def test_compare_performance_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
# extract test stats only
test_stats = experiment.test_stats_full
viz_outputs = ('pdf', 'png')
with TemporaryDirectory() as tmpvizdir:
for viz_output in viz_outputs:
vis_output_pattern_pdf = tmpvizdir + '/*.{}'.format(
viz_output
)
visualize.compare_performance(
[test_stats, test_stats],
output_feature_name=None,
model_names=['Model1', 'Model2'],
output_directory=tmpvizdir,
file_format=viz_output
)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 1 == len(figure_cnt)
def compare_perf():
"compare performance of two models"
test_file = SCRIPT_DIR / 'rotten_tomatoes_test.csv'
output_dir = get_ludwig_output_dir()
model_name = "run"
experiment_name1 = "rt"
experiment_dir = experiment_name1 + '_' + model_name
model_dir1 = output_dir / 'results' / experiment_dir / 'model'
model1 = LudwigModel.load(model_dir1, backend='local')
eval_stats1, predictions1, output_dir1 = model1.evaluate(
dataset=str(test_file))
experiment_name2 = "rt_zscore"
experiment_dir = experiment_name2 + '_' + model_name
model_dir2 = output_dir / 'results' / experiment_dir / 'model'
model2 = LudwigModel.load(model_dir2, backend='local')
eval_stats2, predictions2, output_dir2 = model2.evaluate(
dataset=str(test_file))
list_of_eval_stats = [eval_stats1, eval_stats2]
model_names = [experiment_name1, experiment_name2]
compare_performance(
list_of_eval_stats,
"recommended",
model_names=model_names,
output_directory=output_dir,
file_format="png",
)
print(f'{output_dir=}')
def test_compare_performance_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
"""
experiment = Experiment(csv_filename)
test_stats = experiment.test_stats_full[1]
viz_outputs = ('pdf', 'png')
for viz_output in viz_outputs:
vis_output_pattern_pdf = experiment.model.exp_dir_name + '/*.{}'.format(
viz_output)
visualize.compare_performance(
[test_stats, test_stats],
output_feature_name=None,
model_namess=['Model1', 'Model2'],
output_directory=experiment.model.exp_dir_name,
file_format=viz_output)
figure_cnt = glob.glob(vis_output_pattern_pdf)
assert 2 == len(figure_cnt)
shutil.rmtree(experiment.model.exp_dir_name, ignore_errors=True)
# Define Ludwig model object that drive model training
model = LudwigModel(config=model_id + "_config.yaml",
logging_level=logging.WARN)
# initiate model training
train_stats, _, _ = model.train(
training_set=training_set,
validation_set=val_set,
test_set=test_set,
experiment_name="balance_example",
model_name=model_id,
skip_save_model=True,
)
# evaluate model on test_set
eval_stats, _, _ = model.evaluate(test_set)
# save eval stats for later use
list_of_eval_stats.append(eval_stats)
print(">>>>>>> completed: ", model_id, "\n")
compare_performance(
list_of_eval_stats,
"Response",
model_names=list_of_model_ids,
output_directory="./visualizations",
file_format="png",
)
