losses

Functions for computing losses

QQuantLib.qml4var.losses.compute_integral(y_array, x_array, dask_client=None)

Function for computing numerical integral of inputs arrays.

Parameters:

  • y_array (np.array or dask futures:) – array or futures (only is dask_client is provided) with the y-values for integration. For array expected shape is: shape(n)

  • x_array (np array) – array with the x domain for integration: Shape(n, n_features)

  • dask_client (DASK client) – DASK client to submit computation of the integral. y_array MUST BE a list of futures

Returns:

integral – float or future (if dask_client is provided) with the desired integral computation.

Return type:

float or future

Note

Considerations:

  • if x_array has shape(n, 1): then numpy trapz is used for computing integral.

  • if x_array has shape(n, 2): and dask_client is None numpy trapz is used for computing the double integral.

  • if x_array has shape(n, 2): and dask_client is provided then MonteCarlo integration is used.

  • if x_array has shape(n, > 2): MonteCarlo integration is used

QQuantLib.qml4var.losses.compute_loss(weights, produce_results, loss_function)

Workflow for computing loss:

Parameters:

  • weights (list) – list with the weights for the PQC

  • produce_results (python function) – Function for producing mandatory inputs for computing loss

  • loss_function (python function) – Function for computing loss function

Returns:

loss_ – Loss value

Return type:

float

QQuantLib.qml4var.losses.loss_function_qdml(labels, predict_cdf, predict_pdf, integral, loss_weights=[1.0, 5.0])

Computes the function for Quantum Differential Machine Learning

Parameters:

  • labels (numpy array) – numpy array with the labels. Shape: (-1, 1)

  • predict_cdf (numpy array) – numpy array with the predictions for the CDF. Shape: (-1, 1)

  • predict_pdf (numpy array) – numpy array with the predictions for the PDF. Shape: (-1, 1)

  • integral (float) – float with the integral of the square of the PDF in the desired domain

  • loss_weights (list) – Weights for each part of the Loss function

Returns:

loss_ – evaluation of the loss function for QDML

Return type:

float

QQuantLib.qml4var.losses.mse(labels, prediction)

Compute Mean Square Error

Parameters:

  • labels (numpy array) – numpy array with the labels

  • prediction (numpy array) – numpy array with the predictions

Returns:

mse_v – MSE value

Return type:

float

QQuantLib.qml4var.losses.numeric_gradient(weights, data_x, data_y, loss)

Compute the numeric gradient for some input loss function properly configured.

Parameters:

  • weights (numpy array) – Array with weights for PQC

  • data_x (numpy array) – Array with dataset of the features

  • data_y (numpy array) – Array with targets (labes) dataset

  • loss (function) – function for computing the loss properly configured

Returns:

gradient_i – Array with the gradients of the Loss Function

Return type:

numpy array

QQuantLib.qml4var.losses.trapezoidal_rule(x_domain, y_range)

Computes the integral using the trapezoidal_rule

Parameters:

  • x_domain (numpy array) – domain for computing the integral

  • y_range (numpy array) – range for computing the integral

Returns:

integral – integral computed using the trapezoidal rule

Return type:

float