Source code for ott.initializers.quadratic.initializers

# Copyright OTT-JAX
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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import abc
from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence, Tuple

import jax
import jax.numpy as jnp

from ott.geometry import geometry

  from ott.problems.linear import linear_problem
  from ott.problems.quadratic import quadratic_problem

__all__ = ["BaseQuadraticInitializer", "QuadraticInitializer"]

class BaseQuadraticInitializer(abc.ABC):
  """Base class for quadratic initializers.

    kwargs: Keyword arguments.

  def __init__(self, **kwargs: Any):
    self._kwargs = kwargs

  def __call__(
      self, quad_prob: "quadratic_problem.QuadraticProblem", **kwargs: Any
  ) -> "linear_problem.LinearProblem":
    """Compute the initial linearization of a quadratic problem.

      quad_prob: Quadratic problem to linearize.
      kwargs: Additional keyword arguments.

      Linear problem.
    from ott.problems.linear import linear_problem

    n, m = quad_prob.geom_xx.shape[0], quad_prob.geom_yy.shape[0]
    geom = self._create_geometry(quad_prob, **kwargs)
    assert geom.shape == (n, m), (
        f"Expected geometry of shape `{n, m}`, "
        f"found `{geom.shape}`."
    return linear_problem.LinearProblem(

  def _create_geometry(
      self, quad_prob: "quadratic_problem.QuadraticProblem", **kwargs: Any
  ) -> geometry.Geometry:
    """Compute initial geometry for linearization.

      quad_prob: Quadratic problem.
      kwargs: Additional keyword arguments.

      Geometry used to initialize the linearized problem.

  def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]:  # noqa: D102
    return [], self._kwargs

  def tree_unflatten(  # noqa: D102
      cls, aux_data: Dict[str, Any], children: Sequence[Any]
  ) -> "BaseQuadraticInitializer":
    return cls(*children, **aux_data)

[docs] class QuadraticInitializer(BaseQuadraticInitializer): r"""Initialize a linear problem locally around a selected coupling. If the problem is balanced (``tau_a = 1`` and ``tau_b = 1``), the equation of the cost follows eq. 6, p. 1 of :cite:`peyre:16`. If the problem is unbalanced (``tau_a < 1`` or ``tau_b < 1``), there are two possible cases. A first possibility is to introduce a quadratic KL divergence on the marginals in the objective as done in :cite:`sejourne:21` (``gw_unbalanced_correction = True``), which in turns modifies the local cost matrix. Alternatively, it could be possible to leave the formulation of the local cost unchanged, i.e. follow eq. 6, p. 1 of :cite:`peyre:16` (``gw_unbalanced_correction = False``) and include the unbalanced terms at the level of the linear problem only. Let :math:`P` [num_a, num_b] be the transport matrix, `cost_xx` is the cost matrix of `geom_xx` and `cost_yy` is the cost matrix of `geom_yy`. `left_x` and `right_y` depend on the loss chosen for GW. `gw_unbalanced_correction` is flag indicating whether the unbalanced correction applies. The equation of the local cost can be written as: .. math:: \text{marginal_dep_term} + \text{left}_x(\text{cost_xx}) P \text{right}_y(\text{cost_yy}) + \text{unbalanced_correction} When working with the fused problem, a linear term is added to the cost matrix: `cost_matrix` += `fused_penalty` * `geom_xy.cost_matrix` Args: init_coupling: The coupling to use for initialization. If :obj:`None`, defaults to the product coupling :math:`ab^T`. """ def __init__( self, init_coupling: Optional[jnp.ndarray] = None, **kwargs: Any ): super().__init__(**kwargs) self.init_coupling = init_coupling def _create_geometry( self, quad_prob: "quadratic_problem.QuadraticProblem", *, epsilon: float, relative_epsilon: Optional[bool] = None, **kwargs: Any, ) -> geometry.Geometry: """Compute initial geometry for linearization. Args: quad_prob: Quadratic OT problem. epsilon: Epsilon regularization. relative_epsilon: Flag, use `relative_epsilon` or not in geometry. kwargs: Keyword arguments for :class:`~ott.geometry.geometry.Geometry`. Returns: The initial geometry used to initialize the linearized problem. """ from ott.problems.quadratic import quadratic_problem del kwargs marginal_cost = quad_prob.marginal_dependent_cost(quad_prob.a, quad_prob.b) geom_xx, geom_yy = quad_prob.geom_xx, quad_prob.geom_yy h1, h2 = quad_prob.quad_loss if self.init_coupling is None: tmp1 = quadratic_problem.apply_cost(geom_xx, quad_prob.a, axis=1, fn=h1) tmp2 = quadratic_problem.apply_cost(geom_yy, quad_prob.b, axis=1, fn=h2) tmp = jnp.outer(tmp1, tmp2) else: tmp1 = h1.func(geom_xx.cost_matrix) tmp2 = h2.func(geom_yy.cost_matrix) tmp = tmp1 @ self.init_coupling @ tmp2.T if quad_prob.is_balanced: cost_matrix = marginal_cost.cost_matrix - tmp else: # initialize epsilon for Unbalanced GW according to Sejourne et. al (2021) init_transport = jnp.outer(quad_prob.a, quad_prob.b) marginal_1, marginal_2 = init_transport.sum(1), init_transport.sum(0) epsilon = quadratic_problem.update_epsilon_unbalanced( epsilon=epsilon, transport_mass=marginal_1.sum() ) unbalanced_correction = quad_prob.cost_unbalanced_correction( init_transport, marginal_1, marginal_2, epsilon=epsilon ) cost_matrix = marginal_cost.cost_matrix - tmp + unbalanced_correction cost_matrix += quad_prob.fused_penalty * quad_prob._fused_cost_matrix return geometry.Geometry( cost_matrix=cost_matrix, epsilon=epsilon, relative_epsilon=relative_epsilon ) def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: # noqa: D102 return [self.init_coupling], self._kwargs