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Extract instance, var and constr features into sample

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master
Alinson S. Xavier 4 years ago
parent 061b1349fe
commit 7c4c301611
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3 changed files with 427 additions and 73 deletions
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326
miplearn/features.py
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@ -4,6 +4,7 @@
import collections
import numbers
from copy import copy
from dataclasses import dataclass
from math import log, isfinite
from typing import TYPE_CHECKING, Dict, Optional, List, Hashable, Tuple, Any
@ -176,6 +177,95 @@ class FeaturesExtractor:
self.with_sa = with_sa
self.with_lhs = with_lhs
def extract_after_load_features(
self,
instance: "Instance",
solver: "InternalSolver",
sample: Sample,
) -> None:
variables = solver.get_variables(with_static=True)
constraints = solver.get_constraints(with_static=True, with_lhs=self.with_lhs)
sample.put("var_lower_bounds", variables.lower_bounds)
sample.put("var_names", variables.names)
sample.put("var_obj_coeffs", variables.obj_coeffs)
sample.put("var_types", variables.types)
sample.put("var_upper_bounds", variables.upper_bounds)
sample.put("constr_names", constraints.names)
sample.put("constr_lhs", constraints.lhs)
sample.put("constr_rhs", constraints.rhs)
sample.put("constr_senses", constraints.senses)
self._extract_user_features_vars(instance, sample)
self._extract_user_features_constrs(instance, sample)
self._extract_user_features_instance(instance, sample)
self._extract_var_features_AlvLouWeh2017(sample)
sample.put(
"var_features",
self._combine(
sample,
[
"var_features_AlvLouWeh2017",
"var_features_user",
"var_lower_bounds",
"var_obj_coeffs",
"var_upper_bounds",
],
),
)
def extract_after_lp_features(
self,
solver: "InternalSolver",
sample: Sample,
) -> None:
variables = solver.get_variables(with_static=False, with_sa=self.with_sa)
constraints = solver.get_constraints(with_static=False, with_sa=self.with_sa)
sample.put("lp_var_basis_status", variables.basis_status)
sample.put("lp_var_reduced_costs", variables.reduced_costs)
sample.put("lp_var_sa_lb_down", variables.sa_lb_down)
sample.put("lp_var_sa_lb_up", variables.sa_lb_up)
sample.put("lp_var_sa_obj_down", variables.sa_obj_down)
sample.put("lp_var_sa_obj_up", variables.sa_obj_up)
sample.put("lp_var_sa_ub_down", variables.sa_ub_down)
sample.put("lp_var_sa_ub_up", variables.sa_ub_up)
sample.put("lp_var_values", variables.values)
sample.put("lp_constr_basis_status", constraints.basis_status)
sample.put("lp_constr_dual_values", constraints.dual_values)
sample.put("lp_constr_sa_rhs_down", constraints.sa_rhs_down)
sample.put("lp_constr_sa_rhs_up", constraints.sa_rhs_up)
sample.put("lp_constr_slacks", constraints.slacks)
self._extract_var_features_AlvLouWeh2017(sample, prefix="lp_")
sample.put(
"lp_var_features",
self._combine(
sample,
[
"lp_var_features_AlvLouWeh2017",
"lp_var_reduced_costs",
"lp_var_sa_lb_down",
"lp_var_sa_lb_up",
"lp_var_sa_obj_down",
"lp_var_sa_obj_up",
"lp_var_sa_ub_down",
"lp_var_sa_ub_up",
"lp_var_values",
"var_features_user",
"var_lower_bounds",
"var_obj_coeffs",
"var_upper_bounds",
],
),
)
def extract_after_mip_features(
self,
solver: "InternalSolver",
sample: Sample,
) -> None:
variables = solver.get_variables(with_static=False, with_sa=False)
constraints = solver.get_constraints(with_static=False, with_sa=False)
sample.put("mip_var_values", variables.values)
sample.put("mip_constr_slacks", constraints.slacks)
def extract(
self,
instance: "Instance",
@ -193,13 +283,107 @@ class FeaturesExtractor:
with_lhs=self.with_lhs,
)
if with_static:
self._extract_user_features_vars(instance, features)
self._extract_user_features_constrs(instance, features)
self._extract_user_features_instance(instance, features)
self._extract_alvarez_2017(features)
self._extract_user_features_vars_old(instance, features)
self._extract_user_features_constrs_old(instance, features)
self._extract_user_features_instance_old(instance, features)
self._extract_alvarez_2017_old(features)
return features
def _extract_user_features_vars(
self,
instance: "Instance",
sample: Sample,
) -> None:
categories: List[Optional[Hashable]] = []
user_features: List[Optional[List[float]]] = []
var_features_dict = instance.get_variable_features()
var_categories_dict = instance.get_variable_categories()
var_names = sample.get("var_names")
assert var_names is not None
for (i, var_name) in enumerate(var_names):
if var_name not in var_categories_dict:
user_features.append(None)
categories.append(None)
continue
category: Hashable = var_categories_dict[var_name]
assert isinstance(category, collections.Hashable), (
f"Variable category must be be hashable. "
f"Found {type(category).__name__} instead for var={var_name}."
)
categories.append(category)
user_features_i: Optional[List[float]] = None
if var_name in var_features_dict:
user_features_i = var_features_dict[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}."
)
user_features_i = list(user_features_i)
user_features.append(user_features_i)
sample.put("var_categories", categories)
sample.put("var_features_user", user_features)
def _extract_user_features_constrs(
self,
instance: "Instance",
sample: Sample,
) -> None:
has_static_lazy = instance.has_static_lazy_constraints()
user_features: List[Optional[List[float]]] = []
categories: List[Optional[Hashable]] = []
lazy: List[bool] = []
constr_categories_dict = instance.get_constraint_categories()
constr_features_dict = instance.get_constraint_features()
constr_names = sample.get("constr_names")
assert constr_names is not None
for (cidx, cname) in enumerate(constr_names):
category: Optional[Hashable] = cname
if cname in constr_categories_dict:
category = constr_categories_dict[cname]
if category is None:
user_features.append(None)
categories.append(None)
continue
assert isinstance(category, collections.Hashable), (
f"Constraint category must be hashable. "
f"Found {type(category).__name__} instead for cname={cname}.",
)
categories.append(category)
cf: Optional[List[float]] = None
if cname in constr_features_dict:
cf = constr_features_dict[cname]
if isinstance(cf, np.ndarray):
cf = cf.tolist()
assert isinstance(cf, list), (
f"Constraint features must be a list. "
f"Found {type(cf).__name__} instead for cname={cname}."
)
for f in cf:
assert isinstance(f, numbers.Real), (
f"Constraint features must be a list of numbers. "
f"Found {type(f).__name__} instead for cname={cname}."
)
cf = list(cf)
user_features.append(cf)
if has_static_lazy:
lazy.append(instance.is_constraint_lazy(cname))
else:
lazy.append(False)
sample.put("constr_features_user", user_features)
sample.put("constr_lazy", lazy)
sample.put("constr_categories", categories)
def _extract_user_features_vars_old(
self,
instance: "Instance",
features: Features,
@ -243,7 +427,7 @@ class FeaturesExtractor:
features.variables.categories = categories
features.variables.user_features = user_features
def _extract_user_features_constrs(
def _extract_user_features_constrs_old(
self,
instance: "Instance",
features: Features,
@ -295,6 +479,28 @@ class FeaturesExtractor:
features.constraints.categories = categories
def _extract_user_features_instance(
self,
instance: "Instance",
sample: Sample,
) -> None:
user_features = instance.get_instance_features()
if isinstance(user_features, np.ndarray):
user_features = user_features.tolist()
assert isinstance(user_features, list), (
f"Instance features must be a list. "
f"Found {type(user_features).__name__} instead."
)
for v in user_features:
assert isinstance(v, numbers.Real), (
f"Instance features must be a list of numbers. "
f"Found {type(v).__name__} instead."
)
constr_lazy = sample.get("constr_lazy")
assert constr_lazy is not None
sample.put("instance_features_user", user_features)
sample.put("static_lazy_count", sum(constr_lazy))
def _extract_user_features_instance_old(
self,
instance: "Instance",
features: Features,
@ -318,7 +524,7 @@ class FeaturesExtractor:
lazy_constraint_count=sum(features.constraints.lazy),
)
def _extract_alvarez_2017(self, features: Features) -> None:
def _extract_alvarez_2017_old(self, features: Features) -> None:
assert features.variables is not None
assert features.variables.names is not None
@ -394,6 +600,114 @@ class FeaturesExtractor:
assert isfinite(v), f"non-finite elements detected: {f}"
features.variables.alvarez_2017.append(f)
# Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
# approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
def _extract_var_features_AlvLouWeh2017(
self,
sample: Sample,
prefix: str = "",
) -> None:
obj_coeffs = sample.get("var_obj_coeffs")
obj_sa_down = sample.get("lp_var_sa_obj_down")
obj_sa_up = sample.get("lp_var_sa_obj_up")
values = sample.get(f"lp_var_values")
assert obj_coeffs is not None
pos_obj_coeff_sum = 0.0
neg_obj_coeff_sum = 0.0
for coeff in obj_coeffs:
if coeff > 0:
pos_obj_coeff_sum += coeff
if coeff < 0:
neg_obj_coeff_sum += -coeff
features = []
for i in range(len(obj_coeffs)):
f: List[float] = []
if obj_coeffs is not None:
# Feature 1
f.append(np.sign(obj_coeffs[i]))
# Feature 2
if pos_obj_coeff_sum > 0:
f.append(abs(obj_coeffs[i]) / pos_obj_coeff_sum)
else:
f.append(0.0)
# Feature 3
if neg_obj_coeff_sum > 0:
f.append(abs(obj_coeffs[i]) / neg_obj_coeff_sum)
else:
f.append(0.0)
if values is not None:
# Feature 37
f.append(
min(
values[i] - np.floor(values[i]),
np.ceil(values[i]) - values[i],
)
)
if obj_sa_up is not None:
assert obj_sa_down is not None
assert obj_coeffs is not None
# Convert inf into large finite numbers
sd = max(-1e20, obj_sa_down[i])
su = min(1e20, obj_sa_up[i])
obj = obj_coeffs[i]
# Features 44 and 46
f.append(np.sign(obj_sa_up[i]))
f.append(np.sign(obj_sa_down[i]))
# Feature 47
csign = np.sign(obj)
if csign != 0 and ((obj - sd) / csign) > 0.001:
f.append(log((obj - sd) / csign))
else:
f.append(0.0)
# Feature 48
if csign != 0 and ((su - obj) / csign) > 0.001:
f.append(log((su - obj) / csign))
else:
f.append(0.0)
for v in f:
assert isfinite(v), f"non-finite elements detected: {f}"
features.append(f)
sample.put(f"{prefix}var_features_AlvLouWeh2017", features)
def _combine(
self,
sample: Sample,
attrs: List[str],
) -> List[List[float]]:
combined: List[List[float]] = []
for attr in attrs:
series = sample.get(attr)
if series is None:
continue
if len(combined) == 0:
for i in range(len(series)):
combined.append([])
for (i, s) in enumerate(series):
if s is None:
continue
elif isinstance(s, list):
combined[i].extend([_clipf(sj) for sj in s])
else:
combined[i].append(_clipf(s))
return combined
def _clipf(vi: float) -> float:
if not isfinite(vi):
return max(min(vi, 1e20), -1e20)
return vi
def _clip(v: List[float]) -> None:
for (i, vi) in enumerate(v):

3
miplearn/solvers/learning.py
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@ -168,6 +168,9 @@ class LearningSolver:
# -------------------------------------------------------
logger.info("Extracting features (after-load)...")
initial_time = time.time()
self.extractor.extract_after_load_features(
instance, self.internal_solver, sample
)
features = self.extractor.extract(instance, self.internal_solver)
logger.info(
"Features (after-load) extracted in %.2f seconds"

155
tests/test_features.py
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@ -9,6 +9,7 @@ from miplearn.features import (
InstanceFeatures,
VariableFeatures,
ConstraintFeatures,
Sample,
)
from miplearn.solvers.gurobi import GurobiSolver
from miplearn.solvers.tests import assert_equals
@ -21,55 +22,40 @@ def test_knapsack() -> None:
instance = solver.build_test_instance_knapsack()
model = instance.to_model()
solver.set_instance(instance, model)
solver.solve_lp()
features = FeaturesExtractor().extract(instance, solver)
assert features.variables is not None
assert features.instance is not None
extractor = FeaturesExtractor()
sample = Sample()
# after-load
# -------------------------------------------------------
extractor.extract_after_load_features(instance, solver, sample)
assert_equals(sample.get("var_names"), ["x[0]", "x[1]", "x[2]", "x[3]", "z"])
assert_equals(sample.get("var_lower_bounds"), [0.0, 0.0, 0.0, 0.0, 0.0])
assert_equals(sample.get("var_obj_coeffs"), [505.0, 352.0, 458.0, 220.0, 0.0])
assert_equals(sample.get("var_types"), ["B", "B", "B", "B", "C"])
assert_equals(sample.get("var_upper_bounds"), [1.0, 1.0, 1.0, 1.0, 67.0])
assert_equals(
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,
],
values=[1.0, 0.923077, 1.0, 0.0, 67.0],
alvarez_2017=[
[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, 0.298371, 0.0, 0.0, 1.0, 1.0, 5.232342, 46.051702],
[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
[0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
sample.get("var_categories"),
["default", "default", "default", "default", None],
)
assert_equals(
sample.get("var_features_user"),
[[23.0, 505.0], [26.0, 352.0], [20.0, 458.0], [18.0, 220.0], None],
)
assert_equals(
sample.get("var_features_AlvLouWeh2017"),
[
[1.0, 0.32899, 0.0],
[1.0, 0.229316, 0.0],
[1.0, 0.298371, 0.0],
[1.0, 0.143322, 0.0],
[0.0, 0.0, 0.0],
],
),
)
assert sample.get("var_features") is not None
assert_equals(sample.get("constr_names"), ["eq_capacity"])
assert_equals(
features.constraints,
ConstraintFeatures(
basis_status=["N"],
categories=["eq_capacity"],
dual_values=[13.538462],
names=["eq_capacity"],
lazy=[False],
lhs=[
sample.get("constr_lhs"),
[
[
("x[0]", 23.0),
("x[1]", 26.0),
@ -78,14 +64,71 @@ def test_knapsack() -> None:
("z", -1.0),
],
],
rhs=[0.0],
sa_rhs_down=[-24.0],
sa_rhs_up=[2.0],
senses=["="],
slacks=[0.0],
user_features=[None],
),
)
assert_equals(sample.get("constr_rhs"), [0.0])
assert_equals(sample.get("constr_senses"), ["="])
assert_equals(sample.get("constr_features_user"), [None])
assert_equals(sample.get("constr_categories"), ["eq_capacity"])
assert_equals(sample.get("constr_lazy"), [False])
assert_equals(sample.get("instance_features_user"), [67.0, 21.75])
assert_equals(sample.get("static_lazy_count"), 0)
# after-lp
# -------------------------------------------------------
solver.solve_lp()
extractor.extract_after_lp_features(solver, sample)
assert_equals(
sample.get("lp_var_basis_status"),
["U", "B", "U", "L", "U"],
)
assert_equals(
sample.get("lp_var_reduced_costs"),
[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
)
assert_equals(
sample.get("lp_var_sa_lb_down"),
[-inf, -inf, -inf, -0.111111, -inf],
)
assert_equals(
sample.get("lp_var_sa_lb_up"),
[1.0, 0.923077, 1.0, 1.0, 67.0],
)
assert_equals(
sample.get("lp_var_sa_obj_down"),
[311.384615, 317.777778, 270.769231, -inf, -13.538462],
)
assert_equals(
sample.get("lp_var_sa_obj_up"),
[inf, 570.869565, inf, 243.692308, inf],
)
assert_equals(sample.get("lp_var_sa_ub_down"), [0.913043, 0.923077, 0.9, 0.0, 43.0])
assert_equals(sample.get("lp_var_sa_ub_up"), [2.043478, inf, 2.2, inf, 69.0])
assert_equals(sample.get("lp_var_values"), [1.0, 0.923077, 1.0, 0.0, 67.0])
assert_equals(
sample.get("lp_var_features_AlvLouWeh2017"),
[
[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, 0.298371, 0.0, 0.0, 1.0, 1.0, 5.232342, 46.051702],
[1.0, 0.143322, 0.0, 0.0, 1.0, -1.0, 46.051702, 3.16515],
[0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0],
],
)
assert sample.get("lp_var_features") is not None
assert_equals(sample.get("lp_constr_basis_status"), ["N"])
assert_equals(sample.get("lp_constr_dual_values"), [13.538462])
assert_equals(sample.get("lp_constr_sa_rhs_down"), [-24.0])
assert_equals(sample.get("lp_constr_sa_rhs_up"), [2.0])
assert_equals(sample.get("lp_constr_slacks"), [0.0])
# after-mip
# -------------------------------------------------------
solver.solve()
extractor.extract_after_mip_features(solver, sample)
assert_equals(sample.get("mip_var_values"), [1.0, 0.0, 1.0, 1.0, 61.0])
assert_equals(sample.get("mip_constr_slacks"), [0.0])
features = extractor.extract(instance, solver)
assert_equals(
features.instance,
InstanceFeatures(
@ -138,10 +181,7 @@ def test_constraint_getindex() -> None:
def test_assert_equals() -> None:
assert_equals("hello", "hello")
assert_equals([1.0, 2.0], [1.0, 2.0])
assert_equals(
np.array([1.0, 2.0]),
np.array([1.0, 2.0]),
)
assert_equals(np.array([1.0, 2.0]), np.array([1.0, 2.0]))
assert_equals(
np.array([[1.0, 2.0], [3.0, 4.0]]),
np.array([[1.0, 2.0], [3.0, 4.0]]),
@ -150,9 +190,6 @@ def test_assert_equals() -> None:
VariableFeatures(values=np.array([1.0, 2.0])), # type: ignore
VariableFeatures(values=np.array([1.0, 2.0])), # type: ignore
)
assert_equals(
np.array([True, True]),
[True, True],
)
assert_equals(np.array([True, True]), [True, True])
assert_equals((1.0,), (1.0,))
assert_equals({"x": 10}, {"x": 10})

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