Merge remote-tracking branch 'origin/develop' into enhancement/dataset

# Conflicts:
#	epdb/models.py
#	tests/test_enviformer.py
#	tests/test_model.py

epdb/models.py

@@ -2226,10 +2226,18 @@ class PackageBasedModel(EPModel):
         self.model_status = self.BUILT_NOT_EVALUATED
         self.save()
 
-    def evaluate_model(self, **kwargs):
+    def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None, **kwargs):
         if self.model_status != self.BUILT_NOT_EVALUATED:
             raise ValueError(f"Can't evaluate a model in state {self.model_status}!")
 
+        if multigen:
+            self.multigen_eval = multigen
+            self.save()
+
+        if eval_packages is not None:
+            for p in eval_packages:
+                self.eval_packages.add(p)
+
         self.model_status = self.EVALUATING
         self.save()
 
@@ -2526,7 +2534,6 @@ class RuleBasedRelativeReasoning(PackageBasedModel):
         package: "Package",
         rule_packages: List["Package"],
         data_packages: List["Package"],
-        eval_packages: List["Package"],
         threshold: float = 0.5,
         min_count: int = 10,
         max_count: int = 0,
@@ -2575,10 +2582,6 @@ class RuleBasedRelativeReasoning(PackageBasedModel):
             for p in rule_packages:
                 rbrr.data_packages.add(p)
 
-        if eval_packages:
-            for p in eval_packages:
-                rbrr.eval_packages.add(p)
-
         rbrr.save()
 
         return rbrr
@@ -2633,7 +2636,6 @@ class MLRelativeReasoning(PackageBasedModel):
         package: "Package",
         rule_packages: List["Package"],
         data_packages: List["Package"],
-        eval_packages: List["Package"],
         threshold: float = 0.5,
         name: "str" = None,
         description: str = None,
@@ -2673,10 +2675,6 @@ class MLRelativeReasoning(PackageBasedModel):
             for p in rule_packages:
                 mlrr.data_packages.add(p)
 
-        if eval_packages:
-            for p in eval_packages:
-                mlrr.eval_packages.add(p)
-
         if build_app_domain:
             ad = ApplicabilityDomain.create(
                 mlrr,
@@ -2953,7 +2951,6 @@ class EnviFormer(PackageBasedModel):
     def create(
         package: "Package",
         data_packages: List["Package"],
-        eval_packages: List["Package"],
         threshold: float = 0.5,
         name: "str" = None,
         description: str = None,
@@ -2986,10 +2983,6 @@ class EnviFormer(PackageBasedModel):
         for p in data_packages:
             mod.data_packages.add(p)
 
-        if eval_packages:
-            for p in eval_packages:
-                mod.eval_packages.add(p)
-
         # if build_app_domain:
         #     ad = ApplicabilityDomain.create(mod, app_domain_num_neighbours, app_domain_reliability_threshold,
         #                                     app_domain_local_compatibility_threshold)
@@ -3082,10 +3075,18 @@ class EnviFormer(PackageBasedModel):
         args = {"clz": "EnviFormer"}
         return args
 
-    def evaluate_model(self, **kwargs):
+    def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None, **kwargs):
         if self.model_status != self.BUILT_NOT_EVALUATED:
             raise ValueError(f"Can't evaluate a model in state {self.model_status}!")
 
+        if multigen:
+            self.multigen_eval = multigen
+            self.save()
+
+        if eval_packages is not None:
+            for p in eval_packages:
+                self.eval_packages.add(p)
+
         self.model_status = self.EVALUATING
         self.save()
 
@@ -3226,7 +3227,7 @@ class EnviFormer(PackageBasedModel):
 
             ds = self.load_dataset()
             n_splits = kwargs.get("n_splits", 20)
-            shuff = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state=42)
+            shuff = ShuffleSplit(n_splits=n_splits, test_size=0.1, random_state=42)
 
             # Single gen eval is done in one loop of train then evaluate rather than storing all n_splits trained models
             # this helps reduce the memory footprint.
@@ -3294,7 +3295,7 @@ class EnviFormer(PackageBasedModel):
                 # Compute splits of the collected pathway and evaluate. Like single gen we train and evaluate in each
                 # iteration instead of storing all trained models.
                 for split_id, (train, test) in enumerate(
-                    ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state=42).split(pathways)
+                    ShuffleSplit(n_splits=n_splits, test_size=0.1, random_state=42).split(pathways)
                 ):
                     train_pathways = [pathways[i] for i in train]
                     test_pathways = [pathways[i] for i in test]
@@ -3577,3 +3578,53 @@ class Setting(EnviPathModel):
             self.public = True
         self.global_default = True
         self.save()
+
+
+class JobLogStatus(models.TextChoices):
+    INITIAL = "INITIAL", "Initial"
+    SUCCESS = "SUCCESS", "Success"
+    FAILURE = "FAILURE", "Failure"
+    REVOKED = "REVOKED", "Revoked"
+    IGNORED = "IGNORED", "Ignored"
+
+
+class JobLog(TimeStampedModel):
+    user = models.ForeignKey("epdb.User", models.CASCADE)
+    task_id = models.UUIDField(unique=True)
+    job_name = models.TextField(null=False, blank=False)
+    status = models.CharField(
+        max_length=20,
+        choices=JobLogStatus.choices,
+        default=JobLogStatus.INITIAL,
+    )
+
+    done_at = models.DateTimeField(null=True, blank=True, default=None)
+    task_result = models.TextField(null=True, blank=True, default=None)
+
+    def check_for_update(self):
+        async_res = self.get_result()
+        new_status = async_res.state
+
+        TERMINAL_STATES = [
+            "SUCCESS",
+            "FAILURE",
+            "REVOKED",
+            "IGNORED",
+        ]
+
+        if new_status != self.status and new_status in TERMINAL_STATES:
+            self.status = new_status
+            self.done_at = async_res.date_done
+
+            if new_status == "SUCCESS":
+                self.task_result = async_res.result
+
+            self.save()
+
+            return True
+        return False
+
+    def get_result(self):
+        from celery.result import AsyncResult
+
+        return AsyncResult(str(self.task_id))