Emulator#

The following functions are contained in the twinlab.Emulator class.

Constructor#

Emulator(id[, project, project_owner])

A trainable twinLab emulator.

Setting up#

Emulator.design(priors, num_points[, ...])

Generate an initial design space for an emulator.

Train#

Emulator.train(dataset, inputs, outputs[, ...])

Train an emulator on the twinLab cloud.

Emulator.status([verbose])

Check the status of a training process on the twinLab cloud.

Emulator.view([verbose])

View an emulator that exists on the twinLab cloud.

Emulator.view_train_data([verbose])

View training data with which the emulator was trained in the twinLab cloud.

Emulator.view_test_data([verbose])

View test data on which the emulator was tested in the twinLab cloud.

Explore#

Emulator.summarise([detailed, verbose])

Get a summary of a trained emulator on the twinLab cloud.

Emulator.score([params, verbose])

Score the performance of a trained emulator.

Emulator.benchmark([params, verbose])

Benchmark the predicted uncertainty of a trained emulator.

Emulator.list_processes([verbose])

List all of the processes associated with a given emulator on the twinLab cloud.

Emulator.list_processes_statuses([verbose])

List the status of all processes associated with a given emulator on the twinLab cloud.

Emulator.get_process(process_id[, verbose])

Get the results from a process associated with the emulator on the twinLab cloud.

Predict#

Emulator.predict(df[, params, wait, verbose])

Make predictions using a trained emulator that exists on the twinLab cloud.

Emulator.sample(df, num_samples[, params, ...])

Draw samples from a trained emulator that exists on the twinLab cloud.

Plot#

Emulator.plot(x_axis, y_axis[, x_fixed, ...])

Plot the predictions from an emulator across a single dimension with one and two standard deviation bands.

Emulator.heatmap(x1_axis, x2_axis, y_axis[, ...])

Plot a heatmap of the predictions from an emulator across two dimensions.

Improve#

Emulator.update(df[, df_std, wait, verbose])

Update an emulator with new training data.

Emulator.recommend(num_points, acq_func[, ...])

Draw new recommended data points from a trained emulator that exists on the twinLab cloud.

Emulator.learn(dataset, inputs, outputs, ...)

Perform active learning to improve an emulator on the twinLab cloud.

Emulator.calibrate(df_obs, df_std[, params, ...])

Solve an inverse problem using a trained emulator on the twinLab cloud.

Emulator.maximize([params, wait, verbose])

Finding the maximum the output of a trained emulator that exists on the twinLab cloud.

Delete#

Emulator.delete([verbose])

Delete emulator from the twinLab cloud.

Export#

Emulator.export(file_path, format[, ...])

Export your emulator using a valid file format.

FMU#

Emulator.fmu(file_path, states[, type, os, ...])

Export your emulator as a Functional Mock-up Unit (FMU) following the FMI 2.0 standard.

Lock#

Emulator.lock([verbose])

Lock the emulator to prevent further training.

Emulator.unlock([verbose])

Unlock the emulator to allow further training.