Support Vector Machines (SVM)

Support Vector Machine (SVM) model implementations.

class openadmet.models.architecture.svm.SVMClassifierModel(*, C: float = 1.0, kernel: str = 'rbf', degree: int = 3, gamma: str = 'scale', coef0: float = 0.0, shrinking: bool = True, probability: bool = False, tol: float = 0.001, cache_size: int = 200, class_weight: dict | None = None, verbose: bool = False, max_iter: int = -1, decision_function_shape: str = 'ovr', break_ties: bool = False, random_state: int | None = None)[source]

Bases: SVMModelBase

SVM classification model.

Common parameters for SVM models can be found at: https://scikit-learn.org/stable/modules/svm.html Common parameters that you might want to set include: - C: Regularization parameter - kernel: Specifies the kernel type to be used in the algorithm - degree: Degree of the polynomial kernel function (if using ‘poly’ kernel) - learning_rate: Step size shrinkage used in update to prevent overfitting - objective: Specify the learning task and corresponding objective function - booster: Specify which booster to use, options are gbtree, gblinear or dart - tree_method: Specify the tree construction algorithm used in XGBoost

C: float
break_ties: bool
cache_size: int
class_weight: dict | None
coef0: float
decision_function_shape: str
degree: int
gamma: str
kernel: str
max_iter: int
mod_class

alias of SVC

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

predict_proba(X: ndarray) → ndarray[source]

Predict using the model, returning probabilities for each class.

Parameters:

X (np.ndarray) – Data to predict on

Returns:

Probabilities for each class from the model

Return type:

np.ndarray

probability: bool
random_state: int | None
shrinking: bool
tol: float
type: ClassVar[str] = 'SVMClassifierModel'
verbose: bool
class openadmet.models.architecture.svm.SVMModelBase[source]

Bases: PickleableModelBase

Base class for SVM models.

build()[source]

Prepare the model.

mod_class: ClassVar[type]
model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

predict(X: ndarray, **kwargs) → ndarray[source]

Predict using the model.

Parameters:

  • X (np.ndarray) – Data to predict on

  • **kwargs – Additional keyword arguments for the predict method.

Returns:

Predictions from the model

Return type:

np.ndarray

train(X: ndarray, y: ndarray)[source]

Train the model.

Parameters:

  • X (np.ndarray) – Training data features

  • y (np.ndarray) – Training data labels

type: ClassVar[str]
class openadmet.models.architecture.svm.SVMRegressorModel(*, kernel: str = 'rbf', degree: int = 3, gamma: str = 'scale', coef0: float = 0.0, tol: float = 0.001, C: float = 1.0, epsilon: float = 0.1, shrinking: bool = True, cache_size: int = 200, verbose: bool = False, max_iter: int = -1)[source]

Bases: SVMModelBase

SVM regression model.

Common parameters for SVM models can be found at: https://scikit-learn.org/stable/modules/svm.html

Common parameters that you might want to set include: - C: Regularization parameter - kernel: Specifies the kernel type to be used in the algorithm - degree: Degree of the polynomial kernel function (if using ‘poly’ kernel) - learning_rate: Step size shrinkage used in update to prevent overfitting - objective: Specify the learning task and corresponding objective function - booster: Specify which booster to use, options are gbtree, gblinear or dart - tree_method: Specify the tree construction algorithm used in XGBoost

C: float
cache_size: int
coef0: float
degree: int
epsilon: float
gamma: str
kernel: str
max_iter: int
mod_class

alias of SVR

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

shrinking: bool
tol: float
type: ClassVar[str] = 'SVMRegressorModel'
verbose: bool