
open_vars = {
n: m.add_var(type="binary", name="open_%s" % n)
for n in dat.sites if dat.sites[n]["Center Status"] == "Can Be Center"
}
assert open_vars, "nothing can be center"
assign_slicer = Slicer(assign_vars)
for n, r in dat.sites.items():
if r["Demand"] > 0:
m.add_constraint(m.sum(
assign_vars[n, assign_to]
for _, assign_to in assign_slicer.slice(n, "*")) == 1,
name="must_assign_%s" % n)
for assigned_to, r in dat.sites.items():
if r["Center Status"] == "Can Be Center":
_assign_vars = [
assign_vars[n, assigned_to]
for n, _ in assign_slicer.slice("*", assigned_to)
]
# this is the weak formulation
m.add_constraint(m.sum(_assign_vars) <=
len(_assign_vars) * open_vars[assigned_to],
name="weak_force_open%s" % assigned_to)
number_of_centroids = full_parameters["Number of Centroids"]
m.add_constraint(m.sum(v
for v in open_vars.values()) == number_of_centroids,
name="numCentroids")
m.set_parameters(MIP_Gap=full_parameters["MIP Gap"])
m.set_objective(m.sum(var * get_distance(n, assigned_to) *
dat.sites[n]["Demand"]
for (n, assigned_to), var in assign_vars.items()),
sense="minimize")
if full_parameters["Core Model Type"] == "cplex":
progress.add_cplex_listener("COG Optimization", m.core_model)
worked = m.optimize(*(
[progress.gurobi_call_back_factory("COG Optimization", m.core_model)]
if full_parameters["Core Model Type"] == "gurobi" else []))
assert worked, "testing model set up only for success"
sln = solution_schema.TicDat()
if full_parameters["Core Model Type"] == "gurobi":
sln.parameters["Lower Bound"] = getattr(m.core_model, "objBound",
m.core_model.objVal)
sln.parameters["Upper Bound"] = m.core_model.objVal
else:
lb = m.core_model.solve_details.best_bound
sln.parameters["Lower Bound"] = worked.get_objective_value() if isnan(
lb) else lb
sln.parameters["Upper Bound"] = worked.get_objective_value()
def almostone(x):
return abs(x - 1) < 0.0001
for (n, assigned_to), var in assign_vars.items():
if almostone(m.get_solution_value(var)):
sln.assignments[n, assigned_to] = {}
for n, var in open_vars.items():
if almostone(m.get_solution_value(var)):
sln.openings[n] = {}
return sln