sklearn's score_sample returns log(density)

1 files changed, 5 insertions, 4 deletions

diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py
index 000fdf3d..6906c5bb 100644
--- a/src/python/gudhi/clustering/tomato.py
+++ b/src/python/gudhi/clustering/tomato.py
@@ -54,8 +54,8 @@ class Tomato:
             input_type (str): 'points', 'distance_matrix' or 'neighbors'.
             metric (None|Callable): If None, use Minkowski of parameter p.
             graph_type (str): 'manual', 'knn' or 'radius'. Ignored if input_type is 'neighbors'.
-            density_type (str): 'manual', 'DTM', 'logDTM' or 'kde'.
-            kde_params (dict): if density_type is 'kde', additional parameters passed directly to sklearn.neighbors.KernelDensity.
+            density_type (str): 'manual', 'DTM', 'logDTM', 'KDE' or 'logKDE'.
+            kde_params (dict): if density_type is 'KDE' or 'logKDE', additional parameters passed directly to sklearn.neighbors.KernelDensity.
             k (int): number of neighbors for a knn graph (including the vertex itself). Defaults to 10.
             k_DTM (int): number of neighbors for the DTM density estimation (including the vertex itself). Defaults to k.
             r (float): size of a neighborhood if graph_type is 'radius'
@@ -280,13 +280,14 @@ class Tomato:
             else:
                 weights = -numpy.log(weights)
 
-        if self.density_type_ == "kde":
+        if self.density_type_ == "KDE" or self.density_type_ == "logKDE":
             # FIXME: replace most assert with raise ValueError("blabla")
             assert input_type == "points"
             kde_params = self.params_.get("kde_params", dict())
             from sklearn.neighbors import KernelDensity
-
             weights = KernelDensity(**kde_params).fit(X).score_samples(X)
+            if self.density_type_ == "KDE":
+                weights = numpy.exp(weights)
 
         if self.params_.get("symmetrize_graph"):
             self.neighbors_ = [set(line) for line in self.neighbors_]