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381 lines
14 KiB
381 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, Tuple, KeysView, cast
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import numpy as np
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from miplearn.features.sample import Sample
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from miplearn.solvers.internal import LPSolveStats
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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_array("static_var_lower_bounds", variables.lower_bounds)
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sample.put_array("static_var_names", variables.names)
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sample.put_array("static_var_obj_coeffs", variables.obj_coeffs)
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sample.put_array("static_var_types", variables.types)
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sample.put_array("static_var_upper_bounds", variables.upper_bounds)
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sample.put_array("static_constr_names", constraints.names)
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# sample.put("static_constr_lhs", constraints.lhs)
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sample.put_array("static_constr_rhs", constraints.rhs)
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sample.put_array("static_constr_senses", constraints.senses)
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vars_features_user, var_categories = self._extract_user_features_vars(
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instance, sample
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)
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sample.put_array("static_var_categories", var_categories)
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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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alw17 = self._extract_var_features_AlvLouWeh2017(sample)
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# Build static_var_features
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assert variables.lower_bounds is not None
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assert variables.obj_coeffs is not None
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assert variables.upper_bounds is not None
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sample.put_array(
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"static_var_features",
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np.hstack(
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[
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vars_features_user,
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alw17,
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variables.lower_bounds.reshape(-1, 1),
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variables.obj_coeffs.reshape(-1, 1),
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variables.upper_bounds.reshape(-1, 1),
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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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lp_stats: LPSolveStats,
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) -> None:
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for (k, v) in lp_stats.__dict__.items():
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sample.put_scalar(k, v)
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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_array("lp_var_basis_status", variables.basis_status)
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sample.put_array("lp_var_reduced_costs", variables.reduced_costs)
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sample.put_array("lp_var_sa_lb_down", variables.sa_lb_down)
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sample.put_array("lp_var_sa_lb_up", variables.sa_lb_up)
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sample.put_array("lp_var_sa_obj_down", variables.sa_obj_down)
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sample.put_array("lp_var_sa_obj_up", variables.sa_obj_up)
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sample.put_array("lp_var_sa_ub_down", variables.sa_ub_down)
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sample.put_array("lp_var_sa_ub_up", variables.sa_ub_up)
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sample.put_array("lp_var_values", variables.values)
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sample.put_array("lp_constr_basis_status", constraints.basis_status)
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sample.put_array("lp_constr_dual_values", constraints.dual_values)
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sample.put_array("lp_constr_sa_rhs_down", constraints.sa_rhs_down)
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sample.put_array("lp_constr_sa_rhs_up", constraints.sa_rhs_up)
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sample.put_array("lp_constr_slacks", constraints.slacks)
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alw17 = self._extract_var_features_AlvLouWeh2017(sample)
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# Build lp_var_features
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lp_var_features_list = []
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for f in [
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sample.get_array("static_var_features"),
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alw17,
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]:
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if f is not None:
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lp_var_features_list.append(f)
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for f in [
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variables.reduced_costs,
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variables.sa_lb_down,
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variables.sa_lb_up,
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variables.sa_obj_down,
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variables.sa_obj_up,
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variables.sa_ub_down,
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variables.sa_ub_up,
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variables.values,
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]:
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if f is not None:
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lp_var_features_list.append(f.reshape(-1, 1))
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sample.put_array("lp_var_features", np.hstack(lp_var_features_list))
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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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[
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sample.get_vector_list("static_constr_features"),
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sample.get_array("lp_constr_dual_values"),
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sample.get_array("lp_constr_sa_rhs_down"),
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sample.get_array("lp_constr_sa_rhs_up"),
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sample.get_array("lp_constr_slacks"),
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],
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),
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)
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# Build lp_instance_features
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static_instance_features = sample.get_array("static_instance_features")
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assert static_instance_features is not None
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assert lp_stats.lp_value is not None
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assert lp_stats.lp_wallclock_time is not None
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sample.put_array(
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"lp_instance_features",
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np.hstack(
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[
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static_instance_features,
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lp_stats.lp_value,
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lp_stats.lp_wallclock_time,
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]
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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_array("mip_var_values", variables.values)
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sample.put_array("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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) -> Tuple[np.ndarray, np.ndarray]:
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# Query variable names
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var_names = sample.get_array("static_var_names")
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assert var_names is not None
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# Query variable features
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var_features = instance.get_variable_features(var_names)
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assert isinstance(var_features, np.ndarray), (
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f"Variable features must be a numpy array. "
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f"Found {var_features.__class__} instead."
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)
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assert len(var_features.shape) == 2, (
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f"Variable features must be 2-dimensional array. "
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f"Found array with shape {var_features.shape} instead."
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)
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assert var_features.shape[0] == len(var_names), (
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f"Variable features must have exactly {len(var_names)} rows. "
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f"Found {var_features.shape[0]} rows instead."
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)
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assert var_features.dtype.kind in ["f"], (
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f"Variable features must be floating point numbers. "
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f"Found dtype: {var_features.dtype} instead."
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)
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# Query variable categories
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var_categories = instance.get_variable_categories(var_names)
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assert isinstance(var_categories, np.ndarray), (
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f"Variable categories must be a numpy array. "
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f"Found {var_categories.__class__} instead."
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)
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assert len(var_categories.shape) == 1, (
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f"Variable categories must be a vector. "
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f"Found array with shape {var_categories.shape} instead."
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)
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assert len(var_categories) == len(var_names), (
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f"Variable categories must have exactly {len(var_names)} elements. "
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f"Found {var_features.shape[0]} elements instead."
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)
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assert var_categories.dtype.kind == "S", (
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f"Variable categories must be a numpy array with dtype='S'. "
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f"Found {var_categories.dtype} instead."
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)
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return var_features, var_categories
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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[bytes]] = []
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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_array("static_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, bytes), (
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f"Constraint category must be bytes. "
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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("static_constr_features", user_features)
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sample.put_array("static_constr_categories", np.array(categories, dtype="S"))
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constr_lazy = np.array(lazy, dtype=bool)
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sample.put_array("static_constr_lazy", constr_lazy)
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sample.put_scalar("static_constr_lazy_count", int(constr_lazy.sum()))
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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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features = instance.get_instance_features()
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assert isinstance(features, np.ndarray), (
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f"Instance features must be a numpy array. "
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f"Found {features.__class__} instead."
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)
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assert len(features.shape) == 1, (
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f"Instance features must be a vector. "
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f"Found array with shape {features.shape} instead."
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)
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assert features.dtype.kind in [
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"f"
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], f"Instance features have unsupported dtype: {features.dtype}"
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sample.put_array("static_instance_features", features)
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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(self, sample: Sample) -> np.ndarray:
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obj_coeffs = sample.get_array("static_var_obj_coeffs")
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obj_sa_down = sample.get_array("lp_var_sa_obj_down")
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obj_sa_up = sample.get_array("lp_var_sa_obj_up")
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values = sample.get_array("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 (i, v) in enumerate(f):
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if not isfinite(v):
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f[i] = 0.0
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features.append(f)
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return np.array(features, dtype=float)
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def _combine(
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self,
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items: List,
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) -> List[List[float]]:
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combined: List[List[float]] = []
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for series in items:
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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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