def supervised_umap(ax, manifolds, y, cutoff=50):
x = manifolds[2].reset_index(drop=True)
label = y['risk_score']
c = label < cutoff
m = UMAP(target_metric = "categorical").fit_transform(x, c)
m = m.reset_index(drop=True).to_pandas()
c = c.reset_index(drop=True).to_pandas()
_=ax.scatter(m.loc[c].values[:,0],m[c].values[:,1],alpha=1,s=3,c='blue',label='Low Patient Risk Score (< {})'.format(cutoff))
_=ax.scatter(m.loc[~c].values[:,0],m.loc[~c].values[:,1],alpha=1,s=3,c='red',label='High Patient Risk Score (> {})'.format(cutoff))
ax.set_title('cutoff = {}%'.format(cutoff))
def umap(adata,
min_dist=0.5,
spread=1.0,
n_components=2,
maxiter=None,
alpha=1.0,
gamma=1.0,
negative_sample_rate=5,
init_pos='spectral',
random_state=0,
a=None,
b=None,
copy=False,
method='umap'):
"""Embed the neighborhood graph using UMAP [McInnes18]_.
UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
technique suitable for visualizing high-dimensional data. Besides tending to
be faster than tSNE, it optimizes the embedding such that it best reflects
the topology of the data, which we represent throughout Scanpy using a
neighborhood graph. tSNE, by contrast, optimizes the distribution of
nearest-neighbor distances in the embedding such that these best match the
distribution of distances in the high-dimensional space. We use the
implementation of `umap-learn <https://github.com/lmcinnes/umap>`__
[McInnes18]_. For a few comparisons of UMAP with tSNE, see this `preprint
<https://doi.org/10.1101/298430>`__.
Parameters
----------
adata : :class:`~anndata.AnnData`
Annotated data matrix.
min_dist : `float`, optional (default: 0.5)
The effective minimum distance between embedded points. Smaller values
will result in a more clustered/clumped embedding where nearby points on
the manifold are drawn closer together, while larger values will result
on a more even dispersal of points. The value should be set relative to
the ``spread`` value, which determines the scale at which embedded
points will be spread out. The default of in the `umap-learn` package is
0.1.
spread : `float` (optional, default 1.0)
The effective scale of embedded points. In combination with `min_dist`
this determines how clustered/clumped the embedded points are.
n_components : `int`, optional (default: 2)
The number of dimensions of the embedding.
maxiter : `int`, optional (default: `None`)
The number of iterations (epochs) of the optimization. Called `n_epochs`
in the original UMAP.
alpha : `float`, optional (default: 1.0)
The initial learning rate for the embedding optimization.
gamma : `float` (optional, default 1.0)
Weighting applied to negative samples in low dimensional embedding
optimization. Values higher than one will result in greater weight
being given to negative samples.
negative_sample_rate : `int` (optional, default 5)
The number of negative edge/1-simplex samples to use per positive
edge/1-simplex sample in optimizing the low dimensional embedding.
init_pos : `string` or `np.array`, optional (default: 'spectral')
How to initialize the low dimensional embedding. Called `init` in the
original UMAP.
Options are:
* Any key for `adata.obsm`.
* 'paga': positions from :func:`~scanpy.pl.paga`.
* 'spectral': use a spectral embedding of the graph.
* 'random': assign initial embedding positions at random.
* A numpy array of initial embedding positions.
random_state : `int`, `RandomState` or `None`, optional (default: 0)
If `int`, `random_state` is the seed used by the random number generator;
If `RandomState`, `random_state` is the random number generator;
If `None`, the random number generator is the `RandomState` instance used
by `np.random`.
a : `float` (optional, default `None`)
More specific parameters controlling the embedding. If `None` these
values are set automatically as determined by `min_dist` and
`spread`.
b : `float` (optional, default `None`)
More specific parameters controlling the embedding. If `None` these
values are set automatically as determined by `min_dist` and
`spread`.
copy : `bool` (default: `False`)
Return a copy instead of writing to adata.
method : {`'umap'`, `'rapids'`} (default: `'umap'`)
Use the original 'umap' implementation, or 'rapids' (experimental, GPU only)
Returns
-------
Depending on `copy`, returns or updates `adata` with the following fields.
**X_umap** : `adata.obsm` field
UMAP coordinates of data.
"""
adata = adata.copy() if copy else adata
if 'neighbors' not in adata.uns:
raise ValueError(
'Did not find \'neighbors/connectivities\'. Run `sc.pp.neighbors` first.'
)
start = logg.info('computing UMAP')
if ('params' not in adata.uns['neighbors']
or adata.uns['neighbors']['params']['method'] != 'umap'):
logg.warning(
'neighbors/connectivities have not been computed using umap')
from umap.umap_ import find_ab_params, simplicial_set_embedding
if a is None or b is None:
a, b = find_ab_params(spread, min_dist)
else:
a = a
b = b
if isinstance(init_pos, str) and init_pos in adata.obsm.keys():
init_coords = adata.obsm[init_pos]
elif isinstance(init_pos, str) and init_pos == 'paga':
init_coords = get_init_pos_from_paga(adata, random_state=random_state)
else:
init_coords = init_pos # Let umap handle it
if hasattr(init_coords, "dtype"):
init_coords = check_array(init_coords,
dtype=np.float32,
accept_sparse=False)
random_state = check_random_state(random_state)
neigh_params = adata.uns['neighbors']['params']
X = _choose_representation(adata,
neigh_params.get('use_rep', None),
neigh_params.get('n_pcs', None),
silent=True)
if method == 'umap':
# the data matrix X is really only used for determining the number of connected components
# for the init condition in the UMAP embedding
n_epochs = 0 if maxiter is None else maxiter
X_umap = simplicial_set_embedding(
X,
adata.uns['neighbors']['connectivities'].tocoo(),
n_components,
alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init_coords,
random_state,
neigh_params.get('metric', 'euclidean'),
neigh_params.get('metric_kwds', {}),
verbose=settings.verbosity > 3,
)
elif method == 'rapids':
metric = neigh_params.get('metric', 'euclidean')
if metric != 'euclidean':
raise ValueError(
f'`sc.pp.neighbors` was called with `metric` {metric!r}, '
"but umap `method` 'rapids' only supports the 'euclidean' metric."
)
from cuml import UMAP
n_neighbors = adata.uns['neighbors']['params']['n_neighbors']
n_epochs = 500 if maxiter is None else maxiter # 0 is not a valid value for rapids, unlike original umap
X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
umap = UMAP(
n_neighbors=n_neighbors,
n_components=n_components,
n_epochs=n_epochs,
learning_rate=alpha,
init=init_pos,
min_dist=min_dist,
spread=spread,
negative_sample_rate=negative_sample_rate,
a=a,
b=b,
verbose=settings.verbosity > 3,
)
X_umap = umap.fit_transform(X_contiguous)
adata.obsm['X_umap'] = X_umap # annotate samples with UMAP coordinates
logg.info(
' finished',
time=start,
deep=('added\n'
" 'X_umap', UMAP coordinates (adata.obsm)"),
)
return adata if copy else None
def __init__(self, df, n_clusters, chembl_ids):
self.app = dash.Dash(
__name__, external_stylesheets=external_stylesheets)
self.df = df
self.n_clusters = n_clusters
self.chembl_ids = chembl_ids
# Fetch relavant properties from database.
self.prop_df = self.create_dataframe_molecule_properties(chembl_ids)
self.df['chembl_id'] = chembl_ids
self.df['id'] = self.df.index
self.orig_df = df.copy()
# initialize UMAP
self.umap = UMAP(n_neighbors=100,
a=1.0,
b=1.0,
learning_rate=1.0)
# Construct the UI
self.app.layout = self.constuct_layout()
# Register callbacks for selection inside main figure
self.app.callback(
[Output('selected_clusters', 'value'),
Output('selected_point_cnt', 'children')],
[Input('main-figure', 'clickData'),
Input('main-figure', 'selectedData'),
Input('bt_recluster_clusters', 'n_clicks'),
Input('bt_recluster_points', 'n_clicks'),
Input('northstar_cluster', 'children')],
[State("selected_clusters", "value")]) (self.handle_data_selection)
# Register callbacks for buttons for reclustering selected data
self.app.callback(
[Output('main-figure', 'figure'),
Output('northstar_cluster', 'children')],
[Input('bt_recluster_clusters', 'n_clicks'),
Input('bt_recluster_points', 'n_clicks'),
Input('bt_north_star', 'n_clicks'),
Input('north_star', 'value'),
Input('sl_prop_gradient', 'value'),
Input('sl_nclusters', 'value')],
[State("selected_clusters", "value"),
State("main-figure", "selectedData")]) (self.handle_re_cluster)
# Register callbacks for selection inside main figure to update module details
self.app.callback(
[Output('tb_selected_molecules', 'children'),
Output('sl_mol_props', 'options'),
Output("current_page", "children"),
Output("total_page", "children"),
Output('section_molecule_details', 'style')],
[Input('main-figure', 'selectedData'),
Input('sl_mol_props', 'value'),
Input('sl_prop_gradient', 'value'),
Input("bt_page_prev", "n_clicks"),
Input("bt_page_next", "n_clicks")],
State("current_page", "children")) (self.handle_molecule_selection)
self.app.callback(
Output("hidden1", "children"),
[Input("bt_reset", "n_clicks")]) (self.handle_reset)
self.app.callback(
Output('north_star', 'value'),
[Input({'role': 'bt_star_candidate', 'index': ALL}, 'n_clicks')],
State('north_star', 'value')) \
(self.handle_mark_north_star)
logger.info('Initializing Morgan fingerprints...')
results = db.from_sequence(smiles_list).map(MorganFromSmiles).compute()
np_array_fingerprints = np.stack(results).astype(np.float32)
# take np.array shape (n_mols, nBits) for GPU DataFrame
gdf = np2cudf(np_array_fingerprints)
# prepare one set of clusters
n_clusters = 7
kmeans_float = KMeans(n_clusters=n_clusters)
kmeans_float.fit(gdf)
# UMAP
umap = UMAP(n_neighbors=100,
a=1.0,
b=1.0,
learning_rate=1.0)
Xt = umap.fit_transform(gdf)
gdf.add_column('x', Xt[0].to_array())
gdf.add_column('y', Xt[1].to_array())
gdf.add_column('cluster', kmeans_float.labels_)
# start dash
v = chemvisualize.ChemVisualization(
gdf.copy(), n_clusters, chemblID_list)
logger.info('navigate to https://localhost:5000')
v.start('0.0.0.0')
def draw_sampels(model, dataset, original_labels, algorithm, log_dir, seed,
ind_path, save_dir):
if isinstance(log_dir, str):
log_dir = Path(log_dir)
if isinstance(save_dir, str):
save_dir = Path(save_dir)
os.makedirs(save_dir, exist_ok=True)
algorithm_name = get_algorithms_name(log_dir, [algorithm])[0]
model_dir = log_dir / algorithm_name / str(seed) / "model0"
idx_path = model_dir / "selected_idx.pkl"
selected_idxs = pickle.loads(Path(idx_path).read_bytes())
pt_path = model_dir / "best_model.pt"
model.load_state_dict(torch.load(pt_path))
noise_ind = set(np.load(ind_path))
selected_idx = set(selected_idxs[-1])
labels = np.array(original_labels)
fixed_train_dataloader = DataLoader(dataset,
batch_size=256,
shuffle=False,
num_workers=8)
model.eval()
X = []
with torch.no_grad():
for i, (images, _, _) in enumerate(fixed_train_dataloader):
if torch.cuda.is_available():
images = images.to("cuda:0")
output = model(images)
output = output.cpu().numpy()
X.append(output)
X = np.concatenate(X, axis=0)
tsne = UMAP(n_components=2, random_state=0)
embedding = tsne.fit_transform(X)
emb1 = embedding[:, 0]
emb2 = embedding[:, 1]
targets = ["clean", "noise", "select", "nonsel"]
multi_fig = make_subplots(rows=2,
cols=2,
vertical_spacing=0.05,
horizontal_spacing=0.02)
min_x, max_x = [], []
min_y, max_y = [], []
for n, target in enumerate(targets):
fig = go.Figure()
class_min_x, class_max_x = np.inf, 0
class_min_y, class_max_y = np.inf, 0
for i in range(len(dataset.classes)):
class_ind = set(np.where(labels == i)[0])
if target == "noise":
target_ind = class_ind & noise_ind
elif target == "clean":
target_ind = class_ind - noise_ind
elif target == "select":
target_ind = class_ind & selected_idx
elif target == "nonsel":
target_ind = class_ind - selected_idx
X = emb1[list(target_ind)]
Y = emb2[list(target_ind)]
if len(target_ind) > 0:
class_min_x = min(min(X), class_min_x)
class_max_x = max(max(X), class_max_x)
class_min_y = min(min(Y), class_min_y)
class_max_y = max(max(Y), class_max_y)
if len(dataset.classes) <= 10:
scatter = go.Scattergl(x=X, y=Y, mode="markers")
else:
color = hsv_to_rgb(360 / len(dataset.classes) * i, 1, 1)
scatter = go.Scattergl(
x=X,
y=Y,
mode="markers",
marker_color=f"rgb({color[0]},{color[1]},{color[2]})",
)
fig.add_trace(scatter)
multi_fig.add_trace(
scatter,
row=n // 2 + 1,
col=n % 2 + 1,
)
min_x.append(class_min_x)
max_x.append(class_max_x)
min_y.append(class_min_y)
max_y.append(class_max_y)
fig.update_layout(
autosize=False,
width=1200,
height=800,
margin=go.layout.Margin(
l=0, # left margin
r=0, # right margin
b=0, # bottom margin
t=0, # top margin
),
showlegend=False,
)
fig.update_xaxes(showticklabels=False)
fig.update_yaxes(showticklabels=False)
# fig.write_html(f"{save_dir}/umap-{target}.html")
fig.write_image(f"{save_dir}/umap-{target}.pdf")
multi_fig.update_layout(
autosize=False,
width=3200,
height=2400,
margin=go.layout.Margin(
l=0, # left margin
r=0, # right margin
b=100, # bottom margin
t=0, # top margin
),
font=dict(family="Arial", size=84),
showlegend=False,
)
multi_fig.update_xaxes(title_text="Clean samples", row=1, col=1)
multi_fig.update_xaxes(title_text="Incorrectly labeled samples",
row=1,
col=2)
multi_fig.update_xaxes(title_text="Selected samples", row=2, col=1)
multi_fig.update_xaxes(title_text="Non-selected samples", row=2, col=2)
# scaling
multi_fig.update_xaxes(showticklabels=False,
range=[max(min_x), min(max_x)])
multi_fig.update_yaxes(showticklabels=False,
range=[max(min_y), min(max_y)])
# multi_fig.write_html(f"{save_dir}/umap.html")
multi_fig.write_image(f"{save_dir}/umap.pdf")
def umap(
adata: AnnData,
min_dist: float = 0.5,
spread: float = 1.0,
n_components: int = 2,
maxiter: Optional[int] = None,
alpha: float = 1.0,
gamma: float = 1.0,
negative_sample_rate: int = 5,
init_pos: Union[_InitPos, np.ndarray, None] = 'spectral',
random_state: AnyRandom = 0,
a: Optional[float] = None,
b: Optional[float] = None,
copy: bool = False,
method: Literal['umap', 'rapids'] = 'umap',
neighbors_key: Optional[str] = None,
) -> Optional[AnnData]:
"""\
Embed the neighborhood graph using UMAP [McInnes18]_.
UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
technique suitable for visualizing high-dimensional data. Besides tending to
be faster than tSNE, it optimizes the embedding such that it best reflects
the topology of the data, which we represent throughout Scanpy using a
neighborhood graph. tSNE, by contrast, optimizes the distribution of
nearest-neighbor distances in the embedding such that these best match the
distribution of distances in the high-dimensional space. We use the
implementation of `umap-learn <https://github.com/lmcinnes/umap>`__
[McInnes18]_. For a few comparisons of UMAP with tSNE, see this `preprint
<https://doi.org/10.1101/298430>`__.
Parameters
----------
adata
Annotated data matrix.
min_dist
The effective minimum distance between embedded points. Smaller values
will result in a more clustered/clumped embedding where nearby points on
the manifold are drawn closer together, while larger values will result
on a more even dispersal of points. The value should be set relative to
the ``spread`` value, which determines the scale at which embedded
points will be spread out. The default of in the `umap-learn` package is
0.1.
spread
The effective scale of embedded points. In combination with `min_dist`
this determines how clustered/clumped the embedded points are.
n_components
The number of dimensions of the embedding.
maxiter
The number of iterations (epochs) of the optimization. Called `n_epochs`
in the original UMAP.
alpha
The initial learning rate for the embedding optimization.
gamma
Weighting applied to negative samples in low dimensional embedding
optimization. Values higher than one will result in greater weight
being given to negative samples.
negative_sample_rate
The number of negative edge/1-simplex samples to use per positive
edge/1-simplex sample in optimizing the low dimensional embedding.
init_pos
How to initialize the low dimensional embedding. Called `init` in the
original UMAP. Options are:
* Any key for `adata.obsm`.
* 'paga': positions from :func:`~scanpy.pl.paga`.
* 'spectral': use a spectral embedding of the graph.
* 'random': assign initial embedding positions at random.
* A numpy array of initial embedding positions.
random_state
If `int`, `random_state` is the seed used by the random number generator;
If `RandomState` or `Generator`, `random_state` is the random number generator;
If `None`, the random number generator is the `RandomState` instance used
by `np.random`.
a
More specific parameters controlling the embedding. If `None` these
values are set automatically as determined by `min_dist` and
`spread`.
b
More specific parameters controlling the embedding. If `None` these
values are set automatically as determined by `min_dist` and
`spread`.
copy
Return a copy instead of writing to adata.
method
Use the original 'umap' implementation, or 'rapids' (experimental, GPU only)
neighbors_key
If not specified, umap looks .uns['neighbors'] for neighbors settings
and .obsp['connectivities'] for connectivities
(default storage places for pp.neighbors).
If specified, umap looks .uns[neighbors_key] for neighbors settings and
.obsp[.uns[neighbors_key]['connectivities_key']] for connectivities.
Returns
-------
Depending on `copy`, returns or updates `adata` with the following fields.
**X_umap** : `adata.obsm` field
UMAP coordinates of data.
"""
adata = adata.copy() if copy else adata
if neighbors_key is None:
neighbors_key = 'neighbors'
if neighbors_key not in adata.uns:
raise ValueError(
f'Did not find .uns["{neighbors_key}"]. Run `sc.pp.neighbors` first.'
)
start = logg.info('computing UMAP')
neighbors = NeighborsView(adata, neighbors_key)
if 'params' not in neighbors or neighbors['params']['method'] != 'umap':
logg.warning(
f'.obsp["{neighbors["connectivities_key"]}"] have not been computed using umap'
)
# Compat for umap 0.4 -> 0.5
with warnings.catch_warnings():
# umap 0.5.0
warnings.filterwarnings("ignore", message=r"Tensorflow not installed")
import umap
if version.parse(umap.__version__) >= version.parse("0.5.0"):
def simplicial_set_embedding(*args, **kwargs):
from umap.umap_ import simplicial_set_embedding
X_umap, _ = simplicial_set_embedding(
*args,
densmap=False,
densmap_kwds={},
output_dens=False,
**kwargs,
)
return X_umap
else:
from umap.umap_ import simplicial_set_embedding
from umap.umap_ import find_ab_params
if a is None or b is None:
a, b = find_ab_params(spread, min_dist)
else:
a = a
b = b
adata.uns['umap'] = {'params': {'a': a, 'b': b}}
if isinstance(init_pos, str) and init_pos in adata.obsm.keys():
init_coords = adata.obsm[init_pos]
elif isinstance(init_pos, str) and init_pos == 'paga':
init_coords = get_init_pos_from_paga(
adata, random_state=random_state, neighbors_key=neighbors_key
)
else:
init_coords = init_pos # Let umap handle it
if hasattr(init_coords, "dtype"):
init_coords = check_array(init_coords, dtype=np.float32, accept_sparse=False)
if random_state != 0:
adata.uns['umap']['params']['random_state'] = random_state
random_state = check_random_state(random_state)
neigh_params = neighbors['params']
X = _choose_representation(
adata,
neigh_params.get('use_rep', None),
neigh_params.get('n_pcs', None),
silent=True,
)
if method == 'umap':
# the data matrix X is really only used for determining the number of connected components
# for the init condition in the UMAP embedding
n_epochs = 0 if maxiter is None else maxiter
X_umap = simplicial_set_embedding(
X,
neighbors['connectivities'].tocoo(),
n_components,
alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init_coords,
random_state,
neigh_params.get('metric', 'euclidean'),
neigh_params.get('metric_kwds', {}),
verbose=settings.verbosity > 3,
)
elif method == 'rapids':
metric = neigh_params.get('metric', 'euclidean')
if metric != 'euclidean':
raise ValueError(
f'`sc.pp.neighbors` was called with `metric` {metric!r}, '
"but umap `method` 'rapids' only supports the 'euclidean' metric."
)
from cuml import UMAP
n_neighbors = neighbors['params']['n_neighbors']
n_epochs = (
500 if maxiter is None else maxiter
) # 0 is not a valid value for rapids, unlike original umap
X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
umap = UMAP(
n_neighbors=n_neighbors,
n_components=n_components,
n_epochs=n_epochs,
learning_rate=alpha,
init=init_pos,
min_dist=min_dist,
spread=spread,
negative_sample_rate=negative_sample_rate,
a=a,
b=b,
verbose=settings.verbosity > 3,
random_state=random_state,
)
X_umap = umap.fit_transform(X_contiguous)
adata.obsm['X_umap'] = X_umap # annotate samples with UMAP coordinates
logg.info(
' finished',
time=start,
deep=('added\n' " 'X_umap', UMAP coordinates (adata.obsm)"),
)
return adata if copy else None
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = argparse.ArgumentParser(description="UMAP encoding script")
parser.add_argument(
'--run-name',
dest='run_name',
help=
"Training run directory (for the plot to be placed in the right logs directory)",
required=True)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-f',
'--file-path',
dest='encodings_file_path',
help="encodings file path",
default=argparse.SUPPRESS)
group.add_argument(
'--files-all',
dest='files_all',
action='store_true',
help="cluster using encodings of all available training stages",
default=argparse.SUPPRESS)
args = parser.parse_args(args)
assert (('encodings_file_path' in args.__dict__.keys())
or ('files_all' in args.__dict__.keys()))
if 'encodings_file_path' in args.__dict__.keys():
assert DoesPathExistAndIsFile(
args.encodings_file_path
), f'path {args.encodings_file_path} does not exist or is not a file'
curr_run_name = args.run_name
logs_dir = os.path.join(os.path.abspath('./'), 'logs', curr_run_name)
encodings_dir = os.path.join(logs_dir, 'data_encodings')
umap_plots_dir = os.path.join(logs_dir, 'umap_plots')
assert DoesPathExistAndIsDirectory(
logs_dir), f'path {logs_dir} does not exist or not a directory'
assert DoesPathExistAndIsDirectory(
encodings_dir
), f'path {encodings_dir} does not exist or not a directory'
EnsureDirectoryExists(umap_plots_dir)
if 'encodings_file_path' in args.__dict__.keys():
encodings_files_paths = [args.encodings_file_path]
elif (('files_all' in args.__dict__.keys()) and (args.files_all)):
encodings_files_paths = find_files(encodings_dir, '*encoded.npz')
encodings_files_paths.sort()
for encodings_file_path in tqdm(encodings_files_paths):
enc = np.load(encodings_file_path)
zn = enc['zn']
zc_logits = enc['zc_logits']
labels = enc['labels']
# print('zn shape: ', zn.shape)
# print('zc logits shape: ', zc_logits.shape)
# print('true labels shape:', labels.shape)
umap = UMAP(n_components=2,
verbose=False,
n_epochs=4096,
learning_rate=0.1)
umap_enc = umap.fit_transform(zn)
classes = np.unique(labels)
colors = cm.tab20(np.linspace(0.0, 1.0, len(classes)))
colors = dict(zip(classes, colors))
f = plt.figure(figsize=(6, 6), dpi=300)
for label in classes:
plt.scatter(umap_enc[labels == label, 0],
umap_enc[labels == label, 1],
s=0.5,
color=colors[label],
label=label)
lgnd = plt.legend(fontsize=5)
for hndl in lgnd.legendHandles:
hndl.set_sizes([20])
plt.axis('equal')
plt.tight_layout()
figname = os.path.join(
umap_plots_dir, 'umap-%s.png' %
os.path.splitext(os.path.basename(encodings_file_path))[0])
f.savefig(
figname,
dpi=300,
bbox_inches=0,
pad_inches=0,
)
plt.close()