PlottingΒΆ

Once you have the diagnostic results for a set of learning strategies you can plot the behaviour the evolution of the metrics:

  • Accuracy: fraction of correct classifications;
  • Efficiency: fraction of total SN Ia correctly classified;
  • Purity: fraction of correct Ia classifications;
  • Figure of merit: efficiency x purity with a penalty factor of 3 for false positives (contamination).

The class actsnclass.Canvas enables you do to it using:

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>>> from actsnclass.plot_results import Canvas

>>> # define parameters
>>> path_to_files = ['results/metrics_canonical.dat',
>>>                  'results/metrics_random.dat',
>>>                  'results/metrics_unc.dat']
>>> strategies_list = ['Canonical', 'RandomSampling', 'UncSampling']
>>> output_plot = 'plots/diag.png'

>>> #Initiate the Canvas object, read and plot the results for
>>> # each diagnostic and strategy.
>>> cv = Canvas()
>>> cv.load_diagnostics(path_to_files=path_to_files,
>>>                    strategies_list=strategies_list)
>>> cv.set_plot_dimensions()
>>> cv.plot_diagnostics(output_plot_file=output_plot,
>>>                    strategies_list=strategies_list)

This will generate:

Plot diagnostics evolution.

Alternatively, you can use it directly from the command line.

For example, the result above could also be obtained doing:

>>> make_diagnostic_plots.py -m <path to canonical diag> <path to rand sampling diag>  <path to unc sampling diag>
>>>        -o <path to output plot file> -s Canonical RandomSampling UncSampling

OBS: the color pallete for this project was chosen to honor the work of Piet Mondrian.