def test_plot_learning_curves_create_output_directory():
summary_file_name = join(_my_dir, 'other',
'sample_learning_curve_summary.tsv')
output_dir_name = join(_my_dir, 'other', 'foobar')
plc_cmd_args = [summary_file_name, output_dir_name]
plc.main(argv=plc_cmd_args)
exists(output_dir_name)
def test_plot_learning_curves_argparse():
# A utility function to check that we are setting up argument parsing
# correctly for plot_learning_curves. We are not checking whether the learning
# curves produced are accurate because we have separate tests for that.
# replace the _generate_learning_curve_plots function that's called
# by the main() in plot_learning_curves with a mocked up version
generate_learning_curve_plots_mock = create_autospec(
_generate_learning_curve_plots)
plc._generate_learning_curve_plots = generate_learning_curve_plots_mock
# now call main with some arguments
summary_file_name = join(_my_dir, 'other',
'sample_learning_curve_summary.tsv')
experiment_name = 'sample_learning_curve'
output_dir_name = join(_my_dir, 'other')
plc_cmd_args = [summary_file_name, output_dir_name]
plc.main(argv=plc_cmd_args)
# now check to make sure that _generate_learning_curve_plots (or our mocked up version
# of it) got the arguments that we passed
positional_arguments, keyword_arguments = generate_learning_curve_plots_mock.call_args
eq_(positional_arguments[0], experiment_name)
eq_(positional_arguments[1], output_dir_name)
eq_(positional_arguments[2], summary_file_name)
def test_plot_learning_curves_missing_file():
summary_file_name = join(_my_dir, 'other', 'non_existent_summary.tsv')
output_dir_name = join(_my_dir, 'other')
plc_cmd_args = [summary_file_name, output_dir_name]
plc.main(argv=plc_cmd_args)
def test_plot_learning_curves_no_pandas_no_seaborn():
summary_file_name = join(_my_dir, 'other', 'sample_learning_curve_summary.tsv')
output_dir_name = join(_my_dir, 'other')
plc_cmd_args = [summary_file_name, output_dir_name]
plc.main(argv=plc_cmd_args)