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Add types to knapsack.py

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This commit is contained in:
Alinson S. Xavier
2021-04-07 20:21:28 -05:00
parent 0232219a0e
commit 2c93ff38fc
1 changed files with 73 additions and 59 deletions
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132
miplearn/problems/knapsack.py
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@@ -1,16 +1,16 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import List, Dict
from typing import List, Dict, Optional, Hashable, Any
import numpy as np
import pyomo.environ as pe
from overrides import overrides
from scipy.stats import uniform, randint
from scipy.stats import uniform, randint, rv_discrete
from scipy.stats.distributions import rv_frozen
from miplearn.instance.base import Instance
from miplearn.types import VariableName
from miplearn.types import VariableName, Category
class ChallengeA:
@@ -23,10 +23,10 @@ class ChallengeA:
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
seed: int = 42,
n_training_instances: int = 500,
n_test_instances: int = 50,
) -> None:
np.random.seed(seed)
self.gen = MultiKnapsackGenerator(
n=randint(low=250, high=251),
@@ -57,7 +57,12 @@ class MultiKnapsackInstance(Instance):
same size and items don't shuffle around.
"""
def __init__(self, prices, capacities, weights):
def __init__(
self,
prices: np.ndarray,
capacities: np.ndarray,
weights: np.ndarray,
) -> None:
super().__init__()
assert isinstance(prices, np.ndarray)
assert isinstance(capacities, np.ndarray)
@@ -72,11 +77,11 @@ class MultiKnapsackInstance(Instance):
self.varname_to_index = {f"x[{i}]": i for i in range(self.n)}
@overrides
def to_model(self):
def to_model(self) -> pe.ConcreteModel:
model = pe.ConcreteModel()
model.x = pe.Var(range(self.n), domain=pe.Binary)
model.OBJ = pe.Objective(
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
expr=sum(model.x[j] * self.prices[j] for j in range(self.n)),
sense=pe.maximize,
)
model.eq_capacity = pe.ConstraintList()
@@ -89,8 +94,8 @@ class MultiKnapsackInstance(Instance):
return model
@overrides
def get_instance_features(self):
return [np.mean(self.prices)] + list(self.capacities)
def get_instance_features(self) -> List[float]:
return [float(np.mean(self.prices))] + list(self.capacities)
@overrides
def get_variable_features(self, var_name: VariableName) -> List[float]:
@@ -98,53 +103,57 @@ class MultiKnapsackInstance(Instance):
return [self.prices[index]] + list(self.weights[:, index])
# noinspection PyPep8Naming
class MultiKnapsackGenerator:
def __init__(
self,
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=500),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=False,
w_jitter=uniform(loc=1.0, scale=0.0),
round=True,
n: rv_frozen = randint(low=100, high=101),
m: rv_frozen = randint(low=30, high=31),
w: rv_frozen = randint(low=0, high=1000),
K: rv_frozen = randint(low=500, high=500),
u: rv_frozen = uniform(loc=0.0, scale=1.0),
alpha: rv_frozen = uniform(loc=0.25, scale=0.0),
fix_w: bool = False,
w_jitter: rv_frozen = uniform(loc=1.0, scale=0.0),
round: bool = True,
):
"""Initialize the problem generator.
Instances have a random number of items (or variables) and a random number of knapsacks
(or constraints), as specified by the provided probability distributions `n` and `m`,
respectively. The weight of each item `i` on knapsack `j` is sampled independently from
the provided distribution `w`. The capacity of knapsack `j` is set to:
Instances have a random number of items (or variables) and a random number of
knapsacks (or constraints), as specified by the provided probability
distributions `n` and `m`, respectively. The weight of each item `i` on
knapsack `j` is sampled independently from the provided distribution `w`. The
capacity of knapsack `j` is set to:
alpha_j * sum(w[i,j] for i in range(n)),
where `alpha_j`, the tightness ratio, is sampled from the provided probability
distribution `alpha`. To make the instances more challenging, the costs of the items
are linearly correlated to their average weights. More specifically, the weight of each
item `i` is set to:
where `alpha_j`, the tightness ratio, is sampled from the provided
probability distribution `alpha`. To make the instances more challenging,
the costs of the items are linearly correlated to their average weights. More
specifically, the weight of each item `i` is set to:
sum(w[i,j]/m for j in range(m)) + K * u_i,
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
from the provided probability distributions. Note that `K` is only sample once for the
entire instance.
where `K`, the correlation coefficient, and `u_i`, the correlation
multiplier, are sampled from the provided probability distributions. Note
that `K` is only sample once for the entire instance.
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
also implies that n and m are kept fixed. Although the prices and capacities are derived
from w[i,j], as long as u and K are not constants, the generated instances will still not
be completely identical.
If fix_w=True is provided, then w[i,j] are kept the same in all generated
instances. This also implies that n and m are kept fixed. Although the prices
and capacities are derived from w[i,j], as long as u and K are not constants,
the generated instances will still not 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
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
perturbed weights instead.
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 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 perturbed weights
instead.
By default, all generated prices, weights and capacities are rounded to the nearest integer
number. If `round=False` is provided, this rounding will be disabled.
By default, all generated prices, weights and capacities are rounded to the
nearest integer number. If `round=False` is provided, this rounding will be
disabled.
Parameters
----------
@@ -161,11 +170,13 @@ class MultiKnapsackGenerator:
alpha: rv_continuous
Probability distribution for the tightness ratio
fix_w: boolean
If true, weights are kept the same (minus the noise from w_jitter) in all instances
If true, weights are kept the same (minus the noise from w_jitter) in all
instances
w_jitter: rv_continuous
Probability distribution for random noise added to the weights
round: boolean
If true, all prices, weights and capacities are rounded to the nearest integer
If true, all prices, weights and capacities are rounded to the nearest
integer
"""
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
assert isinstance(m, rv_frozen), "m should be a SciPy probability distribution"
@@ -183,11 +194,16 @@ class MultiKnapsackGenerator:
self.n = n
self.m = m
self.w = w
self.K = K
self.u = u
self.K = K
self.alpha = alpha
self.w_jitter = w_jitter
self.round = round
self.fix_n: Optional[int] = None
self.fix_m: Optional[int] = None
self.fix_w: Optional[np.ndarray] = None
self.fix_u: Optional[np.ndarray] = None
self.fix_K: Optional[float] = None
if fix_w:
self.fix_n = self.n.rvs()
@@ -195,16 +211,14 @@ class MultiKnapsackGenerator:
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():
def generate(self, n_samples: int) -> List[MultiKnapsackInstance]:
def _sample() -> MultiKnapsackInstance:
if self.fix_w is not None:
assert self.fix_m is not None
assert self.fix_n is not None
assert self.fix_u is not None
assert self.fix_K is not None
n = self.fix_n
m = self.fix_m
w = self.fix_w
@@ -249,7 +263,7 @@ class KnapsackInstance(Instance):
}
@overrides
def to_model(self):
def to_model(self) -> pe.ConcreteModel:
model = pe.ConcreteModel()
items = range(len(self.weights))
model.x = pe.Var(items, domain=pe.Binary)
@@ -262,14 +276,14 @@ class KnapsackInstance(Instance):
return model
@overrides
def get_instance_features(self):
def get_instance_features(self) -> List[float]:
return [
self.capacity,
np.average(self.weights),
]
@overrides
def get_variable_features(self, var_name):
def get_variable_features(self, var_name: VariableName) -> List[Category]:
item = self.varname_to_item[var_name]
return [
self.weights[item],
@@ -292,7 +306,7 @@ class GurobiKnapsackInstance(KnapsackInstance):
super().__init__(weights, prices, capacity)
@overrides
def to_model(self):
def to_model(self) -> Any:
import gurobipy as gp
from gurobipy import GRB