def get_pruning_results(project_name, pruning_methods, y_label):
print("Retrieving tasks...")
tasks = Task.get_tasks(project_name=project_name)
print('Done')
# logger = get_clearml_logger(project_name, 'Summary').logger
print(f"Generating graph comparison for {y_label}")
x_values, y_values, labels, num_funcs = compare_clearml_project(
pruning_methods,
project_name=project_name,
tasks=tasks,
x_name='dim_edges',
y_name=y_label)
data = seperate_labels(x_values, y_values, labels, num_funcs)
for n_funcs, n_funcs_data in data.items():
plt.figure()
for method, m_data in n_funcs_data.items():
x = m_data['x']
y = m_data['y']
order = np.argsort(x)
x = [x[i] for i in order]
y = [y[i] for i in order]
plt.plot(x, y, label=method)
plt.title(f"Prunning using {n_funcs} minhash functions")
plt.xlabel("Edge dim")
plt.ylabel("Accuracy")
plt.legend()
plt.show()
def summary_clearml_project(project_name, x_label, seperate_legends):
print("Retrieving tasks...")
tasks = Task.get_tasks(project_name=project_name)
print('Done')
y_labels = set(tasks[0].data.hyperparams['General'])
y_labels.remove(x_label)
logger = get_clearml_logger(project_name, 'Summary').logger
for y_label in y_labels:
print(f"Generating graph comparison for {y_label}")
x_values, y_values, labels = compare_clearml_project(
seperate_legends,
project_name=project_name,
tasks=tasks,
x_name=x_label,
y_name=y_label)
labeled_x, labeled_y, unique_labels = seperate_labels(
x_values, y_values, labels)
for label in unique_labels:
# plt.plot(labeled_x[label], labeled_y[label], label=label)
scatter2d = np.vstack((labeled_x[label], labeled_y[label])).T
logger.report_scatter2d(title=y_label,
series=label,
iteration=0,
scatter=scatter2d,
xaxis=x_label,
yaxis=y_label)
def compare_clearml_project(seperate_legends,
project_name=None,
tasks=None,
x_name=None,
y_name=None):
tasks = Task.get_tasks(
project_name=project_name) if tasks is None else tasks
x_y_values = []
for task in tasks:
try:
x_y_values.append((
float(task.data.hyperparams['Args'][x_name].value),
float(task.data.hyperparams['General'][y_name].value),
task.data.hyperparams['Args'][seperate_legends].value,
float(
task.data.hyperparams['Args']['num_minhash_funcs'].value)))
except Exception as e:
print(e)
pass
x_values = [x for x, y, sep, sep2 in x_y_values]
y_values = [y for x, y, sep, sep2 in x_y_values]
labels1 = [sep for x, y, sep, sep2 in x_y_values]
labels2 = [sep2 for x, y, sep, sep2 in x_y_values]
return x_values, y_values, labels1, labels2
def get_pruning_results(project_name, pruning_methods, y_label):
print("Retrieving tasks...")
tasks = Task.get_tasks(project_name=project_name)
print('Done')
# logger = get_clearml_logger(project_name, 'Summary').logger
print(f"Generating graph comparison for {y_label}")
x_values, y_values, labels = compare_clearml_project(
pruning_methods,
project_name=project_name,
tasks=tasks,
x_name='keep edges',
y_name=y_label)
labeled_x, labeled_y, unique_labels = seperate_labels(
x_values, y_values, labels)
for label in unique_labels:
print(f'======================= {label} =======================')
x = np.array(labeled_x[label])
y = np.array(labeled_y[label])
order = np.argsort(x)
x = x[order]
y = y[order]
for x_, y_ in zip(x, y):
print(f'({x_}, {y_})')
print()
