added kwargs to sim + doc

Author: ievred

ot/da.py

@@ -748,7 +748,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
         return A, b
 
 
-def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha,
+def emd_laplace(a, b, xs, xt, M, reg, eta, alpha,
                 numItermax, stopThr, numInnerItermax,
                 stopInnerThr, log=False, verbose=False, **kwargs):
     r"""Solve the optimal transport problem (OT) with Laplacian regularization
@@ -803,7 +803,11 @@ def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha,
         Print information along iterations
     log : bool, optional
         record log if True
-
+    kwargs : dict
+        Dictionary with attributes 'sim' ('knn' or 'gauss') and
+        'param' (int, float or None) for similarity type and its parameter to be used.
+        If 'param' is None, it is computed as mean pairwise Euclidean distance over the data set
+        or set to 3 when sim is 'gauss' or 'knn', respectively.
 
     Returns
     -------
@@ -830,24 +834,28 @@ def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha,
     ot.optim.cg : General regularized OT
 
     """
-    if sim == 'gauss':
-        if 'rbfparam' not in kwargs:
-            kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2))
-        sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam'])
-        sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam'])
+    if not isinstance(kwargs['param'], (int, float, type(None))):
+        raise ValueError(
+            'Similarity parameter should be an int or a float. Got {type} instead.'.format(type=type(kwargs['param'])))
+
+    if kwargs['sim'] == 'gauss':
+        if kwargs['param'] is None:
+            kwargs['param'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2))
+        sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['param'])
+        sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['param'])
 
-    elif sim == 'knn':
-        if 'nn' not in kwargs:
-            kwargs['nn'] = 5
+    elif kwargs['sim'] == 'knn':
+        if kwargs['param'] is None:
+            kwargs['param'] = 3
 
         from sklearn.neighbors import kneighbors_graph
 
-        sS = kneighbors_graph(xs, kwargs['nn']).toarray()
+        sS = kneighbors_graph(X=xs, n_neighbors=int(kwargs['param'])).toarray()
         sS = (sS + sS.T) / 2
-        sT = kneighbors_graph(xt, kwargs['nn']).toarray()
+        sT = kneighbors_graph(xt, n_neighbors=int(kwargs['param'])).toarray()
         sT = (sT + sT.T) / 2
     else:
-        raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=sim))
+        raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=kwargs['sim']))
 
     lS = laplacian(sS)
     lT = laplacian(sT)
@@ -1721,6 +1729,9 @@ class EMDLaplaceTransport(BaseTransport):
         can occur with large metric values.
     similarity : string, optional (default="knn")
         The similarity to use either knn or gaussian
+    similarity_param : int or float, optional (default=3)
+        Parameter for the similarity: number of nearest neighbors or bandwidth
+        if similarity="knn" or "gaussian", respectively.
     max_iter : int, optional (default=100)
         Max number of BCD iterations
     tol : float, optional (default=1e-5)
@@ -1754,7 +1765,7 @@ class EMDLaplaceTransport(BaseTransport):
     """
 
     def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., alpha=0.5,
-                 metric="sqeuclidean", norm=None, similarity="knn", max_iter=100, tol=1e-9,
+                 metric="sqeuclidean", norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9,
                  max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False,
                  distribution_estimation=distribution_estimation_uniform,
                  out_of_sample_map='ferradans'):
@@ -1765,6 +1776,7 @@ def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., alpha=0.5,
         self.metric = metric
         self.norm = norm
         self.similarity = similarity
+        self.sim_param = similarity_param
         self.max_iter = max_iter
         self.tol = tol
         self.max_inner_iter = max_inner_iter
@@ -1801,10 +1813,14 @@ class label
 
         super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt)
 
+        kwargs = dict()
+        kwargs["sim"] = self.similarity
+        kwargs["param"] = self.sim_param
+
         returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_,
-                                xt=self.xt_, M=self.cost_, reg=self.reg, sim=self.similarity, eta=self.reg_lap, alpha=self.reg_src,
+                                xt=self.xt_, M=self.cost_, reg=self.reg, eta=self.reg_lap, alpha=self.reg_src,
                                 numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter,
-                                stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose)
+                                stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose, **kwargs)
 
         # coupling estimation
         if self.log: