def reset(self):
self._reset_next_step = False
self._ep_return = 0
self._n_steps = 0
# start from src node.
self._curr_node = self._src_node
self._graph_features = copy.deepcopy(self._reset_graph_features)
return restart(self._observation())
def reset_solution(self, sol, obj_val):
# call at the beginning of a local move.
# assert self._globals[Env.GLOBAL_UNFIX_LEFT] == self.k
self._change_sol(sol, obj_val, sol, obj_val)
self._best_quality = self._primal_gap(obj_val)
self._final_quality = self._best_quality
self._qualities = [self._best_quality]
return restart(self._observation())
def reset(self):
if self._reset_next_step:
self._prev_ep_return = self._ep_return
self._ep_return = 0
self._n_steps = 0
# start from src node.
self._curr_node = self._src_node
# reset features
self._graph_features = copy.deepcopy(self._fixed_graph_features)
# path collected so far
self._path = [self._curr_node]
self._reset_next_step = False
return restart(self._observation())
def reset(self):
# fix the graph for a few episodes to reduce load on the disk while loading datasets.
if self._n_resets % self._sample_every_n_resets == 0:
self.milp, self._sol, self._obj = self._sample()
self.mip = SCIPMIPInstance.fromMIPInstance(self.milp.mip)
# clean up previous scip model
if hasattr(self, '_sub_mip_model'):
del_scip_model(self._sub_mip_model)
self._sub_mip_model = self.mip.get_scip_model()
sol, obj, mip = self._sol, self._obj, self.mip
c_f, e_f, v_f = load_pickled_features(self._dataset,
self._dataset_type,
self._milp_choice)
self._var_names = var_names = list(
map(lambda v: v.name.lstrip('t_'), mip.vars))
# call init_features before init_ds
self._init_features(var_names, v_f)
self._init_ds()
self._constraint_nodes = np.float32(c_f['values'])
self._change_sol(sol, obj, sol, obj)
self._best_quality = self._primal_gap(obj)
self._final_quality = self._best_quality
self._qualities = [self._best_quality]
self._unfixed_variables = filter(lambda v: v.vtype() == 'CONTINUOUS',
mip.vars)
self._unfixed_variables = set(
map(lambda v: v.name.lstrip('t_'), self._unfixed_variables))
self._vars_unfixed_so_far = []
# static features computed only once per episode.
if self.config.make_obs_for_graphnet:
raise Exception('Not yet supported!')
self._static_graph_features = self._encode_static_graph_features()
elif self.config.make_obs_for_bipartite_graphnet:
self._static_graph_features = self._encode_static_bipartite_graph_features(
e_f)
self._n_resets += 1
return restart(self._observation())
def reset(self):
self._reset_next_step = False
self._features = np.float32(np.random.randint(0, 2, size=(2, )))
return restart(self._observation())