scikit-learn Training

Trainers for sklearn models.

class openadmet.models.trainer.sklearn.SKLearnGridSearchTrainer(*, param_grid: dict = {})[source]

Bases: SKLearnSearchTrainer

Trainer for sklearn models with grid search.

Variables:

param_grid (dict) – The parameter grid for grid search.

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.

param_grid: dict
train(X: Any, y: Any)[source]

Train the model.

Parameters:

  • X (Any) – Featurized data.

  • y (Any) – Target data.

Returns:

The trained model.

Return type:

ModelBase

class openadmet.models.trainer.sklearn.SKLearnSearchTrainer[source]

Bases: SKLearnTrainer

Trainer for sklearn models with search.

Variables:

search (Any) – The search object (e.g., GridSearchCV).

build()[source]

Unused method for sklearn models.

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.

property search

Return search object (e.g., GridSearchCV).

class openadmet.models.trainer.sklearn.SKLearnTrainer[source]

Bases: TrainerBase

Base trainer for sklearn models.

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.

class openadmet.models.trainer.sklearn.SKlearnBasicTrainer[source]

Bases: SKLearnTrainer

Basic trainer for sklearn models.

build()[source]

Unused method for sklearn models.

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.

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

Train the model.

Parameters:

  • X (Any) – Feature data.

  • y (Any) – Target data.

Returns:

The trained model.

Return type:

ModelBase