def test_roc_curves_from_test_statistics_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualization API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
input_features = [binary_feature(), bag_feature()]
output_features = [binary_feature()]
encoder = 'parallel_cnn'
# Generate test data
data_csv = generate_data(input_features, output_features, csv_filename)
output_feature_name = output_features[0]['name']
input_features[0]['encoder'] = encoder
model = run_api_experiment(input_features, output_features)
data_df = read_csv(data_csv)
model.train(data_df=data_df)
test_stats = model.test(data_df=data_df)[1]
viz_outputs = ('pdf', 'png')
for viz_output in viz_outputs:
vis_output_pattern_pdf = model.exp_dir_name + '/*.{}'.format(
viz_output)
visualize.roc_curves_from_test_statistics(
[test_stats, test_stats],
output_feature_name,
model_namess=['Model1', 'Model2'],
output_directory=model.exp_dir_name,
file_format=viz_output)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
shutil.rmtree(model.exp_dir_name, ignore_errors=True)
def test_roc_curves_from_test_statistics_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualization API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
input_features = [binary_feature(), bag_feature()]
output_features = [binary_feature()]
# Generate test data
data_csv = generate_data(input_features, output_features, csv_filename)
output_feature_name = output_features[0]['name']
model = run_api_experiment(input_features, output_features)
data_df = read_csv(data_csv)
_, _, output_dir = model.train(dataset=data_df)
# extract test metrics
test_stats, _, _ = model.evaluate(dataset=data_df,
collect_overall_stats=True,
output_directory=output_dir)
test_stats = test_stats
viz_outputs = ('pdf', 'png')
for viz_output in viz_outputs:
vis_output_pattern_pdf = os.path.join(output_dir, '*.{}'.format(
viz_output))
visualize.roc_curves_from_test_statistics(
[test_stats, test_stats],
output_feature_name,
model_names=['Model1', 'Model2'],
output_directory=output_dir,
file_format=viz_output
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
shutil.rmtree(output_dir, ignore_errors=True)
def test_roc_curves_from_test_statistics_vis_api(csv_filename):
"""Ensure pdf and png figures can be saved via visualization API call.
:param csv_filename: csv fixture from tests.fixtures.filenames.csv_filename
:return: None
"""
input_features = [binary_feature(), bag_feature()]
output_features = [binary_feature()]
with TemporaryDirectory() as tmpvizdir:
# Generate test data
data_csv = generate_data(input_features, output_features,
os.path.join(tmpvizdir, csv_filename))
output_feature_name = output_features[0]["name"]
model = run_api_experiment(input_features, output_features)
data_df = read_csv(data_csv)
_, _, output_dir = model.train(dataset=data_df,
output_directory=os.path.join(
tmpvizdir, "results"))
# extract test metrics
test_stats, _, _ = model.evaluate(dataset=data_df,
collect_overall_stats=True,
output_directory=output_dir)
test_stats = test_stats
viz_outputs = ("pdf", "png")
for viz_output in viz_outputs:
vis_output_pattern_pdf = os.path.join(output_dir,
f"*.{viz_output}")
visualize.roc_curves_from_test_statistics(
[test_stats, test_stats],
output_feature_name,
model_names=["Model1", "Model2"],
output_directory=output_dir,
file_format=viz_output,
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)