Base Model Classes

Base classes for all models.

class openadmet.models.architecture.model_base.ModelBase[source]

Bases: BaseModel, ABC

Base class for all models.

abstract build()[source]: Prepare the model, abstract method to be implemented by subclasses.

abstract deserialize(param_path: PathLike, serial_path: PathLike)[source]

Deserialize the model, abstract method to be implemented by subclasses.

Parameters:

param_path (PathLike) – Path to load the model parameters from
serial_path (PathLike) – Path to load the model serialization from

property estimator: Get the model estimator.

abstract load(path: PathLike)[source]

Load the model, abstract method to be implemented by subclasses.

Parameters:: path (PathLike) – Path to load the model from

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.

abstract predict(input: Any)[source]

Predict using the model, abstract method to be implemented by subclasses.

Parameters:: input (Any) – Input data to predict on

abstract save(path: PathLike)[source]

Save the model, abstract method to be implemented by subclasses.

Parameters:: path (PathLike) – Path to save the model to

abstract serialize(param_path: PathLike, serial_path: PathLike)[source]

Serialize the model, abstract method to be implemented by subclasses.

Parameters:

param_path (PathLike) – Path to save the model parameters to
serial_path (PathLike) – Path to save the model serialization to

abstract train()[source]: Train the model, abstract method to be implemented by subclasses.

class openadmet.models.architecture.model_base.PickleableModelBase[source]

Bases: ModelBase

An sklearn model that can be pickled using joblib.

load(path: PathLike)[source]

Load the model from a pickle file.

Parameters:: path (PathLike) – Path to load the model from

make_new() → PickleableModelBase[source]: Copy parameters to a new model instance without copying the estimator.

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.

pickleable: ClassVar[bool] = True

save(path: PathLike)[source]

Save the model to a pickle file.

Parameters:: path (PathLike) – Path to save the model to

serialize(param_path: PathLike = 'model.json', serial_path: PathLike = 'model.pkl')[source]

Save the model to a json file and a pickled file.

Parameters:

param_path (PathLike) – Path to save the model parameters to
serial_path (PathLike) – Path to save the pickled model to

openadmet.models.architecture.model_base.get_mod_class(model_type)[source]

Get the model class from the registry by type.

Parameters:: model_type (str) – The registered key for the model (e.g., "XGBRegressorModel").
Returns:: The model class corresponding to the given type.
Return type:: type
Raises:: ValueError – If model_type is not found in the model registry.