Remove source/target masks (#603)

* Remove `{src,tgt}_mask`

* Remove `nan{mean,max,median}`

* Reintroduce subset function

* Bump macOS runners

* Fwd-port `jnp.isscalar`

.github/workflows/tests.yml

@@ -77,9 +77,9 @@ jobs:
         os: [ubuntu-latest]
         include:
         - python-version: '3.9'
-          os: macos-13
-        - python-version: '3.10'
           os: macos-14
+        - python-version: '3.10'
+          os: macos-15
 
     steps:
     - uses: actions/checkout@v4

src/ott/geometry/geometry.py

@@ -21,7 +21,6 @@
 import jax.numpy as jnp
 import jax.scipy as jsp
 import jax.tree_util as jtu
-import numpy as np
 
 from ott import utils
 from ott.geometry import epsilon_scheduler as eps_scheduler
@@ -65,10 +64,6 @@ class Geometry:
     scale_cost: option to rescale the cost matrix. Implemented scalings are
       'median', 'mean', 'std' and 'max_cost'. Alternatively, a float factor can
       be given to rescale the cost such that ``cost_matrix /= scale_cost``.
-    src_mask: Mask specifying valid rows when computing some statistics of
-      :attr:`cost_matrix`, see :attr:`src_mask`.
-    tgt_mask: Mask specifying valid columns when computing some statistics of
-      :attr:`cost_matrix`, see :attr:`tgt_mask`.
 
   Note:
     When defining a :class:`~ott.geometry.geometry.Geometry` through a
@@ -86,18 +81,13 @@ def __init__(
       relative_epsilon: Optional[Literal["mean", "std"]] = None,
       scale_cost: Union[float, Literal["mean", "max_cost", "median",
                                        "std"]] = 1.0,
-      src_mask: Optional[jnp.ndarray] = None,
-      tgt_mask: Optional[jnp.ndarray] = None,
   ):
     self._cost_matrix = cost_matrix
     self._kernel_matrix = kernel_matrix
     self._epsilon_init = epsilon
     self._relative_epsilon = relative_epsilon
     self._scale_cost = scale_cost
 
-    self._src_mask = src_mask
-    self._tgt_mask = tgt_mask
-
   @property
   def cost_rank(self) -> Optional[int]:
     """Output rank of cost matrix, if any was provided."""
@@ -117,14 +107,14 @@ def cost_matrix(self) -> jnp.ndarray:
   @property
   def median_cost_matrix(self) -> float:
     """Median of the :attr:`cost_matrix`."""
-    geom = self._masked_geom(mask_value=jnp.nan)
-    return jnp.nanmedian(geom.cost_matrix)  # will fail for online PC
+    return jnp.median(self.cost_matrix)
 
   @property
   def mean_cost_matrix(self) -> float:
     """Mean of the :attr:`cost_matrix`."""
-    tmp = self._masked_geom().apply_cost(self._n_normed_ones).squeeze()
-    return jnp.sum(tmp * self._m_normed_ones)
+    n, m = self.shape
+    tmp = self.apply_cost(jnp.full((n,), fill_value=1.0 / n))
+    return jnp.sum((1.0 / m) * tmp)
 
   @property
   def std_cost_matrix(self) -> float:
@@ -139,8 +129,9 @@ def std_cost_matrix(self) -> float:
 
     to output :math:`\sigma`.
     """
-    tmp = self._masked_geom().apply_square_cost(self._n_normed_ones).squeeze()
-    tmp = jnp.sum(tmp * self._m_normed_ones) - (self.mean_cost_matrix ** 2)
+    n, m = self.shape
+    tmp = self.apply_square_cost(jnp.full((n,), fill_value=1.0 / n))
+    tmp = jnp.sum((1.0 / m) * tmp) - (self.mean_cost_matrix ** 2)
     return jnp.sqrt(jax.nn.relu(tmp))
 
   @property
@@ -158,7 +149,6 @@ def epsilon_scheduler(self) -> eps_scheduler.Epsilon:
     """Epsilon scheduler."""
     if isinstance(self._epsilon_init, eps_scheduler.Epsilon):
       return self._epsilon_init
-
     # no relative epsilon
     if self._relative_epsilon is None:
       if self._epsilon_init is not None:
@@ -217,23 +207,21 @@ def is_online(self) -> bool:
   @property
   def is_symmetric(self) -> bool:
     """Whether geometry cost/kernel is a symmetric matrix."""
+    n, m = self.shape
     mat = self.kernel_matrix if self.cost_matrix is None else self.cost_matrix
-    return (
-        mat.shape[0] == mat.shape[1] and jnp.all(mat == mat.T)
-    ) if mat is not None else False
+    return (n == m) and jnp.all(mat == mat.T)
 
   @property
-  def inv_scale_cost(self) -> float:
+  def inv_scale_cost(self) -> jnp.ndarray:
     """Compute and return inverse of scaling factor for cost matrix."""
-    if isinstance(self._scale_cost, (int, float, np.number, jax.Array)):
-      return 1.0 / self._scale_cost
-    self = self._masked_geom(mask_value=jnp.nan)
     if self._scale_cost == "max_cost":
-      return 1.0 / jnp.nanmax(self._cost_matrix)
+      return 1.0 / jnp.max(self._cost_matrix)
     if self._scale_cost == "mean":
-      return 1.0 / jnp.nanmean(self._cost_matrix)
+      return 1.0 / jnp.mean(self._cost_matrix)
     if self._scale_cost == "median":
-      return 1.0 / jnp.nanmedian(self._cost_matrix)
+      return 1.0 / jnp.median(self._cost_matrix)
+    if utils.is_scalar(self._scale_cost):
+      return 1.0 / self._scale_cost
     raise ValueError(f"Scaling {self._scale_cost} not implemented.")
 
   def set_scale_cost(self, scale_cost: Union[float, str]) -> "Geometry":
@@ -692,14 +680,14 @@ def to_LRCGeometry(
       i_star = jax.random.randint(rng1, shape=(), minval=0, maxval=n)
       j_star = jax.random.randint(rng2, shape=(), minval=0, maxval=m)
 
-      ci_star = self.subset([i_star], None).cost_matrix.ravel() ** 2  # (m,)
-      cj_star = self.subset(None, [j_star]).cost_matrix.ravel() ** 2  # (n,)
+      ci_star = self.subset(row_ixs=i_star).cost_matrix.ravel() ** 2  # (m,)
+      cj_star = self.subset(col_ixs=j_star).cost_matrix.ravel() ** 2  # (n,)
 
       p_row = cj_star + ci_star[j_star] + jnp.mean(ci_star)  # (n,)
       p_row /= jnp.sum(p_row)
       row_ixs = jax.random.choice(rng3, n, shape=(n_subset,), p=p_row)
       # (n_subset, m)
-      s = self.subset(row_ixs, None).cost_matrix
+      s = self.subset(row_ixs=row_ixs).cost_matrix
       s /= jnp.sqrt(n_subset * p_row[row_ixs][:, None])
 
       p_col = jnp.sum(s ** 2, axis=0)  # (m,)
@@ -720,7 +708,7 @@ def to_LRCGeometry(
       col_ixs = jax.random.choice(rng5, m, shape=(n_subset,))  # (n_subset,)
 
       # (n, n_subset)
-      A_trans = self.subset(None, col_ixs).cost_matrix * inv_scale
+      A_trans = self.subset(col_ixs=col_ixs).cost_matrix * inv_scale
       B = (U[col_ixs, :] @ v * inv_scale)  # (n_subset, k)
       M = jnp.linalg.inv(B.T @ B)  # (k, k)
       V = jnp.linalg.multi_dot([A_trans, B, M.T, v.T])  # (n, k)
@@ -737,187 +725,48 @@ def to_LRCGeometry(
     )
 
   def subset(
-      self, src_ixs: Optional[jnp.ndarray], tgt_ixs: Optional[jnp.ndarray],
-      **kwargs: Any
-  ) -> "Geometry":
-    """Subset rows or columns of a geometry.
-
-    Args:
-      src_ixs: Row indices. If ``None``, use all rows.
-      tgt_ixs: Column indices. If ``None``, use all columns.
-      kwargs: Keyword arguments to override the initialization.
-
-    Returns:
-      The modified geometry.
-    """
-
-    def subset_fn(
-        arr: Optional[jnp.ndarray],
-        src_ixs: Optional[jnp.ndarray],
-        tgt_ixs: Optional[jnp.ndarray],
-    ) -> Optional[jnp.ndarray]:
-      if arr is None:
-        return None
-      if src_ixs is not None:
-        arr = arr[src_ixs, ...]
-      if tgt_ixs is not None:
-        arr = arr[:, tgt_ixs]
-      return arr  # noqa: RET504
-
-    return self._mask_subset_helper(
-        src_ixs,
-        tgt_ixs,
-        fn=subset_fn,
-        propagate_mask=True,
-        **kwargs,
-    )
-
-  def mask(
       self,
-      src_mask: Optional[jnp.ndarray],
-      tgt_mask: Optional[jnp.ndarray],
-      mask_value: float = 0.0,
+      row_ixs: Optional[jnp.ndarray] = None,
+      col_ixs: Optional[jnp.ndarray] = None
   ) -> "Geometry":
-    """Mask rows or columns of a geometry.
-
-    The mask is used only when computing some statistics of the
-    :attr:`cost_matrix`.
-
-        - :attr:`mean_cost_matrix`
-        - :attr:`median_cost_matrix`
-        - :attr:`inv_scale_cost`
+    """Subset rows or columns of a geometry.
 
     Args:
-      src_mask: Row mask. Can be specified either as a boolean array of shape
-        ``[num_a,]`` or as an array of indices. If ``None``, no mask is applied.
-      tgt_mask: Column mask. Can be specified either as a boolean array of shape
-        ``[num_b,]`` or as an array of indices. If ``None``, no mask is applied.
-      mask_value: Value to use for masking.
+      row_ixs: Row indices. If :obj:`None`, use all rows.
+      col_ixs: Column indices. If :obj:`None`, use all columns.
 
     Returns:
-      The masked geometry.
+      The subsetted geometry.
     """
-
-    def mask_fn(
-        arr: Optional[jnp.ndarray],
-        src_mask: Optional[jnp.ndarray],
-        tgt_mask: Optional[jnp.ndarray],
-    ) -> Optional[jnp.ndarray]:
-      if arr is None:
-        return arr
-      assert arr.ndim == 2, arr.ndim
-      if src_mask is not None:
-        arr = jnp.where(src_mask[:, None], arr, mask_value)
-      if tgt_mask is not None:
-        arr = jnp.where(tgt_mask[None, :], arr, mask_value)
-      return arr  # noqa: RET504
-
-    src_mask = self._normalize_mask(src_mask, self.shape[0])
-    tgt_mask = self._normalize_mask(tgt_mask, self.shape[1])
-    return self._mask_subset_helper(
-        src_mask, tgt_mask, fn=mask_fn, propagate_mask=False
-    )
-
-  def _mask_subset_helper(
-      self,
-      src_ixs: Optional[jnp.ndarray],
-      tgt_ixs: Optional[jnp.ndarray],
-      *,
-      fn: Callable[
-          [Optional[jnp.ndarray], Optional[jnp.ndarray], Optional[jnp.ndarray]],
-          Optional[jnp.ndarray]],
-      propagate_mask: bool,
-      **kwargs: Any,
-  ) -> "Geometry":
-    (cost, kernel, eps, src_mask, tgt_mask), aux_data = self.tree_flatten()
-    cost = fn(cost, src_ixs, tgt_ixs)
-    kernel = fn(kernel, src_ixs, tgt_ixs)
-    if propagate_mask:
-      src_mask = self._normalize_mask(src_mask, self.shape[0])
-      tgt_mask = self._normalize_mask(tgt_mask, self.shape[1])
-      src_mask = fn(src_mask, src_ixs, None)
-      tgt_mask = fn(tgt_mask, tgt_ixs, None)
-
-    aux_data = {**aux_data, **kwargs}
-    return type(self).tree_unflatten(
-        aux_data, [cost, kernel, eps, src_mask, tgt_mask]
-    )
-
-  @property
-  def src_mask(self) -> Optional[jnp.ndarray]:
-    """Mask of shape ``[num_a,]`` to compute :attr:`cost_matrix` statistics.
-
-    Specifically, it is used when computing:
-
-      - :attr:`mean_cost_matrix`
-      - :attr:`median_cost_matrix`
-      - :attr:`inv_scale_cost`
-    """
-    return self._normalize_mask(self._src_mask, self.shape[0])
-
-  @property
-  def tgt_mask(self) -> Optional[jnp.ndarray]:
-    """Mask of shape ``[num_b,]`` to compute :attr:`cost_matrix` statistics.
-
-    Specifically, it is used when computing:
-
-      - :attr:`mean_cost_matrix`
-      - :attr:`median_cost_matrix`
-      - :attr:`inv_scale_cost`
-    """
-    return self._normalize_mask(self._tgt_mask, self.shape[1])
+    (cost, kernel, *rest), aux_data = self.tree_flatten()
+    row_ixs = row_ixs if row_ixs is None else jnp.atleast_1d(row_ixs)
+    col_ixs = col_ixs if col_ixs is None else jnp.atleast_1d(col_ixs)
+    if cost is not None:
+      cost = cost if row_ixs is None else cost[row_ixs]
+      cost = cost if col_ixs is None else cost[:, col_ixs]
+    if kernel is not None:
+      kernel = kernel if row_ixs is None else kernel[row_ixs]
+      kernel = kernel if col_ixs is None else kernel[:, col_ixs]
+    return type(self).tree_unflatten(aux_data, (cost, kernel, *rest))
 
   @property
   def dtype(self) -> jnp.dtype:
     """The data type."""
-    return (
-        self._kernel_matrix if self._cost_matrix is None else self._cost_matrix
-    ).dtype
-
-  def _masked_geom(self, mask_value: float = 0.0) -> "Geometry":
-    """Mask geometry based on :attr:`src_mask` and :attr:`tgt_mask`."""
-    src_mask, tgt_mask = self.src_mask, self.tgt_mask
-    if src_mask is None and tgt_mask is None:
-      return self
-    return self.mask(src_mask, tgt_mask, mask_value=mask_value)
-
-  @property
-  def _n_normed_ones(self) -> jnp.ndarray:
-    """Normalized array of shape ``[num_a,]``."""
-    mask = self.src_mask
-    arr = jnp.ones(self.shape[0]) if mask is None else mask
-    return arr / jnp.sum(arr)
-
-  @property
-  def _m_normed_ones(self) -> jnp.ndarray:
-    """Normalized array of shape ``[num_b,]``."""
-    mask = self.tgt_mask
-    arr = jnp.ones(self.shape[1]) if mask is None else mask
-    return arr / jnp.sum(arr)
-
-  @staticmethod
-  def _normalize_mask(mask: Optional[jnp.ndarray],
-                      size: int) -> Optional[jnp.ndarray]:
-    """Convert array of indices to a boolean mask."""
-    if mask is None:
-      return None
-    if not jnp.issubdtype(mask, (bool, jnp.bool_)):
-      mask = jnp.isin(jnp.arange(size), mask)
-    assert mask.shape == (size,)
-    return mask
+    if self._cost_matrix is not None:
+      return self._cost_matrix.dtype
+    return self._kernel_matrix.dtype
 
   def tree_flatten(self):  # noqa: D102
     return (
-        self._cost_matrix, self._kernel_matrix, self._epsilon_init,
-        self._src_mask, self._tgt_mask
+        self._cost_matrix,
+        self._kernel_matrix,
+        self._epsilon_init,
     ), {
         "scale_cost": self._scale_cost,
         "relative_epsilon": self._relative_epsilon,
     }
 
   @classmethod
   def tree_unflatten(cls, aux_data, children):  # noqa: D102
-    cost, kernel, eps, src_mask, tgt_mask = children
-    return cls(
-        cost, kernel, eps, src_mask=src_mask, tgt_mask=tgt_mask, **aux_data
-    )
+    cost, kernel, epsilon = children
+    return cls(cost, kernel_matrix=kernel, epsilon=epsilon, **aux_data)

src/ott/geometry/grid.py

@@ -315,21 +315,6 @@ def transport_from_scalings(
         "cloud geometry instead."
     )
 
-  def subset(
-      self, src_ixs: Optional[jnp.ndarray], tgt_ixs: Optional[jnp.ndarray]
-  ) -> NoReturn:
-    """Not implemented."""
-    raise NotImplementedError("Subsetting is not implemented for grids.")
-
-  def mask(
-      self,
-      src_mask: Optional[jnp.ndarray],
-      tgt_mask: Optional[jnp.ndarray],
-      mask_value: float = 0.0,
-  ) -> NoReturn:
-    """Not implemented."""
-    raise NotImplementedError("Masking is not implemented for grids.")
-
   @property
   def cost_matrix(self) -> jnp.ndarray:
     """Not implemented."""
@@ -425,6 +410,4 @@ def to_LRCGeometry(
         epsilon=self._epsilon_init,
         relative_epsilon=self._relative_epsilon,
         scale_cost=self._scale_cost,
-        src_mask=self.src_mask,
-        tgt_mask=self.tgt_mask,
     )