Reset inputs when changing runs

CHANGELOG.md

@@ -1,7 +1,11 @@
 # Version 1.1.4
 
+## Enhancements
+- Fix lower bounds of dependency versions.
+
 ## Bug-Fixes
 - Don't convert BOHB runs with status 'running' (consistent with SMAC).
+- Reset inputs to fix error when subsequently selecting runs with different configspaces, objectives or budgets (#106)
 
 # Version 1.1.3
 

deepcave/plugins/__init__.py

@@ -360,11 +360,15 @@ def plugin_input_update(pathname: str, *inputs_list: str) -> List[str]:
                             # because `run` would be removed.
                             # Also: We want to keep the current run name.
                             update_dict(_inputs, self.load_inputs())
-
-                            # TODO: Reset only inputs which are not available in another run.
-                            # E.g. if options from budget in run_2 and run_3 are the same
-                            # take the budget from run_2 if changed to run_3. Otherwise,
-                            # reset budgets.
+                            # Reset inputs
+                            if "objective_id" in _inputs.keys():
+                                update_dict(_inputs, {"objective_id": {"value": None}})
+                            if "budget_id" in _inputs.keys():
+                                update_dict(_inputs, {"budget_id": {"value": None}})
+                            if "hyperparameter_name_1" in _inputs.keys():
+                                update_dict(_inputs, {"hyperparameter_name_1": {"value": None}})
+                            if "hyperparameter_name_2" in _inputs.keys():
+                                update_dict(_inputs, {"hyperparameter_name_2": {"value": None}})
 
                         selected_run = run_handler.get_run(_run_id)
 

deepcave/plugins/hyperparameter/pdp.py

@@ -145,10 +145,13 @@ def load_dependency_inputs(self, run, previous_inputs, inputs):
         objective_value = inputs["objective_id"]["value"]
         budget_value = inputs["budget_id"]["value"]
         hp1_value = inputs["hyperparameter_name_1"]["value"]
+        hp2_value = inputs["hyperparameter_name_2"]["value"]
 
         if objective_value is None:
             objective_value = objective_ids[0]
+        if budget_value is None:
             budget_value = budget_ids[-1]
+        if hp1_value is None:
             hp1_value = hp_names[0]
 
         return {
@@ -160,6 +163,7 @@ def load_dependency_inputs(self, run, previous_inputs, inputs):
             },
             "hyperparameter_name_2": {
                 "options": get_checklist_options([None] + hp_names),
+                "value": hp2_value,
             },
         }
 

deepcave/plugins/objective/configuration_cube.py

@@ -126,6 +126,7 @@ def load_dependency_inputs(self, run, _, inputs):
         # Pre-set values
         if objective_value is None:
             objective_value = objective_ids[0]
+        if budget_value is None:
             budget_value = budget_ids[-1]
         else:
             budget_value = inputs["budget_id"]["value"]

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,

requirements.txt

@@ -1,12 +1,12 @@
-wheel
-setuptools
+wheel>=0.41.2
+setuptools==68.2.2
 absl-py>=1.0.0
 jsonlines>=3.0.0
 pandas>=1.3.4
 numpy>=1.22.2
 matplotlib>=3.5.1
 seaborn>=0.13.0
-pyyaml
+pyyaml>=6.0.1
 
 # AutoML packages
 ConfigSpace==0.6.1
@@ -24,4 +24,4 @@ rq>=1.10.1
 # Pinning might be removed for dash>2.3.0
 werkzeug==2.0.3
 
-pyPDPPartitioner
+pyPDPPartitioner>=0.1.8

setup.py

@@ -13,22 +13,22 @@ def read_file(file_name):
     "dev": [
         # Tests
         "pytest>=4.6",
-        "pytest-cov",
-        "pytest-xdist",
-        "pytest-timeout",
-        "mypy",
-        "isort",
-        "black",
-        "pydocstyle",
-        "pre-commit",
-        "flake8",
+        "pytest-cov>=4.1.0",
+        "pytest-xdist>=3.3.1",
+        "pytest-timeout>=2.2.0",
+        "mypy>=1.6.1",
+        "isort>=5.12.0",
+        "black>=23.11.0",
+        "pydocstyle>=6.3.0",
+        "pre-commit>=3.5.0",
+        "flake8>=6.1.0",
         # Docs
         "automl-sphinx-theme>=0.1.10",
     ],
     "examples": [
-        "torch",
-        "torchvision",
-        "pytorch-lightning",
+        "torch>=2.1.0",
+        "torchvision>=0.16.0",
+        "pytorch-lightning>=2.1.1",
     ],
 }