def main():
args = parse_args()
experiment = Run()
params = load_values(args.param_file)
if params:
experiment.log_inputs(**params)
metrics = load_values(args.metric_file)
if metrics:
experiment.log_metrics(**metrics)
if args.tag:
experiment.log_tags(args.tag)
for dataset in load_datasets(args.data_file):
experiment.log_data_ref(**dataset)
if args.capture_png:
imgs = discover_png(experiment.get_outputs_path())
for img in imgs:
if isinstance(img, str):
experiment.log_image(img)
elif isinstance(img, SerialImages):
for idx, path in enumerate(img.paths):
experiment.log_image(path, name=img.name, step=idx)
else:
raise NotImplementedError('We should never get here.')
# https://polyaxon.com/docs/experimentation/tracking/module/#log_data_ref
experiment.log_data_ref('dataset_X', content=X)
experiment.log_data_ref('dataset_y', content=y)
accuracies, classifier = model(X=X,
y=y,
n_estimators=args.n_estimators,
max_features=args.max_features,
min_samples_leaf=args.min_samples_leaf)
accuracy_mean, accuracy_std = (np.mean(accuracies), np.std(accuracies))
values, counts = np.histogram(accuracies)
# Polyaxon
experiment.log_metrics(accuracy_mean=accuracy_mean,
accuracy_std=accuracy_std)
for step in range(accuracies.size):
experiment.log_metrics(accuracy=accuracies[step], step=step)
outpath = os.path.join(experiment.get_outputs_path(), 'model.pkl')
with(open(outpath, 'wb')) as outfile:
pickle.dump(classifier, outfile)
experiment.log_model(
outpath,
name='top cross validation model',
framework='sklearn'
)