def test_compare_classifier_performance_from_pred_vis_api(experiment_to_use):
"""Ensure pdf and png figures can be saved via visualization API call.
:param experiment_to_use: Object containing trained model and results to
test visualization
:return: None
"""
experiment = experiment_to_use
prediction = experiment.predictions
viz_outputs = ('pdf', 'png')
with TemporaryDirectory() as tmpvizdir:
for viz_output in viz_outputs:
vis_output_pattern_pdf = tmpvizdir + '/*.{}'.format(
viz_output)
visualize.compare_classifiers_performance_from_pred(
[prediction, prediction],
experiment.ground_truth,
experiment.ground_truth_metadata,
experiment.output_feature_name,
labels_limit=0,
model_namess=['Model1', 'Model2'],
output_directory=tmpvizdir,
file_format=viz_output
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
def test_compare_classifier_performance_from_pred_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualisation API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
experiment = Experiment(csv_filename)
prediction = experiment.prediction_raw
viz_outputs = ('pdf', 'png')
for viz_output in viz_outputs:
vis_output_pattern_pdf = experiment.model.exp_dir_name + '/*.{}'.format(
viz_output)
visualize.compare_classifiers_performance_from_pred(
[prediction, prediction],
experiment.ground_truth,
experiment.ground_truth_metadata,
experiment.output_feature_name,
labels_limit=0,
model_namess=['Model1', 'Model2'],
output_directory=experiment.model.exp_dir_name,
file_format=viz_output
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
shutil.rmtree(experiment.model.exp_dir_name, ignore_errors=True)
