from sklearn.svm import SVR
from typing import Mapping, Iterable, Literal
from .base import BaseR, HyperparameterSearcher
from optuna.distributions import (
FloatDistribution,
IntDistribution,
CategoricalDistribution,
BaseDistribution,
)
from ....feature_selection import BaseFSR
[docs]
class SVMR(BaseR):
"""Support vector machine regressor.
Hyperparameter optimization is performed automatically during training.
The hyperparameter search process can be modified by the user.
"""
[docs]
def __init__(
self,
type: Literal["linear", "poly", "rbf"] = "rbf",
hyperparam_search_method: Literal["optuna", "grid"] | None = None,
hyperparam_search_space: (
Mapping[str, Iterable | BaseDistribution] | None
) = None,
feature_selectors: list[BaseFSR] | None = None,
max_n_features: int | None = None,
name: str | None = None,
**kwargs,
):
"""
Initializes a SVMR object.
Parameters
----------
type : Literal['linear', 'poly', 'rbf']
Default: 'rbf'. The type of kernel to use.
hyperparam_search_method : Literal[None, 'grid', 'optuna']
Default: None. If None, a model-specific default hyperparameter search
is conducted.
hyperparam_search_space : Mapping[str, Iterable | BaseDistribution]
Default: None. If None, a model-specific default hyperparameter search
is conducted.
feature_selectors : list[BaseFSC]
Default: None. If not None, specifies the feature selectors for the
VotingSelectionReport.
max_n_features : int | None
Default: None.
Only useful if feature_selectors is not None.
If None, then all features with at least 50% support are selected.
model_random_state : int
Default: 42. Random seed for the model.
name : str
Default: None. Determines how the model shows up in the reports.
If None, the name is set to be the class name.
**kwargs : dict
Key word arguments are passed directly into the intialization of the
HyperparameterSearcher class. See below for options.
inner_cv : int | BaseCrossValidator
Default: 5. Number of inner cross validation folds. Inner
cross validation is used for hyperparameter optimization.
inner_cv_seed : int
Default: 42. Random seed for inner cross validation.
n_jobs : int
Default: 1. Number of parallel jobs to run.
verbose : int
Default: 0. Sets the sklearn verbosity level for the sklearn estimator.
2 is the most verbose.
n_trials : int
Default: 100. Number of trials for hyperparameter optimization. Only
used if hyperparam_search_method is 'optuna'.
"""
super().__init__()
if name is None:
self._name = f"SVMR({type})"
else:
self._name = name
self._best_estimator = SVR(kernel=type, max_iter=100)
self._feature_selectors = feature_selectors
self._max_n_features = max_n_features
if (hyperparam_search_method is None) or (hyperparam_search_space is None):
hyperparam_search_method = "optuna"
if type == "linear":
hyperparam_search_space = {
"C": FloatDistribution(1e-2, 1e2, log=True),
"epsilon": FloatDistribution(1e-3, 1e0, log=True),
}
elif type == "poly":
hyperparam_search_space = {
"C": FloatDistribution(1e-2, 1e2, log=True),
"epsilon": FloatDistribution(1e-3, 1e0, log=True),
"degree": IntDistribution(2, 5),
"coef0": IntDistribution(0, 10),
"gamma": CategoricalDistribution(["scale", "auto"]),
}
elif type == "rbf":
hyperparam_search_space = {
"C": FloatDistribution(1e-2, 1e2, log=True),
"epsilon": FloatDistribution(1e-3, 1e0, log=True),
"gamma": CategoricalDistribution(["scale", "auto"]),
}
else:
raise ValueError(f"Invalid value for type: {type}.")
self._hyperparam_searcher = HyperparameterSearcher(
estimator=self._best_estimator,
method=hyperparam_search_method,
hyperparam_grid=hyperparam_search_space,
estimator_name=self._name,
**kwargs,
)
self._validate_inputs()