TestingFuncs module

TestingFuncs.calc_MSE(scaled_inputs, scaled_predictions, scaled_targets, bool_scaled)

Calculates the mean squared error given predictions and target data. Also calculates the forward using the target data.

Parameters:

scaled_inputs: Data scaled for encoding into the circuit. Default range is 0.2 to 0.8. scaled_predictions: Predictions from the PQC in the range of -1 to 1. scaled_targets: Data scaled using the same strategy as scaled_inputs: default range is 0.2 to 0.8. bool_scaled: If False, the MinMaxScaler method “inverse_transform” is used to return the data to their original values before evaluating the MSE.

Returns:

mse: the mean squared error calculated using the either the scaled or real-value predictions and targets, depending on whether bool_scaled is True forward_mse: the mse obtained from a model which predicts each value x_n as the value before it x_n-1.

TestingFuncs.forward(inputs, targets)

A model which predicts each value x_n as the value before it x_n-1.

Parameters:

inputs (numpy.array): Data used by the forward to make a prediction. Shape is (batch_size, n_qubits) where batch_size = train_size//max_steps. targets (numpy.array): True results against which the forward’s predictions are compared. Shape is (batch_size, 1).

Returns:

forward_mse: the mse obtained from the “forward” model.

TestingFuncs.inverse_transform(scaled_data, scaler)

Performs and inverse transform on predictions and targets to return the data to their original values before scaling with MinMaxScaler

Parameters:

scaled_data: data scaled according to the MinMaxScaler. scaler: The MinMaxScaler initialized in training. To be loaded from a dictionary.

Returns:

data: original data before being scaled with MinMaxScaler

TestingFuncs.load_results_params(dict_path)

Loads saved weights, input and target data for testing, scaler for “unscaling” to original data values, indices for unshuffling the testing data and predictions, and n_qubits.

Parameters:

dict_path: Path of the dictionary containing the desired parameters.

Returns:

results_params: the dictionary containing the desired parameters.

TestingFuncs.make_predictions(circuit, weights, inputs)

Uses test data and weights obtained from training to make predictions.

Parameters:

circuit (function): The vmapped circuit to make predictions. weights (Jax tensor): Array of weights obtained from training. inputs (numpy.array): test data. Shape is (batch_size, n_qubits) where batch_size = train_size//max_steps.

Returns:

predictions: An array of predicted values, each evaluated by a PQC using weights and some input data. Each value is an expectation value from the PQC and is therefore scale from -1 to 1.

TestingFuncs.plot(predictions, targets, indices, n_qubits, bool_plot: bool = False, save_plot: str | None = None, mse=None, forward_mse=None, plot_labels=['x-axis', 'y-axis'])

Creates a plot of the original data, the targets and the predictions.

Parameters:

predictions: An array of predicted values, each evaluated by a circuit using weights and some input data. Each value has been unscaled to be in the range of the original data. targets: The true valuues against which the predictions are compared. indices: gives the index of each prediction and target in the original data. n_qubits (int): number of qubits in the circuit bool_plot (bool): If True, a plot of the predicted values over the true values will be shown when the code is run as a cell. save_plot: If None, the plot will not be saved. Otherwise, the plot will be saved to the path given as the string. mse: The MSE from the circuit’s predictions. To be shown on the plot. forward_mse: The MSE from the forward’s predictions. To be shown on the plot. plot_labels: A list of two strings giving the plot’s axis labels. Should be of the form [‘x-axis’,’y-axis’].

TestingFuncs.test(circuit, scaled_inputs, scaled_targets, scaler, weights, save_mse: str | None = None, bool_scaled=False)

Evaluates the performance of the circuit with final weights on the test dataset.

Parameters:

inputs: Test data weights: Weights obtained from training plot_labels: A list containing the x and y axis labels. bool_plot: If True, a plot of the predicted values over the true values will be shown when the code is run as a cell. save_plot: If None, the plot will not be saved. Otherwise, the plot will be saved to the path given as the string. save_mse (str): If None, the mse will not be saved, otherwise, the mse and forward mse will be saved in a directory called ‘MSE to the path given with keys ‘Circuit MSE’ and ‘Forward MSE’. bool_scaled: If True, the mse is calculated using the scaled values. If False, the mse is calculated using the original (unscaled) values.