new fixes

1 files changed, 12 insertions, 15 deletions

diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py
index c5439a67..0659b457 100644
--- a/src/python/gudhi/representations/metrics.py
+++ b/src/python/gudhi/representations/metrics.py
@@ -10,17 +10,9 @@
 import numpy as np
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.metrics import pairwise_distances
-from gudhi.wasserstein import wasserstein_distance as pot_wasserstein_distance
 from gudhi.hera import wasserstein_distance as hera_wasserstein_distance
 from .preprocessing import Padding
 
-try:
-    from .. import bottleneck_distance
-    USE_GUDHI = True
-except ImportError:
-    USE_GUDHI = False
-    print("Gudhi built without CGAL: BottleneckDistance will return a null matrix")
-
 #############################################
 # Metrics ###################################
 #############################################
@@ -111,9 +103,13 @@ def pairwise_persistence_diagram_distances(X, Y=None, metric="bottleneck", **kwa
     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, X, Y, **kwargs))
-    elif metric == "wasserstein" or metric == "pot_wasserstein":
-        return pairwise_distances(XX, YY, metric=sklearn_wrapper(pot_wasserstein_distance,  X, Y, **kwargs))
-    elif metric == "hera_wasserstein":
+    elif metric == "pot_wasserstein":
+        try:
+            from gudhi.wasserstein import wasserstein_distance as pot_wasserstein_distance
+            return pairwise_distances(XX, YY, metric=sklearn_wrapper(pot_wasserstein_distance,  X, Y, **kwargs))
+        except ImportError:
+            print("Gudhi built without POT")
+    elif metric == "wasserstein" or metric == "hera_wasserstein":
         return pairwise_distances(XX, YY, metric=sklearn_wrapper(hera_wasserstein_distance,  X, Y, **kwargs))
     elif metric == "sliced_wasserstein":
         return pairwise_distances(XX, YY, metric=sklearn_wrapper(sliced_wasserstein_distance, X, Y, **kwargs))
@@ -192,16 +188,17 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
         Returns:
             numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise bottleneck distances.
         """
-        if not USE_GUDHI:
-            print("Gudhi built without CGAL: returning a null matrix")
-        Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric="bottleneck", e=self.epsilon) if USE_GUDHI else np.zeros((len(X), len(self.diagrams_)))
+        try:
+            Xfit = pairwise_persistence_diagram_distances(X, self.diagrams_, metric="bottleneck", e=self.epsilon)
+        except ImportError:
+            print("Gudhi built without CGAL")
         return Xfit
 
 class WassersteinDistance(BaseEstimator, TransformerMixin):
     """
     This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. 
     """
-    def __init__(self, order=2, internal_p=2, mode="pot", delta=0.0001):
+    def __init__(self, order=2, internal_p=2, mode="pot", delta=0.01):
         """
         Constructor for the WassersteinDistance class.