interfaces for carp-s and hebo

examples/configs/hebo_mlp.yaml

@@ -0,0 +1,61 @@
+
+defaults:
+  - _self_
+  - override hydra/sweeper: HyperHEBO
+
+
+learning_rate: constant
+learning_rate_init: 0.001
+batch_size: 200
+n_neurons: 10
+n_layer: 1
+solver: adam
+activation: tanh
+
+seed: 42
+epochs: 10  # Default number of epochs
+
+hydra:
+  sweeper:
+    budget: 10
+    search_space:
+      hyperparameters:
+        n_layer:
+          type: uniform_int
+          lower: 1
+          upper: 5
+          default: ${n_layer}
+        n_neurons:
+          type: uniform_int
+          lower: 8
+          upper: 1024
+          log: true
+          default_value: ${n_neurons}
+        activation:
+          type: categorical
+          choices: [ logistic, tanh, relu ]
+          default_value: ${activation}
+        solver:
+          type: categorical
+          choices: [ lbfgs, sgd, adam ]
+          default_value: ${solver}
+        batch_size:
+          type: uniform_int
+          lower: 30
+          upper: 300
+          default_value: ${batch_size}
+        learning_rate:
+          type: categorical
+          choices: [ constant, invscaling, adaptive ]
+          default_value: ${learning_rate}
+        learning_rate_init:
+          type: uniform_float
+          lower: 0.0001
+          upper: 1
+          default_value: ${learning_rate_init}
+          log: true
+
+  run:
+    dir: ./tmp/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: ./tmp/${now:%Y-%m-%d}/${now:%H-%M-%S}

examples/configs/smac_bbo_branin.yaml

@@ -6,6 +6,7 @@ defaults:
 hydra:
   sweeper:
     budget: 10
+    budget_variable: budget
     sweeper_kwargs:
       optimizer_kwargs:
         smac_facade: 

examples/configs/smac_mf_mlp_carps.yaml

@@ -0,0 +1,95 @@
+
+defaults:
+  - _self_
+  - override hydra/sweeper: HyperCARPS
+
+
+learning_rate: constant
+learning_rate_init: 0.001
+batch_size: 200
+n_neurons: 10
+n_layer: 1
+solver: adam
+activation: tanh
+
+seed: 42
+epochs: 10  # Default number of epochs
+
+hydra:
+  sweeper:
+    budget: 10
+    budget_variable: epochs
+    sweeper_kwargs:
+      optimizer_kwargs:
+        _target_: carps.optimizers.smac20.SMAC3Optimizer
+        _partial_: true
+        smac_cfg:
+          smac_class: smac.facade.multi_fidelity_facade.MultiFidelityFacade
+          scenario:
+            seed: ${seed}
+            n_trials: 10
+            deterministic: true
+            n_workers: 1
+            output_directory: ${outdir}/${seed}/smac3_output
+            min_budget: 1
+            max_budget: 10
+          smac_kwargs:
+            dask_client: null
+            intensifier:
+              _target_: smac.intensifier.hyperband.Hyperband
+              _partial_: true
+              eta: 3
+    search_space:
+      hyperparameters:
+        n_layer:
+          type: uniform_int
+          lower: 1
+          upper: 5
+          default: ${n_layer}
+        n_neurons:
+          type: uniform_int
+          lower: 8
+          upper: 1024
+          log: true
+          default_value: ${n_neurons}
+        activation:
+          type: categorical
+          choices: [ logistic, tanh, relu ]
+          default_value: ${activation}
+        solver:
+          type: categorical
+          choices: [ lbfgs, sgd, adam ]
+          default_value: ${solver}
+        batch_size:
+          type: uniform_int
+          lower: 30
+          upper: 300
+          default_value: ${batch_size}
+        learning_rate:
+          type: categorical
+          choices: [ constant, invscaling, adaptive ]
+          default_value: ${learning_rate}
+        learning_rate_init:
+          type: uniform_float
+          lower: 0.0001
+          upper: 1
+          default_value: ${learning_rate_init}
+          log: true
+      conditions:
+        - child: batch_size
+          parent: solver
+          type: IN
+          values: [ sgd, adam ]
+        - child: learning_rate
+          parent: solver
+          type: EQ
+          value: sgd
+        - child: learning_rate_init
+          parent: solver
+          type: IN
+          values: [ sgd, adam ]
+
+  run:
+    dir: ./tmp/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: ./tmp/${now:%Y-%m-%d}/${now:%H-%M-%S}

hydra_plugins/hyper_carp_s/__init__.py

@@ -0,0 +1,3 @@
+from .config import HyperCARPSConfig
+
+__all__ = ["HyperCARPSConfig"]

hydra_plugins/hyper_carp_s/config.py

@@ -0,0 +1,31 @@
+"""Config for HyperCARPS sweeper."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+from hydra.core.config_store import ConfigStore
+
+
+@dataclass
+class HyperCARPSConfig:
+    """Config for HyperCARPS sweeper."""
+
+    _target_: str = "hydra_plugins.hypersweeper.hypersweeper.Hypersweeper"
+    opt_constructor: str = "hydra_plugins.hyper_carp_s.hyper_carp_s.make_carp_s"
+    search_space: dict | None = field(default_factory=dict)
+    resume: str | bool = False
+    budget: Any | None = None
+    budget_variable: str | None = None
+    loading_variable: str | None = None
+    saving_variable: str | None = None
+    sweeper_kwargs: dict | None = field(default_factory=dict)
+
+
+ConfigStore.instance().store(
+    group="hydra/sweeper",
+    name="HyperCARPS",
+    node=HyperCARPSConfig,
+    provider="hypersweeper",
+)

hydra_plugins/hyper_carp_s/hyper_carp_s.py

@@ -0,0 +1,20 @@
+from carps.benchmarks.dummy_problem import DummyProblem
+from hydra_plugins.hypersweeper import Info
+
+class HyperCARPSAdapter:
+    def __init__(self, carps) -> None:
+        self.carps = carps
+
+    def ask(self):
+        carps_info = self.carps.ask()
+        info = Info(carps_info.config, carps_info.budget, None, carps_info.seed)
+        return info, False
+
+    def tell(self, info, value):
+        self.smac.tell(info, value)
+
+def make_carp_s(configspace, carps_args):
+    problem = DummyProblem()
+    problem._configspace = configspace
+    optimizer = carps_args["optimizer"](problem)
+    return HyperCARPSAdapter(optimizer)

hydra_plugins/hyper_hebo/__init__.py

@@ -0,0 +1,3 @@
+from .config import HyperHEBOConfig
+
+__all__ = ["HyperHEBOConfig"]

hydra_plugins/hyper_hebo/config.py

@@ -0,0 +1,31 @@
+"""Config for HyperHEBO sweeper."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+from hydra.core.config_store import ConfigStore
+
+
+@dataclass
+class HyperHEBOConfig:
+    """Config for HyperHEBO sweeper."""
+
+    _target_: str = "hydra_plugins.hypersweeper.hypersweeper.Hypersweeper"
+    opt_constructor: str = "hydra_plugins.hyper_hebo.hyper_hebo.make_hebo"
+    search_space: dict | None = field(default_factory=dict)
+    resume: str | bool = False
+    budget: Any | None = None
+    budget_variable: str | None = None
+    loading_variable: str | None = None
+    saving_variable: str | None = None
+    sweeper_kwargs: dict | None = field(default_factory=dict)
+
+
+ConfigStore.instance().store(
+    group="hydra/sweeper",
+    name="HyperHEBO",
+    node=HyperHEBOConfig,
+    provider="hypersweeper",
+)