mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-09 19:08:51 -06:00
Use compact variable features everywhere
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@@ -10,8 +10,6 @@ from typing import TYPE_CHECKING, Dict, Optional, List, Hashable, Tuple
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import numpy as np
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from miplearn.types import Category
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if TYPE_CHECKING:
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from miplearn.solvers.internal import InternalSolver, LPSolveStats, MIPSolveStats
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from miplearn.instance.base import Instance
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@@ -49,49 +47,31 @@ class VariableFeatures:
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user_features: Optional[Tuple[Optional[Tuple[float, ...]], ...]] = None
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values: Optional[Tuple[float, ...]] = None
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@dataclass
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class Variable:
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basis_status: Optional[str] = None
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category: Optional[Hashable] = None
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lower_bound: Optional[float] = None
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obj_coeff: Optional[float] = None
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reduced_cost: Optional[float] = None
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sa_lb_down: Optional[float] = None
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sa_lb_up: Optional[float] = None
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sa_obj_down: Optional[float] = None
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sa_obj_up: Optional[float] = None
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sa_ub_down: Optional[float] = None
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sa_ub_up: Optional[float] = None
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type: Optional[str] = None
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upper_bound: Optional[float] = None
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user_features: Optional[List[float]] = None
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value: Optional[float] = None
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# Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
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# approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
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alvarez_2017: Optional[List[float]] = None
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alvarez_2017: Optional[List[List[float]]] = None
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def to_list(self) -> List[float]:
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def to_list(self, index: int) -> List[float]:
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features: List[float] = []
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for attr in [
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"lower_bound",
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"obj_coeff",
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"reduced_cost",
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"lower_bounds",
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"obj_coeffs",
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"reduced_costs",
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"sa_lb_down",
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"sa_lb_up",
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"sa_obj_down",
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"sa_obj_up",
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"sa_ub_down",
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"sa_ub_up",
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"upper_bound",
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"value",
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"upper_bounds",
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"values",
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]:
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if getattr(self, attr) is not None:
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features.append(getattr(self, attr))
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features.append(getattr(self, attr)[index])
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for attr in ["user_features", "alvarez_2017"]:
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if getattr(self, attr) is not None:
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features.extend(getattr(self, attr))
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if getattr(self, attr)[index] is not None:
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features.extend(getattr(self, attr)[index])
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_clip(features)
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return features
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@@ -136,7 +116,6 @@ class Constraint:
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class Features:
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instance: Optional[InstanceFeatures] = None
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variables: Optional[VariableFeatures] = None
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variables_old: Optional[Dict[str, Variable]] = None
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constraints: Optional[Dict[str, Constraint]] = None
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lp_solve: Optional["LPSolveStats"] = None
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mip_solve: Optional["MIPSolveStats"] = None
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@@ -169,51 +148,16 @@ class FeaturesExtractor:
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with_static=with_static,
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with_sa=self.with_sa,
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)
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features.variables_old = self.solver.get_variables_old(
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with_static=with_static,
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)
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features.constraints = self.solver.get_constraints(
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with_static=with_static,
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)
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if with_static:
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self._extract_user_features_vars(instance, features)
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self._extract_user_features_vars_old(instance, features)
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self._extract_user_features_constrs(instance, features)
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self._extract_user_features_instance(instance, features)
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self._extract_alvarez_2017(features)
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return features
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def _extract_user_features_vars_old(
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self,
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instance: "Instance",
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features: Features,
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) -> None:
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assert features.variables_old is not None
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for (var_name, var) in features.variables_old.items():
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user_features: Optional[List[float]] = None
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category: Category = instance.get_variable_category(var_name)
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if category is not None:
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assert isinstance(category, collections.Hashable), (
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f"Variable category must be be hashable. "
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f"Found {type(category).__name__} instead for var={var_name}."
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)
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user_features = instance.get_variable_features(var_name)
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if isinstance(user_features, np.ndarray):
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user_features = user_features.tolist()
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assert isinstance(user_features, list), (
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f"Variable features must be a list. "
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f"Found {type(user_features).__name__} instead for "
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f"var={var_name}."
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)
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for v in user_features:
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assert isinstance(v, numbers.Real), (
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f"Variable features must be a list of numbers. "
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f"Found {type(v).__name__} instead "
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f"for var={var_name}."
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)
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var.category = category
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var.user_features = user_features
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def _extract_user_features_vars(
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self,
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instance: "Instance",
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@@ -312,72 +256,80 @@ class FeaturesExtractor:
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)
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def _extract_alvarez_2017(self, features: Features) -> None:
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assert features.variables_old is not None
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assert features.variables is not None
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assert features.variables.names is not None
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obj_coeffs = features.variables.obj_coeffs
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obj_sa_down = features.variables.sa_obj_down
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obj_sa_up = features.variables.sa_obj_up
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values = features.variables.values
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pos_obj_coeff_sum = 0.0
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neg_obj_coeff_sum = 0.0
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for (varname, var) in features.variables_old.items():
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if var.obj_coeff is not None:
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if var.obj_coeff > 0:
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pos_obj_coeff_sum += var.obj_coeff
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if var.obj_coeff < 0:
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neg_obj_coeff_sum += -var.obj_coeff
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if obj_coeffs is not None:
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for coeff in obj_coeffs:
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if coeff > 0:
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pos_obj_coeff_sum += coeff
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if coeff < 0:
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neg_obj_coeff_sum += -coeff
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for (varname, var) in features.variables_old.items():
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assert isinstance(var, Variable)
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features.variables.alvarez_2017 = []
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for i in range(len(features.variables.names)):
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f: List[float] = []
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if var.obj_coeff is not None:
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if obj_coeffs is not None:
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# Feature 1
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f.append(np.sign(var.obj_coeff))
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f.append(np.sign(obj_coeffs[i]))
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# Feature 2
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if pos_obj_coeff_sum > 0:
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f.append(abs(var.obj_coeff) / pos_obj_coeff_sum)
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f.append(abs(obj_coeffs[i]) / pos_obj_coeff_sum)
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else:
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f.append(0.0)
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# Feature 3
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if neg_obj_coeff_sum > 0:
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f.append(abs(var.obj_coeff) / neg_obj_coeff_sum)
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f.append(abs(obj_coeffs[i]) / neg_obj_coeff_sum)
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else:
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f.append(0.0)
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if var.value is not None:
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if values is not None:
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# Feature 37
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f.append(
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min(
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var.value - np.floor(var.value),
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np.ceil(var.value) - var.value,
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values[i] - np.floor(values[i]),
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np.ceil(values[i]) - values[i],
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)
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)
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if var.sa_obj_up is not None:
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assert var.obj_coeff is not None
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assert var.sa_obj_down is not None
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if obj_sa_up is not None:
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assert obj_sa_down is not None
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assert obj_coeffs is not None
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# Convert inf into large finite numbers
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sa_obj_down = max(-1e20, var.sa_obj_down)
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sa_obj_up = min(1e20, var.sa_obj_up)
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sd = max(-1e20, obj_sa_down[i])
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su = min(1e20, obj_sa_up[i])
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obj = obj_coeffs[i]
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# Features 44 and 46
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f.append(np.sign(var.sa_obj_up))
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f.append(np.sign(var.sa_obj_down))
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f.append(np.sign(obj_sa_up[i]))
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f.append(np.sign(obj_sa_down[i]))
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# Feature 47
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csign = np.sign(var.obj_coeff)
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if csign != 0 and ((var.obj_coeff - sa_obj_down) / csign) > 0.001:
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f.append(log((var.obj_coeff - sa_obj_down) / csign))
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csign = np.sign(obj)
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if csign != 0 and ((obj - sd) / csign) > 0.001:
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f.append(log((obj - sd) / csign))
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else:
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f.append(0.0)
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# Feature 48
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if csign != 0 and ((sa_obj_up - var.obj_coeff) / csign) > 0.001:
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f.append(log((sa_obj_up - var.obj_coeff) / csign))
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if csign != 0 and ((su - obj) / csign) > 0.001:
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f.append(log((su - obj) / csign))
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else:
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f.append(0.0)
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for v in f:
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assert isfinite(v), f"non-finite elements detected: {f}"
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var.alvarez_2017 = f
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features.variables.alvarez_2017.append(f)
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def _clip(v: List[float]) -> None:
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