lower the bar for finished trials

code/aind_auto_train/curriculums/coupled_baiting_1p0.py

@@ -259,19 +259,19 @@
         TransitionRule(
             decision=Decision.PROGRESS,
             to_stage=TrainingStage.STAGE_FINAL,
-            condition_description="Finished trials >= 400 and efficiency >= 0.7",
+            condition_description="Finished trials >= 350 and efficiency >= 0.7",
             condition="""lambda metrics:
-                        metrics.finished_trials[-1] >= 400
+                        metrics.finished_trials[-1] >= 350
                         and
                         metrics.foraging_efficiency[-1] >= 0.7
                         """,
         ),
         TransitionRule(
             decision=Decision.ROLLBACK,
             to_stage=TrainingStage.STAGE_2,
-            condition_description="Finished trials < 200 or efficiency < 0.6",
+            condition_description="Finished trials < 250 or efficiency < 0.6",
             condition="""lambda metrics:
-                        metrics.finished_trials[-1] < 200
+                        metrics.finished_trials[-1] < 250
                         or
                         metrics.foraging_efficiency[-1] < 0.6
                         """,
@@ -343,24 +343,24 @@
             decision=Decision.PROGRESS,
             to_stage=TrainingStage.GRADUATED,
             condition_description=("For recent 5 sessions,"
-                                   "mean finished trials >= 500 and mean efficiency >= 0.7 "
+                                   "mean finished trials >= 400 and mean efficiency >= 0.7 "
                                    "and total sessions >= 10 and sessions at final >= 5"),
             condition="""lambda metrics:
                         metrics.session_total >= 10 
                         and
                         metrics.session_at_current_stage >= 5
                         and
-                        np.mean(metrics.finished_trials[-5:]) >= 500
+                        np.mean(metrics.finished_trials[-5:]) >= 400
                         and
                         np.mean(metrics.foraging_efficiency[-5:]) >= 0.7
                         """,
         ),
         TransitionRule(
             decision=Decision.ROLLBACK,
             to_stage=TrainingStage.STAGE_3,
-            condition_description="For recent 2 sessions, mean finished trials < 400 or efficiency < 0.6",
+            condition_description="For recent 2 sessions, mean finished trials < 300 or efficiency < 0.6",
             condition="""lambda metrics:
-                        np.mean(metrics.finished_trials[-2:]) < 400
+                        np.mean(metrics.finished_trials[-2:]) < 300
                         or
                         np.mean(metrics.foraging_efficiency[-2:]) < 0.6
                         """,