[Feature] Show Multi Gen Eval + Batch Prediction (#267)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#267

epdb/models.py

@@ -1744,6 +1744,16 @@ class Pathway(EnviPathModel, AliasMixin, ScenarioMixin):
         # potentially prefetched edge_set
         return self.edge_set.all()
 
+    @property
+    def setting_with_overrides(self):
+        mem_copy = Setting.objects.get(pk=self.setting.pk)
+
+        if "setting_overrides" in self.kv:
+            for k, v in self.kv["setting_overrides"].items():
+                setattr(mem_copy, k, f"{v} (this is an override for this particular pathway)")
+
+        return mem_copy
+
     def _url(self):
         return "{}/pathway/{}".format(self.package.url, self.uuid)
 
@@ -1879,25 +1889,38 @@ class Pathway(EnviPathModel, AliasMixin, ScenarioMixin):
 
         return json.dumps(res)
 
-    def to_csv(self) -> str:
+    def to_csv(self, include_header=True, include_pathway_url=False) -> str:
         import csv
         import io
 
+        header = []
+
+        if include_pathway_url:
+            header += ["Pathway URL"]
+
+        header += [
+            "SMILES",
+            "name",
+            "depth",
+            "probability",
+            "rule_names",
+            "rule_ids",
+            "parent_smiles",
+        ]
+
         rows = []
-        rows.append(
-            [
-                "SMILES",
-                "name",
-                "depth",
-                "probability",
-                "rule_names",
-                "rule_ids",
-                "parent_smiles",
-            ]
-        )
+
+        if include_header:
+            rows.append(header)
+
         for n in self.nodes.order_by("depth"):
             cs = n.default_node_label
-            row = [cs.smiles, cs.name, n.depth]
+            row = []
+
+            if include_pathway_url:
+                row.append(n.pathway.url)
+
+            row += [cs.smiles, cs.name, n.depth]
 
             edges = self.edges.filter(end_nodes__in=[n])
             if len(edges):
@@ -2362,6 +2385,29 @@ class PackageBasedModel(EPModel):
 
         return res
 
+    @property
+    def mg_pr_curve(self):
+        if self.model_status != self.FINISHED:
+            raise ValueError(f"Expected {self.FINISHED} but model is in status {self.model_status}")
+
+        if not self.multigen_eval:
+            raise ValueError("MG PR Curve is only available for multigen models")
+
+        res = []
+
+        thresholds = self.eval_results["multigen_average_precision_per_threshold"].keys()
+
+        for t in thresholds:
+            res.append(
+                {
+                    "precision": self.eval_results["multigen_average_precision_per_threshold"][t],
+                    "recall": self.eval_results["multigen_average_recall_per_threshold"][t],
+                    "threshold": float(t),
+                }
+            )
+
+        return res
+
     @cached_property
     def applicable_rules(self) -> List["Rule"]:
         """
@@ -2565,7 +2611,7 @@ class PackageBasedModel(EPModel):
             for i, root in enumerate(root_compounds):
                 logger.debug(f"Evaluating pathway {i + 1} of {len(root_compounds)}...")
 
-                spw = SPathway(root_nodes=root, prediction_setting=s)
+                spw = SPathway(root_nodes=root.smiles, prediction_setting=s)
                 level = 0
 
                 while not spw.done:
@@ -3771,6 +3817,12 @@ class UserSettingPermission(Permission):
         return f"User: {self.user} has Permission: {self.permission} on Setting: {self.setting}"
 
 
+class ExpansionSchemeChoice(models.TextChoices):
+    BFS = "BFS", "Breadth First Search"
+    DFS = "DFS", "Depth First Search"
+    GREEDY = "GREEDY", "Greedy"
+
+
 class Setting(EnviPathModel):
     public = models.BooleanField(null=False, blank=False, default=False)
     global_default = models.BooleanField(null=False, blank=False, default=False)
@@ -3795,6 +3847,12 @@ class Setting(EnviPathModel):
         null=True, blank=True, verbose_name="Setting Model Threshold", default=0.25
     )
 
+    expansion_scheme = models.CharField(
+        max_length=20,
+        choices=ExpansionSchemeChoice.choices,
+        default=ExpansionSchemeChoice.BFS,
+    )
+
     def _url(self):
         return "{}/setting/{}".format(s.SERVER_URL, self.uuid)
 
@@ -3833,7 +3891,7 @@ class Setting(EnviPathModel):
         """Decision Method whether to expand on a certain Node or not"""
         if pathway.num_nodes() >= self.max_nodes:
             logger.info(
-                f"Pathway has {pathway.num_nodes()} which exceeds the limit of {self.max_nodes}"
+                f"Pathway has {pathway.num_nodes()} Nodes which exceeds the limit of {self.max_nodes}"
             )
             return []
 
@@ -3931,3 +3989,8 @@ class JobLog(TimeStampedModel):
         if self.job_name == "engineer_pathways":
             return ast.literal_eval(self.task_result)
         return self.task_result
+
+    def is_result_downloadable(self):
+        downloadable = ["batch_predict"]
+
+        return self.job_name in downloadable