ANL-CEEESA
/
MIPLearn
mirror of https://github.com/ANL-CEEESA/MIPLearn.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

AlvLouWeh2017: Implement M1 features

Browse Source
master
Alinson S. Xavier 4 years ago
parent fabb13dc7a
commit 53a7c8f84a
No known key found for this signature in database
GPG Key ID: DCA0DAD4D2F58624
2 changed files with 188 additions and 89 deletions
Show Stats Download Patch File Download Diff File

149
miplearn/features/extractor.py
Unescape View File

@ -6,6 +6,7 @@ from math import log, isfinite
from typing import TYPE_CHECKING, List, Tuple, Optional from typing import TYPE_CHECKING, List, Tuple, Optional
import numpy as np import numpy as np
from scipy.sparse import coo_matrix
from miplearn.features.sample import Sample from miplearn.features.sample import Sample
from miplearn.solvers.internal import LPSolveStats from miplearn.solvers.internal import LPSolveStats
@ -15,6 +16,7 @@ if TYPE_CHECKING:
from miplearn.instance.base import Instance from miplearn.instance.base import Instance
# noinspection PyPep8Naming
class FeaturesExtractor: class FeaturesExtractor:
def __init__( def __init__(
self, self,
@ -23,6 +25,7 @@ class FeaturesExtractor:
) -> None: ) -> None:
self.with_sa = with_sa self.with_sa = with_sa
self.with_lhs = with_lhs self.with_lhs = with_lhs
self.var_features_user: Optional[np.ndarray] = None
def extract_after_load_features( def extract_after_load_features(
self, self,
@ -65,6 +68,7 @@ class FeaturesExtractor:
vars_features_user, vars_features_user,
var_categories, var_categories,
) = self._extract_user_features_vars(instance, sample) ) = self._extract_user_features_vars(instance, sample)
self.var_features_user = vars_features_user
sample.put_array("static_var_categories", var_categories) sample.put_array("static_var_categories", var_categories)
assert variables.lower_bounds is not None assert variables.lower_bounds is not None
assert variables.obj_coeffs is not None assert variables.obj_coeffs is not None
@ -74,12 +78,11 @@ class FeaturesExtractor:
np.hstack( np.hstack(
[ [
vars_features_user, vars_features_user,
self._extract_var_features_AlvLouWeh2017( self._compute_AlvLouWeh2017(
obj_coeffs=variables.obj_coeffs, A=constraints.lhs,
b=constraints.rhs,
c=variables.obj_coeffs,
), ),
variables.lower_bounds.reshape(-1, 1),
variables.obj_coeffs.reshape(-1, 1),
variables.upper_bounds.reshape(-1, 1),
] ]
), ),
) )
@ -112,11 +115,13 @@ class FeaturesExtractor:
# Variable features # Variable features
lp_var_features_list = [] lp_var_features_list = []
for f in [ for f in [
sample.get_array("static_var_features"), self.var_features_user,
self._extract_var_features_AlvLouWeh2017( self._compute_AlvLouWeh2017(
obj_coeffs=sample.get_array("static_var_obj_coeffs"), A=sample.get_sparse("static_constr_lhs"),
obj_sa_up=variables.sa_obj_up, b=sample.get_array("static_constr_rhs"),
obj_sa_down=variables.sa_obj_down, c=sample.get_array("static_var_obj_coeffs"),
c_sa_up=variables.sa_obj_up,
c_sa_down=variables.sa_obj_down,
values=variables.values, values=variables.values,
), ),
]: ]:
@ -315,90 +320,108 @@ class FeaturesExtractor:
], f"Instance features have unsupported {features.dtype}" ], f"Instance features have unsupported {features.dtype}"
sample.put_array("static_instance_features", features) sample.put_array("static_instance_features", features)
# Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based @classmethod
# approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195. def _compute_AlvLouWeh2017(
# noinspection PyPep8Naming cls,
def _extract_var_features_AlvLouWeh2017( A: Optional[coo_matrix] = None,
self, b: Optional[np.ndarray] = None,
obj_coeffs: Optional[np.ndarray] = None, c: Optional[np.ndarray] = None,
obj_sa_down: Optional[np.ndarray] = None, c_sa_down: Optional[np.ndarray] = None,
obj_sa_up: Optional[np.ndarray] = None, c_sa_up: Optional[np.ndarray] = None,
values: Optional[np.ndarray] = None, values: Optional[np.ndarray] = None,
) -> np.ndarray: ) -> np.ndarray:
assert obj_coeffs is not None """
obj_coeffs = obj_coeffs.astype(float) Computes static variable features described in:
_fix_infinity(obj_coeffs) Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
nvars = len(obj_coeffs) approximation of strong branching. INFORMS Journal on Computing, 29(1),
185-195.
if obj_sa_down is not None: """
obj_sa_down = obj_sa_down.astype(float) assert b is not None
_fix_infinity(obj_sa_down) assert c is not None
nvars = len(c)
if obj_sa_up is not None:
obj_sa_up = obj_sa_up.astype(float) c_pos_sum = c[c > 0].sum()
_fix_infinity(obj_sa_up) c_neg_sum = -c[c < 0].sum()
if values is not None:
values = values.astype(float)
_fix_infinity(values)
pos_obj_coeffs_sum = obj_coeffs[obj_coeffs > 0].sum()
neg_obj_coeffs_sum = -obj_coeffs[obj_coeffs < 0].sum()
curr = 0 curr = 0
max_n_features = 8 max_n_features = 30
features = np.zeros((nvars, max_n_features)) features = np.zeros((nvars, max_n_features))
def push(v: np.ndarray) -> None:
nonlocal curr
features[:, curr] = v
curr += 1
with np.errstate(divide="ignore", invalid="ignore"): with np.errstate(divide="ignore", invalid="ignore"):
# Feature 1 # Feature 1
features[:, curr] = np.sign(obj_coeffs) push(np.sign(c))
curr += 1
# Feature 2 # Feature 2
if abs(pos_obj_coeffs_sum) > 0: push(np.abs(c) / c_pos_sum)
features[:, curr] = np.abs(obj_coeffs) / pos_obj_coeffs_sum
curr += 1
# Feature 3 # Feature 3
if abs(neg_obj_coeffs_sum) > 0: push(np.abs(c) / c_neg_sum)
features[:, curr] = np.abs(obj_coeffs) / neg_obj_coeffs_sum
curr += 1 if A is not None:
M1 = A.T.multiply(1.0 / np.abs(b)).T.tocsr()
M1_pos = M1[b > 0, :]
if M1_pos.shape[0] > 0:
M1_pos_max = M1_pos.max(axis=0).todense()
M1_pos_min = M1_pos.min(axis=0).todense()
else:
M1_pos_max = np.zeros(nvars)
M1_pos_min = np.zeros(nvars)
M1_neg = M1[b < 0, :]
if M1_neg.shape[0] > 0:
M1_neg_max = M1_neg.max(axis=0).todense()
M1_neg_min = M1_neg.min(axis=0).todense()
else:
M1_neg_max = np.zeros(nvars)
M1_neg_min = np.zeros(nvars)
# Features 4-11
push(np.sign(M1_pos_min))
push(np.sign(M1_pos_max))
push(np.abs(M1_pos_min))
push(np.abs(M1_pos_max))
push(np.sign(M1_neg_min))
push(np.sign(M1_neg_max))
push(np.abs(M1_neg_min))
push(np.abs(M1_neg_max))
# Feature 37 # Feature 37
if values is not None: if values is not None:
features[:, curr] = np.minimum( push(
np.minimum(
values - np.floor(values), values - np.floor(values),
np.ceil(values) - values, np.ceil(values) - values,
) )
curr += 1 )
# Feature 44 # Feature 44
if obj_sa_up is not None: if c_sa_up is not None:
features[:, curr] = np.sign(obj_sa_up) push(np.sign(c_sa_up))
curr += 1
# Feature 46 # Feature 46
if obj_sa_down is not None: if c_sa_down is not None:
features[:, curr] = np.sign(obj_sa_down) push(np.sign(c_sa_down))
curr += 1
# Feature 47 # Feature 47
if obj_sa_down is not None: if c_sa_down is not None:
features[:, curr] = np.log( push(np.log(c - c_sa_down / np.sign(c)))
obj_coeffs - obj_sa_down / np.sign(obj_coeffs)
)
curr += 1
# Feature 48 # Feature 48
if obj_sa_up is not None: if c_sa_up is not None:
features[:, curr] = np.log(obj_coeffs - obj_sa_up / np.sign(obj_coeffs)) push(np.log(c - c_sa_up / np.sign(c)))
curr += 1
features = features[:, 0:curr] features = features[:, 0:curr]
_fix_infinity(features) _fix_infinity(features)
return features return features
def _fix_infinity(m: np.ndarray) -> None: def _fix_infinity(m: Optional[np.ndarray]) -> None:
if m is None:
return
masked = np.ma.masked_invalid(m) masked = np.ma.masked_invalid(m)
max_values = np.max(masked, axis=0) max_values = np.max(masked, axis=0)
min_values = np.min(masked, axis=0) min_values = np.min(masked, axis=0)

124
tests/features/test_extractor.py
Unescape View File

@ -65,11 +65,67 @@ def test_knapsack() -> None:
sample.get_array("static_var_features"), sample.get_array("static_var_features"),
np.array( np.array(
[ [
[23.0, 505.0, 1.0, 0.32899, 0.0, 505.0, 1.0], [
[26.0, 352.0, 1.0, 0.229316, 0.0, 352.0, 1.0], 23.0,
[20.0, 458.0, 1.0, 0.298371, 0.0, 458.0, 1.0], 505.0,
[18.0, 220.0, 1.0, 0.143322, 0.0, 220.0, 1.0], 1.0,
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 67.0], 0.32899,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
[
26.0,
352.0,
1.0,
0.229316,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
[
20.0,
458.0,
1.0,
0.298371,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
[
18.0,
220.0,
1.0,
0.143322,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
] ]
), ),
) )
@ -156,11 +212,15 @@ def test_knapsack() -> None:
505.0, 505.0,
1.0, 1.0,
0.32899, 0.32899,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0, 0.0,
505.0,
1.0,
1.0,
0.32899,
0.0, 0.0,
1.0, 1.0,
1.0, 1.0,
@ -180,11 +240,15 @@ def test_knapsack() -> None:
352.0, 352.0,
1.0, 1.0,
0.229316, 0.229316,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0, 0.0,
352.0,
1.0,
1.0,
0.229316,
0.076923, 0.076923,
1.0, 1.0,
1.0, 1.0,
@ -204,11 +268,15 @@ def test_knapsack() -> None:
458.0, 458.0,
1.0, 1.0,
0.298371, 0.298371,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0, 0.0,
458.0,
1.0,
1.0,
0.298371,
0.0, 0.0,
1.0, 1.0,
1.0, 1.0,
@ -228,15 +296,19 @@ def test_knapsack() -> None:
220.0, 220.0,
1.0, 1.0,
0.143322, 0.143322,
1e20,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0, 0.0,
220.0,
1.0,
1.0,
0.143322,
0.0, 0.0,
1.0, 1.0,
-1.0, -1.0,
5.453347, 5.265874,
0.0, 0.0,
-23.692308, -23.692308,
-0.111111, -0.111111,
@ -254,13 +326,17 @@ def test_knapsack() -> None:
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
67.0, 0.0,
0.0,
0.0,
0.0,
0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
1.0, 1.0,
-1.0, -1.0,
5.453347, 5.265874,
0.0, 0.0,
13.538462, 13.538462,
-0.111111, -0.111111,

Write Preview
Loading…
Cancel
Save