Allow to load multi-objective SMAC3v2 and add example

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Version 1.1.4
 
+## Enhancements
+- Fix lower bounds of dependency versions.
+- Allow to load multi-objective SMAC3v2 and add example (#69)
+
 ## Bug-Fixes
 - Don't convert BOHB runs with status 'running' (consistent with SMAC).
 

deepcave/runs/converters/smac3v2.py

@@ -42,10 +42,17 @@ def from_path(cls, path):
             configspace = cs_json.read(f.read())
 
         # Read objectives
-        # We have to define it ourselves, because we don't know the type of the objective
+        with (path / "scenario.json").open() as json_file:
+            all_data = json.load(json_file)
+            objectives = all_data["objectives"]
+
+        obj_list = list()
+        if not isinstance(objectives, list):
+            objectives = [objectives]
         # Only lock lower
-        objective1 = Objective("Cost", lower=0)
-        objective2 = Objective("Time", lower=0)
+        for obj in objectives:
+            obj_list.append(Objective(obj, lower=0))
+        obj_list.append(Objective("Time", lower=0))
 
         # Read meta
         with (path / "scenario.json").open() as json_file:
@@ -54,7 +61,7 @@ def from_path(cls, path):
 
         # Let's create a new run object
         run = SMAC3v2Run(
-            name=path.stem, configspace=configspace, objectives=[objective1, objective2], meta=meta
+            name=path.stem, configspace=configspace, objectives=obj_list, meta=meta
         )
 
         # We have to set the path manually
@@ -118,7 +125,7 @@ def from_path(cls, path):
 
             if status != Status.SUCCESS:
                 # We don't want cost included which are failed
-                cost = None
+                cost = [None] * len(cost) if isinstance(cost, list) else None
                 time = None
             else:
                 time = endtime - starttime
@@ -134,7 +141,7 @@ def from_path(cls, path):
                 origin = config_origins[config_id]
 
             run.add(
-                costs=[cost, time],
+                costs=cost + [time] if isinstance(cost, list) else [cost, time],
                 config=config,
                 budget=budget,
                 start_time=starttime,

examples/record/mnist_pytorch.py

@@ -63,15 +63,14 @@ def __init__(self, activation="relu", learning_rate=1e-4, dropout_rate=0.1, batc
             ]
         )
 
-        self.accuracy = Accuracy()
+        self.accuracy = Accuracy(task="multiclass", num_classes=self.num_classes)
 
     def prepare_data(self):
         # download
         MNIST(self.data_dir, train=True, download=True)
         MNIST(self.data_dir, train=False, download=True)
 
     def setup(self, stage=None):
-
         # Assign train/val datasets for use in dataloaders
         if stage == "fit" or stage is None:
             mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
@@ -257,10 +256,9 @@ def get_configspace(seed):
 
                     # The model weights are trained
                     trainer = pl.Trainer(
-                        accelerator="gpu",
+                        accelerator="cpu",
                         devices=1,
                         num_sanity_val_steps=0,  # No validation sanity
-                        auto_scale_batch_size="power",
                         deterministic=True,
                         min_epochs=epochs,
                         max_epochs=epochs,

logs/SMAC3v2/multi-objective/run_1/configspace.json

@@ -0,0 +1,17 @@
+{
+  "hyperparameters": [
+    {
+      "name": "x",
+      "type": "uniform_float",
+      "log": false,
+      "lower": -2.0,
+      "upper": 2.0,
+      "default": 0.0,
+      "q": null
+    }
+  ],
+  "conditions": [],
+  "forbiddens": [],
+  "python_module_version": "0.6.1",
+  "json_format_version": 0.4
+}