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Tool: Assortment Optimizer

Tool function for assortment and pricing optimization.

LatentClassAssortmentOptimizer

Bases: object

Assortment optimizer for latent class models.

Implementation of the paper: Isabel Méndez-Díaz, Juan José Miranda-Bront, Gustavo Vulcano, Paula Zabala, A branch-and-cut algorithm for the latent-class logit assortment problem, Discrete Applied Mathematics, Volume 164, Part 1, 2014, Pages 246-263, ISSN 0166-218X, https://doi.org/10.1016/j.dam.2012.03.003.

Source code in choice_learn/toolbox/assortment_optimizer.py 
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class LatentClassAssortmentOptimizer(object):
    """Assortment optimizer for latent class models.

    Implementation of the paper:
    Isabel Méndez-Díaz, Juan José Miranda-Bront, Gustavo Vulcano, Paula Zabala,
    A branch-and-cut algorithm for the latent-class logit assortment problem,
    Discrete Applied Mathematics,
    Volume 164, Part 1,
    2014,
    Pages 246-263,
    ISSN 0166-218X,
    https://doi.org/10.1016/j.dam.2012.03.003.
    """

    def __new__(
        cls,
        solver,
        class_weights,
        class_utilities,
        itemwise_values,
        assortment_size,
        outside_option_given=False,
    ):
        """Create the AssortmentOptimizer object.

        Basically used to handle the choice of solver.

        Parameters
        ----------
        solver: str
            Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.
        class_weights: Iterable
            List of weights for each latent class.
        class_utilities: Iterable
            List of utilities for each item of each latent class.
            Must have a shape of (n_classes, n_items)
        itemwise_values: Iterable
            List of to-be-optimized values for each item, e.g. prices.
        assortment_size : int
            maximum size of the requested assortment.
        outside_option_given : bool
            Whether the outside option is given or not (and thus is automatically added).
        """
        if solver.lower() == "gurobi":
            from .gurobi_opt import GurobiLatentClassAssortmentOptimizer

            return GurobiLatentClassAssortmentOptimizer(
                class_weights=class_weights,
                class_utilities=class_utilities,
                itemwise_values=itemwise_values,
                assortment_size=assortment_size,
                outside_option_given=outside_option_given,
            )
        if solver.lower() == "or-tools" or solver.lower() == "ortools":
            from .or_tools_opt import ORToolsLatentClassAssortmentOptimizer

            return ORToolsLatentClassAssortmentOptimizer(
                class_weights=class_weights,
                class_utilities=class_utilities,
                itemwise_values=itemwise_values,
                assortment_size=assortment_size,
                outside_option_given=outside_option_given,
            )

        raise ValueError("Unknown solver. Please choose between 'gurobi' and 'or-tools'.")

__new__(solver, class_weights, class_utilities, itemwise_values, assortment_size, outside_option_given=False)

Create the AssortmentOptimizer object.

Basically used to handle the choice of solver.

Parameters:

Name Type Description Default
solver

Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.

 required
class_weights

List of weights for each latent class.

 required
class_utilities

List of utilities for each item of each latent class. Must have a shape of (n_classes, n_items)

 required
itemwise_values

List of to-be-optimized values for each item, e.g. prices.

 required
assortment_size int

maximum size of the requested assortment.

 required
outside_option_given bool

Whether the outside option is given or not (and thus is automatically added).

 False
Source code in choice_learn/toolbox/assortment_optimizer.py 
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def __new__(
    cls,
    solver,
    class_weights,
    class_utilities,
    itemwise_values,
    assortment_size,
    outside_option_given=False,
):
    """Create the AssortmentOptimizer object.

    Basically used to handle the choice of solver.

    Parameters
    ----------
    solver: str
        Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.
    class_weights: Iterable
        List of weights for each latent class.
    class_utilities: Iterable
        List of utilities for each item of each latent class.
        Must have a shape of (n_classes, n_items)
    itemwise_values: Iterable
        List of to-be-optimized values for each item, e.g. prices.
    assortment_size : int
        maximum size of the requested assortment.
    outside_option_given : bool
        Whether the outside option is given or not (and thus is automatically added).
    """
    if solver.lower() == "gurobi":
        from .gurobi_opt import GurobiLatentClassAssortmentOptimizer

        return GurobiLatentClassAssortmentOptimizer(
            class_weights=class_weights,
            class_utilities=class_utilities,
            itemwise_values=itemwise_values,
            assortment_size=assortment_size,
            outside_option_given=outside_option_given,
        )
    if solver.lower() == "or-tools" or solver.lower() == "ortools":
        from .or_tools_opt import ORToolsLatentClassAssortmentOptimizer

        return ORToolsLatentClassAssortmentOptimizer(
            class_weights=class_weights,
            class_utilities=class_utilities,
            itemwise_values=itemwise_values,
            assortment_size=assortment_size,
            outside_option_given=outside_option_given,
        )

    raise ValueError("Unknown solver. Please choose between 'gurobi' and 'or-tools'.")

LatentClassPricingOptimizer

Bases: object

Assortment optimizer for latent class models with additional pricing optimization.

Implementation of the paper: Isabel Méndez-Díaz, Juan José Miranda-Bront, Gustavo Vulcano, Paula Zabala, A branch-and-cut algorithm for the latent-class logit assortment problem, Discrete Applied Mathematics, Volume 164, Part 1, 2014, Pages 246-263, ISSN 0166-218X, https://doi.org/10.1016/j.dam.2012.03.003.

Source code in choice_learn/toolbox/assortment_optimizer.py 
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class LatentClassPricingOptimizer(object):
    """Assortment optimizer for latent class models with additional pricing optimization.

    Implementation of the paper:
    Isabel Méndez-Díaz, Juan José Miranda-Bront, Gustavo Vulcano, Paula Zabala,
    A branch-and-cut algorithm for the latent-class logit assortment problem,
    Discrete Applied Mathematics,
    Volume 164, Part 1,
    2014,
    Pages 246-263,
    ISSN 0166-218X,
    https://doi.org/10.1016/j.dam.2012.03.003.
    """

    def __new__(
        cls,
        solver,
        class_weights,
        class_utilities,
        itemwise_values,
        assortment_size,
        outside_option_given=False,
    ):
        """Create the AssortmentOptimizer object.

        Basically used to handle the choice of solver.

        Parameters
        ----------
        solver: str
            Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.
        class_weights: Iterable
            List of weights for each latent class.
        class_utilities: Iterable
            List of utilities for each item of each latent class.
            Must have a shape of (n_classes, n_items)
        itemwise_values: Iterable
            List of to-be-optimized values for each item, e.g. prices.
        assortment_size : int
            maximum size of the requested assortment.
        outside_option_given : bool
            Whether the outside option is given or not (and thus is automatically added).
        """
        if solver.lower() == "gurobi":
            from .gurobi_opt import GurobiLatentClassPricingOptimizer

            return GurobiLatentClassPricingOptimizer(
                class_weights=class_weights,
                class_utilities=class_utilities,
                itemwise_values=itemwise_values,
                assortment_size=assortment_size,
                outside_option_given=outside_option_given,
            )
        if solver.lower() == "or-tools" or solver.lower() == "ortools":
            from .or_tools_opt import ORToolsLatentClassPricingOptimizer

            return ORToolsLatentClassPricingOptimizer(
                class_weights=class_weights,
                class_utilities=class_utilities,
                itemwise_values=itemwise_values,
                assortment_size=assortment_size,
                outside_option_given=outside_option_given,
            )

        raise ValueError("Unknown solver. Please choose between 'gurobi' and 'or-tools'.")

__new__(solver, class_weights, class_utilities, itemwise_values, assortment_size, outside_option_given=False)

Create the AssortmentOptimizer object.

Basically used to handle the choice of solver.

Parameters:

Name Type Description Default
solver

Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.

 required
class_weights

List of weights for each latent class.

 required
class_utilities

List of utilities for each item of each latent class. Must have a shape of (n_classes, n_items)

 required
itemwise_values

List of to-be-optimized values for each item, e.g. prices.

 required
assortment_size int

maximum size of the requested assortment.

 required
outside_option_given bool

Whether the outside option is given or not (and thus is automatically added).

 False
Source code in choice_learn/toolbox/assortment_optimizer.py 
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def __new__(
    cls,
    solver,
    class_weights,
    class_utilities,
    itemwise_values,
    assortment_size,
    outside_option_given=False,
):
    """Create the AssortmentOptimizer object.

    Basically used to handle the choice of solver.

    Parameters
    ----------
    solver: str
        Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.
    class_weights: Iterable
        List of weights for each latent class.
    class_utilities: Iterable
        List of utilities for each item of each latent class.
        Must have a shape of (n_classes, n_items)
    itemwise_values: Iterable
        List of to-be-optimized values for each item, e.g. prices.
    assortment_size : int
        maximum size of the requested assortment.
    outside_option_given : bool
        Whether the outside option is given or not (and thus is automatically added).
    """
    if solver.lower() == "gurobi":
        from .gurobi_opt import GurobiLatentClassPricingOptimizer

        return GurobiLatentClassPricingOptimizer(
            class_weights=class_weights,
            class_utilities=class_utilities,
            itemwise_values=itemwise_values,
            assortment_size=assortment_size,
            outside_option_given=outside_option_given,
        )
    if solver.lower() == "or-tools" or solver.lower() == "ortools":
        from .or_tools_opt import ORToolsLatentClassPricingOptimizer

        return ORToolsLatentClassPricingOptimizer(
            class_weights=class_weights,
            class_utilities=class_utilities,
            itemwise_values=itemwise_values,
            assortment_size=assortment_size,
            outside_option_given=outside_option_given,
        )

    raise ValueError("Unknown solver. Please choose between 'gurobi' and 'or-tools'.")

MNLAssortmentOptimizer

Bases: object

Base class for assortment optimization.

Source code in choice_learn/toolbox/assortment_optimizer.py 
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class MNLAssortmentOptimizer(object):
    """Base class for assortment optimization."""

    def __new__(
        cls, solver, utilities, itemwise_values, assortment_size, outside_option_given=False
    ):
        """Create the AssortmentOptimizer object.

        Basically used to handle the choice of solver.

        Parameters
        ----------
        solver: str
            Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.
        utilities : Iterable
            List of utilities for each item.
        itemwise_values: Iterable
            List of to-be-optimized values for each item, e.g. prices.
        assortment_size : int
            maximum size of the requested assortment.
        outside_option_given : bool
            Whether the outside option is given or not (and thus is automatically added).
        """
        if solver.lower() == "gurobi":
            from .gurobi_opt import GurobiMNLAssortmentOptimizer

            return GurobiMNLAssortmentOptimizer(
                utilities=utilities,
                itemwise_values=itemwise_values,
                assortment_size=assortment_size,
                outside_option_given=outside_option_given,
            )
        if solver.lower() == "or-tools" or solver.lower() == "ortools":
            from .or_tools_opt import ORToolsMNLAssortmentOptimizer

            return ORToolsMNLAssortmentOptimizer(
                utilities=utilities,
                itemwise_values=itemwise_values,
                assortment_size=assortment_size,
                outside_option_given=outside_option_given,
            )

        raise ValueError("Unknown solver. Please choose between 'gurobi' and 'or-tools'.")

__new__(solver, utilities, itemwise_values, assortment_size, outside_option_given=False)

Create the AssortmentOptimizer object.

Basically used to handle the choice of solver.

Parameters:

Name Type Description Default
solver

Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.

 required
utilities Iterable

List of utilities for each item.

 required
itemwise_values

List of to-be-optimized values for each item, e.g. prices.

 required
assortment_size int

maximum size of the requested assortment.

 required
outside_option_given bool

Whether the outside option is given or not (and thus is automatically added).

 False
Source code in choice_learn/toolbox/assortment_optimizer.py 
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def __new__(
    cls, solver, utilities, itemwise_values, assortment_size, outside_option_given=False
):
    """Create the AssortmentOptimizer object.

    Basically used to handle the choice of solver.

    Parameters
    ----------
    solver: str
        Name of the solver to be used. Currently only "gurobi" and "or-tools" is supported.
    utilities : Iterable
        List of utilities for each item.
    itemwise_values: Iterable
        List of to-be-optimized values for each item, e.g. prices.
    assortment_size : int
        maximum size of the requested assortment.
    outside_option_given : bool
        Whether the outside option is given or not (and thus is automatically added).
    """
    if solver.lower() == "gurobi":
        from .gurobi_opt import GurobiMNLAssortmentOptimizer

        return GurobiMNLAssortmentOptimizer(
            utilities=utilities,
            itemwise_values=itemwise_values,
            assortment_size=assortment_size,
            outside_option_given=outside_option_given,
        )
    if solver.lower() == "or-tools" or solver.lower() == "ortools":
        from .or_tools_opt import ORToolsMNLAssortmentOptimizer

        return ORToolsMNLAssortmentOptimizer(
            utilities=utilities,
            itemwise_values=itemwise_values,
            assortment_size=assortment_size,
            outside_option_given=outside_option_given,
        )

    raise ValueError("Unknown solver. Please choose between 'gurobi' and 'or-tools'.")