def test_hierarchy(args):
wnids = get_wnids_from_dataset(args.dataset)
path = get_graph_path_from_args(**vars(args))
print("==> Reading from {}".format(path))
G = read_graph(path)
G_name = Path(path).stem
leaves_seen, wnid_set1 = match_wnid_leaves(wnids, G, G_name)
print_stats(leaves_seen, wnid_set1, G_name, "leaves")
leaves_seen, wnid_set2 = match_wnid_nodes(wnids, G, G_name)
print_stats(leaves_seen, wnid_set2, G_name, "nodes")
num_roots = len(list(get_roots(G)))
if num_roots == 1:
Colors.green("Found just 1 root.")
else:
Colors.red(f"Found {num_roots} roots. Should be only 1.")
if len(wnid_set1) == len(wnid_set2) == 0 and num_roots == 1:
Colors.green("==> All checks pass!")
else:
Colors.red("==> Test failed")
def generate_hierarchy_vis(args):
path = get_graph_path_from_args(**vars(args))
print('==> Reading from {}'.format(path))
G = read_graph(path)
roots = list(get_roots(G))
num_roots = len(roots)
root = args.vis_root or next(get_roots(G))
assert root in G, f'Node {root} is not a valid node. Nodes: {G.nodes}'
dataset = None
if args.dataset and args.vis_leaf_images:
cls = getattr(data, args.dataset)
dataset = cls(root='./data', train=False, download=True)
color_info = get_color_info(G,
args.color,
color_leaves=not args.vis_no_color_leaves,
color_path_to=args.vis_color_path_to,
color_nodes=args.vis_color_nodes or ())
node_to_conf = generate_node_conf(args.vis_node_conf)
tree = build_tree(G,
root,
color_info=color_info,
force_labels_left=args.vis_force_labels_left or [],
dataset=dataset,
include_leaf_images=args.vis_leaf_images,
image_resize_factor=args.vis_image_resize_factor,
include_fake_sublabels=args.vis_fake_sublabels,
node_to_conf=node_to_conf)
graph = build_graph(G)
if num_roots > 1:
Colors.red(f'Found {num_roots} roots! Should be only 1: {roots}')
else:
print(f'Found just {num_roots} root.')
fname = generate_vis_fname(**vars(args))
parent = Path(fwd()).parent
generate_vis(str(parent / 'nbdt/templates/tree-template.html'),
tree,
fname,
zoom=args.vis_zoom,
straight_lines=not args.vis_curved,
show_sublabels=args.vis_sublabels,
height=args.vis_height,
width=args.vis_width,
dark=args.vis_dark,
margin_top=args.vis_margin_top,
margin_left=args.vis_margin_left,
hide=args.vis_hide or [],
above_dy=args.vis_above_dy,
below_dy=args.vis_below_dy,
scale=args.vis_scale,
root_y=args.vis_root_y,
colormap=args.vis_colormap)
def generate_hierarchy_vis(args):
path = get_graph_path_from_args(**vars(args))
print('==> Reading from {}'.format(path))
G = read_graph(path)
roots = list(get_roots(G))
num_roots = len(roots)
root = next(get_roots(G))
dataset = None
if args.dataset:
cls = getattr(data, args.dataset)
dataset = cls(root='./data', train=False, download=True)
color_info = get_color_info(G,
args.color,
color_leaves=not args.vis_no_color_leaves,
color_path_to=args.vis_color_path_to,
color_nodes=args.vis_color_nodes or ())
tree = build_tree(G,
root,
color_info=color_info,
force_labels_left=args.vis_force_labels_left or [],
dataset=dataset,
include_leaf_images=args.vis_leaf_images,
image_resize_factor=args.vis_image_resize_factor)
graph = build_graph(G)
if num_roots > 1:
Colors.red(f'Found {num_roots} roots! Should be only 1: {roots}')
else:
print(f'Found just {num_roots} root.')
fname = generate_vis_fname(**vars(args))
parent = Path(fwd()).parent
generate_vis(str(parent / 'nbdt/templates/tree-template.html'),
tree,
'tree',
fname,
zoom=args.vis_zoom,
straight_lines=not args.vis_curved,
show_sublabels=args.vis_sublabels,
height=args.vis_height,
dark=args.vis_dark)
def generate_hierarchy_vis(args):
path_hie = get_graph_path_from_args(**vars(args))
print("==> Reading from {}".format(path_hie))
G = read_graph(path_hie)
path_html = f"./{generate_vis_fname(**vars(args))}.html"
kwargs = vars(args)
dataset = None
if args.dataset and args.vis_leaf_images:
cls = getattr(data, kwargs.pop('dataset'))
dataset = cls(root="./data", train=False, download=True)
kwargs.pop('dataset', '')
kwargs.pop('fname', '')
return generate_hierarchy_vis_from(G,
dataset,
path_html,
verbose=True,
**kwargs)
def generate_hierarchy(
dataset,
method,
seed=0,
branching_factor=2,
extra=0,
no_prune=False,
fname="",
path="",
single_path=False,
induced_linkage="ward",
induced_affinity="euclidean",
checkpoint=None,
arch=None,
model=None,
**kwargs,
):
wnids = get_wnids_from_dataset(dataset)
if method == "wordnet":
G = build_minimal_wordnet_graph(wnids, single_path)
elif method == "random":
G = build_random_graph(wnids,
seed=seed,
branching_factor=branching_factor)
elif method == "induced":
G = build_induced_graph(
wnids,
dataset=dataset,
checkpoint=checkpoint,
model=arch,
linkage=induced_linkage,
affinity=induced_affinity,
branching_factor=branching_factor,
state_dict=model.state_dict() if model is not None else None,
)
else:
raise NotImplementedError(f'Method "{method}" not yet handled.')
print_graph_stats(G, "matched")
assert_all_wnids_in_graph(G, wnids)
if not no_prune:
G = prune_single_successor_nodes(G)
print_graph_stats(G, "pruned")
assert_all_wnids_in_graph(G, wnids)
if extra > 0:
G, n_extra, n_imaginary = augment_graph(G, extra, True)
print(f"[extra] \t Extras: {n_extra} \t Imaginary: {n_imaginary}")
print_graph_stats(G, "extra")
assert_all_wnids_in_graph(G, wnids)
path = get_graph_path_from_args(
dataset=dataset,
method=method,
seed=seed,
branching_factor=branching_factor,
extra=extra,
no_prune=no_prune,
fname=fname,
path=path,
single_path=single_path,
induced_linkage=induced_linkage,
induced_affinity=induced_affinity,
checkpoint=checkpoint,
arch=arch,
)
write_graph(G, path)
Colors.green("==> Wrote tree to {}".format(path))
return path