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Small fixes to Alvarez2017 features

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This commit is contained in:
Alinson S. Xavier
2021-04-11 08:03:17 -05:00
parent c39231cb18
commit d85a63f869
2 changed files with 91 additions and 64 deletions
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119
miplearn/features.py
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@@ -58,6 +58,28 @@ class Variable:
# approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195. # approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
alvarez_2017: Optional[List[float]] = None alvarez_2017: Optional[List[float]] = None
def to_list(self) -> List[float]:
features: List[float] = []
for attr in [
"lower_bound",
"obj_coeff",
"reduced_cost",
"sa_lb_down",
"sa_lb_up",
"sa_obj_down",
"sa_obj_up",
"sa_ub_down",
"sa_ub_up",
"upper_bound",
"value",
]:
if getattr(self, attr) is not None:
features.append(getattr(self, attr))
for attr in ["user_features", "alvarez_2017"]:
if getattr(self, attr) is not None:
features.extend(getattr(self, attr))
return features
@dataclass @dataclass
class Constraint: class Constraint:
@@ -88,16 +110,23 @@ class FeaturesExtractor:
) -> None: ) -> None:
self.solver = internal_solver self.solver = internal_solver
def extract(self, instance: "Instance") -> None: def extract(self, instance: "Instance") -> Features:
instance.features.variables = self.solver.get_variables() features = Features()
instance.features.constraints = self.solver.get_constraints() features.variables = self.solver.get_variables()
self._extract_user_features_vars(instance) features.constraints = self.solver.get_constraints()
self._extract_user_features_constrs(instance) self._extract_user_features_vars(instance, features)
self._extract_user_features_instance(instance) self._extract_user_features_constrs(instance, features)
self._extract_alvarez_2017(instance) self._extract_user_features_instance(instance, features)
self._extract_alvarez_2017(features)
return features
def _extract_user_features_vars(self, instance: "Instance"): def _extract_user_features_vars(
for (var_name, var) in instance.features.variables.items(): self,
instance: "Instance",
features: Features,
) -> None:
assert features.variables is not None
for (var_name, var) in features.variables.items():
user_features: Optional[List[float]] = None user_features: Optional[List[float]] = None
category: Category = instance.get_variable_category(var_name) category: Category = instance.get_variable_category(var_name)
if category is not None: if category is not None:
@@ -122,9 +151,14 @@ class FeaturesExtractor:
var.category = category var.category = category
var.user_features = user_features var.user_features = user_features
def _extract_user_features_constrs(self, instance: "Instance"): def _extract_user_features_constrs(
self,
instance: "Instance",
features: Features,
) -> None:
assert features.constraints is not None
has_static_lazy = instance.has_static_lazy_constraints() has_static_lazy = instance.has_static_lazy_constraints()
for (cid, constr) in instance.features.constraints.items(): for (cid, constr) in features.constraints.items():
user_features = None user_features = None
category = instance.get_constraint_category(cid) category = instance.get_constraint_category(cid)
if category is not None: if category is not None:
@@ -148,8 +182,12 @@ class FeaturesExtractor:
constr.category = category constr.category = category
constr.user_features = user_features constr.user_features = user_features
def _extract_user_features_instance(self, instance: "Instance"): def _extract_user_features_instance(
assert instance.features.constraints is not None self,
instance: "Instance",
features: Features,
) -> None:
assert features.constraints is not None
user_features = instance.get_instance_features() user_features = instance.get_instance_features()
if isinstance(user_features, np.ndarray): if isinstance(user_features, np.ndarray):
user_features = user_features.tolist() user_features = user_features.tolist()
@@ -163,49 +201,48 @@ class FeaturesExtractor:
f"Found {type(v).__name__} instead." f"Found {type(v).__name__} instead."
) )
lazy_count = 0 lazy_count = 0
for (cid, cdict) in instance.features.constraints.items(): for (cid, cdict) in features.constraints.items():
if cdict.lazy: if cdict.lazy:
lazy_count += 1 lazy_count += 1
instance.features.instance = InstanceFeatures( features.instance = InstanceFeatures(
user_features=user_features, user_features=user_features,
lazy_constraint_count=lazy_count, lazy_constraint_count=lazy_count,
) )
def _extract_alvarez_2017(self, instance: "Instance"): def _extract_alvarez_2017(self, features: Features) -> None:
assert instance.features is not None assert features.variables is not None
assert instance.features.variables is not None
pos_obj_coeff_sum = 0.0 pos_obj_coeff_sum = 0.0
neg_obj_coeff_sum = 0.0 neg_obj_coeff_sum = 0.0
for (varname, var) in instance.features.variables.items(): for (varname, var) in features.variables.items():
if var.obj_coeff is not None: if var.obj_coeff is not None:
if var.obj_coeff > 0: if var.obj_coeff > 0:
pos_obj_coeff_sum += var.obj_coeff pos_obj_coeff_sum += var.obj_coeff
if var.obj_coeff < 0: if var.obj_coeff < 0:
neg_obj_coeff_sum += -var.obj_coeff neg_obj_coeff_sum += -var.obj_coeff
for (varname, var) in instance.features.variables.items(): for (varname, var) in features.variables.items():
assert isinstance(var, Variable) assert isinstance(var, Variable)
features = [] f: List[float] = []
if var.obj_coeff is not None: if var.obj_coeff is not None:
# Feature 1 # Feature 1
features.append(np.sign(var.obj_coeff)) f.append(np.sign(var.obj_coeff))
# Feature 2 # Feature 2
if pos_obj_coeff_sum > 0: if pos_obj_coeff_sum > 0:
features.append(abs(var.obj_coeff) / pos_obj_coeff_sum) f.append(abs(var.obj_coeff) / pos_obj_coeff_sum)
else: else:
features.append(0.0) f.append(0.0)
# Feature 3 # Feature 3
if neg_obj_coeff_sum > 0: if neg_obj_coeff_sum > 0:
features.append(abs(var.obj_coeff) / neg_obj_coeff_sum) f.append(abs(var.obj_coeff) / neg_obj_coeff_sum)
else: else:
features.append(0.0) f.append(0.0)
if var.value is not None: if var.value is not None:
# Feature 37 # Feature 37
features.append( f.append(
min( min(
var.value - np.floor(var.value), var.value - np.floor(var.value),
np.ceil(var.value) - var.value, np.ceil(var.value) - var.value,
@@ -213,25 +250,29 @@ class FeaturesExtractor:
) )
if var.sa_obj_up is not None: if var.sa_obj_up is not None:
assert var.obj_coeff is not None
assert var.sa_obj_down is not None assert var.sa_obj_down is not None
csign = np.sign(var.obj_coeff) # Convert inf into large finite numbers
sa_obj_down = max(-1e20, var.sa_obj_down)
sa_obj_up = min(1e20, var.sa_obj_up)
# Features 44 and 46 # Features 44 and 46
features.append(np.sign(var.sa_obj_up)) f.append(np.sign(var.sa_obj_up))
features.append(np.sign(var.sa_obj_down)) f.append(np.sign(var.sa_obj_down))
# Feature 47 # Feature 47
f47 = log((var.obj_coeff - var.sa_obj_down) / csign) csign = np.sign(var.obj_coeff)
if isfinite(f47): if csign != 0 and ((var.obj_coeff - sa_obj_down) / csign) > 0.001:
features.append(f47) f.append(log((var.obj_coeff - sa_obj_down) / csign))
else: else:
features.append(0.0) f.append(0.0)
# Feature 48 # Feature 48
f48 = log((var.sa_obj_up - var.obj_coeff) / csign) if csign != 0 and ((sa_obj_up - var.obj_coeff) / csign) > 0.001:
if isfinite(f48): f.append(log((sa_obj_up - var.obj_coeff) / csign))
features.append(f48)
else: else:
features.append(0.0) f.append(0.0)
var.alvarez_2017 = features for v in f:
assert isfinite(v), f"non-finite elements detected: {f}"
var.alvarez_2017 = f

36
tests/test_features.py
View File

@@ -21,9 +21,13 @@ def test_knapsack() -> None:
solver.set_instance(instance, model) solver.set_instance(instance, model)
solver.solve_lp() solver.solve_lp()
FeaturesExtractor(solver).extract(instance) features = FeaturesExtractor(solver).extract(instance)
assert features.variables is not None
assert features.constraints is not None
assert features.instance is not None
assert_equals( assert_equals(
_round_variables(instance.features.variables), _round_variables(features.variables),
{ {
"x[0]": Variable( "x[0]": Variable(
basis_status="U", basis_status="U",
@@ -41,7 +45,7 @@ def test_knapsack() -> None:
upper_bound=1.0, upper_bound=1.0,
user_features=[23.0, 505.0], user_features=[23.0, 505.0],
value=1.0, value=1.0,
alvarez_2017=[1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 0.0], alvarez_2017=[1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702],
), ),
"x[1]": Variable( "x[1]": Variable(
basis_status="B", basis_status="B",
@@ -86,16 +90,7 @@ def test_knapsack() -> None:
upper_bound=1.0, upper_bound=1.0,
user_features=[20.0, 458.0], user_features=[20.0, 458.0],
value=1.0, value=1.0,
alvarez_2017=[ alvarez_2017=[1.0, 0.298371, 0.0, 0.0, 1.0, 1.0, 5.232342, 46.051702],
1.0,
0.298371,
0.0,
0.0,
1.0,
1.0,
5.232342,
0.0,
],
), ),
"x[3]": Variable( "x[3]": Variable(
basis_status="L", basis_status="L",
@@ -113,21 +108,12 @@ def test_knapsack() -> None:
upper_bound=1.0, upper_bound=1.0,
user_features=[18.0, 220.0], user_features=[18.0, 220.0],
value=0.0, value=0.0,
alvarez_2017=[ alvarez_2017=[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
1.0,
0.143322,
0.0,
0.0,
1.0,
-1.0,
0.0,
3.16515,
],
), ),
}, },
) )
assert_equals( assert_equals(
_round_constraints(instance.features.constraints), _round_constraints(features.constraints),
{ {
"eq_capacity": Constraint( "eq_capacity": Constraint(
basis_status="N", basis_status="N",
@@ -145,7 +131,7 @@ def test_knapsack() -> None:
}, },
) )
assert_equals( assert_equals(
instance.features.instance, features.instance,
InstanceFeatures( InstanceFeatures(
user_features=[67.0, 21.75], user_features=[67.0, 21.75],
lazy_constraint_count=0, lazy_constraint_count=0,