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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.instance</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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 gzip
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import json
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from abc import ABC, abstractmethod
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from typing import Any, List
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import numpy as np
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from miplearn.types import TrainingSample
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class Instance(ABC):
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"""
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Abstract class holding all the data necessary to generate a concrete model of the
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problem.
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In the knapsack problem, for example, this class could hold the number of items,
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their weights and costs, as well as the size of the knapsack. Objects
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implementing this class are able to convert themselves into a concrete
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optimization model, which can be optimized by a solver, or into arrays of
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features, which can be provided as inputs to machine learning models.
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"""
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def __init__(self):
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self.training_data: List[TrainingSample] = []
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@abstractmethod
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def to_model(self) -> Any:
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"""
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Returns the optimization model corresponding to this instance.
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"""
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pass
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def get_instance_features(self):
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"""
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Returns a 1-dimensional Numpy array of (numerical) features describing the
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entire instance.
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The array is used by LearningSolver to determine how similar two instances
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are. It may also be used to predict, in combination with variable-specific
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features, the values of binary decision variables in the problem.
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There is not necessarily a one-to-one correspondence between models and
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instance features: the features may encode only part of the data necessary to
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generate the complete model. Features may also be statistics computed from
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the original data. For example, in the knapsack problem, an implementation
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may decide to provide as instance features only the average weights, average
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prices, number of items and the size of the knapsack.
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The returned array MUST have the same length for all relevant instances of
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the problem. If two instances map into arrays of different lengths,
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they cannot be solved by the same LearningSolver object.
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By default, returns [0].
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"""
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return np.zeros(1)
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def get_variable_features(self, var, index):
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"""
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Returns a 1-dimensional array of (numerical) features describing a particular
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decision variable.
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The argument `var` is a pyomo.core.Var object, which represents a collection
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of decision variables. The argument `index` specifies which variable in the
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collection is the relevant one.
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In combination with instance features, variable features are used by
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LearningSolver to predict, among other things, the optimal value of each
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decision variable before the optimization takes place. In the knapsack
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problem, for example, an implementation could provide as variable features
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the weight and the price of a specific item.
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Like instance features, the arrays returned by this method MUST have the same
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length for all variables within the same category, for all relevant instances
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of the problem.
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By default, returns [0].
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"""
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return np.zeros(1)
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def get_variable_category(self, var, index):
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"""
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Returns the category (a string, an integer or any hashable type) for each
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decision variable.
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If two variables have the same category, LearningSolver will use the same
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internal ML model to predict the values of both variables. If the returned
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category is None, ML models will ignore the variable.
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By default, returns "default".
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"""
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return "default"
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def get_constraint_features(self, cid):
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return np.zeros(1)
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def get_constraint_category(self, cid):
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return cid
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def has_static_lazy_constraints(self):
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return False
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def has_dynamic_lazy_constraints(self):
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return False
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def is_constraint_lazy(self, cid):
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return False
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def find_violated_lazy_constraints(self, model):
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"""
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Returns lazy constraint violations found for the current solution.
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After solving a model, LearningSolver will ask the instance to identify which
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lazy constraints are violated by the current solution. For each identified
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violation, LearningSolver will then call the build_lazy_constraint, add the
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generated Pyomo constraint to the model, then resolve the problem. The
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process repeats until no further lazy constraint violations are found.
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Each "violation" is simply a string, a tuple or any other hashable type which
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allows the instance to identify unambiguously which lazy constraint should be
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generated. In the Traveling Salesman Problem, for example, a subtour
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violation could be a frozen set containing the cities in the subtour.
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For a concrete example, see TravelingSalesmanInstance.
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"""
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return []
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def build_lazy_constraint(self, model, violation):
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"""
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Returns a Pyomo constraint which fixes a given violation.
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This method is typically called immediately after
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find_violated_lazy_constraints. The violation object provided to this method
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|
is exactly the same object returned earlier by
|
|
find_violated_lazy_constraints. After some training, LearningSolver may
|
|
decide to proactively build some lazy constraints at the beginning of the
|
|
optimization process, before a solution is even available. In this case,
|
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build_lazy_constraints will be called without a corresponding call to
|
|
find_violated_lazy_constraints.
|
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The implementation should not directly add the constraint to the model. The
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constraint will be added by LearningSolver after the method returns.
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For a concrete example, see TravelingSalesmanInstance.
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"""
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pass
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def find_violated_user_cuts(self, model):
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return []
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def build_user_cut(self, model, violation):
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pass
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def load(self, filename):
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with gzip.GzipFile(filename, "r") as f:
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data = json.loads(f.read().decode("utf-8"))
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self.__dict__ = data
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def dump(self, filename):
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data = json.dumps(self.__dict__, indent=2).encode("utf-8")
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with gzip.GzipFile(filename, "w") as f:
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f.write(data)</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.instance.Instance"><code class="flex name class">
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<span>class <span class="ident">Instance</span></span>
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</code></dt>
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<dd>
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<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
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problem.</p>
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<p>In the knapsack problem, for example, this class could hold the number of items,
|
|
their weights and costs, as well as the size of the knapsack. Objects
|
|
implementing this class are able to convert themselves into a concrete
|
|
optimization model, which can be optimized by a solver, or into arrays of
|
|
features, which can be provided as inputs to machine learning models.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class Instance(ABC):
|
|
"""
|
|
Abstract class holding all the data necessary to generate a concrete model of the
|
|
problem.
|
|
|
|
In the knapsack problem, for example, this class could hold the number of items,
|
|
their weights and costs, as well as the size of the knapsack. Objects
|
|
implementing this class are able to convert themselves into a concrete
|
|
optimization model, which can be optimized by a solver, or into arrays of
|
|
features, which can be provided as inputs to machine learning models.
|
|
"""
|
|
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|
def __init__(self):
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|
self.training_data: List[TrainingSample] = []
|
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@abstractmethod
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def to_model(self) -> Any:
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|
"""
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Returns the optimization model corresponding to this instance.
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|
"""
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pass
|
|
|
|
def get_instance_features(self):
|
|
"""
|
|
Returns a 1-dimensional Numpy array of (numerical) features describing the
|
|
entire instance.
|
|
|
|
The array is used by LearningSolver to determine how similar two instances
|
|
are. It may also be used to predict, in combination with variable-specific
|
|
features, the values of binary decision variables in the problem.
|
|
|
|
There is not necessarily a one-to-one correspondence between models and
|
|
instance features: the features may encode only part of the data necessary to
|
|
generate the complete model. Features may also be statistics computed from
|
|
the original data. For example, in the knapsack problem, an implementation
|
|
may decide to provide as instance features only the average weights, average
|
|
prices, number of items and the size of the knapsack.
|
|
|
|
The returned array MUST have the same length for all relevant instances of
|
|
the problem. If two instances map into arrays of different lengths,
|
|
they cannot be solved by the same LearningSolver object.
|
|
|
|
By default, returns [0].
|
|
"""
|
|
return np.zeros(1)
|
|
|
|
def get_variable_features(self, var, index):
|
|
"""
|
|
Returns a 1-dimensional array of (numerical) features describing a particular
|
|
decision variable.
|
|
|
|
The argument `var` is a pyomo.core.Var object, which represents a collection
|
|
of decision variables. The argument `index` specifies which variable in the
|
|
collection is the relevant one.
|
|
|
|
In combination with instance features, variable features are used by
|
|
LearningSolver to predict, among other things, the optimal value of each
|
|
decision variable before the optimization takes place. In the knapsack
|
|
problem, for example, an implementation could provide as variable features
|
|
the weight and the price of a specific item.
|
|
|
|
Like instance features, the arrays returned by this method MUST have the same
|
|
length for all variables within the same category, for all relevant instances
|
|
of the problem.
|
|
|
|
By default, returns [0].
|
|
"""
|
|
return np.zeros(1)
|
|
|
|
def get_variable_category(self, var, index):
|
|
"""
|
|
Returns the category (a string, an integer or any hashable type) for each
|
|
decision variable.
|
|
|
|
If two variables have the same category, LearningSolver will use the same
|
|
internal ML model to predict the values of both variables. If the returned
|
|
category is None, ML models will ignore the variable.
|
|
|
|
By default, returns "default".
|
|
"""
|
|
return "default"
|
|
|
|
def get_constraint_features(self, cid):
|
|
return np.zeros(1)
|
|
|
|
def get_constraint_category(self, cid):
|
|
return cid
|
|
|
|
def has_static_lazy_constraints(self):
|
|
return False
|
|
|
|
def has_dynamic_lazy_constraints(self):
|
|
return False
|
|
|
|
def is_constraint_lazy(self, cid):
|
|
return False
|
|
|
|
def find_violated_lazy_constraints(self, model):
|
|
"""
|
|
Returns lazy constraint violations found for the current solution.
|
|
|
|
After solving a model, LearningSolver will ask the instance to identify which
|
|
lazy constraints are violated by the current solution. For each identified
|
|
violation, LearningSolver will then call the build_lazy_constraint, add the
|
|
generated Pyomo constraint to the model, then resolve the problem. The
|
|
process repeats until no further lazy constraint violations are found.
|
|
|
|
Each "violation" is simply a string, a tuple or any other hashable type which
|
|
allows the instance to identify unambiguously which lazy constraint should be
|
|
generated. In the Traveling Salesman Problem, for example, a subtour
|
|
violation could be a frozen set containing the cities in the subtour.
|
|
|
|
For a concrete example, see TravelingSalesmanInstance.
|
|
"""
|
|
return []
|
|
|
|
def build_lazy_constraint(self, model, violation):
|
|
"""
|
|
Returns a Pyomo constraint which fixes a given violation.
|
|
|
|
This method is typically called immediately after
|
|
find_violated_lazy_constraints. The violation object provided to this method
|
|
is exactly the same object returned earlier by
|
|
find_violated_lazy_constraints. After some training, LearningSolver may
|
|
decide to proactively build some lazy constraints at the beginning of the
|
|
optimization process, before a solution is even available. In this case,
|
|
build_lazy_constraints will be called without a corresponding call to
|
|
find_violated_lazy_constraints.
|
|
|
|
The implementation should not directly add the constraint to the model. The
|
|
constraint will be added by LearningSolver after the method returns.
|
|
|
|
For a concrete example, see TravelingSalesmanInstance.
|
|
"""
|
|
pass
|
|
|
|
def find_violated_user_cuts(self, model):
|
|
return []
|
|
|
|
def build_user_cut(self, model, violation):
|
|
pass
|
|
|
|
def load(self, filename):
|
|
with gzip.GzipFile(filename, "r") as f:
|
|
data = json.loads(f.read().decode("utf-8"))
|
|
self.__dict__ = data
|
|
|
|
def dump(self, filename):
|
|
data = json.dumps(self.__dict__, indent=2).encode("utf-8")
|
|
with gzip.GzipFile(filename, "w") as f:
|
|
f.write(data)</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Subclasses</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.problems.knapsack.MultiKnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</a></li>
|
|
<li><a title="miplearn.problems.knapsack.KnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></li>
|
|
<li><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></li>
|
|
<li><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></li>
|
|
<li><a title="miplearn.problems.stab.MaxWeightStableSetInstance" href="problems/stab.html#miplearn.problems.stab.MaxWeightStableSetInstance">MaxWeightStableSetInstance</a></li>
|
|
<li><a title="miplearn.problems.tsp.TravelingSalesmanInstance" href="problems/tsp.html#miplearn.problems.tsp.TravelingSalesmanInstance">TravelingSalesmanInstance</a></li>
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|
<li><a title="miplearn.components.steps.tests.test_convert_tight.SampleInstance" href="components/steps/tests/test_convert_tight.html#miplearn.components.steps.tests.test_convert_tight.SampleInstance">SampleInstance</a></li>
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</ul>
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<h3>Methods</h3>
|
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<dl>
|
|
<dt id="miplearn.instance.Instance.build_lazy_constraint"><code class="name flex">
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|
<span>def <span class="ident">build_lazy_constraint</span></span>(<span>self, model, violation)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns a Pyomo constraint which fixes a given violation.</p>
|
|
<p>This method is typically called immediately after
|
|
find_violated_lazy_constraints. The violation object provided to this method
|
|
is exactly the same object returned earlier by
|
|
find_violated_lazy_constraints. After some training, LearningSolver may
|
|
decide to proactively build some lazy constraints at the beginning of the
|
|
optimization process, before a solution is even available. In this case,
|
|
build_lazy_constraints will be called without a corresponding call to
|
|
find_violated_lazy_constraints.</p>
|
|
<p>The implementation should not directly add the constraint to the model. The
|
|
constraint will be added by LearningSolver after the method returns.</p>
|
|
<p>For a concrete example, see TravelingSalesmanInstance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def build_lazy_constraint(self, model, violation):
|
|
"""
|
|
Returns a Pyomo constraint which fixes a given violation.
|
|
|
|
This method is typically called immediately after
|
|
find_violated_lazy_constraints. The violation object provided to this method
|
|
is exactly the same object returned earlier by
|
|
find_violated_lazy_constraints. After some training, LearningSolver may
|
|
decide to proactively build some lazy constraints at the beginning of the
|
|
optimization process, before a solution is even available. In this case,
|
|
build_lazy_constraints will be called without a corresponding call to
|
|
find_violated_lazy_constraints.
|
|
|
|
The implementation should not directly add the constraint to the model. The
|
|
constraint will be added by LearningSolver after the method returns.
|
|
|
|
For a concrete example, see TravelingSalesmanInstance.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.build_user_cut"><code class="name flex">
|
|
<span>def <span class="ident">build_user_cut</span></span>(<span>self, model, violation)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def build_user_cut(self, model, violation):
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.dump"><code class="name flex">
|
|
<span>def <span class="ident">dump</span></span>(<span>self, filename)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def dump(self, filename):
|
|
data = json.dumps(self.__dict__, indent=2).encode("utf-8")
|
|
with gzip.GzipFile(filename, "w") as f:
|
|
f.write(data)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.find_violated_lazy_constraints"><code class="name flex">
|
|
<span>def <span class="ident">find_violated_lazy_constraints</span></span>(<span>self, model)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns lazy constraint violations found for the current solution.</p>
|
|
<p>After solving a model, LearningSolver will ask the instance to identify which
|
|
lazy constraints are violated by the current solution. For each identified
|
|
violation, LearningSolver will then call the build_lazy_constraint, add the
|
|
generated Pyomo constraint to the model, then resolve the problem. The
|
|
process repeats until no further lazy constraint violations are found.</p>
|
|
<p>Each "violation" is simply a string, a tuple or any other hashable type which
|
|
allows the instance to identify unambiguously which lazy constraint should be
|
|
generated. In the Traveling Salesman Problem, for example, a subtour
|
|
violation could be a frozen set containing the cities in the subtour.</p>
|
|
<p>For a concrete example, see TravelingSalesmanInstance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def find_violated_lazy_constraints(self, model):
|
|
"""
|
|
Returns lazy constraint violations found for the current solution.
|
|
|
|
After solving a model, LearningSolver will ask the instance to identify which
|
|
lazy constraints are violated by the current solution. For each identified
|
|
violation, LearningSolver will then call the build_lazy_constraint, add the
|
|
generated Pyomo constraint to the model, then resolve the problem. The
|
|
process repeats until no further lazy constraint violations are found.
|
|
|
|
Each "violation" is simply a string, a tuple or any other hashable type which
|
|
allows the instance to identify unambiguously which lazy constraint should be
|
|
generated. In the Traveling Salesman Problem, for example, a subtour
|
|
violation could be a frozen set containing the cities in the subtour.
|
|
|
|
For a concrete example, see TravelingSalesmanInstance.
|
|
"""
|
|
return []</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.find_violated_user_cuts"><code class="name flex">
|
|
<span>def <span class="ident">find_violated_user_cuts</span></span>(<span>self, model)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def find_violated_user_cuts(self, model):
|
|
return []</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.get_constraint_category"><code class="name flex">
|
|
<span>def <span class="ident">get_constraint_category</span></span>(<span>self, cid)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def get_constraint_category(self, cid):
|
|
return cid</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.get_constraint_features"><code class="name flex">
|
|
<span>def <span class="ident">get_constraint_features</span></span>(<span>self, cid)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def get_constraint_features(self, cid):
|
|
return np.zeros(1)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.get_instance_features"><code class="name flex">
|
|
<span>def <span class="ident">get_instance_features</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns a 1-dimensional Numpy array of (numerical) features describing the
|
|
entire instance.</p>
|
|
<p>The array is used by LearningSolver to determine how similar two instances
|
|
are. It may also be used to predict, in combination with variable-specific
|
|
features, the values of binary decision variables in the problem.</p>
|
|
<p>There is not necessarily a one-to-one correspondence between models and
|
|
instance features: the features may encode only part of the data necessary to
|
|
generate the complete model. Features may also be statistics computed from
|
|
the original data. For example, in the knapsack problem, an implementation
|
|
may decide to provide as instance features only the average weights, average
|
|
prices, number of items and the size of the knapsack.</p>
|
|
<p>The returned array MUST have the same length for all relevant instances of
|
|
the problem. If two instances map into arrays of different lengths,
|
|
they cannot be solved by the same LearningSolver object.</p>
|
|
<p>By default, returns [0].</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def get_instance_features(self):
|
|
"""
|
|
Returns a 1-dimensional Numpy array of (numerical) features describing the
|
|
entire instance.
|
|
|
|
The array is used by LearningSolver to determine how similar two instances
|
|
are. It may also be used to predict, in combination with variable-specific
|
|
features, the values of binary decision variables in the problem.
|
|
|
|
There is not necessarily a one-to-one correspondence between models and
|
|
instance features: the features may encode only part of the data necessary to
|
|
generate the complete model. Features may also be statistics computed from
|
|
the original data. For example, in the knapsack problem, an implementation
|
|
may decide to provide as instance features only the average weights, average
|
|
prices, number of items and the size of the knapsack.
|
|
|
|
The returned array MUST have the same length for all relevant instances of
|
|
the problem. If two instances map into arrays of different lengths,
|
|
they cannot be solved by the same LearningSolver object.
|
|
|
|
By default, returns [0].
|
|
"""
|
|
return np.zeros(1)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.get_variable_category"><code class="name flex">
|
|
<span>def <span class="ident">get_variable_category</span></span>(<span>self, var, index)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns the category (a string, an integer or any hashable type) for each
|
|
decision variable.</p>
|
|
<p>If two variables have the same category, LearningSolver will use the same
|
|
internal ML model to predict the values of both variables. If the returned
|
|
category is None, ML models will ignore the variable.</p>
|
|
<p>By default, returns "default".</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def get_variable_category(self, var, index):
|
|
"""
|
|
Returns the category (a string, an integer or any hashable type) for each
|
|
decision variable.
|
|
|
|
If two variables have the same category, LearningSolver will use the same
|
|
internal ML model to predict the values of both variables. If the returned
|
|
category is None, ML models will ignore the variable.
|
|
|
|
By default, returns "default".
|
|
"""
|
|
return "default"</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.get_variable_features"><code class="name flex">
|
|
<span>def <span class="ident">get_variable_features</span></span>(<span>self, var, index)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns a 1-dimensional array of (numerical) features describing a particular
|
|
decision variable.</p>
|
|
<p>The argument <code>var</code> is a pyomo.core.Var object, which represents a collection
|
|
of decision variables. The argument <code>index</code> specifies which variable in the
|
|
collection is the relevant one.</p>
|
|
<p>In combination with instance features, variable features are used by
|
|
LearningSolver to predict, among other things, the optimal value of each
|
|
decision variable before the optimization takes place. In the knapsack
|
|
problem, for example, an implementation could provide as variable features
|
|
the weight and the price of a specific item.</p>
|
|
<p>Like instance features, the arrays returned by this method MUST have the same
|
|
length for all variables within the same category, for all relevant instances
|
|
of the problem.</p>
|
|
<p>By default, returns [0].</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def get_variable_features(self, var, index):
|
|
"""
|
|
Returns a 1-dimensional array of (numerical) features describing a particular
|
|
decision variable.
|
|
|
|
The argument `var` is a pyomo.core.Var object, which represents a collection
|
|
of decision variables. The argument `index` specifies which variable in the
|
|
collection is the relevant one.
|
|
|
|
In combination with instance features, variable features are used by
|
|
LearningSolver to predict, among other things, the optimal value of each
|
|
decision variable before the optimization takes place. In the knapsack
|
|
problem, for example, an implementation could provide as variable features
|
|
the weight and the price of a specific item.
|
|
|
|
Like instance features, the arrays returned by this method MUST have the same
|
|
length for all variables within the same category, for all relevant instances
|
|
of the problem.
|
|
|
|
By default, returns [0].
|
|
"""
|
|
return np.zeros(1)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.has_dynamic_lazy_constraints"><code class="name flex">
|
|
<span>def <span class="ident">has_dynamic_lazy_constraints</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def has_dynamic_lazy_constraints(self):
|
|
return False</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.has_static_lazy_constraints"><code class="name flex">
|
|
<span>def <span class="ident">has_static_lazy_constraints</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def has_static_lazy_constraints(self):
|
|
return False</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.is_constraint_lazy"><code class="name flex">
|
|
<span>def <span class="ident">is_constraint_lazy</span></span>(<span>self, cid)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def is_constraint_lazy(self, cid):
|
|
return False</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.load"><code class="name flex">
|
|
<span>def <span class="ident">load</span></span>(<span>self, filename)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def load(self, filename):
|
|
with gzip.GzipFile(filename, "r") as f:
|
|
data = json.loads(f.read().decode("utf-8"))
|
|
self.__dict__ = data</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.instance.Instance.to_model"><code class="name flex">
|
|
<span>def <span class="ident">to_model</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Returns the optimization model corresponding to this instance.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">@abstractmethod
|
|
def to_model(self) -> Any:
|
|
"""
|
|
Returns the optimization model corresponding to this instance.
|
|
"""
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.instance.Instance" href="#miplearn.instance.Instance">Instance</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.build_user_cut" href="#miplearn.instance.Instance.build_user_cut">build_user_cut</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.dump" href="#miplearn.instance.Instance.dump">dump</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.find_violated_user_cuts" href="#miplearn.instance.Instance.find_violated_user_cuts">find_violated_user_cuts</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_constraint_category" href="#miplearn.instance.Instance.get_constraint_category">get_constraint_category</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_constraint_features" href="#miplearn.instance.Instance.get_constraint_features">get_constraint_features</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.has_dynamic_lazy_constraints" href="#miplearn.instance.Instance.has_dynamic_lazy_constraints">has_dynamic_lazy_constraints</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.has_static_lazy_constraints" href="#miplearn.instance.Instance.has_static_lazy_constraints">has_static_lazy_constraints</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.is_constraint_lazy" href="#miplearn.instance.Instance.is_constraint_lazy">is_constraint_lazy</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.load" href="#miplearn.instance.Instance.load">load</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.to_model" href="#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</nav>
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