visualizations

Module of classes used to create visualizations of data produced by the experiment and learners.

class mloop.visualizations.ControllerVisualizer(filename, file_type='pkl', **kwargs)

Bases: object

ControllerVisualizer creates figures from a Controller Archive.

Parameters:filename (String) – Filename of the GaussianProcessLearner archive. Keyword Arguments:  file_type (String) – Can be ‘mat’ for matlab, ‘pkl’ for pickle or ‘txt’ for text. Default ‘pkl’.

plot_cost_vs_run()

Create a plot of the costs versus run number.

plot_parameters_vs_cost()

Create a plot of the parameters versus run number.

plot_parameters_vs_run()

Create a plot of the parameters versus run number.

class mloop.visualizations.DifferentialEvolutionVisualizer(filename, file_type='pkl', **kwargs)

Bases: object

DifferentialEvolutionVisualizer creates figures from a differential evolution archive.

Parameters:filename (String) – Filename of the DifferentialEvolutionVisualizer archive. Keyword Arguments:  file_type (String) – Can be ‘mat’ for matlab, ‘pkl’ for pickle or ‘txt’ for text. Default ‘pkl’.

plot_costs_vs_generations()

Create a plot of the costs versus run number.

plot_params_vs_generations()

Create a plot of the parameters versus run number.

class mloop.visualizations.GaussianProcessVisualizer(filename, file_type='pkl', **kwargs)

Bases: mloop.learners.GaussianProcessLearner

GaussianProcessVisualizer extends of GaussianProcessLearner, designed not to be used as a learner, but to instead post process a GaussianProcessLearner archive file and produce useful data for visualization of the state of the learner. Fixes the Gaussian process hyperparameters to what was last found during the run.

Parameters:filename (String) – Filename of the GaussianProcessLearner archive. Keyword Arguments:  file_type (String) – Can be ‘mat’ for matlab, ‘pkl’ for pickle or ‘txt’ for text. Default ‘pkl’.

plot_cross_sections()

Produce a figure of the cross section about best cost and parameters

plot_hyperparameters_vs_run()

return_cross_sections(points=100, cross_section_center=None)

Finds the predicted global minima, then returns a list of vectors of parameters values, costs and uncertainties, corresponding to the 1D cross sections along each parameter axis through the predicted global minima.

Keyword Arguments:  

• points (int) – the number of points to sample along each cross section. Default value is 100.
• cross_section_center (array) – parameter array where the centre of the cross section should be taken. If None, the parameters for the best returned cost are used.

Returns:

a tuple (cross_arrays, cost_arrays, uncer_arrays) cross_parameter_arrays (list): a list of arrays for each cross section, with the values of the varied parameter going from the minimum to maximum value. cost_arrays (list): a list of arrays for the costs evaluated along each cross section about the minimum. uncertainty_arrays (list): a list of uncertainties

run()

Overides the GaussianProcessLearner multiprocessor run routine. Does nothing but makes a warning.

class mloop.visualizations.NeuralNetVisualizer(filename, file_type='pkl', **kwargs)

Bases: mloop.learners.NeuralNetLearner

NeuralNetVisualizer extends of NeuralNetLearner, designed not to be used as a learner, but to instead post process a NeuralNetLearner archive file and produce useful data for visualization of the state of the learner.

Parameters:filename (String) – Filename of the GaussianProcessLearner archive. Keyword Arguments:  file_type (String) – Can be ‘mat’ for matlab, ‘pkl’ for pickle or ‘txt’ for text. Default ‘pkl’.

do_cross_sections(upload)

Produce a figure of the cross section about best cost and parameters

plot_density_surface()

Produce a density plot of the cost surface (only works when there are 2 parameters)

plot_losses()

Produce a figure of the loss as a function of training run.

plot_surface()

Produce a figure of the cost surface (only works when there are 2 parameters)

return_cross_sections(points=100, cross_section_center=None)

Finds the predicted global minima, then returns a list of vectors of parameters values, costs and uncertainties, corresponding to the 1D cross sections along each parameter axis through the predicted global minima.

Keyword Arguments:  

• points (int) – the number of points to sample along each cross section. Default value is 100.
• cross_section_center (array) – parameter array where the centre of the cross section should be taken. If None, the parameters for the best returned cost are used.

Returns:

a tuple (cross_arrays, cost_arrays, uncer_arrays) cross_parameter_arrays (list): a list of arrays for each cross section, with the values of the varied parameter going from the minimum to maximum value. cost_arrays (list): a list of arrays for the costs evaluated along each cross section about the minimum. uncertainty_arrays (list): a list of uncertainties

run()

Overides the GaussianProcessLearner multiprocessor run routine. Does nothing but makes a warning.

mloop.visualizations._color_from_controller_name(controller_name)

Gives a color (as a number betweeen zero an one) corresponding to each controller name string.

mloop.visualizations._color_list_from_num_of_params(num_of_params)

Gives a list of colors based on the number of parameters.

mloop.visualizations.configure_plots()

Configure the setting for the plots.

mloop.visualizations.create_controller_visualizations(filename, file_type='pkl', plot_cost_vs_run=True, plot_parameters_vs_run=True, plot_parameters_vs_cost=True)

Runs the plots for a controller file.

Parameters:

filename (Optional [string]) – Filename for the controller archive.

Keyword Arguments:  

• file_type (Optional [string]) – File type ‘pkl’ pickle, ‘mat’ matlab or ‘txt’ text.
• plot_cost_vs_run (Optional [bool]) – If True plot cost versus run number, else do not. Default True.
• plot_parameters_vs_run (Optional [bool]) – If True plot parameters versus run number, else do not. Default True.
• plot_parameters_vs_cost (Optional [bool]) – If True plot parameters versus cost number, else do not. Default True.

mloop.visualizations.create_differential_evolution_learner_visualizations(filename, file_type='pkl', plot_params_vs_generations=True, plot_costs_vs_generations=True)

Runs the plots from a differential evolution learner file.

Parameters:

filename (Optional [string]) – Filename for the differential evolution archive. Must provide datetime or filename. Default None.

Keyword Arguments:  

• file_type (Optional [string]) – File type ‘pkl’ pickle, ‘mat’ matlab or ‘txt’ text.
• plot_params_generations (Optional [bool]) – If True plot parameters vs generations, else do not. Default True.
• plot_costs_generations (Optional [bool]) – If True plot costs vs generations, else do not. Default True.

mloop.visualizations.create_gaussian_process_learner_visualizations(filename, file_type='pkl', plot_cross_sections=True, plot_hyperparameters_vs_run=True)

Runs the plots from a gaussian process learner file.

Parameters:

filename (Optional [string]) – Filename for the gaussian process archive. Must provide datetime or filename. Default None.

Keyword Arguments:  

• file_type (Optional [string]) – File type ‘pkl’ pickle, ‘mat’ matlab or ‘txt’ text.
• plot_cross_sections (Optional [bool]) – If True plot predict landscape cross sections, else do not. Default True.

mloop.visualizations.create_neural_net_learner_visualizations(filename, file_type='pkl', plot_cross_sections=True, upload_cross_sections=False)

Creates plots from a neural nets learner file.

Parameters:

filename (Optional [string]) – Filename for the neural net archive. Must provide datetime or filename. Default None.

Keyword Arguments:  

• file_type (Optional [string]) – File type ‘pkl’ pickle, ‘mat’ matlab or ‘txt’ text.
• plot_cross_sections (Optional [bool]) – If True plot predict landscape cross sections, else do not. Default True.

mloop.visualizations.show_all_default_visualizations(controller, show_plots=True)

Plots all visualizations available for a controller, and it’s internal learners.

Parameters:controller (Controller) – The controller to extract plots from Keyword Arguments:  show_plots (Controller) – Determine whether to run plt.show() at the end or not. For debugging.

mloop.visualizations.show_all_default_visualizations_from_archive(controller_filename, learner_filename, controller_type, show_plots=True, upload_cross_sections=False)