""" Simulated Task consisting in training a Support Vector Machine (SVM).
"""
import typing
from dataclasses import dataclass
from functools import partial
from typing import Callable, ClassVar, Dict, List, Tuple
from orion.benchmark.task.profet.model_utils import get_default_architecture
from orion.benchmark.task.profet.profet_task import ProfetTask
if typing.TYPE_CHECKING:
import torch
[docs]class ProfetSvmTask(ProfetTask):
"""Simulated Task consisting in training a Support Vector Machine."""
[docs] @dataclass
class ModelConfig(ProfetTask.ModelConfig):
"""Config for training the Profet model on an SVM task."""
benchmark: str = "svm"
json_file_name: ClassVar[str] = "data_sobol_svm.json"
get_architecture: ClassVar[Callable[[int], "torch.nn.Module"]] = partial(
get_default_architecture, classification=True
)
""" Callable that takes the input dimensionality and returns the network to be trained. """
hidden_space: ClassVar[int] = 5
normalize_targets: ClassVar[bool] = False
log_cost: ClassVar[bool] = True
log_target: ClassVar[bool] = False
shapes: ClassVar[Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...]]] = (
(200, 2),
(26, 200),
(26, 200),
)
y_min: ClassVar[float] = 0.0
y_max: ClassVar[float] = 1.0
c_min: ClassVar[float] = 0.0
c_max: ClassVar[float] = 697154.4010462761
[docs] def call(self, C: float, gamma: float) -> List[Dict]:
return super().call(C=C, gamma=gamma)
[docs] def get_search_space(self) -> Dict[str, str]:
return dict(
C="loguniform(np.exp(-10), np.exp(10))",
gamma="loguniform(np.exp(-10), np.exp(10))",
)