c3.optimizers.sensitivity

Module for Sensitivity Analysis. This allows the sweeping of the goal function in a given range of parameters to ascertain whether the dataset being used is sensitive to changes in the parameters of interest

Module Contents

class c3.optimizers.sensitivity.Sensitivity(sampling: str, batch_sizes: Dict[str, int], pmap: c3.parametermap.ParameterMap, datafiles: Dict[str, str], state_labels: Dict[str, List[Any]], sweep_map: List[List[Tuple[str]]], sweep_bounds: List[List[int]], algorithm: str, estimator: str = None, estimator_list: List[str] = None, dir_path: str = None, run_name: str = None, options={})

Bases: c3.optimizers.modellearning.ModelLearning

Class for Sensitivity Analysis, subclassed from Model Learning

Parameters

• sampling (str) – Name of the sampling method from library

• batch_sizes (Dict[str, int]) – Number of points to select from the dataset

• pmap (ParameterMap) – Model parameter map

• datafiles (Dict[str, str]) – The datafiles for each of the learning datasets

• state_labels (Dict[str, List[Any]]) – The labels for the excited states of the system

• sweep_map (List[List[List[str]]]) – Map of variables to be swept in exp_opt_map format

• sweep_bounds (List[List[int]]) – List of upper and lower bounds for each sweeping variable

• algorithm (str) – Name of the sweeping algorithm from the library

• estimator (str, optional) – Name of estimator method from library, by default None

• estimator_list (List[str], optional) – List of different estimators to be used, by default None

• dir_path (str, optional) – Path to save sensitivity logs, by default None

• run_name (str, optional) – Name of the experiment run, by default None

• options (dict, optional) – Options for the sweeping algorithm, by default {}

Raises

NotImplementedError – When trying to set the estimator or estimator_list

sensitivity()

Run the sensitivity analysis.

log_setup() → None

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

• run_name (str) – User specified name for the run

read_data(datafiles: Dict[str, str]) → None

Open data files and read in experiment results.

Parameters

datafiles (dict) – List of paths for files that contain learning data.

select_from_data(batch_size) → List[int]

Select a subset of each dataset to compute the goal function on.

Parameters

batch_size (int) – Number of points to select

Returns

Indeces of the selected data points.

Return type

list

learn_model() → None

Performs the model learning by minimizing the figure of merit.

confirm() → None

Compute the validation set, i.e. the value of the goal function on all points of the dataset that were not used for learning.

goal_run(current_params: tensorflow.constant) → tensorflow.float64

Evaluate the figure of merit for the current model parameters.

Parameters

current_params (tf.Tensor) – Current model parameters

Returns

Figure of merit

Return type

tf.float64

goal_run_with_grad(current_params)

Same as goal_run but with gradient. Very resource intensive. Unoptimized at the moment.

replace_logdir(new_logdir)

Specify a new filepath to store the log.

Parameters

new_logdir –

set_created_by(config) → None

Store the config file location used to created this optimizer.

load_best(init_point, extend_bounds=False) → None

Load a previous parameter point to start the optimization from. Legacy wrapper. Method moved to Parametermap.

Parameters

• init_point (str) – File location of the initial point

• extend_bounds (bool) – Whether or not to allow the loaded optimal parameters’ bounds to be extended if they exceed those specified.

start_log() → None

Initialize the log with current time.

end_log() → None

Finish the log by recording current time and total runtime.

log_best_unitary() → None

Save the best unitary in the log.

log_parameters(params) → None

Log the current status. Write parameters to log. Update the current best parameters. Call plotting functions as set up.

lookup_gradient(x)

Return the stored gradient for a given parameter set.

Parameters

x (np.array) – Parameter set.

Returns

Value of the gradient.

Return type

np.array

fct_to_min(input_parameters: Union[numpy.ndarray, tensorflow.constant]) → Union[numpy.ndarray, tensorflow.constant]

Wrapper for the goal function.

Parameters

input_parameters ([np.array, tf.constant]) – Vector of parameters in the optimizer friendly way.

Returns

Value of the goal function. Float if input is np.array else tf.constant

Return type

[np.ndarray, tf.constant]

fct_to_min_autograd(x)

Wrapper for the goal function, including evaluation and storage of the gradient.

Parameters

xnp.array

Vector of parameters in the optimizer friendly way.

float

Value of the goal function.