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378 lines
14 KiB
378 lines
14 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import collections
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import numbers
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from dataclasses import dataclass
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from math import log, isfinite
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from typing import TYPE_CHECKING, Dict, Optional, List, Hashable, Tuple
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import numpy as np
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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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@dataclass
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class InstanceFeatures:
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user_features: Optional[List[float]] = None
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lazy_constraint_count: int = 0
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def to_list(self) -> List[float]:
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features: List[float] = []
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if self.user_features is not None:
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features.extend(self.user_features)
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_clip(features)
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return features
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@dataclass
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class VariableFeatures:
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names: Optional[List[str]] = None
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basis_status: Optional[List[str]] = None
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categories: Optional[List[Optional[Hashable]]] = None
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lower_bounds: Optional[List[float]] = None
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obj_coeffs: Optional[List[float]] = None
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reduced_costs: Optional[List[float]] = None
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sa_lb_down: Optional[List[float]] = None
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sa_lb_up: Optional[List[float]] = None
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sa_obj_down: Optional[List[float]] = None
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sa_obj_up: Optional[List[float]] = None
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sa_ub_down: Optional[List[float]] = None
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sa_ub_up: Optional[List[float]] = None
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types: Optional[List[str]] = None
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upper_bounds: Optional[List[float]] = None
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user_features: Optional[List[Optional[List[float]]]] = None
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values: Optional[List[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[List[float]]] = None
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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_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_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)[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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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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@dataclass
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class ConstraintFeatures:
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basis_status: Optional[List[str]] = None
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categories: Optional[List[Optional[Hashable]]] = None
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dual_values: Optional[List[float]] = None
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names: Optional[List[str]] = None
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lazy: Optional[List[bool]] = None
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lhs: Optional[List[List[Tuple[str, float]]]] = None
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rhs: Optional[List[float]] = None
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sa_rhs_down: Optional[List[float]] = None
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sa_rhs_up: Optional[List[float]] = None
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senses: Optional[List[str]] = None
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slacks: Optional[List[float]] = None
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user_features: Optional[List[Optional[List[float]]]] = None
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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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"dual_values",
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"rhs",
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"slacks",
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]:
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if getattr(self, attr) is not None:
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features.append(getattr(self, attr)[index])
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for attr in ["user_features"]:
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if getattr(self, attr) is not None:
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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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def __getitem__(self, selected: List[bool]) -> "ConstraintFeatures":
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return ConstraintFeatures(
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basis_status=self._filter(self.basis_status, selected),
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categories=self._filter(self.categories, selected),
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dual_values=self._filter(self.dual_values, selected),
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names=self._filter(self.names, selected),
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lazy=self._filter(self.lazy, selected),
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lhs=self._filter(self.lhs, selected),
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rhs=self._filter(self.rhs, selected),
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sa_rhs_down=self._filter(self.sa_rhs_down, selected),
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sa_rhs_up=self._filter(self.sa_rhs_up, selected),
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senses=self._filter(self.senses, selected),
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slacks=self._filter(self.slacks, selected),
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user_features=self._filter(self.user_features, selected),
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)
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def _filter(
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self,
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obj: Optional[List],
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selected: List[bool],
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) -> Optional[List]:
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if obj is None:
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return None
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return [obj[i] for (i, selected_i) in enumerate(selected) if selected_i]
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@dataclass
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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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constraints: Optional[ConstraintFeatures] = None
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lp_solve: Optional["LPSolveStats"] = None
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mip_solve: Optional["MIPSolveStats"] = None
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extra: Optional[Dict] = None
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@dataclass
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class Sample:
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after_load: Optional[Features] = None
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after_lp: Optional[Features] = None
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after_mip: Optional[Features] = None
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class FeaturesExtractor:
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def __init__(
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self,
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with_sa: bool = True,
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with_lhs: bool = True,
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) -> None:
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self.with_sa = with_sa
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self.with_lhs = with_lhs
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def extract(
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self,
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instance: "Instance",
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solver: "InternalSolver",
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with_static: bool = True,
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) -> Features:
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features = Features()
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features.variables = solver.get_variables(
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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.constraints = solver.get_constraints(
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with_static=with_static,
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with_sa=self.with_sa,
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with_lhs=self.with_lhs,
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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_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(
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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 is not None
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assert features.variables.names is not None
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categories: List[Optional[Hashable]] = []
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user_features: List[Optional[List[float]]] = []
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var_features_dict = instance.get_variable_features()
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var_categories_dict = instance.get_variable_categories()
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for (i, var_name) in enumerate(features.variables.names):
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if var_name not in var_categories_dict:
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user_features.append(None)
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categories.append(None)
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continue
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category: Hashable = var_categories_dict[var_name]
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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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categories.append(category)
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user_features_i: Optional[List[float]] = None
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if var_name in var_features_dict:
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user_features_i = var_features_dict[var_name]
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if isinstance(user_features_i, np.ndarray):
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user_features_i = user_features_i.tolist()
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assert isinstance(user_features_i, list), (
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f"Variable features must be a list. "
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f"Found {type(user_features_i).__name__} instead for "
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f"var={var_name}."
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)
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for v in user_features_i:
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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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user_features_i = list(user_features_i)
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user_features.append(user_features_i)
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features.variables.categories = categories
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features.variables.user_features = user_features
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def _extract_user_features_constrs(
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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.constraints is not None
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assert features.constraints.names is not None
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has_static_lazy = instance.has_static_lazy_constraints()
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user_features: List[Optional[List[float]]] = []
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categories: List[Optional[Hashable]] = []
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lazy: List[bool] = []
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for (cidx, cname) in enumerate(features.constraints.names):
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cf: Optional[List[float]] = None
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category: Optional[Hashable] = instance.get_constraint_category(cname)
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if category is not None:
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categories.append(category)
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assert isinstance(category, collections.Hashable), (
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f"Constraint category must be hashable. "
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f"Found {type(category).__name__} instead for cname={cname}.",
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)
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cf = instance.get_constraint_features(cname)
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if isinstance(cf, np.ndarray):
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cf = cf.tolist()
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assert isinstance(cf, list), (
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f"Constraint features must be a list. "
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f"Found {type(cf).__name__} instead for cname={cname}."
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)
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for f in cf:
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assert isinstance(f, numbers.Real), (
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f"Constraint features must be a list of numbers. "
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f"Found {type(f).__name__} instead for cname={cname}."
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)
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user_features.append(list(cf))
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else:
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user_features.append(None)
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categories.append(None)
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if has_static_lazy:
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lazy.append(instance.is_constraint_lazy(cname))
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else:
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lazy.append(False)
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features.constraints.user_features = user_features
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features.constraints.lazy = lazy
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features.constraints.categories = categories
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def _extract_user_features_instance(
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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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user_features = instance.get_instance_features()
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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"Instance features must be a list. "
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f"Found {type(user_features).__name__} instead."
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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"Instance features must be a list of numbers. "
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f"Found {type(v).__name__} instead."
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)
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assert features.constraints is not None
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assert features.constraints.lazy is not None
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features.instance = InstanceFeatures(
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user_features=user_features,
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lazy_constraint_count=sum(features.constraints.lazy),
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)
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def _extract_alvarez_2017(self, features: Features) -> 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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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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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 obj_coeffs is not None:
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# Feature 1
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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(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(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 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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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 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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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(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(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 ((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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features.variables.alvarez_2017.append(f)
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def _clip(v: List[float]) -> None:
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for (i, vi) in enumerate(v):
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if not isfinite(vi):
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v[i] = max(min(vi, 1e20), -1e20)
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