def _build_decomposition_tree(self):
# TODO: fix this. Add default empty separators set.
s = self._s + [set()]
# TODO: check connectivity order.
prev_model = self._model.slice(self._u[-1], s[-2] | self._m[-1] | s[-1])
tree = DecompositionTree(self._model)
tree.add_node(prev_model)
tree.set_root(prev_model)
for i in xrange(len(self._u) - 2, -1, -1):
model = self._model.slice(self._u[i], s[i + 1] | self._m[i] | s[i])
tree.add_node(model)
tree.add_edge(prev_model, model,
[self.get_model().get_columns_names()[i] for i in s[i + 1]])
prev_model = model
self._decomposition_tree = tree
def decompose(self, initial_cols=[0], max_separator_size=0,
merge_empty_blocks=True):
'''Decomposes model into submodels starting by initial cols. By default
starts from column 0. Default max separator size is 11.
:param initial_cols: A list of integers.
:param max_separator_size: An integer that represents max available
separator size.
:param merge_empty_blocks: ``True`` or ``False``, whether or not we need to
merge empty blocks.
'''
if max_separator_size:
raise NotImplementedError()
logging.info('Decompose model %s', self._model.get_name())
self._used=[]
self._used2=[]
self._p=[]
m = self._model.get_rows_coefficients()
j_to_i_mapping = {}
for j in range(m.shape[1]):
j_to_i_mapping[j] = set()
# TODO(d2rk): use interaction graph?
g = networkx.Graph()
g.add_nodes_from(range(m.shape[1]))
for i in xrange(m.shape[0]):
J_ = _get_indices(m, i)
for j in range(len(J_) - 1):
j_to_i_mapping[J_[j]].add(i)
for j_ in range(j + 1, len(J_)):
g.add_edge(J_[j], J_[j_])
j_to_i_mapping[J_[-1]].add(i)
def get_neighbors(nodes):
neighbors = set()
for node in nodes:
neighbors.update(g.neighbors(node))
neighbors.update(nodes)
return neighbors
def U(m_):
u_=set()
for i in xrange(m.shape[0]):
ok = True
K_ = _get_indices(m, i)
for j in K_:
ok &= j in m_
if ok:
u_.add(i)
return u_
self._m = [set(initial_cols) | get_neighbors(set(initial_cols))]
self._s = [set()]
self._u = [set()]
i = len(self._m)
J = get_neighbors(self._m[i - 1])
while True:
M_ = J - self._m[i - 1] - self._s[i - 1]
if not len(M_):
break
T = get_neighbors(M_)
J_ = T - M_
self._m.append(M_)
self._u.append(set())
self._s.append(J_ & J)
self._m[i - 1] -= self._s[i]
J = T
i += 1
for j in range(i):
current= self._m[j] | self._s[j]
if j+1 < i:
current.update(self._s[j+1])
self._u[j] = U(current)
tree = DecompositionTree(self._model)
for j in range(m.shape[1]):
self._p.append(j)
self._used.append(0)
self._used2.append(0)
self._layers=[]
self._layermodel=[]
for j in range(i):
self._layers.append([])
self._layermodel.append([])
for j in range(i-1,-1,-1):
current=self._m[j] | self._s[j]
separator=set() | self._s[j]
if j+1<i:
current.update(self._s[j+1])
separator.update(self._s[j+1])
for k in current:
self._used[k]=1
for k in current - separator:
T=get_neighbors([k])
for _k in T:
if self._used[_k]:
self.unite_components(k,_k)
for k in current:
if not self._used2[k]:
self._layers[j].append(set())
for _k in current:
if self.get_component(k)==self.get_component(_k):
self._layers[j][-1].add(_k)
self._used2[_k]=1
u=U(self._layers[j][-1])
self._layermodel[j].append(self._model.slice(u, self._layers[j][-1]))
tree.add_node(self._layermodel[j][-1])
if j!=i-1:
for _k in range(len(self._layers[j+1])):
if len( self._layers[j][-1] & self._layers[j+1][_k] )>0:
tree.add_edge(self._layermodel[j][-1], self._layermodel[j+1][_k],
[self.get_model().get_columns_names()[i] for i in self._layers[j][-1] & self._layers[j+1][_k]])
for k in separator:
T=get_neighbors([k])
for _k in T:
if self._used[_k]:
self.unite_components(k,_k)
tree.set_root(self._layermodel[0][0])
self._decomposition_tree = tree
def modify(self):
list_of_models = []
names = []
tmpbool = True
for node in self._tree.get_nodes():
m = node.get_model()
names.append(node.get_name())
kmodel = knapsack_model.KnapsackModel(m)
kmodel.mp_model_to_knapsack(m)
new_model = mp_model_builder.MPModelBuilder().build_from_scratch(kmodel.get_profits(), [kmodel.get_weights()], ['L'], [kmodel.get_max_weight()], ["new_model"], [0 for j in range(m.get_num_columns())], [1 for j in range(m.get_num_columns())], m.columns_names)
new_model.set_objective(kmodel.get_profits())
new_model.set_name(m.get_name())
list_of_models.append(new_model)
G = nx.DiGraph(self._tree)
for edge in dfs_edges(G, self._tree.get_root()):
fnode = edge[0]
snode = edge[1]
for i in range(len(names)):
if names[i] == fnode:
fmodel = list_of_models[i]
elif names[i] == snode:
smodel = list_of_models[i]
if tmpbool:
new_tree = DecompositionTree(self._tree._model)
root = Node(fmodel)
new_tree.set_root(root)
tmpbool = False
fbool = False
if not fmodel in new_tree.get_models():
new_tree.add_node(fmodel)
fbool = True
if not smodel in new_tree.get_models():
new_tree.add_node(smodel)
fbool = True
if fbool:
shared_vars_names = []
fvars = fmodel.columns_names
svars = smodel.columns_names
for p in fvars:
if p in svars:
shared_vars_names.append(p)
new_tree.add_edge(fmodel, smodel, shared_vars_names)
return new_tree
