palma

palma#

Subpackages#

Attributes#

`logger`
`set_logger`

Classes#

`ModelEvaluation`
`ModelSelector`	Wrapper to optimizers selecting the best model for a Project.
`Project`	Represents a machine learning project with various components

Package Contents#

class palma.ModelEvaluation(estimator)#

__date#

__model_id#

__estimator#

__components#

estimator_name#

metrics#

add(component, name=None)#

fit(project: palma.base.project.Project)#

__get_fit_estimators(X, y, indexes)#

__compute_predictions(project, indexes, estimators)#

property id: str#

property components#

property unfit_estimator#

class palma.ModelSelector(engine: str | palma.base.engine.BaseOptimizer, engine_parameters: Dict)#

Wrapper to optimizers selecting the best model for a Project.

The optimization can be launched with the start method. Once the optimization is done, the best model can be accessed as the best_model_ attribute.

Parameters:

- engine (str): Currently accepted values are “FlamlOptimizer” or: “AutoSklearnOptimizer” (the latter is deprecatted).
- engine_parameters (dict): parameters passed to the engine.

Methods

- start(project: Project): look for best model

__date#

__run_id#

__parameters#

start(project: Project)#

property run_id: str#

class palma.Project(project_name: str, problem: str)#

Represents a machine learning project with various components and logging capabilities.

Parameters:

project_name (str): The name of the project.
problem (str): The description of the machine learning problem.: Accepted values: “classification” or “regression”.

Attributes:

base_index (List[int]): List of base indices for the project.
components (dict): Dictionary containing project components.
date (datetime): The date and time when the project was created.
project_id (str): Unique identifier for the project.
is_started (bool): Indicates whether the project has been started.
problem (str): Description of the machine learning problem.
validation_strategy (ValidationStrategy): The validation strategy used in the project.
project_name (str): The name of the project.
study_name (str): The randomly generated study name.
X (pd.DataFrame): The feature data for the project.
y (pd.Series): The target variable for the project.

Methods

add(component: Component) -> None: Adds a component to the project.
start(X: pd.DataFrame, y: pd.Series, splitter, X_test=None, y_test=None, groups=None, kwargs) -> None:**	Starts the project with the specified data and validation strategy.

__project_name#

__date#

__study_name#

__problem#

__components#

__is_started = False#

__component_list = []#

add(component: Component) → None#

start(X: pandas.DataFrame, y: pandas.Series, splitter, X_test=None, y_test=None, groups=None, **kwargs) → None#

__call_components(object_: Project) → None#

property components: dict#

property date: datetime.datetime#

property project_id: str#

property is_started: bool#

property problem: str#

property validation_strategy: palma.base.splitting_strategy.ValidationStrategy#

property project_name: str#

property study_name: str#

property X: pandas.DataFrame#

property y: pandas.Series#

palma.logger#

palma.set_logger#