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BaseProcessor

Bases: BaseLoader, ABC

Abstract base class defining essential data processing methods.

This class provides core processing capabilities such as loading and saving data, along with abstract methods that must be implemented by any subclass. These methods include data imputation, feature creation, and outcome variable generation for specialized data processing.

Inherits
  • BaseLoader: Provides loading and saving capabilities for processed data.
  • ABC: Specifies abstract methods for subclasses to implement.

Parameters:

Name Type Description Default
behavior bool

If True, includes behavior columns in the data processing.

 required

Attributes:

Name Type Description
behavior bool

Flag indicating whether to include behavior columns during data processing.

Methods:

Name Description
load_data

Load processed data from the specified path and file.
save_data

Save processed data to the specified path and file.
Abstract Methods
  • impute_missing_values: Impute missing values in the DataFrame.
  • create_tooth_features: Generate features related to tooth data.
  • create_outcome_variables: Create outcome variables for analysis.
  • process_data: Clean, impute, and scale the data.
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class BaseProcessor(BaseLoader, ABC):
    """Abstract base class defining essential data processing methods.

    This class provides core processing capabilities such as loading and saving
    data, along with abstract methods that must be implemented by any subclass.
    These methods include data imputation, feature creation, and outcome variable
    generation for specialized data processing.

    Inherits:
        - `BaseLoader`: Provides loading and saving capabilities for processed data.
        - `ABC`: Specifies abstract methods for subclasses to implement.

    Args:
        behavior (bool): If True, includes behavior columns in the data processing.

    Attributes:
        behavior (bool): Flag indicating whether to include behavior columns
            during data processing.

    Methods:
        load_data: Load processed data from the specified path and file.
        save_data: Save processed data to the specified path and file.

    Abstract Methods:
        - `impute_missing_values`: Impute missing values in the DataFrame.
        - `create_tooth_features`: Generate features related to tooth data.
        - `create_outcome_variables`: Create outcome variables for analysis.
        - `process_data`: Clean, impute, and scale the data.
    """

    def __init__(self, behavior: bool) -> None:
        """Initializes the BaseProcessor with behavior flag."""
        super().__init__()
        self.behavior = behavior

    def load_data(
        self,
        path: Union[str, Path] = Path("data/raw/raw_data.xlsx"),
    ) -> pd.DataFrame:
        """Loads the dataset and validates required columns.

        Args:
            path (str, optional): Directory where dataset is located.
                Defaults to Path("data/raw/raw_data.xlsx").

        Returns:
            pd.DataFrame: The loaded DataFrame.

        Raises:
            ValueError: If any required columns are missing.
        """
        path = Path(path)
        if not path.is_absolute():
            path = Path.cwd() / path

        if not path.exists():
            raise FileNotFoundError(f"File not found: {path}")

        df = pd.read_excel(path, header=[1])

        actual_columns_lower = {col.lower(): col for col in df.columns}
        required_columns_lower = [col.lower() for col in self.required_columns]

        missing_columns = [
            col for col in required_columns_lower if col not in actual_columns_lower
        ]
        if missing_columns:
            missing_columns_names = [
                self.required_columns[required_columns_lower.index(col)]
                for col in missing_columns
            ]
            warnings.warn(
                f"Warning: Missing columns: {', '.join(missing_columns_names)}",
                stacklevel=2,
            )

        available_required_columns = [
            col for col in required_columns_lower if col in actual_columns_lower
        ]
        actual_required_columns = [
            actual_columns_lower[col] for col in available_required_columns
        ]

        if self.behavior:
            behavior_columns_lower = [
                col.lower() for col in self.behavior_columns["binary"]
            ] + [col.lower() for col in self.behavior_columns["categorical"]]
            missing_behavior_columns = [
                col for col in behavior_columns_lower if col not in actual_columns_lower
            ]
            if missing_behavior_columns:
                missing_behavior_names = [
                    col.capitalize() for col in missing_behavior_columns
                ]
                warnings.warn(
                    f"Warning: Missing cols: {', '.join(missing_behavior_names)}",
                    stacklevel=2,
                )

            available_behavior_columns = [
                col for col in behavior_columns_lower if col in actual_columns_lower
            ]
            actual_required_columns += [
                actual_columns_lower[col] for col in available_behavior_columns
            ]

        return df[actual_required_columns]

    def save_data(
        self,
        df: pd.DataFrame,
        path: Union[str, Path] = Path("data/processed/processed_data.csv"),
    ) -> None:
        """Saves the processed DataFrame to a CSV file.

        Args:
            df (pd.DataFrame): The processed DataFrame.
            path (str, optional): Directory where dataset is saved.
                Defaults to Path("data/processed/processed_data.csv".
        """
        super().save_data(df=df, path=path)

    @abstractmethod
    def impute_missing_values(self, df: pd.DataFrame):
        """Imputes missing values in the DataFrame.

        Args:
            df (pd.DataFrame): The DataFrame with potential missing values.
        """

    @abstractmethod
    def create_tooth_features(self, df: pd.DataFrame):
        """Creates additional features related to tooth data.

        Args:
            df (pd.DataFrame): The DataFrame containing tooth data.
        """

    @abstractmethod
    def create_outcome_variables(self, df: pd.DataFrame):
        """Generates outcome variables for analysis.

        Args:
            df (pd.DataFrame): The DataFrame with original outcome variables.
        """

    @abstractmethod
    def process_data(self, df: pd.DataFrame):
        """Processes dataset with data cleaning, imputations and scaling.

        Args:
            df (pd.DataFrame): The input DataFrame.
        """

__init__(behavior)

Initializes the BaseProcessor with behavior flag.

Source code in periomod/data/_basedata.py 
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def __init__(self, behavior: bool) -> None:
    """Initializes the BaseProcessor with behavior flag."""
    super().__init__()
    self.behavior = behavior

create_outcome_variables(df) abstractmethod

Generates outcome variables for analysis.

Parameters:

Name Type Description Default
df DataFrame

The DataFrame with original outcome variables.

 required
Source code in periomod/data/_basedata.py 
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@abstractmethod
def create_outcome_variables(self, df: pd.DataFrame):
    """Generates outcome variables for analysis.

    Args:
        df (pd.DataFrame): The DataFrame with original outcome variables.
    """

create_tooth_features(df) abstractmethod

Creates additional features related to tooth data.

Parameters:

Name Type Description Default
df DataFrame

The DataFrame containing tooth data.

 required
Source code in periomod/data/_basedata.py 
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@abstractmethod
def create_tooth_features(self, df: pd.DataFrame):
    """Creates additional features related to tooth data.

    Args:
        df (pd.DataFrame): The DataFrame containing tooth data.
    """

impute_missing_values(df) abstractmethod

Imputes missing values in the DataFrame.

Parameters:

Name Type Description Default
df DataFrame

The DataFrame with potential missing values.

 required
Source code in periomod/data/_basedata.py 
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@abstractmethod
def impute_missing_values(self, df: pd.DataFrame):
    """Imputes missing values in the DataFrame.

    Args:
        df (pd.DataFrame): The DataFrame with potential missing values.
    """

load_data(path=Path('data/raw/raw_data.xlsx'))

Loads the dataset and validates required columns.

Parameters:

Name Type Description Default
path str

Directory where dataset is located. Defaults to Path("data/raw/raw_data.xlsx").

 Path('data/raw/raw_data.xlsx')

Returns:

Type Description
DataFrame

pd.DataFrame: The loaded DataFrame.

Raises:

Type Description
ValueError

If any required columns are missing.
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def load_data(
    self,
    path: Union[str, Path] = Path("data/raw/raw_data.xlsx"),
) -> pd.DataFrame:
    """Loads the dataset and validates required columns.

    Args:
        path (str, optional): Directory where dataset is located.
            Defaults to Path("data/raw/raw_data.xlsx").

    Returns:
        pd.DataFrame: The loaded DataFrame.

    Raises:
        ValueError: If any required columns are missing.
    """
    path = Path(path)
    if not path.is_absolute():
        path = Path.cwd() / path

    if not path.exists():
        raise FileNotFoundError(f"File not found: {path}")

    df = pd.read_excel(path, header=[1])

    actual_columns_lower = {col.lower(): col for col in df.columns}
    required_columns_lower = [col.lower() for col in self.required_columns]

    missing_columns = [
        col for col in required_columns_lower if col not in actual_columns_lower
    ]
    if missing_columns:
        missing_columns_names = [
            self.required_columns[required_columns_lower.index(col)]
            for col in missing_columns
        ]
        warnings.warn(
            f"Warning: Missing columns: {', '.join(missing_columns_names)}",
            stacklevel=2,
        )

    available_required_columns = [
        col for col in required_columns_lower if col in actual_columns_lower
    ]
    actual_required_columns = [
        actual_columns_lower[col] for col in available_required_columns
    ]

    if self.behavior:
        behavior_columns_lower = [
            col.lower() for col in self.behavior_columns["binary"]
        ] + [col.lower() for col in self.behavior_columns["categorical"]]
        missing_behavior_columns = [
            col for col in behavior_columns_lower if col not in actual_columns_lower
        ]
        if missing_behavior_columns:
            missing_behavior_names = [
                col.capitalize() for col in missing_behavior_columns
            ]
            warnings.warn(
                f"Warning: Missing cols: {', '.join(missing_behavior_names)}",
                stacklevel=2,
            )

        available_behavior_columns = [
            col for col in behavior_columns_lower if col in actual_columns_lower
        ]
        actual_required_columns += [
            actual_columns_lower[col] for col in available_behavior_columns
        ]

    return df[actual_required_columns]

process_data(df) abstractmethod

Processes dataset with data cleaning, imputations and scaling.

Parameters:

Name Type Description Default
df DataFrame

The input DataFrame.

 required
Source code in periomod/data/_basedata.py 
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@abstractmethod
def process_data(self, df: pd.DataFrame):
    """Processes dataset with data cleaning, imputations and scaling.

    Args:
        df (pd.DataFrame): The input DataFrame.
    """

save_data(df, path=Path('data/processed/processed_data.csv'))

Saves the processed DataFrame to a CSV file.

Parameters:

Name Type Description Default
df DataFrame

The processed DataFrame.

 required
path str

Directory where dataset is saved. Defaults to Path("data/processed/processed_data.csv".

 Path('data/processed/processed_data.csv')
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def save_data(
    self,
    df: pd.DataFrame,
    path: Union[str, Path] = Path("data/processed/processed_data.csv"),
) -> None:
    """Saves the processed DataFrame to a CSV file.

    Args:
        df (pd.DataFrame): The processed DataFrame.
        path (str, optional): Directory where dataset is saved.
            Defaults to Path("data/processed/processed_data.csv".
    """
    super().save_data(df=df, path=path)