modules.applications.qml.generative_modeling.transformations.pit.PIT

class PIT

Bases: Transformation

The transformation of the original probability distribution to the distribution of its uniformly distributed cumulative marginals is known as the copula.

__init__()

Constructor method.

Methods

__init__()

Constructor method.

compute_discretization(n_qubits, n_registered)

Compute discretization for the grid.

compute_discretization_efficient(n_qubits, ...)

Compute grid discretization.

emp_integral_trans(data)

Applies the empirical integral transformation to the given data.

fit_transform(data)

Takes the data points and applies the PIT.

generate_samples(results, bin_data, n_registers)

Generate samples based on measurement results and the grid bins.

generate_samples_efficient(results, ...[, noisy])

Generate samples efficiently using numpy arrays based on measurement results and the grid bins.

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 empty dict as this transformation has no configurable settings.

get_requirements()

Returns requirements of this module.

get_submodule(option)

Submodule is instantiated according to the information given in self.sub_options.

inverse_transform(data)

Applies the inverse transformation to the full data set.

postprocess(input_data, config, **kwargs)

Does the reverse transformation.

preprocess(input_data, config, **kwargs)

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

reverse_transform(input_data)

Transforms the solution back to the representation needed for validation/evaluation.

transform(input_data, config)

Transforms the input dataset using PIT transformation and computes histograms of the training dataset in the transformed space.
static compute_discretization(n_qubits: int, n_registered: int) → ndarray

Compute discretization for the grid.

Parameters:

  • n_qubits -- Total number of qubits

  • n_registered -- Number of qubits to be registered

Returns:

Discretization data

static compute_discretization_efficient(n_qubits: int, n_registers: int) → ndarray

Compute grid discretization.

Parameters:

  • n_qubits -- Total number of qubits

  • n_registers -- Number of qubits to be registered

Returns:

Discretization data

emp_integral_trans(data: ndarray) → ndarray

Applies the empirical integral transformation to the given data.

Parameters:

data -- Data points

Returns:

Empirically transformed data points

fit_transform(data: ndarray) → ndarray

Takes the data points and applies the PIT.

Parameters:

data -- Data samples

Returns:

Transformed data points

static generate_samples(results: ndarray, bin_data: ndarray, n_registers: int, noisy: bool = True) → ndarray

Generate samples based on measurement results and the grid bins.

Parameters:

  • results -- Results of measurements

  • bin_data -- Binned data

  • n_registers -- Number of registers

  • noisy -- Flag indicating whether to add noise

Returns:

Generated samples

static generate_samples_efficient(results, bin_data: ndarray, n_registers: int, noisy: bool = True) → ndarray

Generate samples efficiently using numpy arrays based on measurement results and the grid bins.

Parameters:

  • results -- Results of measurements

  • bin_data -- Binned data

  • n_registers -- Number of registers

  • noisy -- Flag indicating whether to add noise

Returns:

Generated samples

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

get_default_submodule(option: str) → CircuitCopula

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

get_parameter_options() → dict

Returns empty dict as this transformation has no configurable settings.

Returns:

Empty dict

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

inverse_transform(data: ndarray) → ndarray

Applies the inverse transformation to the full data set.

Parameters:

data -- Data set

Returns:

Data set after applying the inverse transformation

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

Does the reverse transformation.

Parameters:

  • input_data -- Dictionary containing information of previously executed modules

  • config -- Dictionary containing additional information

  • kwargs -- Dictionary containing additional information

Returns:

Tuple with the dictionary and the time the postprocessing took

preprocess(input_data: dict, config: dict, **kwargs: dict) → tuple[dict, 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 transformation

  • kwargs -- Additional optional arguments

Returns:

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

reverse_transform(input_data: dict) → dict

Transforms the solution back to the representation needed for validation/evaluation.

Parameters:

input_data -- Dictionary containing the solution

Returns:

Dictionary with solution transformed accordingly

transform(input_data: dict, config: dict) → dict

Transforms the input dataset using PIT transformation and computes histograms of the training dataset in the transformed space.

Parameters:

  • input_data -- Dataset

  • config -- Config with the parameters specified in Config class

Returns:

Dict with PIT transformation, time it took to map it