Source code for evalml.data_checks.data_check_action_option

"""Recommended action returned by a DataCheck."""

from enum import Enum

from evalml.data_checks.data_check_action import DataCheckAction
from evalml.data_checks.data_check_action_code import DataCheckActionCode
from evalml.utils import classproperty


[docs]class DataCheckActionOption:
    """A recommended action option returned by a DataCheck.

        It contains an action code that indicates what the
        action should be, a data check name that indicates what data check was used to generate the action, and
        parameters and metadata which can be used to further refine the action.

    Args:
        action_code (DataCheckActionCode): Action code associated with the action option.
        data_check_name (str): Name of the data check that produced this option.
        parameters (dict): Parameters associated with the action option. Defaults to None.
        metadata (dict, optional): Additional useful information associated with the action option. Defaults to None.


    Examples:
        >>> parameters = {
        ...     "global_parameter_name": {
        ...         "parameter_type": "global",
        ...         "type": "float",
        ...         "default_value": 0.0,
        ...     },
        ...     "column_parameter_name": {
        ...         "parameter_type": "column",
        ...         "columns": {
        ...             "a": {
        ...                 "impute_strategy": {
        ...                     "categories": ["mean", "most_frequent"],
        ...                     "type": "category",
        ...                     "default_value": "mean",
        ...                 },
        ...             "constant_fill_value": {"type": "float", "default_value": 0},
        ...             },
        ...         },
        ...     },
        ... }
        >>> data_check_action = DataCheckActionOption(DataCheckActionCode.DROP_COL, None, metadata={}, parameters=parameters)


    """

    def __init__(self, action_code, data_check_name, parameters=None, metadata=None):
        self.action_code = action_code
        self.data_check_name = data_check_name
        self.parameters = parameters or {}
        self.metadata = {"columns": None, "rows": None}
        if metadata is not None:
            self.metadata.update(metadata)
        self._validate_parameters()

    def __eq__(self, other):
        """Check for equality.

        Two DataCheckActionOption objs are considered equivalent if all of their attributes are equivalent.

        Args:
            other: An object to compare equality with.

        Returns:
            bool: True if the other object is considered an equivalent data check action, False otherwise.
        """
        attributes_to_check = [
            "action_code",
            "data_check_name",
            "parameters",
            "metadata",
        ]
        for attribute in attributes_to_check:
            if getattr(self, attribute) != getattr(other, attribute):
                return False
        return True

[docs]    def to_dict(self):
        """Return a dictionary form of the data check action option."""
        action_option_dict = {
            "code": self.action_code.name,
            "data_check_name": self.data_check_name,
            "metadata": self.metadata,
        }

        parameters_dict = self.parameters.copy()
        for parameter_dict in parameters_dict.values():
            parameter_dict["parameter_type"] = (
                DCAOParameterType.handle_dcao_parameter_type(
                    parameter_dict["parameter_type"],
                ).value
            )

        action_option_dict.update({"parameters": parameters_dict})
        return action_option_dict

[docs]    @staticmethod
    def convert_dict_to_option(action_dict):
        """Convert a dictionary into a DataCheckActionOption.

        Args:
            action_dict: Dictionary to convert into an action option. Should have keys "code", "data_check_name", and "metadata".

        Raises:
            ValueError: If input dictionary does not have keys `code` and `metadata` and if the `metadata` dictionary does not have keys `columns` and `rows`.

        Returns:
            DataCheckActionOption object from the input dictionary.
        """
        if "code" not in action_dict or "metadata" not in action_dict:
            raise ValueError(
                "The input dictionary should have the keys `code` and `metadata`.",
            )
        if (
            "columns" not in action_dict["metadata"]
            and "rows" not in action_dict["metadata"]
        ):
            raise ValueError(
                "The metadata dictionary should have the keys `columns` or `rows`. Set to None if not using.",
            )

        return DataCheckActionOption(
            action_code=DataCheckActionCode._all_values[action_dict["code"]],
            metadata=action_dict["metadata"],
            data_check_name=(
                action_dict["data_check_name"]
                if "data_check_name" in action_dict
                else None
            ),
            parameters=(
                action_dict["parameters"] if "parameters" in action_dict else None
            ),
        )

    def _validate_parameters(self):
        """Validate parameters associated with the action option."""
        for _, parameter_value in self.parameters.items():
            if "parameter_type" not in parameter_value:
                raise ValueError("Each parameter must have a parameter_type key.")

            try:
                parameter_type = DCAOParameterType.handle_dcao_parameter_type(
                    parameter_value["parameter_type"],
                )
            except KeyError as ke:
                raise ValueError(
                    "Each parameter must have a parameter_type key with a value of `global` or `column`. "
                    + str(ke),
                )

            if parameter_type == DCAOParameterType.GLOBAL:
                if "type" not in parameter_value:
                    raise ValueError("Each global parameter must have a type key.")
            elif parameter_type == DCAOParameterType.COLUMN:
                if "columns" not in parameter_value:
                    raise ValueError(
                        "Each `column` parameter type must also have a `columns` key indicating which columns the parameter should address.",
                    )
                columns = parameter_value["columns"]
                if not isinstance(columns, dict):
                    raise ValueError(
                        "`columns` must be a dictionary, where each key is the name of a column and the associated value is a dictionary of parameters for that column.",
                    )
                for column_parameters in columns.values():
                    for column_parameter_values in column_parameters.values():
                        if "type" not in column_parameter_values:
                            raise ValueError(
                                "Each column parameter must have a type key.",
                            )
                        if "default_value" not in column_parameter_values:
                            raise ValueError(
                                "Each column parameter must have a default_value key.",
                            )

[docs]    def get_action_from_defaults(self):
        """Returns an action based on the defaults parameters.

        Returns:
            DataCheckAction: An based on the defaults parameters the option.
        """
        parameters = self.parameters
        actions_parameters = {}
        for parameter, parameter_info in parameters.items():
            parameter_type = DCAOParameterType.handle_dcao_parameter_type(
                parameter_info["parameter_type"],
            )
            if parameter_type == DCAOParameterType.GLOBAL:
                actions_parameters[parameter] = parameter_info["default_value"]
            elif parameter_type == DCAOParameterType.COLUMN:
                actions_parameters[parameter] = {}
                column_parameters = parameter_info["columns"]
                for (
                    column_parameter_name,
                    column_parameter_values,
                ) in column_parameters.items():
                    actions_parameters[parameter][column_parameter_name] = {}
                    for (
                        column_specific_parameter,
                        column_specific_parameter_value,
                    ) in column_parameter_values.items():
                        actions_parameters[parameter][column_parameter_name][
                            column_specific_parameter
                        ] = column_specific_parameter_value["default_value"]

        metadata = self.metadata
        metadata.update({"parameters": actions_parameters})
        return DataCheckAction(
            self.action_code,
            self.data_check_name,
            metadata=metadata,
        )


[docs]class DCAOParameterType(Enum):
    """Enum for data check action option parameter type."""

    GLOBAL = "global"
    """Global parameter type. Parameters that apply to the entire data set."""

    COLUMN = "column"
    """Column parameter type. Parameters that apply to a specific column in the data set."""

    def __str__(self):
        """String representation of the DCAOParameterType enum."""
        parameter_type_dict = {
            DCAOParameterType.GLOBAL.name: "global",
            DCAOParameterType.COLUMN.name: "column",
        }
        return parameter_type_dict[self.name]

    @classproperty
    def _all_values(cls):
        return {pt.value.upper(): pt for pt in cls.all_parameter_types}

    @classproperty
    def all_parameter_types(cls):
        """Get a list of all defined parameter types.

        Returns:
            list(DCAOParameterType): List of all defined parameter types.
        """
        return list(cls)

[docs]    @staticmethod
    def handle_dcao_parameter_type(dcao_parameter_type):
        """Handles the data check action option parameter type by either returning the DCAOParameterType enum or converting from a str.

        Args:
            dcao_parameter_type (str or DCAOParameterType): Data check action option parameter type that needs to be handled.

        Returns:
            DCAOParameterType enum

        Raises:
            KeyError: If input is not a valid DCAOParameterType enum value.
            ValueError: If input is not a string or DCAOParameterType object.

        """
        if isinstance(dcao_parameter_type, str):
            try:
                tpe = DCAOParameterType._all_values[dcao_parameter_type.upper()]
            except KeyError:
                raise KeyError(
                    "Parameter type '{}' does not exist".format(dcao_parameter_type),
                )
            return tpe
        if isinstance(dcao_parameter_type, DCAOParameterType):
            return dcao_parameter_type
        raise ValueError(
            "`handle_dcao_parameter_type` was not passed a str or DCAOParameterType object",
        )


[docs]class DCAOParameterAllowedValuesType(Enum):
    """Enum for data check action option parameter allowed values type."""

    CATEGORICAL = "categorical"
    """Categorical allowed values type. Parameters that have a set of allowed values."""

    NUMERICAL = "numerical"
    """Numerical allowed values type. Parameters that have a range of allowed values."""