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363 lines
14 KiB
363 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 math import log, isfinite
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from typing import TYPE_CHECKING, Dict, Optional, List, Any
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import numpy as np
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from miplearn.features.sample import Sample
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if TYPE_CHECKING:
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from miplearn.solvers.internal import InternalSolver
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from miplearn.instance.base import Instance
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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_after_load_features(
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self,
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instance: "Instance",
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solver: "InternalSolver",
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sample: Sample,
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) -> None:
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variables = solver.get_variables(with_static=True)
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constraints = solver.get_constraints(with_static=True, with_lhs=self.with_lhs)
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sample.put_vector("var_lower_bounds", variables.lower_bounds)
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sample.put_vector("var_names", variables.names)
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sample.put_vector("var_obj_coeffs", variables.obj_coeffs)
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sample.put_vector("var_types", variables.types)
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sample.put_vector("var_upper_bounds", variables.upper_bounds)
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sample.put_vector("constr_names", constraints.names)
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sample.put("constr_lhs", constraints.lhs)
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sample.put_vector("constr_rhs", constraints.rhs)
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sample.put_vector("constr_senses", constraints.senses)
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self._extract_user_features_vars(instance, sample)
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self._extract_user_features_constrs(instance, sample)
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self._extract_user_features_instance(instance, sample)
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self._extract_var_features_AlvLouWeh2017(sample)
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sample.put_vector_list(
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"var_features",
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self._combine(
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sample,
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[
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"var_features_AlvLouWeh2017",
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"var_features_user",
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"var_lower_bounds",
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"var_obj_coeffs",
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"var_upper_bounds",
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],
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),
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)
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def extract_after_lp_features(
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self,
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solver: "InternalSolver",
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sample: Sample,
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) -> None:
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variables = solver.get_variables(with_static=False, with_sa=self.with_sa)
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constraints = solver.get_constraints(with_static=False, with_sa=self.with_sa)
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sample.put_vector("lp_var_basis_status", variables.basis_status)
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sample.put_vector("lp_var_reduced_costs", variables.reduced_costs)
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sample.put_vector("lp_var_sa_lb_down", variables.sa_lb_down)
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sample.put_vector("lp_var_sa_lb_up", variables.sa_lb_up)
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sample.put_vector("lp_var_sa_obj_down", variables.sa_obj_down)
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sample.put_vector("lp_var_sa_obj_up", variables.sa_obj_up)
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sample.put_vector("lp_var_sa_ub_down", variables.sa_ub_down)
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sample.put_vector("lp_var_sa_ub_up", variables.sa_ub_up)
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sample.put_vector("lp_var_values", variables.values)
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sample.put_vector("lp_constr_basis_status", constraints.basis_status)
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sample.put_vector("lp_constr_dual_values", constraints.dual_values)
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sample.put_vector("lp_constr_sa_rhs_down", constraints.sa_rhs_down)
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sample.put_vector("lp_constr_sa_rhs_up", constraints.sa_rhs_up)
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sample.put_vector("lp_constr_slacks", constraints.slacks)
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self._extract_var_features_AlvLouWeh2017(sample, prefix="lp_")
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sample.put_vector_list(
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"lp_var_features",
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self._combine(
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sample,
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[
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"lp_var_features_AlvLouWeh2017",
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"lp_var_reduced_costs",
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"lp_var_sa_lb_down",
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"lp_var_sa_lb_up",
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"lp_var_sa_obj_down",
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"lp_var_sa_obj_up",
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"lp_var_sa_ub_down",
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"lp_var_sa_ub_up",
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"lp_var_values",
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"var_features_user",
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"var_lower_bounds",
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"var_obj_coeffs",
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"var_upper_bounds",
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],
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),
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)
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sample.put_vector_list(
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"lp_constr_features",
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self._combine(
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sample,
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[
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"constr_features_user",
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"lp_constr_dual_values",
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"lp_constr_sa_rhs_down",
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"lp_constr_sa_rhs_up",
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"lp_constr_slacks",
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],
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),
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)
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instance_features_user = sample.get("instance_features_user")
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assert instance_features_user is not None
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sample.put_vector(
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"lp_instance_features",
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instance_features_user
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+ [
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sample.get("lp_value"),
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sample.get("lp_wallclock_time"),
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],
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)
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def extract_after_mip_features(
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self,
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solver: "InternalSolver",
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sample: Sample,
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) -> None:
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variables = solver.get_variables(with_static=False, with_sa=False)
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constraints = solver.get_constraints(with_static=False, with_sa=False)
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sample.put_vector("mip_var_values", variables.values)
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sample.put_vector("mip_constr_slacks", constraints.slacks)
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def _extract_user_features_vars(
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self,
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instance: "Instance",
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sample: Sample,
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) -> None:
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categories: List[Optional[str]] = []
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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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var_names = sample.get("var_names")
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assert var_names is not None
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for (i, var_name) in enumerate(var_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: str = var_categories_dict[var_name]
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assert isinstance(category, str), (
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f"Variable category must be a string. "
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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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sample.put("var_categories", categories)
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sample.put_vector_list("var_features_user", 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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sample: Sample,
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) -> 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[str]] = []
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lazy: List[bool] = []
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constr_categories_dict = instance.get_constraint_categories()
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constr_features_dict = instance.get_constraint_features()
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constr_names = sample.get("constr_names")
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assert constr_names is not None
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for (cidx, cname) in enumerate(constr_names):
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category: Optional[str] = cname
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if cname in constr_categories_dict:
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category = constr_categories_dict[cname]
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if category is None:
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user_features.append(None)
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categories.append(None)
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continue
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assert isinstance(category, str), (
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f"Constraint category must be a string. "
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f"Found {type(category).__name__} instead for cname={cname}.",
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)
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categories.append(category)
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cf: Optional[List[float]] = None
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if cname in constr_features_dict:
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cf = constr_features_dict[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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cf = list(cf)
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user_features.append(cf)
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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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sample.put_vector_list("constr_features_user", user_features)
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sample.put_vector("constr_lazy", lazy)
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sample.put("constr_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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sample: Sample,
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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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constr_lazy = sample.get("constr_lazy")
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assert constr_lazy is not None
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sample.put_vector("instance_features_user", user_features)
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sample.put_scalar("static_lazy_count", sum(constr_lazy))
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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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def _extract_var_features_AlvLouWeh2017(
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self,
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sample: Sample,
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prefix: str = "",
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) -> None:
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obj_coeffs = sample.get("var_obj_coeffs")
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obj_sa_down = sample.get("lp_var_sa_obj_down")
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obj_sa_up = sample.get("lp_var_sa_obj_up")
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values = sample.get(f"lp_var_values")
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assert obj_coeffs is not None
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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 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 = []
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for i in range(len(obj_coeffs)):
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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.append(f)
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sample.put_vector_list(f"{prefix}var_features_AlvLouWeh2017", features)
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def _combine(
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self,
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sample: Sample,
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attrs: List[str],
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) -> List[List[float]]:
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combined: List[List[float]] = []
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for attr in attrs:
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series = sample.get(attr)
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if series is None:
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continue
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if len(combined) == 0:
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for i in range(len(series)):
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combined.append([])
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for (i, s) in enumerate(series):
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if s is None:
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continue
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elif isinstance(s, list):
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combined[i].extend([_clip(sj) for sj in s])
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else:
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combined[i].append(_clip(s))
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return combined
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def _clip(vi: float) -> float:
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if not isfinite(vi):
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return max(min(vi, 1e20), -1e20)
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return vi
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