import types
from typing import Any, Literal, Mapping, Optional, Tuple, Type, Union
from anndata import AnnData
from moscot import _constants
from moscot._types import (
ArrayLike,
CostKwargs_t,
OttCostFnMap_t,
Policy_t,
ProblemStage_t,
QuadInitializer_t,
ScaleCost_t,
)
from moscot.base.problems.compound_problem import B, CompoundProblem, K
from moscot.base.problems.problem import OTProblem
from moscot.problems._utils import handle_cost, handle_joint_attr
from moscot.problems.cross_modality._mixins import CrossModalityTranslationMixin
from moscot.utils.subset_policy import DummyPolicy, ExternalStarPolicy
__all__ = ["TranslationProblem"]
[docs]
class TranslationProblem(CrossModalityTranslationMixin[K, OTProblem], CompoundProblem[K, OTProblem]):
"""Class for integrating single-cell multi-omics data, based on :cite:`demetci-scot:22`.
Parameters
----------
adata_src
Annotated data object containing the source modality.
adata_tgt
Annotated data object containing the target modality.
kwargs
Keyword arguments for :class:`~moscot.base.problems.CompoundProblem`.
"""
def __init__(self, adata_src: AnnData, adata_tgt: AnnData, **kwargs: Any):
super().__init__(adata_src, **kwargs)
self._adata_tgt = adata_tgt
def _create_policy( # type: ignore[override]
self,
policy: Literal["external_star"] = "external_star",
key: Optional[str] = None,
**kwargs: Any,
) -> Union[DummyPolicy, ExternalStarPolicy[K]]:
del policy
if key is None:
return DummyPolicy(self.adata, **kwargs)
return ExternalStarPolicy(self.adata, key=key, **kwargs)
def _create_problem(
self,
src: K,
tgt: K,
src_mask: ArrayLike,
tgt_mask: ArrayLike,
**kwargs: Any,
) -> OTProblem:
del tgt_mask
return self._base_problem_type(
adata=self.adata_src,
adata_tgt=self.adata_tgt,
src_obs_mask=src_mask,
tgt_obs_mask=None,
src_key=src,
tgt_key=tgt,
**kwargs,
)
[docs]
def prepare(
self,
src_attr: Union[str, Mapping[str, Any]],
tgt_attr: Union[str, Mapping[str, Any]],
joint_attr: Optional[Union[str, Mapping[str, Any]]] = None,
batch_key: Optional[str] = None,
cost: OttCostFnMap_t = "sq_euclidean",
cost_kwargs: CostKwargs_t = types.MappingProxyType({}),
a: Optional[Union[bool, str]] = None,
b: Optional[Union[bool, str]] = None,
**kwargs: Any,
) -> "TranslationProblem[K]":
"""Prepare the translation problem.
.. seealso::
- See :doc:`../notebooks/tutorials/600_tutorial_translation` on how to prepare the translation problem.
Parameters
----------
src_attr
How to get the data for the source modality:
- :class:`str` - a key in :attr:`~anndata.AnnData.obsm` where the data is stored.
- :class:`dict` - it should contain ``'attr'`` and ``'key'``, the attribute and the key
in :class:`~anndata.AnnData`, and optionally ``'tag'``, one of :class:`~moscot.utils.tagged_array.Tag`.
By default, :attr:`tag = 'point_cloud' <moscot.utils.tagged_array.Tag.POINT_CLOUD>` is used.
tgt_attr
How to get the data for the target modality:
- :class:`str` - a key in :attr:`~anndata.AnnData.obsm` where the data is stored.
- :class:`dict` - it should contain ``'attr'`` and ``'key'``, the attribute and the key
in :class:`~anndata.AnnData`, and optionally ``'tag'``, one of :class:`~moscot.utils.tagged_array.Tag`.
By default, :attr:`tag = 'point_cloud' <moscot.utils.tagged_array.Tag.POINT_CLOUD>` is used.
joint_attr
How to get the data for the :term:`linear term` in the :term:`fused <fused Gromov-Wasserstein>` case:
- :obj:`None` - the pure :term:`Gromov-Wasserstein` case is used.
- :class:`str` - a key in :attr:`~anndata.AnnData.obsm` where the data is stored.
- :class:`dict` - it should contain ``'attr'`` and ``'key'``, the attribute and key in
:class:`~anndata.AnnData`, and optionally ``'tag'`` from the
:class:`tags <moscot.utils.tagged_array.Tag>`.
By default, :attr:`tag = 'point_cloud' <moscot.utils.tagged_array.Tag.POINT_CLOUD>` is used.
batch_key
Key in :attr:`~anndata.AnnData.obs` specifying the batch.
cost
Cost function to use. Valid options are:
- :class:`str` - name of the cost function for all terms, see :func:`~moscot.costs.get_available_costs`.
- :class:`dict` - a dictionary with the following keys and values:
- ``'xy'`` - cost function for the :term:`linear term`.
- ``'x'`` - cost function for the source modality.
- ``'y'`` - cost function for the target modality.
cost_kwargs
Keyword arguments for the :class:`~moscot.base.cost.BaseCost` or any backend-specific cost.
a
Source :term:`marginals`. Valid options are:
- :class:`str` - key in :attr:`~anndata.AnnData.obs` where the source marginals are stored.
- :class:`bool` - if :obj:`True`,
:meth:`estimate the marginals <moscot.base.problems.OTProblem.estimate_marginals>`,
otherwise use uniform marginals.
- :obj:`None` - uniform marginals.
b
Target :term:`marginals`. Valid options are:
- :class:`str` - key in :attr:`~anndata.AnnData.obs` where the target marginals are stored.
- :class:`bool` - if :obj:`True`,
:meth:`estimate the marginals <moscot.base.problems.OTProblem.estimate_marginals>`,
otherwise use uniform marginals.
- :obj:`None` - uniform marginals.
kwargs
Keyword arguments for :meth:`~moscot.base.problems.CompoundProblem.prepare`.
Returns
-------
Returns self and updates the following fields:
- :attr:`problems` - the prepared subproblems.
- :attr:`solutions` - set to an empty :class:`dict`.
- :attr:`batch_key` - key in :attr:`~anndata.AnnData.obs` where batches are stored.
- :attr:`stage` - set to ``'prepared'``.
- :attr:`problem_kind` - set to ``'quadratic'``.
"""
self._src_attr = {"attr": "obsm", "key": src_attr} if isinstance(src_attr, str) else src_attr
self._tgt_attr = {"attr": "obsm", "key": tgt_attr} if isinstance(tgt_attr, str) else tgt_attr
self.batch_key = batch_key
if joint_attr is None:
xy = {} # type: ignore[var-annotated]
else:
xy, kwargs = handle_joint_attr(joint_attr, kwargs)
_, dim_src = getattr(self.adata_src, xy["x_attr"])[xy["x_key"]].shape
_, dim_tgt = getattr(self.adata_tgt, xy["y_attr"])[xy["y_key"]].shape
if dim_src != dim_tgt:
raise ValueError(
f"The dimensions of `joint_attr` do not match. "
f"The joint attribute in the source distribution has dimension {dim_src}, "
f"while the joint attribute in the target distribution has dimension {dim_tgt}."
)
xy, x, y = handle_cost(
xy=xy, x=self._src_attr, y=self._tgt_attr, cost=cost, cost_kwargs=cost_kwargs, **kwargs # type: ignore[arg-type]
)
if xy:
kwargs["xy"] = xy
return super().prepare(x=x, y=y, policy="external_star", key=batch_key, cost=cost, a=a, b=b, **kwargs) # type: ignore[return-value] # noqa: E501
[docs]
def solve( # type: ignore[override]
self,
alpha: Optional[float] = 1.0,
epsilon: float = 1e-2,
tau_a: float = 1.0,
tau_b: float = 1.0,
rank: int = -1,
scale_cost: ScaleCost_t = "mean",
batch_size: Optional[int] = None,
stage: Union[ProblemStage_t, Tuple[ProblemStage_t, ...]] = ("prepared", "solved"),
initializer: QuadInitializer_t = None,
initializer_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
jit: bool = True,
min_iterations: int = 5,
max_iterations: int = 50,
threshold: float = 1e-3,
linear_solver_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
device: Optional[Literal["cpu", "gpu", "tpu"]] = None,
**kwargs: Any,
) -> "TranslationProblem[K]":
r"""Solve the translation problem.
.. seealso::
- See :doc:`../notebooks/tutorials/600_tutorial_translation` on how to
solve the :class:`~moscot.problems.cross_modality.TranslationProblem`.
Parameters
----------
alpha
Parameter in :math:`(0, 1]` that interpolates between the :term:`quadratic term` and
the :term:`linear term`. :math:`\alpha = 1` corresponds to the pure :term:`Gromov-Wasserstein` problem while
:math:`\alpha \to 0` corresponds to the pure :term:`linear problem`.
epsilon
:term:`Entropic regularization`.
tau_a
Parameter in :math:`(0, 1]` that defines how much :term:`unbalanced <unbalanced OT problem>` is the problem
on the source :term:`marginals`. If :math:`1`, the problem is :term:`balanced <balanced OT problem>`.
tau_b
Parameter in :math:`(0, 1]` that defines how much :term:`unbalanced <unbalanced OT problem>` is the problem
on the target :term:`marginals`. If :math:`1`, the problem is :term:`balanced <balanced OT problem>`.
rank
Rank of the :term:`low-rank OT` solver :cite:`scetbon:21b`.
If :math:`-1`, full-rank solver :cite:`peyre:2016` is used.
scale_cost
How to re-scale the cost matrices. If a :class:`float`, the cost matrices
will be re-scaled as :math:`\frac{\text{cost}}{\text{scale_cost}}`.
batch_size
Number of rows/columns of the cost matrix to materialize during the solver iterations.
Larger value will require more memory.
stage
Stage by which to filter the :attr:`problems` to be solved.
initializer
How to initialize the solution. If :obj:`None`, ``'default'`` will be used for a full-rank solver and
``'rank2'`` for a low-rank solver.
initializer_kwargs
Keyword arguments for the ``initializer``.
jit
Whether to :func:`~jax.jit` the underlying :mod:`ott` solver.
min_iterations
Minimum number of :term:`(fused) GW <Gromov-Wasserstein>` iterations.
max_iterations
Maximum number of :term:`(fused) GW <Gromov-Wasserstein>` iterations.
threshold
Convergence threshold of the :term:`GW <Gromov-Wasserstein>` solver.
linear_solver_kwargs
Keyword arguments for the inner :term:`linear problem` solver.
device
Transfer the solution to a different device, see :meth:`~moscot.base.output.BaseSolverOutput.to`.
If :obj:`None`, keep the output on the original device.
kwargs
Keyword arguments for :meth:`~moscot.base.problems.CompoundProblem.solve`.
Returns
-------
Returns self and updates the following fields:
- :attr:`solutions` - the :term:`OT` solutions for each subproblem.
- :attr:`stage` - set to ``'solved'``.
"""
return super().solve(
alpha=alpha,
epsilon=epsilon,
tau_a=tau_a,
tau_b=tau_b,
rank=rank,
scale_cost=scale_cost,
batch_size=batch_size,
stage=stage,
initializer=initializer,
initializer_kwargs=initializer_kwargs,
jit=jit,
min_iterations=min_iterations,
max_iterations=max_iterations,
threshold=threshold,
linear_solver_kwargs=linear_solver_kwargs,
device=device,
**kwargs,
) # type: ignore[return-value]
@property
def adata_src(self) -> AnnData:
"""Source data."""
return self.adata
@property
def adata_tgt(self) -> AnnData:
"""Target data."""
return self._adata_tgt
@property
def _base_problem_type(self) -> Type[B]:
return OTProblem # type: ignore[return-value]
@property
def _valid_policies(self) -> Tuple[Policy_t, ...]:
return _constants.EXTERNAL_STAR, _constants.DUMMY # type: ignore[return-value]