fix n_iter report

benchmarks/ridge/consolidate_result_csv.py

@@ -94,7 +94,9 @@
     BACKEND_PROVIDER: str,
     COMPUTE_DEVICE: str,
     COMPUTE_RUNTIME: str,
-    RESULT_NB_ITERATIONS: np.int64,
+    # NB: following should be int but str is more practical because it enables
+    # use of missing values for solver for which it doesn't apply.
+    RESULT_NB_ITERATIONS: str,
     OBJECTIVE_FUNCTION_VALUE: np.float64,
     SOLVER: str,
     PLATFORM: str,

benchmarks/ridge/objective.py

@@ -1,3 +1,4 @@
+import numbers
 from datetime import datetime
 
 from benchopt import BaseObjective, safe_import_context
@@ -87,6 +88,23 @@ def evaluate_result(self, weights, intercept, n_iter, **solver_parameters):
         all_parameters.update(
             {("solver_param_" + key): value for key, value in solver_parameters.items()}
         )
+
+        if not (isinstance(n_iter, numbers.Number) or (n_iter is None)):
+            n_iter = set(n_iter)
+            if len(n_iter) > 1:
+                raise ValueError(
+                    "In multitarget mode, the same number of iterations is expected "
+                    "for all targets, to keep reports comparable."
+                )
+            n_iter = n_iter.pop()
+
+        # NB: str for n_iter is a more practical type because it enables
+        # using missing values for solvers for which it doesn't apply
+        if n_iter is None:
+            n_iter = ""
+        else:
+            n_iter = str(n_iter)
+
         return dict(
             value=value,
             n_iter=n_iter,

benchmarks/ridge/solvers/scikit_learn.py

@@ -64,20 +64,10 @@ def run(self, _):
         self.n_iter_ = estimator.n_iter_
 
     def get_result(self):
-        n_iter = self.n_iter_
-        if isinstance(n_iter, list):
-            n_iter = set(n_iter)
-            if len(n_iter) > 1:
-                raise ValueError(
-                    "In multitarget mode, the same number of iterations is expected "
-                    "for all targets, to keep reports comparable."
-                )
-            n_iter = n_iter.pop()
-
         return dict(
             weights=self.weights,
             intercept=self.intercept,
-            n_iter=n_iter,
+            n_iter=self.n_iter_,
             version_info=f"scikit-learn {version('scikit-learn')}",
             __name=self.name,
             **self._parameters,