palma

Subpackages

• palma.base
  • palma.base.engine
  • palma.base.model
  • palma.base.model_selection
  • palma.base.project
  • palma.base.splitting_strategy
• palma.components
  • palma.components.base
  • palma.components.checker
  • palma.components.dashboard
  • palma.components.data_checker
  • palma.components.data_profiler
  • palma.components.logger
  • palma.components.performance
• palma.datasets
• palma.preprocessing
  • palma.preprocessing.na_encoder
  • palma.preprocessing.pca
• palma.utils
  • palma.utils.checker
  • palma.utils.names
  • palma.utils.plotting
  • palma.utils.utils

Package Contents

Classes

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

Attributes

logger
set_logger

class palma.ModelEvaluation(estimator)

property id: str

property components

property unfit_estimator

add(component, name=None)

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

__get_fit_estimators(X, y, indexes)

__compute_predictions(project, indexes, estimators)

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

property run_id: str

start(project: Project)

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.

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

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

palma.logger

palma.set_logger