modules.applications.qml.generative_modeling.data.data_handler.data_handler_generative.DataHandlerGenerative

class DataHandlerGenerative(name: str)

Bases: Core, DataHandler, ABC

The task of the DataHandler module is to translate the application’s data and problem specification into preprocessed format.

__init__(name: str): Constructor method.

Methods

`__init__`(name)	Constructor method.
`data_load`(gen_mod, config)	Helps to ensure that the model can effectively learn the underlying patterns and structure of the data, and produce high-quality outputs.
`evaluate`(solution)	Computes the best loss values.
`generalization`()	Computes generalization metrics.
`get_available_submodule_options`()	Gets the list of available options.
`get_available_submodules`(option)	If the module has submodules depending on certain options, this method should adjust the submodule_options accordingly.
`get_default_submodule`(option)	Given an option string by the user, this returns a submodule.
`get_depending_parameters`(option, config)	If the module has parameters depending on certain options, this method should return the parameters for the given option.
`get_parameter_options`()	Returns the parameters for a given module.
`get_requirements`()	Returns requirements of this module.
`get_submodule`(option)	Submodule is instantiated according to the information given in self.sub_options.
`postprocess`(input_data, config, **kwargs)	In this module, the postprocessing step is transforming the data to the correct target format.
`preprocess`(input_data, config, **kwargs)	In this module, the preprocessing step is transforming the data to the correct target format.
`tb_to_pd`(logdir, rep)	Converts TensorBoard event files in the specified log directory into a pandas DataFrame and saves it as a pickle file.

abstract data_load(gen_mod: dict, config: dict) → tuple[any, float]

Helps to ensure that the model can effectively learn the underlying patterns and structure of the data, and produce high-quality outputs.

Parameters:

gen_mod -- Dictionary with collected information of the previous modules
config -- Config specifying the parameters of the data handler

Returns:

Mapped problem and the time it took to create the mapping

abstract evaluate(solution: any) → tuple[any, float]

Computes the best loss values.

Parameters:: solution -- Solution data
Returns:: Evaluation data and the time it took to create it

generalization() → tuple[dict, float]

Computes generalization metrics.

Returns:: Evaluation and the time it took to create it

get_available_submodule_options() → list

Gets the list of available options.

Returns:: List of module options

get_available_submodules(option: list) → list

If the module has submodules depending on certain options, this method should adjust the submodule_options accordingly.

Parameters:: option -- List of chosen options
Returns:: List of available submodules

abstract get_default_submodule(option: str) → Core

Given an option string by the user, this returns a submodule.

Parameters:: option -- String with the chosen submodule
Returns:: Module of type Core

get_depending_parameters(option: str, config: dict) → dict

If the module has parameters depending on certain options, this method should return the parameters for the given option.

Parameters:

option -- The chosen option
config -- Current config dictionary

Returns:

The parameters for the given option

abstract get_parameter_options() → dict

Returns the parameters for a given module.

Should always be in this format:

{
   "parameter_name":{
      "values":[1, 2, 3],
      "description":"How many nodes do you need?"
   },
    "parameter_name_2":{
      "values":["x", "y"],
      "description":"Which type of problem do you want?"
   }
}

Returns:: Available settings for this application

static get_requirements() → list[dict]

Returns requirements of this module.

Returns:: List of dict with requirements of this module

get_submodule(option: str) → Core

Submodule is instantiated according to the information given in self.sub_options. If self.sub_options is None, get_default_submodule is called as a fallback.

Parameters:: option -- String with the options
Returns:: Instance of a module

postprocess(input_data: dict, config: dict, **kwargs) → tuple[dict, float]

In this module, the postprocessing step is transforming the data to the correct target format.

Parameters:

input_data -- Original data
config -- Config specifying the parameters of the training
kwargs -- Optional additional settings

Returns:

Tuple with an output_dictionary and the time it took

preprocess(input_data: dict, config: dict, **kwargs) → tuple[any, float]

In this module, the preprocessing step is transforming the data to the correct target format.

Parameters:

input_data -- Collected information of the benchmarking process
config -- Config specifying the parameters of the training
kwargs -- Optional additional settings

Returns:

Tuple with transformed problem and the time it took to map it

static tb_to_pd(logdir: str, rep: str) → None

Converts TensorBoard event files in the specified log directory into a pandas DataFrame and saves it as a pickle file.

Parameters:

logdir -- Path to the log directory containing TensorBoard event files
rep -- Repetition counter