tab_err#

Submodules#

Classes#

`ErrorMechanism`	Error Mechanism Abstract Base Class.
`ErrorModel`	Combines an error mechanism and error type and defines how many percent of the column should be perturbed.
`ErrorType`	Error Type Abstract Base Class.

Package Contents#

class tab_err.ErrorMechanism(condition_to_column: int | str | None = None, seed: int | None = None)#

Bases: abc.ABC

Error Mechanism Abstract Base Class.

sample(data: pandas.DataFrame, column: str | int, error_rate: float, error_mask: pandas.DataFrame | None = None) → pandas.DataFrame#

Returns an error mask for locations to introduce errors in a pandas DataFrame.

Description:: Does error checking for the abstract method ‘_sample’. Assigns the _random_generator attribute. Calls subclass _sample method.

Parameters:

data (pd.DataFrame) – DataFrame containing the column to add errors to
column (str | int) – The column of ‘data’ to create an error mask for
error_rate (float) – Percentage of rows to be affected by errors in range [0,1].
error_mask (pd.DataFrame | None, optional) – An existing error mask to add more errors to in the case of the mid-/high-level APIs. Defaults to None.

Raises:

ValueError – If error rate is out of the [0,1] interval, a ValueError is thrown
TypeError – If the ‘data’ argument is not a pandas dataframe or the data is empty, a TypeError is thrown
ValueError – If required and there are not 2 columns in the ‘data’ argument, a ValueError is thrown.

Returns:

Updated dataframe with the generated error mask

Return type:

pd.DataFrame

condition_to_column = None#

class tab_err.ErrorModel#

Combines an error mechanism and error type and defines how many percent of the column should be perturbed.

error_mechanism#

Instance of an ErrorMechanism that will be applied.

Type:: ErrorMechanism

error_type#

Instance of an ErrorType that will be applied.

Type:: ErrorType

error_rate#

Defines how many percent should be perturbed.

Type:: float

apply(data: pandas.DataFrame, column: str | int) → tuple[pandas.DataFrame, pandas.DataFrame]#

Applies the defined ErrorModel to the given column of a pandas DataFrame.

Parameters:

data (pd.DataFrame) – The pandas DataFrame to create errors in.
column (str | int) – The column to create errors in.

Returns:

The first element is a copy of ‘data’ with errors.
The second element is the associated error mask.

Return type:

tuple[pd.DataFrame, pd.DataFrame]

error_mechanism: tab_err.ErrorMechanism#

error_rate: float#

error_type: tab_err.ErrorType#

class tab_err.ErrorType(config: tab_err.error_type._config.ErrorTypeConfig | dict | None = None, seed: int | None = None)#

Bases: abc.ABC

Error Type Abstract Base Class.

apply(data: pandas.DataFrame, error_mask: pandas.DataFrame, column: str | int) → pandas.Series#

Applies an ErrorType to a column of ‘data’. Does type and shape checking and creates a random number generator.

Parameters:

data (pd.DataFrame) – The Pandas DataFrame containing the column where errors are to be introduced.
error_mask (pd.DataFrame) – The Pandas DataFrame containing the error mask for ‘column’.
column (str | int) – The index in the ‘data’ and ‘error_mask’ DataFrames where errors are to be introduced.

Returns:

The data column, ‘column’, after errors of ErrorType at the locations specified by ‘error_mask’ are introduced.

Return type:

pd.Series

classmethod from_dict(data: dict[str, Any]) → ErrorType#

Deserialize an ErrorType object from a dictionary.

Parameters:: data (dict[str, Any]) – A dictionary representation of the ErrorType object.
Returns:: An ErrorType object deserialized from the dictionary.
Return type:: ErrorType

get_valid_columns(data: pandas.DataFrame) → list[str | int]#: Finds the valid columns to which the error type can be applied. Wrapper around _get_valid_columns.

to_dict() → dict[str, Any]#

Serialized the ErrorType object into a dictionary.

Returns:: A dictionary representation of the ErrorType object.
Return type:: dict[str, Any]