added sklearn trick

2 files changed, 20 insertions, 37 deletions

diff --git a/src/python/gudhi/representations/kernel_methods.py b/src/python/gudhi/representations/kernel_methods.py
index bbbb7c31..d89f69ab 100644
--- a/src/python/gudhi/representations/kernel_methods.py
+++ b/src/python/gudhi/representations/kernel_methods.py
@@ -62,27 +62,17 @@ def pairwise_persistence_diagram_kernels(X, Y=None, metric="sliced_wasserstein",
     :param metric: kernel to use. It can be either a string ("sliced_wasserstein", "persistence_scale_space", "persistence_weighted_gaussian", "persistence_fisher") or a function taking two numpy arrays of shape (nx2) and (mx2) as inputs.
     :returns: kernel matrix, i.e., numpy array of shape (num diagrams 1 x num diagrams 2)
     :rtype: float
-    """
-    if Y is None:
-        YY = None
-        pX = Padding(use=True).fit_transform(X)
-        diag_len = len(pX[0])
-        XX = np.reshape(np.vstack(pX), [-1, diag_len*3])
-    else:
-        nX, nY = len(X), len(Y)
-        pD = Padding(use=True).fit_transform(X + Y)
-        diag_len = len(pD[0])
-        XX = np.reshape(np.vstack(pD[:nX]), [-1, diag_len*3])
-        YY = np.reshape(np.vstack(pD[nX:]), [-1, diag_len*3])
-
+    """    
+    XX = np.reshape(np.arange(len(X)), [-1,1])
+    YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1])
     if metric == "sliced_wasserstein":
         return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="sliced_wasserstein", num_directions=kwargs["num_directions"]) / kwargs["bandwidth"])
     elif metric == "persistence_fisher":
         return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="persistence_fisher", kernel_approx=kwargs["kernel_approx"], bandwidth=kwargs["bandwidth"]) / kwargs["bandwidth_fisher"])
     elif metric == "persistence_scale_space":
-        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_scale_space_kernel, **kwargs))
+        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_scale_space_kernel, X, Y, **kwargs))
     elif metric == "persistence_weighted_gaussian":
-        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_weighted_gaussian_kernel, **kwargs))
+        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_weighted_gaussian_kernel, X, Y, **kwargs))
     else:
         return pairwise_kernels(XX, YY, metric=sklearn_wrapper(metric, **kwargs))
 
diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py
index cc788994..fead8aa0 100644
--- a/src/python/gudhi/representations/metrics.py
+++ b/src/python/gudhi/representations/metrics.py
@@ -85,13 +85,16 @@ def persistence_fisher_distance(D1, D2, kernel_approx=None, bandwidth=1.):
             vectorj = vectorj/vectorj_sum
         return np.arccos(  min(np.dot(np.sqrt(vectori), np.sqrt(vectorj)), 1.)  )
 
-def sklearn_wrapper(metric, **kwargs):
+def sklearn_wrapper(metric, X, Y, **kwargs):
     """
-    This function is a wrapper for any metric between two persistence diagrams that takes two numpy arrays of shapes (nx2) and (mx2) as arguments. It turns the metric into another that takes flattened and padded diagrams as inputs. 
+    This function is a wrapper for any metric between two persistence diagrams that takes two numpy arrays of shapes (nx2) and (mx2) as arguments.
     """
-    def flat_metric(D1, D2):
-        DD1, DD2 = np.reshape(D1, [-1,3]), np.reshape(D2, [-1,3])
-        return metric(DD1[DD1[:,2]==1,0:2], DD2[DD2[:,2]==1,0:2], **kwargs)
+    if Y is None:
+        def flat_metric(a, b):
+            return metric(X[int(a[0])], X[int(b[0])], **kwargs)
+    else:
+        def flat_metric(a, b):
+            return metric(X[int(a[0])], Y[int(b[0])], **kwargs)
     return flat_metric
 
 def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", **kwargs):
@@ -103,28 +106,18 @@ def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", **kwa
     :returns: distance matrix, i.e., numpy array of shape (num diagrams 1 x num diagrams 2)
     :rtype: float
     """
-    if Y is None:
-        YY = None
-        pX = Padding(use=True).fit_transform(X)
-        diag_len = len(pX[0])
-        XX = np.reshape(np.vstack(pX), [-1, diag_len*3])
-    else:
-        nX, nY = len(X), len(Y)
-        pD = Padding(use=True).fit_transform(X + Y)
-        diag_len = len(pD[0])
-        XX = np.reshape(np.vstack(pD[:nX]), [-1, diag_len*3])
-        YY = np.reshape(np.vstack(pD[nX:]), [-1, diag_len*3])
-
+    XX = np.reshape(np.arange(len(X)), [-1,1])
+    YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1]) 
     if metric == "bottleneck":
-        return pairwise_distances(XX, YY, metric=sklearn_wrapper(bottleneck_distance, **kwargs))
+        return pairwise_distances(XX, YY, metric=sklearn_wrapper(bottleneck_distance, X, Y, **kwargs))
     elif metric == "wasserstein":
-        return pairwise_distances(XX, YY, metric=sklearn_wrapper(wasserstein_distance, **kwargs))
+        return pairwise_distances(XX, YY, metric=sklearn_wrapper(wasserstein_distance, X, Y, **kwargs))
     elif metric == "sliced_wasserstein":
-        return pairwise_distances(XX, YY, metric=sklearn_wrapper(sliced_wasserstein_distance, **kwargs))
+        return pairwise_distances(XX, YY, metric=sklearn_wrapper(sliced_wasserstein_distance, X, Y, **kwargs))
     elif metric == "persistence_fisher":
-        return pairwise_distances(XX, YY, metric=sklearn_wrapper(persistence_fisher_distance, **kwargs))
+        return pairwise_distances(XX, YY, metric=sklearn_wrapper(persistence_fisher_distance, X, Y, **kwargs))
     else:
-        return pairwise_distances(XX, YY, metric=sklearn_wrapper(metric, **kwargs))
+        return pairwise_distances(XX, YY, metric=sklearn_wrapper(metric, X, Y, **kwargs))
 
 class SlicedWassersteinDistance(BaseEstimator, TransformerMixin):
     """