def apply(self, solver: Solver):
for i, lineup in enumerate(self.context.exclude_lineups, start=1):
variables = [self.players_dict[player] for player in lineup]
solver.add_constraint(variables,
None,
SolverSign.LTE,
self.max_repeating_players,
name='exclude_lineups_%d' % i)
def _create_constraints(self, solver: Solver, players_dict: Dict[Player, Any]) -> None:
for positions, total_for_positions in self.positions.items():
min_exposure_players = [p for p in self.min_exposure_players if list_intersection(p.positions, positions)]
if not min_exposure_players:
continue
total_force = sum(self.min_exposure_players[p] for p in min_exposure_players) - \
total_for_positions * (self.remaining_lineups - 1)
if total_force > 0:
variables = [players_dict[p] for p in min_exposure_players]
total_force = min(total_force, ceil(total_force / self.remaining_lineups))
solver.add_constraint(variables, None, SolverSign.GTE, total_force)
for player, total_lineups in self.min_exposure_players.items():
if total_lineups >= self.remaining_lineups:
solver.add_constraint([players_dict[player]], None, SolverSign.EQ, 1)
def _create_constraints(
self,
solver: Solver,
players_dict: Dict[Player, Any],
):
players_in_stack = defaultdict(set) # type: Dict[Player, Set[Any]]
for stack in self.stacks:
combinations_variables = {}
for group in stack.groups:
group_name = self._build_group_name(group)
sub_groups = group.get_all_players_groups()
if self._is_reached_exposure(group) or (
group.parent
and self._is_reached_exposure(group.parent)):
for group_players, _, max_from_group in sub_groups:
if max_from_group is None:
continue
solver.add_constraint(
[players_dict[p] for p in group_players], None,
SolverSign.EQ, 0)
max_group = sorted(sub_groups, key=lambda t: t[1])[0]
if max_group[1]:
solver.add_constraint(
[players_dict[p] for p in max_group[0]], None,
SolverSign.LTE, max_group[1] - 1)
continue
solver_variable = None
if any(sub_group[1] is not None for sub_group in sub_groups):
solver_variable = solver.add_variable(group_name)
combinations_variables[group_name] = solver_variable
for group_players, group_min, group_max in sub_groups:
variables = [players_dict[p] for p in group_players]
if group_min is not None:
if not stack.can_intersect:
for player in group_players:
players_in_stack[player].add(solver_variable)
solver.add_constraint(variables, None, SolverSign.GTE,
group_min * solver_variable)
if group_max is not None:
solver.add_constraint(variables, None,
SolverSign.LTE, group_max)
elif group_max is not None:
solver_variable = solver.add_variable(
group_name, min_value=0, max_value=group_max)
solver.add_constraint(variables, None, SolverSign.EQ,
solver_variable)
if combinations_variables:
solver.add_constraint(combinations_variables.values(), None,
SolverSign.GTE, 1)
for player, stacks_vars in players_in_stack.items():
if len(stacks_vars) > 1:
solver.add_constraint(stacks_vars, None, SolverSign.LTE, 1)
def _create_constraints(
self,
solver: Solver,
players_dict: Dict[Player, Any],
):
players_in_stack = defaultdict(set) # type: Dict[Player, Set[Any]]
for stack in self.stacks:
combinations_variables = {}
for group in stack.groups:
group_name = ('stack_%s_%s' % (stack.uuid, group.uuid)).replace('-', '_')
sub_groups = group.get_all_players_groups()
if group.max_exposure is not None and group.max_exposure <= self.used_groups[group_name] / self.total_lineups:
max_group = sorted(sub_groups, key=lambda t: t[1])[0]
variables = [players_dict[p] for p in max_group[0]]
solver.add_constraint(variables, None, SolverSign.LTE, max_group[1] - 1)
continue
if any(sub_group[1] is not None for sub_group in sub_groups):
solver_variable = solver.add_variable(group_name)
combinations_variables[group_name] = solver_variable
for group_players, group_min, group_max in sub_groups:
variables = [players_dict[p] for p in group_players]
if group_min is not None:
if not stack.can_intersect:
for player in group_players:
players_in_stack[player].add(solver_variable)
solver.add_constraint(variables, None, SolverSign.GTE, group_min * solver_variable)
if group_max is not None:
solver.add_constraint(variables, None, SolverSign.LTE, group_max)
if combinations_variables:
solver.add_constraint(combinations_variables.values(), None, SolverSign.GTE, 1)
for player, stacks_vars in players_in_stack.items():
if len(stacks_vars) > 1:
solver.add_constraint(stacks_vars, None, SolverSign.LTE, 1)
def _create_constraints(
self,
solver: Solver,
):
players_in_stack = defaultdict(set) # type: Dict[Player, Set[Any]]
for stack in self.stacks:
combinations_variables = {}
for group in stack.groups:
group_name = self._build_group_name(group)
sub_groups = group.get_all_players_groups()
if self.exposure_strategy.is_reached_exposure(group_name) or \
(group.parent and self.exposure_strategy.is_reached_exposure(
self._build_group_name(group.parent))):
for sub_group in sub_groups:
if sub_group.max_from_group is None:
continue
solver.add_constraint(
[self.players_dict[p] for p in sub_group.players],
None, SolverSign.EQ, 0)
max_group = sorted(
sub_groups,
key=lambda g: g.min_from_group)[0] # type: ignore
if max_group.min_from_group:
solver.add_constraint(
[self.players_dict[p] for p in max_group.players],
None, SolverSign.LTE, max_group.min_from_group - 1)
continue
solver_variable = None
if any(sub_group.min_from_group is not None
for sub_group in sub_groups):
solver_variable = solver.add_variable(group_name)
if group.depends_on is None:
combinations_variables[group_name] = solver_variable
for sub_group in sub_groups:
variables = [
self.players_dict[p] for p in sub_group.players
if p in self.players_dict
]
if sub_group.min_from_group is not None:
if not stack.can_intersect:
for player in sub_group.players:
players_in_stack[player].add(solver_variable)
solver.add_constraint(
variables, None, SolverSign.GTE,
sub_group.min_from_group * solver_variable)
if sub_group.max_from_group is not None:
solver.add_constraint(variables, None,
SolverSign.LTE,
sub_group.max_from_group)
elif sub_group.max_from_group is not None:
solver_variable = solver.add_variable(
group_name,
min_value=0,
max_value=sub_group.max_from_group)
solver.add_constraint(variables, None, SolverSign.EQ,
solver_variable)
if group.depends_on is not None:
total_players_var = solver.add_variable(
'total_players_%s' % group_name,
min_value=0,
max_value=sub_group.max_from_group
or len(variables))
solver.add_constraint(variables, None, SolverSign.EQ,
total_players_var)
depend_var = self.players_dict.get(group.depends_on, 0)
solver.add_constraint(
[total_players_var], None, SolverSign.GTE,
depend_var * (sub_group.min_from_group or 1))
if group.strict_depend:
solver.add_constraint(
[total_players_var], None, SolverSign.LTE,
depend_var *
(sub_group.max_from_group or len(variables)))
if combinations_variables:
solver.add_constraint(combinations_variables.values(), None,
SolverSign.GTE, 1)
for player, stacks_vars in players_in_stack.items():
if len(stacks_vars) > 1:
solver.add_constraint(stacks_vars, None, SolverSign.LTE, 1)
