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BaseConfig

Base class to initialize Hydra configuration.

This class loads and sets various configuration parameters used across the package, providing easy access to these parameters by initializing them from a Hydra configuration file.

Parameters:

Name Type Description Default
config_path str

Path to the Hydra config directory.

 '../config'
config_name str

Name of the configuration file (without extension).

 'config'

Attributes:

Name Type Description
group_col str

Column name used for group-based splitting.
y str

Target column name in the dataset.
target_state int

Random state of target encoding.
learner_state int

Random state of learners.
xgb_obj_binary str

Objective function for binary classification in XGBoost.
xgb_loss_binary str

Loss function for binary classification in XGBoost.
xgb_obj_multi str

Objective function for multiclass classification in XGBoost.
xgb_loss_multi str

Loss function for multiclass classification in XGBoost.
lr_solver_binary str

Solver type for binary classification in logistic regression.
lr_solver_multi str

Solver type for multiclass classification in logistic regression.
lr_multi_loss str

Loss function for multiclass logistic regression.
patient_columns List[str]

List of column names related to patient data.
tooth_columns List[str]

List of column names related to tooth data.
side_columns List[str]

List of column names related to side data.
feature_mapping dict[str, str]

Mapping of feature names for plotting.
cat_vars List[str]

List of categorical variables in the dataset.
bin_vars List[str]

List of binary variables in the dataset.
scale_vars List[str]

List of numeric variables to scale in preprocessing.
behavior_columns Dict[str, List[str]]

Dictionary categorizing behavior-related columns by type.
task_cols List[str]

List of task-specific columns in the dataset.
no_train_cols List[str]

Columns excluded from training.
infect_vars List[str]

Columns indicating infection status.
cat_map Dict[str, int]

Mapping of categorical features and their maximum values for encoding.
target_cols List[str]

Columns related to the prediction target.
all_cat_vars List[str]

Combined list of categorical variables for encoding.
required_columns List[str]

Combined list of columns required in the dataset for analysis.
rs_state int

State for random search parameter selection.
Example 
config = BaseConfig()
print(config.tooth_columns)
Note

This class assumes Hydra configuration files are correctly set up and stored at config_path. Make sure the file structure and values are properly defined within the configuration.

Source code in periomod/base.py 
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class BaseConfig:
    """Base class to initialize Hydra configuration.

    This class loads and sets various configuration parameters used across the package,
    providing easy access to these parameters by initializing them from a Hydra
    configuration file.

    Args:
        config_path (str): Path to the Hydra config directory.
        config_name (str): Name of the configuration file (without extension).

    Attributes:
        group_col (str): Column name used for group-based splitting.
        y (str): Target column name in the dataset.
        target_state (int): Random state of target encoding.
        learner_state (int): Random state of learners.
        xgb_obj_binary (str): Objective function for binary classification in XGBoost.
        xgb_loss_binary (str): Loss function for binary classification in XGBoost.
        xgb_obj_multi (str): Objective function for multiclass classification in
            XGBoost.
        xgb_loss_multi (str): Loss function for multiclass classification in XGBoost.
        lr_solver_binary (str): Solver type for binary classification in logistic
            regression.
        lr_solver_multi (str): Solver type for multiclass classification in logistic
            regression.
        lr_multi_loss (str): Loss function for multiclass logistic regression.
        patient_columns (List[str]): List of column names related to patient data.
        tooth_columns (List[str]): List of column names related to tooth data.
        side_columns (List[str]): List of column names related to side data.
        feature_mapping (dict[str, str]): Mapping of feature names for plotting.
        cat_vars (List[str]): List of categorical variables in the dataset.
        bin_vars (List[str]): List of binary variables in the dataset.
        scale_vars (List[str]): List of numeric variables to scale in preprocessing.
        behavior_columns (Dict[str, List[str]]): Dictionary categorizing
            behavior-related columns by type.
        task_cols (List[str]): List of task-specific columns in the dataset.
        no_train_cols (List[str]): Columns excluded from training.
        infect_vars (List[str]): Columns indicating infection status.
        cat_map (Dict[str, int]): Mapping of categorical features and their maximum
            values for encoding.
        target_cols (List[str]): Columns related to the prediction target.
        all_cat_vars (List[str]): Combined list of categorical variables for encoding.
        required_columns (List[str]): Combined list of columns required in the dataset
            for analysis.
        rs_state (int): State for random search parameter selection.

    Example:
        ```
        config = BaseConfig()
        print(config.tooth_columns)
        ```

    Note:
        This class assumes Hydra configuration files are correctly set up and stored at
        `config_path`. Make sure the file structure and values are properly defined
        within the configuration.
    """

    def __init__(
        self, config_path: str = "../config", config_name: str = "config"
    ) -> None:
        """Initializes the Hydra configuration for use in other classes."""
        with hydra.initialize(config_path=config_path, version_base="1.2"):
            cfg = hydra.compose(config_name=config_name)

        self.group_col = cfg.resample.group_col
        self.y = cfg.resample.y
        self.target_state = cfg.resample.target_state
        self.learner_state = cfg.learner.learner_state
        self.xgb_obj_binary = cfg.learner.xgb_obj_binary
        self.xgb_loss_binary = cfg.learner.xgb_loss_binary
        self.xgb_obj_multi = cfg.learner.xgb_obj_multi
        self.xgb_loss_multi = cfg.learner.xgb_loss_multi
        self.lr_solver_binary = cfg.learner.lr_solver_binary
        self.lr_solver_multi = cfg.learner.lr_solver_multi
        self.lr_multi_loss = cfg.learner.lr_multi_loss
        self.patient_columns = cfg.data.patient_columns
        self.tooth_columns = cfg.data.tooth_columns
        self.side_columns = cfg.data.side_columns
        self.feature_mapping = cfg.data.feature_mapping
        self.cat_vars = cfg.data.cat_vars
        self.bin_vars = cfg.data.bin_vars
        self.scale_vars = cfg.data.scale_vars
        self.behavior_columns = cfg.data.behavior_columns
        self.task_cols = cfg.data.task_cols
        self.no_train_cols = cfg.data.no_train_cols
        self.infect_vars = cfg.data.infect_cols
        self.cat_map = cfg.data.cat_map
        self.target_cols = cfg.data.target_cols
        self.all_cat_vars = self.cat_vars + cfg.data.behavior_columns["categorical"]
        self.required_columns = (
            self.patient_columns + self.tooth_columns + self.side_columns
        )
        self.rs_state = cfg.tuning.rs_state

__init__(config_path='../config', config_name='config')

Initializes the Hydra configuration for use in other classes.

Source code in periomod/base.py 
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def __init__(
    self, config_path: str = "../config", config_name: str = "config"
) -> None:
    """Initializes the Hydra configuration for use in other classes."""
    with hydra.initialize(config_path=config_path, version_base="1.2"):
        cfg = hydra.compose(config_name=config_name)

    self.group_col = cfg.resample.group_col
    self.y = cfg.resample.y
    self.target_state = cfg.resample.target_state
    self.learner_state = cfg.learner.learner_state
    self.xgb_obj_binary = cfg.learner.xgb_obj_binary
    self.xgb_loss_binary = cfg.learner.xgb_loss_binary
    self.xgb_obj_multi = cfg.learner.xgb_obj_multi
    self.xgb_loss_multi = cfg.learner.xgb_loss_multi
    self.lr_solver_binary = cfg.learner.lr_solver_binary
    self.lr_solver_multi = cfg.learner.lr_solver_multi
    self.lr_multi_loss = cfg.learner.lr_multi_loss
    self.patient_columns = cfg.data.patient_columns
    self.tooth_columns = cfg.data.tooth_columns
    self.side_columns = cfg.data.side_columns
    self.feature_mapping = cfg.data.feature_mapping
    self.cat_vars = cfg.data.cat_vars
    self.bin_vars = cfg.data.bin_vars
    self.scale_vars = cfg.data.scale_vars
    self.behavior_columns = cfg.data.behavior_columns
    self.task_cols = cfg.data.task_cols
    self.no_train_cols = cfg.data.no_train_cols
    self.infect_vars = cfg.data.infect_cols
    self.cat_map = cfg.data.cat_map
    self.target_cols = cfg.data.target_cols
    self.all_cat_vars = self.cat_vars + cfg.data.behavior_columns["categorical"]
    self.required_columns = (
        self.patient_columns + self.tooth_columns + self.side_columns
    )
    self.rs_state = cfg.tuning.rs_state