def test_show_examples_supervised(self, _):
with testing.mock_data(num_examples=20):
ds, ds_info = load.load('imagenet2012',
split='train',
with_info=True,
as_supervised=True)
visualization.show_examples(ds, ds_info)
def _as_df(ds_name: str, **kwargs) -> pandas.DataFrame:
"""Loads the dataset as `pandas.DataFrame`."""
with testing.mock_data(num_examples=3):
ds, ds_info = load.load(ds_name,
split='train',
with_info=True,
**kwargs)
df = as_dataframe.as_dataframe(ds, ds_info)
return df
def test_show_examples_graph_with_colors_and_labels(self, _):
with testing.mock_data(num_examples=20):
ds, ds_info = load.load('ogbg_molpcba',
split='train',
with_info=True)
# Dictionaries used to map nodes and edges to colors.
atomic_numbers_to_elements = {
6: 'C',
7: 'N',
8: 'O',
9: 'F',
14: 'Si',
15: 'P',
16: 'S',
17: 'Cl',
35: 'Br,'
}
elements_to_colors = {
element: f'C{index}'
for index, element in enumerate(
atomic_numbers_to_elements.values())
}
bond_types_to_colors = {num: f'C{num}' for num in range(4)}
# Node colors are atomic numbers.
def node_color_fn(graph):
atomic_numbers = 1 + graph['node_feat'][:, 0].numpy()
return {
index:
elements_to_colors[atomic_numbers_to_elements[atomic_number]]
for index, atomic_number in enumerate(atomic_numbers)
}
# Node labels are element names.
def node_label_fn(graph):
atomic_numbers = 1 + graph['node_feat'][:, 0].numpy()
return {
index: atomic_numbers_to_elements[atomic_number]
for index, atomic_number in enumerate(atomic_numbers)
}
# Edge colors are bond types.
def edge_color_fn(graph):
bonds = graph['edge_index'].numpy()
bond_types = graph['edge_feat'][:, 0].numpy()
return {
tuple(bond): bond_types_to_colors[bond_type]
for bond, bond_type in zip(bonds, bond_types)
}
visualization.show_examples(ds,
ds_info,
node_color_fn=node_color_fn,
node_label_fn=node_label_fn,
edge_color_fn=edge_color_fn)
def test_show_examples(self, mock_fig):
with testing.mock_data(num_examples=20):
ds, ds_info = registered.load('imagenet2012',
split='train',
with_info=True)
visualization.show_examples(ds_info, ds)
ds, ds_info = registered.load('crema_d',
split='validation',
with_info=True)
visualization.show_examples(ds_info, ds)
def test_show_examples(self):
with testing.mock_data():
builder = registered.builder('imagenet2012')
visualization.show_statistics(builder.info)
def test_show_examples_missing_sample(self, _):
with testing.mock_data(num_examples=3):
ds, ds_info = load.load('imagenet2012',
split='train',
with_info=True)
visualization.show_examples(ds.take(3), ds_info)
def test_show_examples_graph(self, _):
with testing.mock_data(num_examples=20):
ds, ds_info = load.load('ogbg_molpcba',
split='train',
with_info=True)
visualization.show_examples(ds, ds_info)
