First part of the fix

1 files changed, 40 insertions, 20 deletions

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 84bc99a2..711c32a7 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -44,11 +44,15 @@ class PersistenceImage(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.im_range)).any():
-            new_X = BirthPersistenceTransform().fit_transform(X)
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(new_X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.im_range = np.where(np.isnan(np.array(self.im_range)), np.array([mx, Mx, my, My]), np.array(self.im_range))
+        try:
+            if np.isnan(np.array(self.im_range)).any():
+                new_X = BirthPersistenceTransform().fit_transform(X)
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(new_X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.im_range = np.where(np.isnan(np.array(self.im_range)), np.array([mx, Mx, my, My]), np.array(self.im_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -120,9 +124,13 @@ class Landscape(BaseEstimator, TransformerMixin):
             y (n x 1 array): persistence diagram labels (unused).
         """
         if self.nan_in_range.any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
+            try:
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
+            except ValueError:
+                # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+                pass
         return self
 
     def transform(self, X):
@@ -218,10 +226,14 @@ class Silhouette(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.sample_range)).any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        try:
+            if np.isnan(np.array(self.sample_range)).any():
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -307,10 +319,14 @@ class BettiCurve(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.sample_range)).any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        try:
+            if np.isnan(np.array(self.sample_range)).any():
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):
@@ -374,10 +390,14 @@ class Entropy(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        if np.isnan(np.array(self.sample_range)).any():
-            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
-            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        try:
+            if np.isnan(np.array(self.sample_range)).any():
+                pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+                [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+                self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        except ValueError:
+            # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507
+            pass
         return self
 
     def transform(self, X):