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Remove tuples from VariableFeatures

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master
Alinson S. Xavier 4 years ago
parent fa969cf066
commit f9ac65bf9c
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8 changed files with 98 additions and 111 deletions
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40
miplearn/features.py
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@ -30,22 +30,22 @@ class InstanceFeatures:
@dataclass @dataclass
class VariableFeatures: class VariableFeatures:
names: Optional[Tuple[str, ...]] = None names: Optional[List[str]] = None
basis_status: Optional[Tuple[str, ...]] = None basis_status: Optional[List[str]] = None
categories: Optional[Tuple[Optional[Hashable], ...]] = None categories: Optional[List[Optional[Hashable]]] = None
lower_bounds: Optional[Tuple[float, ...]] = None lower_bounds: Optional[List[float]] = None
obj_coeffs: Optional[Tuple[float, ...]] = None obj_coeffs: Optional[List[float]] = None
reduced_costs: Optional[Tuple[float, ...]] = None reduced_costs: Optional[List[float]] = None
sa_lb_down: Optional[Tuple[float, ...]] = None sa_lb_down: Optional[List[float]] = None
sa_lb_up: Optional[Tuple[float, ...]] = None sa_lb_up: Optional[List[float]] = None
sa_obj_down: Optional[Tuple[float, ...]] = None sa_obj_down: Optional[List[float]] = None
sa_obj_up: Optional[Tuple[float, ...]] = None sa_obj_up: Optional[List[float]] = None
sa_ub_down: Optional[Tuple[float, ...]] = None sa_ub_down: Optional[List[float]] = None
sa_ub_up: Optional[Tuple[float, ...]] = None sa_ub_up: Optional[List[float]] = None
types: Optional[Tuple[str, ...]] = None types: Optional[List[str]] = None
upper_bounds: Optional[Tuple[float, ...]] = None upper_bounds: Optional[List[float]] = None
user_features: Optional[Tuple[Optional[Tuple[float, ...]], ...]] = None user_features: Optional[List[Optional[List[float]]]] = None
values: Optional[Tuple[float, ...]] = None values: Optional[List[float]] = None
# Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based # Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
# approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195. # approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
@ -190,7 +190,7 @@ class FeaturesExtractor:
assert features.variables is not None assert features.variables is not None
assert features.variables.names is not None assert features.variables.names is not None
categories: List[Hashable] = [] categories: List[Hashable] = []
user_features: List[Optional[Tuple[float, ...]]] = [] user_features: List[Optional[List[float]]] = []
for (i, var_name) in enumerate(features.variables.names): for (i, var_name) in enumerate(features.variables.names):
category: Hashable = instance.get_variable_category(var_name) category: Hashable = instance.get_variable_category(var_name)
user_features_i: Optional[List[float]] = None user_features_i: Optional[List[float]] = None
@ -217,9 +217,9 @@ class FeaturesExtractor:
if user_features_i is None: if user_features_i is None:
user_features.append(None) user_features.append(None)
else: else:
user_features.append(tuple(user_features_i)) user_features.append(user_features_i)
features.variables.categories = tuple(categories) features.variables.categories = categories
features.variables.user_features = tuple(user_features) features.variables.user_features = user_features
def _extract_user_features_constrs( def _extract_user_features_constrs(
self, self,

38
miplearn/solvers/gurobi.py
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@ -75,12 +75,12 @@ class GurobiSolver(InternalSolver):
self._cname_to_constr: Dict[str, "gurobipy.Constr"] = {} self._cname_to_constr: Dict[str, "gurobipy.Constr"] = {}
self._gp_vars: Tuple["gurobipy.Var", ...] = tuple() self._gp_vars: Tuple["gurobipy.Var", ...] = tuple()
self._gp_constrs: Tuple["gurobipy.Constr", ...] = tuple() self._gp_constrs: Tuple["gurobipy.Constr", ...] = tuple()
self._var_names: Tuple[str, ...] = tuple() self._var_names: List[str] = []
self._constr_names: Tuple[str, ...] = tuple() self._constr_names: Tuple[str, ...] = tuple()
self._var_types: Tuple[str, ...] = tuple() self._var_types: List[str] = []
self._var_lbs: Tuple[float, ...] = tuple() self._var_lbs: List[float] = []
self._var_ubs: Tuple[float, ...] = tuple() self._var_ubs: List[float] = []
self._var_obj_coeffs: Tuple[float, ...] = tuple() self._var_obj_coeffs: List[float] = []
if self.lazy_cb_frequency == 1: if self.lazy_cb_frequency == 1:
self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL] self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
@ -328,8 +328,8 @@ class GurobiSolver(InternalSolver):
obj_coeffs = self._var_obj_coeffs obj_coeffs = self._var_obj_coeffs
if self._has_lp_solution: if self._has_lp_solution:
reduced_costs = tuple(model.getAttr("rc", self._gp_vars)) reduced_costs = model.getAttr("rc", self._gp_vars)
basis_status = tuple( basis_status = list(
map( map(
_parse_gurobi_vbasis, _parse_gurobi_vbasis,
model.getAttr("vbasis", self._gp_vars), model.getAttr("vbasis", self._gp_vars),
@ -337,15 +337,15 @@ class GurobiSolver(InternalSolver):
) )
if with_sa: if with_sa:
sa_obj_up = tuple(model.getAttr("saobjUp", self._gp_vars)) sa_obj_up = model.getAttr("saobjUp", self._gp_vars)
sa_obj_down = tuple(model.getAttr("saobjLow", self._gp_vars)) sa_obj_down = model.getAttr("saobjLow", self._gp_vars)
sa_ub_up = tuple(model.getAttr("saubUp", self._gp_vars)) sa_ub_up = model.getAttr("saubUp", self._gp_vars)
sa_ub_down = tuple(model.getAttr("saubLow", self._gp_vars)) sa_ub_down = model.getAttr("saubLow", self._gp_vars)
sa_lb_up = tuple(model.getAttr("salbUp", self._gp_vars)) sa_lb_up = model.getAttr("salbUp", self._gp_vars)
sa_lb_down = tuple(model.getAttr("salbLow", self._gp_vars)) sa_lb_down = model.getAttr("salbLow", self._gp_vars)
if model.solCount > 0: if model.solCount > 0:
values = tuple(model.getAttr("x", self._gp_vars)) values = model.getAttr("x", self._gp_vars)
return VariableFeatures( return VariableFeatures(
names=self._var_names, names=self._var_names,
@ -600,12 +600,12 @@ class GurobiSolver(InternalSolver):
self._cname_to_constr = cname_to_constr self._cname_to_constr = cname_to_constr
self._gp_vars = tuple(gp_vars) self._gp_vars = tuple(gp_vars)
self._gp_constrs = tuple(gp_constrs) self._gp_constrs = tuple(gp_constrs)
self._var_names = tuple(var_names) self._var_names = var_names
self._constr_names = tuple(constr_names) self._constr_names = tuple(constr_names)
self._var_types = tuple(var_types) self._var_types = var_types
self._var_lbs = tuple(var_lbs) self._var_lbs = var_lbs
self._var_ubs = tuple(var_ubs) self._var_ubs = var_ubs
self._var_obj_coeffs = tuple(var_obj_coeffs) self._var_obj_coeffs = var_obj_coeffs
def __getstate__(self) -> Dict: def __getstate__(self) -> Dict:
return { return {

37
miplearn/solvers/pyomo/base.py
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@ -337,33 +337,20 @@ class BasePyomoSolver(InternalSolver):
if self._has_lp_solution or self._has_mip_solution: if self._has_lp_solution or self._has_mip_solution:
values.append(v.value) values.append(v.value)
types_t: Optional[Tuple[str, ...]] = None def _none_if_empty(obj: Any) -> Any:
upper_bounds_t: Optional[Tuple[float, ...]] = None if len(obj) == 0:
lower_bounds_t: Optional[Tuple[float, ...]] = None return None
obj_coeffs_t: Optional[Tuple[float, ...]] = None else:
reduced_costs_t: Optional[Tuple[float, ...]] = None return obj
values_t: Optional[Tuple[float, ...]] = None
if with_static:
types_t = tuple(types)
upper_bounds_t = tuple(upper_bounds)
lower_bounds_t = tuple(lower_bounds)
obj_coeffs_t = tuple(obj_coeffs)
if self._has_lp_solution:
reduced_costs_t = tuple(reduced_costs)
if self._has_lp_solution or self._has_mip_solution:
values_t = tuple(values)
return VariableFeatures( return VariableFeatures(
names=tuple(names), names=_none_if_empty(names),
types=types_t, types=_none_if_empty(types),
upper_bounds=upper_bounds_t, upper_bounds=_none_if_empty(upper_bounds),
lower_bounds=lower_bounds_t, lower_bounds=_none_if_empty(lower_bounds),
obj_coeffs=obj_coeffs_t, obj_coeffs=_none_if_empty(obj_coeffs),
reduced_costs=reduced_costs_t, reduced_costs=_none_if_empty(reduced_costs),
values=values_t, values=_none_if_empty(values),
) )
@overrides @overrides

34
miplearn/solvers/tests/__init__.py
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@ -57,11 +57,11 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
assert_equals( assert_equals(
solver.get_variables(), solver.get_variables(),
VariableFeatures( VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"), names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
lower_bounds=(0.0, 0.0, 0.0, 0.0, 0.0), lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
upper_bounds=(1.0, 1.0, 1.0, 1.0, 67.0), upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
types=("B", "B", "B", "B", "C"), types=["B", "B", "B", "B", "C"],
obj_coeffs=(505.0, 352.0, 458.0, 220.0, 0.0), obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
), ),
) )
@ -100,16 +100,16 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
_filter_attrs( _filter_attrs(
solver.get_variable_attrs(), solver.get_variable_attrs(),
VariableFeatures( VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"), names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
basis_status=("U", "B", "U", "L", "U"), basis_status=["U", "B", "U", "L", "U"],
reduced_costs=(193.615385, 0.0, 187.230769, -23.692308, 13.538462), reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
sa_lb_down=(-inf, -inf, -inf, -0.111111, -inf), sa_lb_down=[-inf, -inf, -inf, -0.111111, -inf],
sa_lb_up=(1.0, 0.923077, 1.0, 1.0, 67.0), sa_lb_up=[1.0, 0.923077, 1.0, 1.0, 67.0],
sa_obj_down=(311.384615, 317.777778, 270.769231, -inf, -13.538462), sa_obj_down=[311.384615, 317.777778, 270.769231, -inf, -13.538462],
sa_obj_up=(inf, 570.869565, inf, 243.692308, inf), sa_obj_up=[inf, 570.869565, inf, 243.692308, inf],
sa_ub_down=(0.913043, 0.923077, 0.9, 0.0, 43.0), sa_ub_down=[0.913043, 0.923077, 0.9, 0.0, 43.0],
sa_ub_up=(2.043478, inf, 2.2, inf, 69.0), sa_ub_up=[2.043478, inf, 2.2, inf, 69.0],
values=(1.0, 0.923077, 1.0, 0.0, 67.0), values=[1.0, 0.923077, 1.0, 0.0, 67.0],
), ),
), ),
) )
@ -153,8 +153,8 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
_filter_attrs( _filter_attrs(
solver.get_variable_attrs(), solver.get_variable_attrs(),
VariableFeatures( VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"), names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
values=(1.0, 0.0, 1.0, 1.0, 61.0), values=[1.0, 0.0, 1.0, 1.0, 61.0],
), ),
), ),
) )

8
tests/components/test_primal.py
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@ -29,8 +29,8 @@ def sample() -> Sample:
after_load=Features( after_load=Features(
instance=InstanceFeatures(), instance=InstanceFeatures(),
variables=VariableFeatures( variables=VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]"), names=["x[0]", "x[1]", "x[2]", "x[3]"],
categories=("default", None, "default", "default"), categories=["default", None, "default", "default"],
), ),
), ),
after_lp=Features( after_lp=Features(
@ -38,8 +38,8 @@ def sample() -> Sample:
), ),
after_mip=Features( after_mip=Features(
variables=VariableFeatures( variables=VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]"), names=["x[0]", "x[1]", "x[2]", "x[3]"],
values=(0.0, 1.0, 1.0, 0.0), values=[0.0, 1.0, 1.0, 0.0],
) )
), ),
) )

6
tests/problems/test_tsp.py
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@ -44,7 +44,7 @@ def test_instance() -> None:
features = instance.samples[0].after_mip features = instance.samples[0].after_mip
assert features is not None assert features is not None
assert features.variables is not None assert features.variables is not None
assert features.variables.values == (1.0, 0.0, 1.0, 1.0, 0.0, 1.0) assert features.variables.values == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
assert features.mip_solve is not None assert features.mip_solve is not None
assert features.mip_solve.mip_lower_bound == 4.0 assert features.mip_solve.mip_lower_bound == 4.0
assert features.mip_solve.mip_upper_bound == 4.0 assert features.mip_solve.mip_upper_bound == 4.0
@ -75,7 +75,7 @@ def test_subtour() -> None:
assert lazy_enforced is not None assert lazy_enforced is not None
assert len(lazy_enforced) > 0 assert len(lazy_enforced) > 0
assert features.variables is not None assert features.variables is not None
assert features.variables.values == ( assert features.variables.values == [
1.0, 1.0,
0.0, 0.0,
0.0, 0.0,
@ -91,6 +91,6 @@ def test_subtour() -> None:
0.0, 0.0,
1.0, 1.0,
1.0, 1.0,
) ]
solver.fit([instance]) solver.fit([instance])
solver.solve(instance) solver.solve(instance)

4
tests/solvers/test_learning_solver.py
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@ -41,7 +41,7 @@ def test_learning_solver(
after_mip = sample.after_mip after_mip = sample.after_mip
assert after_mip is not None assert after_mip is not None
assert after_mip.variables is not None assert after_mip.variables is not None
assert after_mip.variables.values == (1.0, 0.0, 1.0, 1.0, 61.0) assert after_mip.variables.values == [1.0, 0.0, 1.0, 1.0, 61.0]
assert after_mip.mip_solve is not None assert after_mip.mip_solve is not None
assert after_mip.mip_solve.mip_lower_bound == 1183.0 assert after_mip.mip_solve.mip_lower_bound == 1183.0
assert after_mip.mip_solve.mip_upper_bound == 1183.0 assert after_mip.mip_solve.mip_upper_bound == 1183.0
@ -51,7 +51,7 @@ def test_learning_solver(
after_lp = sample.after_lp after_lp = sample.after_lp
assert after_lp is not None assert after_lp is not None
assert after_lp.variables is not None assert after_lp.variables is not None
assert _round(after_lp.variables.values) == (1.0, 0.923077, 1.0, 0.0, 67.0) assert _round(after_lp.variables.values) == [1.0, 0.923077, 1.0, 0.0, 67.0]
assert after_lp.lp_solve is not None assert after_lp.lp_solve is not None
assert after_lp.lp_solve.lp_value is not None assert after_lp.lp_solve.lp_value is not None
assert round(after_lp.lp_solve.lp_value, 3) == 1287.923 assert round(after_lp.lp_solve.lp_value, 3) == 1287.923

42
tests/test_features.py
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@ -31,28 +31,28 @@ def test_knapsack() -> None:
assert_equals( assert_equals(
_round(features.variables), _round(features.variables),
VariableFeatures( VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"), names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
basis_status=("U", "B", "U", "L", "U"), basis_status=["U", "B", "U", "L", "U"],
categories=("default", "default", "default", "default", None), categories=["default", "default", "default", "default", None],
lower_bounds=(0.0, 0.0, 0.0, 0.0, 0.0), lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
obj_coeffs=(505.0, 352.0, 458.0, 220.0, 0.0), obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
reduced_costs=(193.615385, 0.0, 187.230769, -23.692308, 13.538462), reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
sa_lb_down=(-inf, -inf, -inf, -0.111111, -inf), sa_lb_down=[-inf, -inf, -inf, -0.111111, -inf],
sa_lb_up=(1.0, 0.923077, 1.0, 1.0, 67.0), sa_lb_up=[1.0, 0.923077, 1.0, 1.0, 67.0],
sa_obj_down=(311.384615, 317.777778, 270.769231, -inf, -13.538462), sa_obj_down=[311.384615, 317.777778, 270.769231, -inf, -13.538462],
sa_obj_up=(inf, 570.869565, inf, 243.692308, inf), sa_obj_up=[inf, 570.869565, inf, 243.692308, inf],
sa_ub_down=(0.913043, 0.923077, 0.9, 0.0, 43.0), sa_ub_down=[0.913043, 0.923077, 0.9, 0.0, 43.0],
sa_ub_up=(2.043478, inf, 2.2, inf, 69.0), sa_ub_up=[2.043478, inf, 2.2, inf, 69.0],
types=("B", "B", "B", "B", "C"), types=["B", "B", "B", "B", "C"],
upper_bounds=(1.0, 1.0, 1.0, 1.0, 67.0), upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
user_features=( user_features=[
(23.0, 505.0), [23.0, 505.0],
(26.0, 352.0), [26.0, 352.0],
(20.0, 458.0), [20.0, 458.0],
(18.0, 220.0), [18.0, 220.0],
None, None,
), ],
values=(1.0, 0.923077, 1.0, 0.0, 67.0), values=[1.0, 0.923077, 1.0, 0.0, 67.0],
alvarez_2017=[ alvarez_2017=[
[1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702], [1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702],
[1.0, 0.229316, 0.0, 0.076923, 1.0, 1.0, 3.532875, 5.388476], [1.0, 0.229316, 0.0, 0.076923, 1.0, 1.0, 3.532875, 5.388476],

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