[MRG] Fix None init plan in unbalanced lbfgs solvers

RELEASES.md

@@ -17,6 +17,7 @@
 - Backend implementation of `ot.dist` for (PR #701)
 - Updated documentation Quickstart guide and User guide with new API (PR #726)
 - Fix jax version for auto-grad (PR #732)
+- Fix reg_div function compatibility with numpy in `ot.unbalanced.lbfgsb_unbalanced` via new function `ot.utils.fun_to_numpy` (PR #731)
 
 #### Closed issues
 - Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)

ot/unbalanced/_lbfgs.py

@@ -9,12 +9,11 @@
 #
 # License: MIT License
 
-import warnings
 import numpy as np
 from scipy.optimize import minimize, Bounds
 
 from ..backend import get_backend
-from ..utils import list_to_array, get_parameter_pair
+from ..utils import list_to_array, get_parameter_pair, fun_to_numpy
 
 
 def _get_loss_unbalanced(a, b, c, M, reg, reg_m1, reg_m2, reg_div="kl", regm_div="kl"):
@@ -46,9 +45,9 @@
         Divergence used for regularization.
         Can take three values: 'entropy' (negative entropy), or
         'kl' (Kullback-Leibler) or 'l2' (half-squared) or a tuple
-        of two calable functions returning the reg term and its derivative.
+        of two callable functions returning the reg term and its derivative.
         Note that the callable functions should be able to handle Numpy arrays
-        and not tesors from the backend
+        and not tensors from the backend
     regm_div: string, optional
         Divergence to quantify the difference between the marginals.
         Can take three values: 'kl' (Kullback-Leibler) or 'l2' (half-squared) or 'tv' (Total Variation)
@@ -206,26 +205,27 @@
         loss matrix
     reg: float
         regularization term >=0
-    c : array-like (dim_a, dim_b), optional (default = None)
-        Reference measure for the regularization.
-        If None, then use :math:`\mathbf{c} = \mathbf{a} \mathbf{b}^T`.
     reg_m: float or indexable object of length 1 or 2
         Marginal relaxation term: nonnegative (including 0) but cannot be infinity.
         If :math:`\mathrm{reg_{m}}` is a scalar or an indexable object of length 1,
         then the same :math:`\mathrm{reg_{m}}` is applied to both marginal relaxations.
         If :math:`\mathrm{reg_{m}}` is an array, it must be a Numpy array.
-    reg_div: string, optional
+    c : array-like (dim_a, dim_b), optional (default = None)
+        Reference measure for the regularization.
+        If None, then use :math:`\mathbf{c} = \mathbf{a} \mathbf{b}^T`.
+    reg_div: string or pair of callable functions, optional (default = 'kl')
         Divergence used for regularization.
         Can take three values: 'entropy' (negative entropy), or
         'kl' (Kullback-Leibler) or 'l2' (half-squared) or a tuple
-        of two calable functions returning the reg term and its derivative.
+        of two callable functions returning the reg term and its derivative.
         Note that the callable functions should be able to handle Numpy arrays
-        and not tesors from the backend
-    regm_div: string, optional
+        and not tensors from the backend, otherwise functions will be converted to Numpy
+        leading to a computational overhead.
+    regm_div: string, optional (default = 'kl')
         Divergence to quantify the difference between the marginals.
         Can take three values: 'kl' (Kullback-Leibler) or 'l2' (half-squared) or 'tv' (Total Variation)
-    G0: array-like (dim_a, dim_b)
-        Initialization of the transport matrix
+    G0: array-like (dim_a, dim_b), optional (default = None)
+        Initialization of the transport matrix. None corresponds to uniform product.
     numItermax : int, optional
         Max number of iterations
     stopThr : float, optional
@@ -267,26 +267,14 @@
     ot.unbalanced.sinkhorn_unbalanced2 : Entropic regularized OT loss
     """
 
-    # wrap the callable function to handle numpy arrays
-    if isinstance(reg_div, tuple):
-        f0, df0 = reg_div
-        try:
-            f0(G0)
-            df0(G0)
-        except BaseException:
-            warnings.warn(
-                "The callable functions should be able to handle numpy arrays, wrapper ar added to handle this which comes with overhead"
-            )
-
-            def f(x):
-                return nx.to_numpy(f0(nx.from_numpy(x, type_as=M0)))
-
-            def df(x):
-                return nx.to_numpy(df0(nx.from_numpy(x, type_as=M0)))
-
-            reg_div = (f, df)
+    # test settings
+    regm_div = regm_div.lower()
+    if regm_div not in ["kl", "l2", "tv"]:
+        raise ValueError(
+            "Unknown regm_div = {}. Must be either 'kl', 'l2' or 'tv'".format(regm_div)
+        )
 
-    else:
+    if isinstance(reg_div, str):
         reg_div = reg_div.lower()
         if reg_div not in ["entropy", "kl", "l2"]:
             raise ValueError(
@@ -295,16 +283,11 @@
                 )
             )
 
-    regm_div = regm_div.lower()
-    if regm_div not in ["kl", "l2", "tv"]:
-        raise ValueError(
-            "Unknown regm_div = {}. Must be either 'kl', 'l2' or 'tv'".format(regm_div)
-        )
-
+    # convert all inputs to numpy arrays
     reg_m1, reg_m2 = get_parameter_pair(reg_m)
 
     M, a, b = list_to_array(M, a, b)
-    nx = get_backend(M, a, b)
+    nx = get_backend(M, a, b, G0)
     M0 = M
 
     dim_a, dim_b = M.shape
@@ -315,10 +298,22 @@
         b = nx.ones(dim_b, type_as=M) / dim_b
 
     # convert to numpy
-    a, b, M, reg_m1, reg_m2, reg = nx.to_numpy(a, b, M, reg_m1, reg_m2, reg)
+    if nx.__name__ == "numpy":  # remaining parameters which can be arrays
+        reg_m1, reg_m2, reg = nx.to_numpy(reg_m1, reg_m2, reg)
+    else:
+        a, b, M, reg_m1, reg_m2, reg = nx.to_numpy(a, b, M, reg_m1, reg_m2, reg)
+
     G0 = a[:, None] * b[None, :] if G0 is None else nx.to_numpy(G0)
     c = a[:, None] * b[None, :] if c is None else nx.to_numpy(c)
 
+    # potentially convert the callable function to handle numpy arrays
+    if isinstance(reg_div, tuple):
+        f0, df0 = reg_div
+        f = fun_to_numpy(f0, G0, nx, warn=True)
+        df = fun_to_numpy(df0, G0, nx, warn=True)
+
+        reg_div = (f, df)
+
     _func = _get_loss_unbalanced(a, b, c, M, reg, reg_m1, reg_m2, reg_div, regm_div)
 
     res = minimize(
@@ -399,26 +394,27 @@
         loss matrix
     reg: float
         regularization term >=0
-    c : array-like (dim_a, dim_b), optional (default = None)
-        Reference measure for the regularization.
-        If None, then use :math:`\mathbf{c} = \mathbf{a} \mathbf{b}^T`.
     reg_m: float or indexable object of length 1 or 2
         Marginal relaxation term: nonnegative (including 0) but cannot be infinity.
         If :math:`\mathrm{reg_{m}}` is a scalar or an indexable object of length 1,
         then the same :math:`\mathrm{reg_{m}}` is applied to both marginal relaxations.
         If :math:`\mathrm{reg_{m}}` is an array, it must be a Numpy array.
-    reg_div: string, optional
+    c : array-like (dim_a, dim_b), optional (default = None)
+        Reference measure for the regularization.
+        If None, then use :math:`\mathbf{c} = \mathbf{a} \mathbf{b}^T`.
+    reg_div: string or pair of callable functions, optional (default = 'kl')
         Divergence used for regularization.
         Can take three values: 'entropy' (negative entropy), or
         'kl' (Kullback-Leibler) or 'l2' (half-squared) or a tuple
-        of two calable functions returning the reg term and its derivative.
+        of two callable functions returning the reg term and its derivative.
         Note that the callable functions should be able to handle Numpy arrays
-        and not tesors from the backend
-    regm_div: string, optional
+        and not tensors from the backend, otherwise functions will be converted to Numpy
+        leading to a computational overhead.
+    regm_div: string, optional (default = 'kl')
         Divergence to quantify the difference between the marginals.
         Can take three values: 'kl' (Kullback-Leibler) or 'l2' (half-squared) or 'tv' (Total Variation)
-    G0: array-like (dim_a, dim_b)
-        Initialization of the transport matrix
+    G0: array-like (dim_a, dim_b), optional (default = None)
+        Initialization of the transport matrix. None corresponds to uniform product.
     returnCost: string, optional (default = "linear")
         If `returnCost` = "linear", then return the linear part of the unbalanced OT loss.
         If `returnCost` = "total", then return the total unbalanced OT loss.

ot/utils.py

@@ -1473,3 +1473,43 @@
             'numThreads should either be "max" or a strictly positive integer'
         )
     return numThreads
+
+
+def fun_to_numpy(fun, arr, nx, warn=True):
+    """Convert a function to a numpy function.
+
+    Parameters
+    ----------
+    fun : callable
+        The function to convert.
+    arr : array-like
+        The input to test the function. Can be from any backend.
+    nx : Backend
+        The backend to use for the conversion.
+    warn : bool, optional
+        Whether to raise a warning if the function is not compatible with numpy.
+        Default is True.
+    Returns
+    -------
+    fun_numpy : callable
+        The converted function.
+    """
+    if arr is None:
+        raise ValueError("arr should not be None to test fun")
+
+    nx_arr = get_backend(arr)
+    if nx_arr.__name__ != "numpy":
+        arr = nx.to_numpy(arr)
+    try:
+        fun(arr)
+        return fun
+    except BaseException:
+        if warn:
+            warnings.warn(
+                "The callable function should be able to handle numpy arrays, a compatible function is created and comes with overhead"
+            )
+
+        def fun_numpy(x):
+            return nx.to_numpy(fun(nx.from_numpy(x)))
+
+        return fun_numpy

test/test_utils.py

@@ -731,3 +731,21 @@ def test_exp_bures(nx):
     # exp_\Lambda(log_\Lambda(Sigma)) = Sigma
     Sigma_exp = ot.utils.exp_bures(Lambda, T - nx.eye(d, type_as=T))
     np.testing.assert_allclose(nx.to_numpy(Sigma), nx.to_numpy(Sigma_exp), atol=1e-5)
+
+
+def test_fun_to_numpy(nx):
+    arr = np.arange(5)
+    arrb = nx.from_numpy(arr)
+
+    def fun(x):  # backend function
+        return nx.sum(x)
+
+    fun_numpy = ot.utils.fun_to_numpy(fun, arrb, nx, warn=True)
+
+    res = nx.to_numpy(fun(arrb))
+    res_np = fun_numpy(arr)
+
+    np.testing.assert_allclose(res, res_np)
+
+    with pytest.raises(ValueError):
+        ot.utils.fun_to_numpy(fun, None, nx, warn=True)