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Rename features.variables to variables_old; update FeatureExtractor

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This commit is contained in:
Alinson S. Xavier
2021-04-15 06:54:27 -05:00
parent 08f0bedbe0
commit fec0113722
9 changed files with 150 additions and 173 deletions
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26
miplearn/components/primal.py
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@@ -104,7 +104,7 @@ class PrimalSolutionComponent(Component):
def sample_predict(self, sample: Sample) -> Solution:
assert sample.after_load is not None
assert sample.after_load.variables is not None
assert sample.after_load.variables_old is not None
# Compute y_pred
x, _ = self.sample_xy(None, sample)
@@ -125,9 +125,9 @@ class PrimalSolutionComponent(Component):
).T
# Convert y_pred into solution
solution: Solution = {v: None for v in sample.after_load.variables.keys()}
solution: Solution = {v: None for v in sample.after_load.variables_old.keys()}
category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
for (var_name, var_features) in sample.after_load.variables.items():
for (var_name, var_features) in sample.after_load.variables_old.items():
category = var_features.category
if category not in category_offset:
continue
@@ -150,8 +150,8 @@ class PrimalSolutionComponent(Component):
y: Dict = {}
assert sample.after_load is not None
assert sample.after_load.instance is not None
assert sample.after_load.variables is not None
for (var_name, var) in sample.after_load.variables.items():
assert sample.after_load.variables_old is not None
for (var_name, var) in sample.after_load.variables_old.items():
# Initialize categories
category = var.category
if category is None:
@@ -162,17 +162,17 @@ class PrimalSolutionComponent(Component):
# Features
features = list(sample.after_load.instance.to_list())
features.extend(sample.after_load.variables[var_name].to_list())
features.extend(sample.after_load.variables_old[var_name].to_list())
if sample.after_lp is not None:
assert sample.after_lp.variables is not None
features.extend(sample.after_lp.variables[var_name].to_list())
assert sample.after_lp.variables_old is not None
features.extend(sample.after_lp.variables_old[var_name].to_list())
x[category].append(features)
# Labels
if sample.after_mip is not None:
assert sample.after_mip.variables is not None
assert sample.after_mip.variables[var_name] is not None
opt_value = sample.after_mip.variables[var_name].value
assert sample.after_mip.variables_old is not None
assert sample.after_mip.variables_old[var_name] is not None
opt_value = sample.after_mip.variables_old[var_name].value
assert opt_value is not None
assert 0.0 - 1e-5 <= opt_value <= 1.0 + 1e-5, (
f"Variable {var_name} has non-binary value {opt_value} in the "
@@ -190,9 +190,9 @@ class PrimalSolutionComponent(Component):
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
assert sample.after_mip is not None
assert sample.after_mip.variables is not None
assert sample.after_mip.variables_old is not None
solution_actual = sample.after_mip.variables
solution_actual = sample.after_mip.variables_old
solution_pred = self.sample_predict(sample)
vars_all, vars_one, vars_zero = set(), set(), set()
pred_one_positive, pred_zero_positive = set(), set()

67
miplearn/features.py
View File

@@ -34,7 +34,7 @@ class InstanceFeatures:
class VariableFeatures:
names: Optional[Tuple[str, ...]] = None
basis_status: Optional[Tuple[str, ...]] = None
categories: Optional[Tuple[Hashable, ...]] = None
categories: Optional[Tuple[Optional[Hashable], ...]] = None
lower_bounds: Optional[Tuple[float, ...]] = None
obj_coeffs: Optional[Tuple[float, ...]] = None
reduced_costs: Optional[Tuple[float, ...]] = None
@@ -46,7 +46,7 @@ class VariableFeatures:
sa_ub_up: Optional[Tuple[float, ...]] = None
types: Optional[Tuple[str, ...]] = None
upper_bounds: Optional[Tuple[float, ...]] = None
user_features: Optional[Tuple[Tuple[float, ...]]] = None
user_features: Optional[Tuple[Optional[Tuple[float, ...]], ...]] = None
values: Optional[Tuple[float, ...]] = None
@@ -135,7 +135,8 @@ class Constraint:
@dataclass
class Features:
instance: Optional[InstanceFeatures] = None
variables: Optional[Dict[str, Variable]] = None
variables: Optional[VariableFeatures] = None
variables_old: Optional[Dict[str, Variable]] = None
constraints: Optional[Dict[str, Constraint]] = None
lp_solve: Optional["LPSolveStats"] = None
mip_solve: Optional["MIPSolveStats"] = None
@@ -153,8 +154,10 @@ class FeaturesExtractor:
def __init__(
self,
internal_solver: "InternalSolver",
with_sa: bool = True,
) -> None:
self.solver = internal_solver
self.with_sa = with_sa
def extract(
self,
@@ -162,7 +165,11 @@ class FeaturesExtractor:
with_static: bool = True,
) -> Features:
features = Features()
features.variables = self.solver.get_variables_old(
features.variables = self.solver.get_variables(
with_static=with_static,
with_sa=self.with_sa,
)
features.variables_old = self.solver.get_variables_old(
with_static=with_static,
)
features.constraints = self.solver.get_constraints(
@@ -170,18 +177,19 @@ class FeaturesExtractor:
)
if with_static:
self._extract_user_features_vars(instance, features)
self._extract_user_features_vars_old(instance, features)
self._extract_user_features_constrs(instance, features)
self._extract_user_features_instance(instance, features)
self._extract_alvarez_2017(features)
return features
def _extract_user_features_vars(
def _extract_user_features_vars_old(
self,
instance: "Instance",
features: Features,
) -> None:
assert features.variables is not None
for (var_name, var) in features.variables.items():
assert features.variables_old is not None
for (var_name, var) in features.variables_old.items():
user_features: Optional[List[float]] = None
category: Category = instance.get_variable_category(var_name)
if category is not None:
@@ -206,6 +214,45 @@ class FeaturesExtractor:
var.category = category
var.user_features = user_features
def _extract_user_features_vars(
self,
instance: "Instance",
features: Features,
) -> None:
assert features.variables is not None
assert features.variables.names is not None
categories: List[Hashable] = []
user_features: List[Optional[Tuple[float, ...]]] = []
for (i, var_name) in enumerate(features.variables.names):
category: Hashable = instance.get_variable_category(var_name)
user_features_i: Optional[List[float]] = None
if category is not None:
assert isinstance(category, collections.Hashable), (
f"Variable category must be be hashable. "
f"Found {type(category).__name__} instead for var={var_name}."
)
user_features_i = instance.get_variable_features(var_name)
if isinstance(user_features_i, np.ndarray):
user_features_i = user_features_i.tolist()
assert isinstance(user_features_i, list), (
f"Variable features must be a list. "
f"Found {type(user_features_i).__name__} instead for "
f"var={var_name}."
)
for v in user_features_i:
assert isinstance(v, numbers.Real), (
f"Variable features must be a list of numbers. "
f"Found {type(v).__name__} instead "
f"for var={var_name}."
)
categories.append(category)
if user_features_i is None:
user_features.append(None)
else:
user_features.append(tuple(user_features_i))
features.variables.categories = tuple(categories)
features.variables.user_features = tuple(user_features)
def _extract_user_features_constrs(
self,
instance: "Instance",
@@ -265,18 +312,18 @@ class FeaturesExtractor:
)
def _extract_alvarez_2017(self, features: Features) -> None:
assert features.variables is not None
assert features.variables_old is not None
pos_obj_coeff_sum = 0.0
neg_obj_coeff_sum = 0.0
for (varname, var) in features.variables.items():
for (varname, var) in features.variables_old.items():
if var.obj_coeff is not None:
if var.obj_coeff > 0:
pos_obj_coeff_sum += var.obj_coeff
if var.obj_coeff < 0:
neg_obj_coeff_sum += -var.obj_coeff
for (varname, var) in features.variables.items():
for (varname, var) in features.variables_old.items():
assert isinstance(var, Variable)
f: List[float] = []
if var.obj_coeff is not None:

5
miplearn/solvers/gurobi.py
View File

@@ -393,13 +393,12 @@ class GurobiSolver(InternalSolver):
else:
raise Exception(f"unknown vbasis: {basis_status}")
names, upper_bounds, lower_bounds, types, values = None, None, None, None, None
upper_bounds, lower_bounds, types, values = None, None, None, None
obj_coeffs, reduced_costs, basis_status = None, None, None
sa_obj_up, sa_ub_up, sa_lb_up = None, None, None
sa_obj_down, sa_ub_down, sa_lb_down = None, None, None
if with_static:
names = self._var_names
upper_bounds = self._var_ubs
lower_bounds = self._var_lbs
types = self._var_types
@@ -426,7 +425,7 @@ class GurobiSolver(InternalSolver):
values = tuple(model.getAttr("x", self._gp_vars))
return VariableFeatures(
names=names,
names=self._var_names,
upper_bounds=upper_bounds,
lower_bounds=lower_bounds,
types=types,

6
miplearn/solvers/pyomo/base.py
View File

@@ -210,13 +210,13 @@ class BasePyomoSolver(InternalSolver):
for idx in var:
v = var[idx]
if with_static:
# Variable name
if idx is None:
names.append(str(var))
else:
names.append(f"{var}[{idx}]")
if with_static:
# Variable type
if v.domain == pyomo.core.Binary:
types.append("B")
@@ -250,7 +250,6 @@ class BasePyomoSolver(InternalSolver):
if self._has_lp_solution or self._has_mip_solution:
values.append(v.value)
names_t: Optional[Tuple[str, ...]] = None
types_t: Optional[Tuple[str, ...]] = None
upper_bounds_t: Optional[Tuple[float, ...]] = None
lower_bounds_t: Optional[Tuple[float, ...]] = None
@@ -259,7 +258,6 @@ class BasePyomoSolver(InternalSolver):
values_t: Optional[Tuple[float, ...]] = None
if with_static:
names_t = tuple(names)
types_t = tuple(types)
upper_bounds_t = tuple(upper_bounds)
lower_bounds_t = tuple(lower_bounds)
@@ -272,7 +270,7 @@ class BasePyomoSolver(InternalSolver):
values_t = tuple(values)
return VariableFeatures(
names=names_t,
names=tuple(names),
types=types_t,
upper_bounds=upper_bounds_t,
lower_bounds=lower_bounds_t,

6
miplearn/solvers/tests/__init__.py
View File

@@ -138,6 +138,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
_filter_attrs(
solver.get_variable_attrs(),
VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
basis_status=("U", "B", "U", "L", "U"),
reduced_costs=(193.615385, 0.0, 187.230769, -23.692308, 13.538462),
sa_lb_down=(-inf, -inf, -inf, -0.111111, -inf),
@@ -200,7 +201,10 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
_round(solver.get_variables(with_static=False)),
_filter_attrs(
solver.get_variable_attrs(),
VariableFeatures(values=(1.0, 0.0, 1.0, 1.0, 61.0)),
VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
values=(1.0, 0.0, 1.0, 1.0, 61.0),
),
),
)

12
tests/components/test_primal.py
View File

@@ -28,7 +28,7 @@ def sample() -> Sample:
sample = Sample(
after_load=Features(
instance=InstanceFeatures(),
variables={
variables_old={
"x[0]": Variable(category="default"),
"x[1]": Variable(category=None),
"x[2]": Variable(category="default"),
@@ -36,7 +36,7 @@ def sample() -> Sample:
},
),
after_lp=Features(
variables={
variables_old={
"x[0]": Variable(),
"x[1]": Variable(),
"x[2]": Variable(),
@@ -44,7 +44,7 @@ def sample() -> Sample:
},
),
after_mip=Features(
variables={
variables_old={
"x[0]": Variable(value=0.0),
"x[1]": Variable(value=1.0),
"x[2]": Variable(value=1.0),
@@ -53,13 +53,13 @@ def sample() -> Sample:
),
)
sample.after_load.instance.to_list = Mock(return_value=[5.0]) # type: ignore
sample.after_lp.variables["x[0]"].to_list = Mock( # type: ignore
sample.after_lp.variables_old["x[0]"].to_list = Mock( # type: ignore
return_value=[0.0, 0.0]
)
sample.after_lp.variables["x[2]"].to_list = Mock( # type: ignore
sample.after_lp.variables_old["x[2]"].to_list = Mock( # type: ignore
return_value=[1.0, 0.0]
)
sample.after_lp.variables["x[3]"].to_list = Mock( # type: ignore
sample.after_lp.variables_old["x[3]"].to_list = Mock( # type: ignore
return_value=[1.0, 1.0]
)
return sample

28
tests/problems/test_tsp.py
View File

@@ -43,13 +43,13 @@ def test_instance() -> None:
assert instance.samples[0].after_mip is not None
features = instance.samples[0].after_mip
assert features is not None
assert features.variables is not None
assert features.variables["x[(0, 1)]"].value == 1.0
assert features.variables["x[(0, 2)]"].value == 0.0
assert features.variables["x[(0, 3)]"].value == 1.0
assert features.variables["x[(1, 2)]"].value == 1.0
assert features.variables["x[(1, 3)]"].value == 0.0
assert features.variables["x[(2, 3)]"].value == 1.0
assert features.variables_old is not None
assert features.variables_old["x[(0, 1)]"].value == 1.0
assert features.variables_old["x[(0, 2)]"].value == 0.0
assert features.variables_old["x[(0, 3)]"].value == 1.0
assert features.variables_old["x[(1, 2)]"].value == 1.0
assert features.variables_old["x[(1, 3)]"].value == 0.0
assert features.variables_old["x[(2, 3)]"].value == 1.0
assert features.mip_solve is not None
assert features.mip_solve.mip_lower_bound == 4.0
assert features.mip_solve.mip_upper_bound == 4.0
@@ -79,12 +79,12 @@ def test_subtour() -> None:
lazy_enforced = features.extra["lazy_enforced"]
assert lazy_enforced is not None
assert len(lazy_enforced) > 0
assert features.variables is not None
assert features.variables["x[(0, 1)]"].value == 1.0
assert features.variables["x[(0, 4)]"].value == 1.0
assert features.variables["x[(1, 2)]"].value == 1.0
assert features.variables["x[(2, 3)]"].value == 1.0
assert features.variables["x[(3, 5)]"].value == 1.0
assert features.variables["x[(4, 5)]"].value == 1.0
assert features.variables_old is not None
assert features.variables_old["x[(0, 1)]"].value == 1.0
assert features.variables_old["x[(0, 4)]"].value == 1.0
assert features.variables_old["x[(1, 2)]"].value == 1.0
assert features.variables_old["x[(2, 3)]"].value == 1.0
assert features.variables_old["x[(3, 5)]"].value == 1.0
assert features.variables_old["x[(4, 5)]"].value == 1.0
solver.fit([instance])
solver.solve(instance)

28
tests/solvers/test_learning_solver.py
View File

@@ -39,12 +39,12 @@ def test_learning_solver(
after_mip = sample.after_mip
assert after_mip is not None
assert after_mip.variables is not None
assert after_mip.variables_old is not None
assert after_mip.mip_solve is not None
assert after_mip.variables["x[0]"].value == 1.0
assert after_mip.variables["x[1]"].value == 0.0
assert after_mip.variables["x[2]"].value == 1.0
assert after_mip.variables["x[3]"].value == 1.0
assert after_mip.variables_old["x[0]"].value == 1.0
assert after_mip.variables_old["x[1]"].value == 0.0
assert after_mip.variables_old["x[2]"].value == 1.0
assert after_mip.variables_old["x[3]"].value == 1.0
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_log is not None
@@ -52,16 +52,16 @@ def test_learning_solver(
after_lp = sample.after_lp
assert after_lp is not None
assert after_lp.variables is not None
assert after_lp.variables_old is not None
assert after_lp.lp_solve is not None
assert after_lp.variables["x[0]"].value is not None
assert after_lp.variables["x[1]"].value is not None
assert after_lp.variables["x[2]"].value is not None
assert after_lp.variables["x[3]"].value is not None
assert round(after_lp.variables["x[0]"].value, 3) == 1.000
assert round(after_lp.variables["x[1]"].value, 3) == 0.923
assert round(after_lp.variables["x[2]"].value, 3) == 1.000
assert round(after_lp.variables["x[3]"].value, 3) == 0.000
assert after_lp.variables_old["x[0]"].value is not None
assert after_lp.variables_old["x[1]"].value is not None
assert after_lp.variables_old["x[2]"].value is not None
assert after_lp.variables_old["x[3]"].value is not None
assert round(after_lp.variables_old["x[0]"].value, 3) == 1.000
assert round(after_lp.variables_old["x[1]"].value, 3) == 0.923
assert round(after_lp.variables_old["x[2]"].value, 3) == 1.000
assert round(after_lp.variables_old["x[3]"].value, 3) == 0.000
assert after_lp.lp_solve.lp_value is not None
assert round(after_lp.lp_solve.lp_value, 3) == 1287.923
assert after_lp.lp_solve.lp_log is not None

133
tests/test_features.py
View File

@@ -7,9 +7,15 @@ from miplearn.features import (
InstanceFeatures,
Variable,
Constraint,
VariableFeatures,
)
from miplearn.solvers.gurobi import GurobiSolver
from miplearn.solvers.tests import assert_equals, _round_variables, _round_constraints
from miplearn.solvers.tests import (
assert_equals,
_round_variables,
_round_constraints,
_round,
)
inf = float("inf")
@@ -22,113 +28,36 @@ def test_knapsack() -> None:
solver.solve_lp()
features = FeaturesExtractor(solver).extract(instance)
assert features.variables is not None
assert features.variables_old is not None
assert features.constraints is not None
assert features.instance is not None
assert_equals(
_round_variables(features.variables),
{
"x[0]": Variable(
basis_status="U",
category="default",
lower_bound=0.0,
obj_coeff=505.0,
reduced_cost=193.615385,
sa_lb_down=-inf,
sa_lb_up=1.0,
sa_obj_down=311.384615,
sa_obj_up=inf,
sa_ub_down=0.913043,
sa_ub_up=2.043478,
type="B",
upper_bound=1.0,
user_features=[23.0, 505.0],
value=1.0,
alvarez_2017=[1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702],
_round(features.variables),
VariableFeatures(
names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
basis_status=("U", "B", "U", "L", "U"),
categories=("default", "default", "default", "default", None),
lower_bounds=(0.0, 0.0, 0.0, 0.0, 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),
sa_lb_down=(-inf, -inf, -inf, -0.111111, -inf),
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_up=(inf, 570.869565, inf, 243.692308, inf),
sa_ub_down=(0.913043, 0.923077, 0.9, 0.0, 43.0),
sa_ub_up=(2.043478, inf, 2.2, inf, 69.0),
types=("B", "B", "B", "B", "C"),
upper_bounds=(1.0, 1.0, 1.0, 1.0, 67.0),
user_features=(
(23.0, 505.0),
(26.0, 352.0),
(20.0, 458.0),
(18.0, 220.0),
None,
),
"x[1]": Variable(
basis_status="B",
category="default",
lower_bound=0.0,
obj_coeff=352.0,
reduced_cost=0.0,
sa_lb_down=-inf,
sa_lb_up=0.923077,
sa_obj_down=317.777778,
sa_obj_up=570.869565,
sa_ub_down=0.923077,
sa_ub_up=inf,
type="B",
upper_bound=1.0,
user_features=[26.0, 352.0],
value=0.923077,
alvarez_2017=[
1.0,
0.229316,
0.0,
0.076923,
1.0,
1.0,
3.532875,
5.388476,
],
values=(1.0, 0.923077, 1.0, 0.0, 67.0),
),
"x[2]": Variable(
basis_status="U",
category="default",
lower_bound=0.0,
obj_coeff=458.0,
reduced_cost=187.230769,
sa_lb_down=-inf,
sa_lb_up=1.0,
sa_obj_down=270.769231,
sa_obj_up=inf,
sa_ub_down=0.9,
sa_ub_up=2.2,
type="B",
upper_bound=1.0,
user_features=[20.0, 458.0],
value=1.0,
alvarez_2017=[1.0, 0.298371, 0.0, 0.0, 1.0, 1.0, 5.232342, 46.051702],
),
"x[3]": Variable(
basis_status="L",
category="default",
lower_bound=0.0,
obj_coeff=220.0,
reduced_cost=-23.692308,
sa_lb_down=-0.111111,
sa_lb_up=1.0,
sa_obj_down=-inf,
sa_obj_up=243.692308,
sa_ub_down=0.0,
sa_ub_up=inf,
type="B",
upper_bound=1.0,
user_features=[18.0, 220.0],
value=0.0,
alvarez_2017=[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
),
"z": Variable(
basis_status="U",
category=None,
lower_bound=0.0,
obj_coeff=0.0,
reduced_cost=13.538462,
sa_lb_down=-inf,
sa_lb_up=67.0,
sa_obj_down=-13.538462,
sa_obj_up=inf,
sa_ub_down=43.0,
sa_ub_up=69.0,
type="C",
upper_bound=67.0,
user_features=None,
value=67.0,
alvarez_2017=[0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
),
},
)
assert_equals(
_round_constraints(features.constraints),