Save each benchmark before the end

config_files/config_test.yml

 # The base configuration of the benchmark
 Base :
   log: True
-  name: ["awa-tiger-wolf-all"]
+  name: ["plausible", "koukou"]
   label: "_"
   type: ".hdf5"
   views:
-  pathf: "/home/baptiste/Documents/Datasets/AWA/base/"
+  pathf: "../data/"
   nice: 0
   random_state: 42
   nb_cores: 1
@@ -25,7 +25,7 @@ Classification:
   type: ["multiview", "monoview"]
   algos_monoview: ["decision_tree", "adaboost", "random_forest" ]
   algos_multiview: ["weighted_linear_early_fusion",]
-  stats_iter: 1
+  stats_iter: 2
   metrics: ["accuracy_score", "f1_score"]
   metric_princ: "f1_score"
   hps_type: "randomized_search-equiv"

multiview_platform/mono_multi_view_classifiers/exec_classif.py

@@ -771,7 +771,10 @@ def exec_benchmark(nb_cores, stats_iter, nb_multiclass,
     #         benchmark_arguments_dictionaries[0])]
     # else:
     for arguments in benchmark_arguments_dictionaries:
-        results += [exec_one_benchmark_mono_core(dataset_var=dataset_var, **arguments)]
+        benchmark_results = exec_one_benchmark_mono_core(dataset_var=dataset_var, **arguments)
+        from .result_analysis import analyze_biclass
+        analyze_biclass([benchmark_results], benchmark_arguments_dictionaries, stats_iter, metrics, example_ids=dataset_var.example_ids)
+        results += [benchmark_results]
     logging.debug("Done:\t Executing all the needed biclass benchmarks")
 
     # Do everything with flagging