last fixes

eaf.py

@@ -43,19 +43,17 @@ def main(eaf_args=None):
     if the.not_parallel:
         results = []
         for i in range(the.nb_run):
-            # print(f"{i}th run")
             results.append(run(args))
     else:
 
         def f(i):
-            # print(f"{i}th run")
             return run(args)
 
         results = Parallel(n_jobs=num_cores - 1)(
             delayed(f)(i) for i in range(the.nb_run)
         )
 
-    lens =[len(result[-1]) for result in results]
+    lens = [len(result[-1]) for result in results]
     histories = [
         np.array([val for val, _ in history] + [1] * (the.iters - len(history)))
         for _, _, _, _, _, history in results
@@ -64,26 +62,37 @@ def main(eaf_args=None):
     histories = np.array([np.maximum.accumulate(history) for history in histories])
     masks = []
     for idx, _ in enumerate(histories):
-        masks.append(np.concatenate((np.zeros(lens[idx],),np.ones(the.iters - lens[idx],))))
-    
+        masks.append(
+            np.concatenate(
+                (
+                    np.zeros(
+                        lens[idx],
+                    ),
+                    np.ones(
+                        the.iters - lens[idx],
+                    ),
+                )
+            )
+        )
+
     histories = np.ma.array(histories, mask=masks).T
 
     buff = np.empty((1, the.iters))
     for quality_threshold in the.quality_threshold:
         curr_quality = histories >= quality_threshold
-        curr_quality = curr_quality.mean(axis=-1)
+        curr_quality = np.sum(curr_quality, axis=1) / the.nb_run
+        curr_quality = np.maximum.accumulate(curr_quality)
         curr_quality = np.expand_dims(curr_quality, axis=0)
         buff = np.concatenate((buff, curr_quality))
 
     _, coef, initial, _, _, _ = results[0]
-    t = [i * coef for i in range(histories.shape[0])]
-    t[0] = initial
+    t = [i * coef + initial for i in range(histories.shape[0])]
 
     plt.figure()
 
     plt.title(f"{the.solver}, threshold {the.quality_threshold}")
     for i in range(1, buff.shape[0]):
-        plt.step(t, buff[i,~buff[i].mask][0])
+        plt.step(t, buff[i, ~buff[i].mask][0])
 
     plt.legend(
         [
@@ -91,6 +100,10 @@ def main(eaf_args=None):
             for i in range(len(the.quality_threshold))
         ]
     )
+
+    plt.xlabel("Number of calls to objective function")
+    plt.ylabel("Frequency of reaching")
+    
     plt.show()