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Merge branch 'master' into feature/tsp

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Alinson S. Xavier
2020-02-24 14:22:20 -06:00
parent 2dc66696f9 f22aecb14d
commit f713a399a8
107 changed files with 13071 additions and 914 deletions
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6
miplearn/problems/__init__.py
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@@ -1,3 +1,3 @@
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
# Written by Alinson S. Xavier <axavier@anl.gov>
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.

28
miplearn/problems/knapsack.py
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@@ -1,6 +1,6 @@
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
# Written by Alinson S. Xavier <axavier@anl.gov>
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
import miplearn
from miplearn import Instance
@@ -13,7 +13,7 @@ from scipy.stats.distributions import rv_frozen
class ChallengeA:
"""
- 250 variables, 10 constraints, fixed weights
- w ~ U(100, 900), jitter ~ U(-100, 100)
- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
- K = 500, u ~ U(0., 1.)
- alpha = 0.25
"""
@@ -25,12 +25,12 @@ class ChallengeA:
np.random.seed(seed)
self.gen = MultiKnapsackGenerator(n=randint(low=250, high=251),
m=randint(low=10, high=11),
w=uniform(loc=100.0, scale=900.0),
w=uniform(loc=0.0, scale=1000.0),
K=uniform(loc=500.0, scale=0.0),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=True,
w_jitter=uniform(loc=-100.0, scale=200.0),
w_jitter=uniform(loc=0.95, scale=0.1),
)
np.random.seed(seed + 1)
self.training_instances = self.gen.generate(n_training_instances)
@@ -104,7 +104,7 @@ class MultiKnapsackGenerator:
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=False,
w_jitter=randint(low=0, high=1),
w_jitter=uniform(loc=1.0, scale=0.0),
round=True,
):
"""Initialize the problem generator.
@@ -133,7 +133,7 @@ class MultiKnapsackGenerator:
be completely identical.
If a probability distribution w_jitter is provided, then item weights will be set to
w[i,j] + gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
fix_w=True, this argument may be used to generate instances where the weight of each item
is roughly the same, but not exactly identical, across all instances. The prices of the
items and the capacities of the knapsacks will be calculated as above, but using these
@@ -186,10 +186,14 @@ class MultiKnapsackGenerator:
self.fix_n = self.n.rvs()
self.fix_m = self.m.rvs()
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
self.fix_u = self.u.rvs(self.fix_n)
self.fix_K = self.K.rvs()
else:
self.fix_n = None
self.fix_m = None
self.fix_w = None
self.fix_u = None
self.fix_K = None
def generate(self, n_samples):
def _sample():
@@ -197,13 +201,15 @@ class MultiKnapsackGenerator:
n = self.fix_n
m = self.fix_m
w = self.fix_w
u = self.fix_u
K = self.fix_K
else:
n = self.n.rvs()
m = self.m.rvs()
w = np.array([self.w.rvs(n) for _ in range(m)])
w = w + np.array([self.w_jitter.rvs(n) for _ in range(m)])
K = self.K.rvs()
u = self.u.rvs(n)
u = self.u.rvs(n)
K = self.K.rvs()
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
alpha = self.alpha.rvs(m)
p = np.array([w[:,j].sum() / m + K * u[j] for j in range(n)])
b = np.array([w[i,:].sum() * alpha[i] for i in range(m)])

6
miplearn/problems/stab.py
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@@ -1,6 +1,6 @@
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
# Written by Alinson S. Xavier <axavier@anl.gov>
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
import numpy as np
import pyomo.environ as pe

4
miplearn/problems/tests/__init__.py
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@@ -0,0 +1,4 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.

35
miplearn/problems/tests/test_knapsack.py
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@@ -1,6 +1,6 @@
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
# Written by Alinson S. Xavier <axavier@anl.gov>
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import LearningSolver
from miplearn.problems.knapsack import MultiKnapsackGenerator, MultiKnapsackInstance
@@ -23,32 +23,3 @@ def test_knapsack_generator():
assert round(np.mean(w_sum), -1) == 500.
assert round(np.mean(p_sum), -1) == 1250.
assert round(np.mean(b_sum), -3) == 25000.
def test_knapsack_fixed_weights_jitter():
gen = MultiKnapsackGenerator(n=randint(low=50, high=51),
m=randint(low=10, high=11),
w=randint(low=0, high=1000),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=0.0),
alpha=uniform(loc=0.50, scale=0.0),
fix_w=True,
w_jitter=randint(low=0, high=1),
)
instances = gen.generate(100)
w = [instance.weights[0,0] for instance in instances]
assert np.std(w) == 0.
gen = MultiKnapsackGenerator(n=randint(low=1, high=2),
m=randint(low=10, high=11),
w=randint(low=1000, high=1001),
K=randint(low=500, high=501),
u=uniform(loc=1.0, scale=0.0),
alpha=uniform(loc=0.50, scale=0.0),
fix_w=True,
w_jitter=randint(low=0, high=1001),
)
instances = gen.generate(5_000)
w = [instance.weights[0,0] for instance in instances]
assert round(np.std(w), -1) == 290.
assert round(np.mean(w), -2) == 1500.

6
miplearn/problems/tests/test_stab.py
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@@ -1,6 +1,6 @@
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
# Written by Alinson S. Xavier <axavier@anl.gov>
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn import LearningSolver
from miplearn.problems.stab import MaxWeightStableSetInstance