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MIPLearn v0.3

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Alinson S. Xavier
2023-06-08 11:25:39 -05:00
parent 6cc253a903
commit 1ea989d48a
172 changed files with 10495 additions and 24812 deletions
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miplearn/extractors/AlvLouWeh2017.py Normal file
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import Tuple, Optional
import numpy as np
from miplearn.extractors.abstract import FeaturesExtractor
from miplearn.h5 import H5File
class AlvLouWeh2017Extractor(FeaturesExtractor):
def __init__(
self,
with_m1: bool = True,
with_m2: bool = True,
with_m3: bool = True,
):
self.with_m1 = with_m1
self.with_m2 = with_m2
self.with_m3 = with_m3
def get_instance_features(self, h5: H5File) -> np.ndarray:
raise NotImplemented()
def get_var_features(self, h5: H5File) -> np.ndarray:
"""
Computes static variable features described in:
Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
approximation of strong branching. INFORMS Journal on Computing, 29(1),
185-195.
"""
A = h5.get_sparse("static_constr_lhs")
b = h5.get_array("static_constr_rhs")
c = h5.get_array("static_var_obj_coeffs")
c_sa_up = h5.get_array("lp_var_sa_obj_up")
c_sa_down = h5.get_array("lp_var_sa_obj_down")
values = h5.get_array("lp_var_values")
assert A is not None
assert b is not None
assert c is not None
nvars = len(c)
curr = 0
max_n_features = 40
features = np.zeros((nvars, max_n_features))
def push(v: np.ndarray) -> None:
nonlocal curr
assert v.shape == (nvars,), f"{v.shape} != ({nvars},)"
features[:, curr] = v
curr += 1
def push_sign_abs(v: np.ndarray) -> None:
assert v.shape == (nvars,), f"{v.shape} != ({nvars},)"
push(np.sign(v))
push(np.abs(v))
def maxmin(M: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
M_max = np.ravel(M.max(axis=0).todense())
M_min = np.ravel(M.min(axis=0).todense())
return M_max, M_min
with np.errstate(divide="ignore", invalid="ignore"):
# Feature 1
push(np.sign(c))
# Feature 2
c_pos_sum = c[c > 0].sum()
push(np.abs(c) / c_pos_sum)
# Feature 3
c_neg_sum = -c[c < 0].sum()
push(np.abs(c) / c_neg_sum)
if A is not None and self.with_m1:
# Compute A_ji / |b_j|
M1 = A.T.multiply(1.0 / np.abs(b)).T.tocsr()
# Select rows with positive b_j and compute max/min
M1_pos = M1[b > 0, :]
if M1_pos.shape[0] > 0:
M1_pos_max = np.asarray(M1_pos.max(axis=0).todense()).flatten()
M1_pos_min = np.asarray(M1_pos.min(axis=0).todense()).flatten()
else:
M1_pos_max = np.zeros(nvars)
M1_pos_min = np.zeros(nvars)
# Select rows with negative b_j and compute max/min
M1_neg = M1[b < 0, :]
if M1_neg.shape[0] > 0:
M1_neg_max = np.asarray(M1_neg.max(axis=0).todense()).flatten()
M1_neg_min = np.asarray(M1_neg.min(axis=0).todense()).flatten()
else:
M1_neg_max = np.zeros(nvars)
M1_neg_min = np.zeros(nvars)
# Features 4-11
push_sign_abs(M1_pos_min)
push_sign_abs(M1_pos_max)
push_sign_abs(M1_neg_min)
push_sign_abs(M1_neg_max)
if A is not None and self.with_m2:
# Compute |c_i| / A_ij
M2 = A.power(-1).multiply(np.abs(c)).tocsc()
# Compute max/min
M2_max, M2_min = maxmin(M2)
# Make copies of M2 and erase elements based on sign(c)
M2_pos_max = M2_max.copy()
M2_neg_max = M2_max.copy()
M2_pos_min = M2_min.copy()
M2_neg_min = M2_min.copy()
M2_pos_max[c <= 0] = 0
M2_pos_min[c <= 0] = 0
M2_neg_max[c >= 0] = 0
M2_neg_min[c >= 0] = 0
# Features 12-19
push_sign_abs(M2_pos_min)
push_sign_abs(M2_pos_max)
push_sign_abs(M2_neg_min)
push_sign_abs(M2_neg_max)
if A is not None and self.with_m3:
# Compute row sums
S_pos = A.maximum(0).sum(axis=1)
S_neg = np.abs(A.minimum(0).sum(axis=1))
# Divide A by positive and negative row sums
M3_pos = A.multiply(1 / S_pos).tocsr()
M3_neg = A.multiply(1 / S_neg).tocsr()
# Remove +inf and -inf generated by division by zero
M3_pos.data[~np.isfinite(M3_pos.data)] = 0.0
M3_neg.data[~np.isfinite(M3_neg.data)] = 0.0
M3_pos.eliminate_zeros()
M3_neg.eliminate_zeros()
# Split each matrix into positive and negative parts
M3_pos_pos = M3_pos.maximum(0)
M3_pos_neg = -(M3_pos.minimum(0))
M3_neg_pos = M3_neg.maximum(0)
M3_neg_neg = -(M3_neg.minimum(0))
# Calculate max/min
M3_pos_pos_max, M3_pos_pos_min = maxmin(M3_pos_pos)
M3_pos_neg_max, M3_pos_neg_min = maxmin(M3_pos_neg)
M3_neg_pos_max, M3_neg_pos_min = maxmin(M3_neg_pos)
M3_neg_neg_max, M3_neg_neg_min = maxmin(M3_neg_neg)
# Features 20-35
push_sign_abs(M3_pos_pos_max)
push_sign_abs(M3_pos_pos_min)
push_sign_abs(M3_pos_neg_max)
push_sign_abs(M3_pos_neg_min)
push_sign_abs(M3_neg_pos_max)
push_sign_abs(M3_neg_pos_min)
push_sign_abs(M3_neg_neg_max)
push_sign_abs(M3_neg_neg_min)
# Feature 36: only available during B&B
# Feature 37
if values is not None:
push(
np.minimum(
values - np.floor(values),
np.ceil(values) - values,
)
)
# Features 38-43: only available during B&B
# Feature 44
if c_sa_up is not None:
assert c_sa_down is not None
# Features 44 and 46
push(np.sign(c_sa_up))
push(np.sign(c_sa_down))
# Feature 45 is duplicated
# Feature 47-48
push(np.log(c - c_sa_down / np.sign(c)))
push(np.log(c - c_sa_up / np.sign(c)))
# Features 49-64: only available during B&B
features = features[:, 0:curr]
_fix_infinity(features)
return features
def get_constr_features(self, h5: H5File) -> np.ndarray:
raise NotImplemented()
def _fix_infinity(m: Optional[np.ndarray]) -> None:
if m is None:
return
masked = np.ma.masked_invalid(m) # type: ignore
max_values = np.max(masked, axis=0)
min_values = np.min(masked, axis=0)
m[:] = np.maximum(np.minimum(m, max_values), min_values)
m[~np.isfinite(m)] = 0.0