Correlation plot with nans

pypesto/visualize/misc.py

@@ -1,3 +1,4 @@
+import logging
 import warnings
 from collections.abc import Iterable
 from numbers import Number
@@ -23,6 +24,8 @@
 from ..util import assign_clusters, delete_nan_inf
 from .clust_color import assign_colors_for_list
 
+logger = logging.getLogger(__name__)
+
 
 def process_result_list(
     results: Union[Result, list[Result]], colors=None, legends=None
@@ -303,8 +306,8 @@ def process_start_indices(
     """
     Process the start_indices.
 
-    Create an array of indices if a number was provided and checks that the
-    indices do not exceed the max_index.
+    Create an array of indices if a number was provided, checks that the indices
+    do not exceed the max_index and removes starts with non-finite fval.
 
     Parameters
     ----------
@@ -323,7 +326,7 @@ def process_start_indices(
         start_indices = ALL
     if isinstance(start_indices, str):
         if start_indices == ALL:
-            return np.asarray(range(len(result.optimize_result)))
+            start_indices = np.asarray(range(len(result.optimize_result)))
         elif start_indices == ALL_CLUSTERED:
             clust_ind, clust_size = assign_clusters(
                 delete_nan_inf(result.optimize_result.fval)[1]
@@ -336,12 +339,12 @@ def process_start_indices(
             start_indices = np.concatenate(
                 [np.where(clust_ind == i_clust)[0] for i_clust in clust_gr2]
             )
-            return start_indices
+            start_indices = start_indices
         elif start_indices == FIRST_CLUSTER:
             clust_ind = assign_clusters(
                 delete_nan_inf(result.optimize_result.fval)[1]
             )[0]
-            return np.where(clust_ind == 0)[0]
+            start_indices = np.where(clust_ind == 0)[0]
         else:
             raise ValueError(
                 f"Permissible values for start_indices are {ALL}, "
@@ -359,6 +362,18 @@ def process_start_indices(
         if start_index < len(result.optimize_result)
     ]
 
+    # filter out the indices that are not finite
+    start_indices_unfiltered = len(start_indices)
+    start_indices = [
+        start_index
+        for start_index in start_indices
+        if np.isfinite(result.optimize_result[start_index].fval)
+    ]
+    if len(start_indices) != start_indices_unfiltered:
+        logger.warning(
+            "Some start indices were removed due to inf or nan function values."
+        )
+
     return np.asarray(start_indices)
 
 

pypesto/visualize/parameters.py

@@ -601,23 +601,10 @@ def optimization_scatter(
     parameter_indices = process_parameter_indices(
         parameter_indices=parameter_indices, result=result
     )
-    # remove all start indices that encounter an inf value at the start
-    # resulting in optimize_result[start]["x"] being None
-    start_indices_finite = start_indices[
-        [
-            result.optimize_result[i_start]["x"] is not None
-            for i_start in start_indices
-        ]
-    ]
-    # compare start_indices with start_indices_finite and log a warning
-    if len(start_indices) != len(start_indices_finite):
-        logger.warning(
-            "Some start indices were removed due to inf values at the start."
-        )
     # put all parameters into a dataframe, where columns are parameters
     parameters = [
         result.optimize_result[i_start]["x"][parameter_indices]
-        for i_start in start_indices_finite
+        for i_start in start_indices
     ]
     x_labels = [
         result.problem.x_names[parameter_index]

test/visualize/test_visualize.py

@@ -1179,3 +1179,15 @@ def test_sacess_history():
     )
     sacess.minimize(problem)
     sacess_history(sacess.histories)
+
+
+@pytest.mark.parametrize(
+    "result_creation",
+    [create_optimization_result, create_optimization_result_nan_inf],
+)
+@close_fig
+def test_parameters_correlation_matrix(result_creation):
+    """Test pypesto.visualize.parameters_correlation_matrix"""
+    result = result_creation()
+
+    visualize.parameters_correlation_matrix(result)