palma package#
Subpackages#
- palma.base package
- palma.components package
- Submodules
- palma.components.base module
- palma.components.checker module
- palma.components.dashboard module
- palma.components.data_checker module
- palma.components.data_profiler module
- palma.components.logger module
- palma.components.performance module
- Module contents
- palma.preprocessing package
- Submodules
- palma.preprocessing.na_encoder module
- palma.preprocessing.pca module
PCAPCA.get_correlation()PCA.get_individual_contributions()PCA.get_variables_contributions()PCA.nb_componentPCA.plot_circle_corr()PCA.plot_correlation_matrix()PCA.plot_cumulated_variance()PCA.plot_eigen_values()PCA.plot_factorial_plan()PCA.plot_var_cp()PCA.plot_variance_bar()PCA.set_nb_components()PCA.transform()
- Module contents
- palma.utils package
Module contents#
- class palma.ModelEvaluation(estimator)#
Bases:
object- Attributes:
- components
- id
- unfit_estimator
Methods
add
fit
- add(component, name=None)#
- property components#
- property id: str#
- property unfit_estimator#
- class palma.ModelSelector(engine: str | BaseOptimizer, engine_parameters: Dict)#
Bases:
objectWrapper to optimizers selecting the best model for a Project.
The optimization can be launched with the
startmethod. Once the optimization is done, the best model can be accessed as thebest_model_attribute.- Parameters:
- - engine (str): Currently accepted values are “FlamlOptimizer” or
“AutoSklearnOptimizer” (the latter is deprecatted).
- - engine_parameters (dict): parameters passed to the engine.
- Attributes:
- run_id
Methods
- start(project: Project): look for best model
- property run_id: str#
- class palma.Project(project_name: str, problem: str)#
Bases:
objectRepresents 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 X: DataFrame#
- property components: dict#
- property date: datetime#
- property is_started: bool#
- property problem: str#
- property project_id: str#
- property project_name: str#
- start(X: DataFrame, y: Series, splitter, X_test=None, y_test=None, groups=None, **kwargs) None#
- property study_name: str#
- property validation_strategy: ValidationStrategy#
- property y: Series#
- palma.set_logger(_logger) None#
- Parameters:
- _logger: Logger
Define the logger to use.
>>> from palma import logger, set_logger >>> from palma.components import FileSystemLogger >>> from palma.components import MLFlowLogger >>> set_logger(MLFlowLogger(uri=".")) >>> set_logger(FileSystemLogger(uri="."))