def visualize_trained_network_results(data_dict, z_dim=100):
plt.close("all")
data_name = data_dict.get('name', None)
source_key = data_dict.get('source_key', None)
target_key = data_dict.get('target_key', None)
cell_type_key = data_dict.get("cell_type", None)
data = sc.read(f"../data/{data_name}/train_{data_name}.h5ad")
cell_types = data.obs[cell_type_key].unique().tolist()
spec_cell_type = data_dict.get("spec_cell_types", None)
if spec_cell_type is not []:
cell_types = spec_cell_type
for cell_type in cell_types:
path_to_save = f"../results/CVAE/{data_name}/{cell_type}/{z_dim}/{source_key} to {target_key}/Visualizations/"
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
train_data = data.copy()[~((data.obs['condition'] == target_key) &
(data.obs[cell_type_key] == cell_type))]
cell_type_adata = data[data.obs[cell_type_key] == cell_type]
network = trvae.CVAE(
x_dimension=data.shape[1],
z_dimension=z_dim,
model_path=f"../models/CVAE/{data_name}/{cell_type}/{z_dim}/cvae")
network.restore_model()
if sparse.issparse(data.X):
data.X = data.X.A
feed_data = data.X
train_labels, _ = trvae.label_encoder(data)
fake_labels = np.ones(train_labels.shape)
latent_with_true_labels = network.to_latent(feed_data, train_labels)
latent_with_fake_labels = network.to_latent(feed_data, fake_labels)
mmd_latent_with_true_labels = network.to_mmd_layer(
feed_data, train_labels)
mmd_latent_with_fake_labels = network.to_mmd_layer(
feed_data, fake_labels)
cell_type_ctrl = cell_type_adata.copy()[
cell_type_adata.obs['condition'] == source_key]
print(cell_type_ctrl.shape, cell_type_adata.shape)
pred_celltypes = network.predict(cell_type_ctrl,
labels=np.ones(
(cell_type_ctrl.shape[0], 1)))
pred_adata = anndata.AnnData(X=pred_celltypes)
pred_adata.obs['condition'] = ['predicted'] * pred_adata.shape[0]
pred_adata.var = cell_type_adata.var
if data_name == "pbmc":
sc.tl.rank_genes_groups(cell_type_adata,
groupby="condition",
n_genes=100,
method="wilcoxon")
top_100_genes = cell_type_adata.uns["rank_genes_groups"]["names"][
target_key].tolist()
gene_list = top_100_genes[:10]
else:
sc.tl.rank_genes_groups(cell_type_adata,
groupby="condition",
n_genes=100,
method="wilcoxon")
top_50_down_genes = cell_type_adata.uns["rank_genes_groups"][
"names"][source_key].tolist()
top_50_up_genes = cell_type_adata.uns["rank_genes_groups"][
"names"][target_key].tolist()
top_100_genes = top_50_up_genes + top_50_down_genes
gene_list = top_50_down_genes[:5] + top_50_up_genes[:5]
cell_type_adata = cell_type_adata.concatenate(pred_adata)
trvae.plotting.reg_mean_plot(
cell_type_adata,
top_100_genes=top_100_genes,
gene_list=gene_list,
condition_key='condition',
axis_keys={
"x": 'predicted',
'y': target_key
},
labels={
'x': 'pred stim',
'y': 'real stim'
},
legend=False,
fontsize=20,
textsize=14,
title=cell_type,
path_to_save=os.path.join(
path_to_save, f'rcvae_reg_mean_{data_name}_{cell_type}.pdf'))
trvae.plotting.reg_var_plot(
cell_type_adata,
top_100_genes=top_100_genes,
gene_list=gene_list,
condition_key='condition',
axis_keys={
"x": 'predicted',
'y': target_key
},
labels={
'x': 'pred stim',
'y': 'real stim'
},
legend=False,
fontsize=20,
textsize=14,
title=cell_type,
path_to_save=os.path.join(
path_to_save, f'rcvae_reg_var_{data_name}_{cell_type}.pdf'))
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
latent_with_true_labels = sc.AnnData(X=latent_with_true_labels)
latent_with_true_labels.obs['condition'] = data.obs['condition'].values
latent_with_true_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
latent_with_fake_labels = sc.AnnData(X=latent_with_fake_labels)
latent_with_fake_labels.obs['condition'] = data.obs['condition'].values
latent_with_fake_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
mmd_latent_with_true_labels = sc.AnnData(X=mmd_latent_with_true_labels)
mmd_latent_with_true_labels.obs['condition'] = data.obs[
'condition'].values
mmd_latent_with_true_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
mmd_latent_with_fake_labels = sc.AnnData(X=mmd_latent_with_fake_labels)
mmd_latent_with_fake_labels.obs['condition'] = data.obs[
'condition'].values
mmd_latent_with_fake_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
color = ['condition', cell_type_key]
sc.pp.neighbors(train_data)
sc.tl.umap(train_data)
sc.pl.umap(train_data,
color=color,
save=f'_{data_name}_{cell_type}_train_data',
show=False)
sc.pp.neighbors(latent_with_true_labels)
sc.tl.umap(latent_with_true_labels)
sc.pl.umap(latent_with_true_labels,
color=color,
save=f"_{data_name}_{cell_type}_latent_with_true_labels",
show=False)
sc.pp.neighbors(latent_with_fake_labels)
sc.tl.umap(latent_with_fake_labels)
sc.pl.umap(latent_with_fake_labels,
color=color,
save=f"_{data_name}_{cell_type}_latent_with_fake_labels",
show=False)
sc.pp.neighbors(mmd_latent_with_true_labels)
sc.tl.umap(mmd_latent_with_true_labels)
sc.pl.umap(
mmd_latent_with_true_labels,
color=color,
save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels",
show=False)
sc.pp.neighbors(mmd_latent_with_fake_labels)
sc.tl.umap(mmd_latent_with_fake_labels)
sc.pl.umap(
mmd_latent_with_fake_labels,
color=color,
save=f"_{data_name}_{cell_type}_mmd_latent_with_fake_labels",
show=False)
sc.pl.violin(cell_type_adata,
keys=top_100_genes[0],
groupby='condition',
save=f"_{data_name}_{cell_type}_{top_100_genes[0]}",
show=False)
plt.close("all")
def visualize_trained_network_results_multimodal(data_dict, z_dim=100):
plt.close("all")
data_name = data_dict.get('name', None)
source_key = data_dict.get('source_key', None)
target_key = data_dict.get('target_key', None)
data = sc.read(f"../data/{data_name}/train_{data_name}.h5ad")
path_to_save = f"../results/RCVAE/{data_name}/{z_dim}/{source_key} to {target_key}/Visualizations/"
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
network = trvae.trVAE(
x_dimension=data.shape[1],
z_dimension=z_dim,
model_path=f"../models/RCVAE/{data_name}/{z_dim}/",
)
network.restore_model()
if sparse.issparse(data.X):
data.X = data.X.A
feed_data = data.X
train_labels, _ = trvae.label_encoder(data)
fake_labels = np.ones(train_labels.shape)
latent_with_true_labels = network.to_latent(feed_data, train_labels)
latent_with_fake_labels = network.to_latent(feed_data, fake_labels)
mmd_latent_with_true_labels = network.to_mmd_layer(network,
feed_data,
train_labels,
feed_fake=False)
mmd_latent_with_fake_labels = network.to_mmd_layer(network,
feed_data,
train_labels,
feed_fake=True)
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
latent_with_true_labels = sc.AnnData(X=latent_with_true_labels)
latent_with_true_labels.obs['condition'] = data.obs['condition'].values
# latent_with_true_labels.obs[cell_type_key] = data.obs[cell_type_key].values
latent_with_fake_labels = sc.AnnData(X=latent_with_fake_labels)
latent_with_fake_labels.obs['condition'] = data.obs['condition'].values
# latent_with_fake_labels.obs[cell_type_key] = data.obs[cell_type_key].values
mmd_latent_with_true_labels = sc.AnnData(X=mmd_latent_with_true_labels)
mmd_latent_with_true_labels.obs['condition'] = data.obs['condition'].values
# mmd_latent_with_true_labels.obs[cell_type_key] = data.obs[cell_type_key].values
mmd_latent_with_fake_labels = sc.AnnData(X=mmd_latent_with_fake_labels)
mmd_latent_with_fake_labels.obs['condition'] = data.obs['condition'].values
# mmd_latent_with_fake_labels.obs[cell_type_key] = data.obs[cell_type_key].values
color = ['condition']
sc.pp.neighbors(data)
sc.tl.umap(data)
sc.pl.umap(data, color=color, save=f'_{data_name}_train_data', show=False)
sc.pp.neighbors(latent_with_true_labels)
sc.tl.umap(latent_with_true_labels)
sc.pl.umap(latent_with_true_labels,
color=color,
save=f"_{data_name}_latent_with_true_labels",
show=False)
sc.pp.neighbors(latent_with_fake_labels)
sc.tl.umap(latent_with_fake_labels)
sc.pl.umap(latent_with_fake_labels,
color=color,
save=f"_{data_name}__latent_with_fake_labels",
show=False)
sc.pp.neighbors(mmd_latent_with_true_labels)
sc.tl.umap(mmd_latent_with_true_labels)
sc.pl.umap(mmd_latent_with_true_labels,
color=color,
save=f"_{data_name}_mmd_latent_with_true_labels",
show=False)
sc.pp.neighbors(mmd_latent_with_fake_labels)
sc.tl.umap(mmd_latent_with_fake_labels)
sc.pl.umap(mmd_latent_with_fake_labels,
color=color,
save=f"_{data_name}_mmd_latent_with_fake_labels",
show=False)
plt.close("all")
def visualize_trained_network_results(data_dict,
z_dim=100,
mmd_dimension=128,
loss_fn='mse'):
plt.close("all")
data_name = data_dict['name']
source_keys = data_dict.get("source_conditions")
target_keys = data_dict.get("target_conditions")
cell_type_key = data_dict.get("cell_type", None)
need_merge = data_dict.get('need_merge', False)
label_encoder = data_dict.get('label_encoder', None)
condition_key = data_dict.get('condition', 'condition')
if need_merge:
data, _ = merge_data(data_dict)
else:
data = sc.read(f"../data/{data_name}/{data_name}.h5ad")
if loss_fn != 'mse':
data = normalize_hvg(data,
filter_min_counts=False,
normalize_input=False,
logtrans_input=True)
cell_types = data.obs[cell_type_key].unique().tolist()
spec_cell_type = data_dict.get("spec_cell_types", None)
if spec_cell_type:
cell_types = spec_cell_type
for cell_type in cell_types:
path_to_save = f"../results/RCVAEMulti/{data_name}/{cell_type}/{z_dim}/Visualizations/"
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
train_data = data.copy()[~(
(data.obs[condition_key].isin(target_keys)) &
(data.obs[cell_type_key] == cell_type))]
cell_type_adata = data.copy()[data.obs[cell_type_key] == cell_type]
n_conditions = len(train_data.obs[condition_key].unique().tolist())
network = trvae.trVAEMulti(
x_dimension=data.shape[1],
z_dimension=z_dim,
loss_fn=loss_fn,
n_conditions=n_conditions,
mmd_dimension=mmd_dimension,
model_path=f"../models/RCVAEMulti/{data_name}/{cell_type}/{z_dim}/",
)
network.restore_model()
if sparse.issparse(data.X):
data.X = data.X.A
feed_data = data
train_labels, _ = trvae.label_encoder(data, label_encoder,
condition_key)
fake_labels = []
for i in range(n_conditions):
fake_labels.append(np.zeros(train_labels.shape) + i)
latent_with_true_labels = network.to_z_latent(feed_data.X,
train_labels)
latent_with_fake_labels = [
network.to_z_latent(feed_data.X, fake_labels[i])
for i in range(n_conditions)
]
mmd_latent_with_true_labels = network.to_mmd_layer(feed_data,
train_labels,
feed_fake=0)
mmd_latent_with_fake_labels = [
network.to_mmd_layer(feed_data, train_labels, feed_fake=i)
for i in range(n_conditions)
]
if data_name in ["pbmc", 'endo_norm']:
sc.tl.rank_genes_groups(cell_type_adata,
groupby=condition_key,
n_genes=100,
method="wilcoxon")
top_100_genes = cell_type_adata.uns["rank_genes_groups"]["names"][
target_keys[-1]].tolist()
gene_list = top_100_genes[:10]
elif data_name in ['pancreas', 'nmuil_count']:
gene_list = None
top_100_genes = None
else:
sc.tl.rank_genes_groups(cell_type_adata,
groupby=condition_key,
n_genes=100,
method="wilcoxon")
top_50_down_genes = cell_type_adata.uns["rank_genes_groups"][
"names"][source_keys[0]].tolist()
top_50_up_genes = cell_type_adata.uns["rank_genes_groups"][
"names"][target_keys[-1]].tolist()
top_100_genes = top_50_up_genes + top_50_down_genes
gene_list = top_50_down_genes[:5] + top_50_up_genes[:5]
perturbation_list = data_dict.get("perturbation", [])
pred_adatas = None
for source, dest, name, source_label, target_label in perturbation_list:
print(source, dest, name)
pred_adata = visualize_multi_perturbation_between(
network,
cell_type_adata,
pred_adatas,
source_condition=source,
target_condition=dest,
name=name,
source_label=source_label,
target_label=target_label,
cell_type=cell_type,
data_name=data_name,
top_100_genes=top_100_genes,
gene_list=gene_list,
path_to_save=path_to_save,
condition_key=condition_key)
if pred_adatas is None:
pred_adatas = pred_adata
else:
pred_adatas = pred_adatas.concatenate(pred_adata)
pred_adatas.write_h5ad(
filename=f"../data/reconstructed/RCVAEMulti/{data_name}.h5ad")
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
color = [condition_key, cell_type_key]
latent_with_true_labels = sc.AnnData(X=latent_with_true_labels)
latent_with_true_labels.obs[condition_key] = data.obs[
condition_key].values
latent_with_true_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
latent_with_fake_labels = [
sc.AnnData(X=latent_with_fake_labels[i])
for i in range(n_conditions)
]
for i in range(n_conditions):
latent_with_fake_labels[i].obs[condition_key] = data.obs[
condition_key].values
latent_with_fake_labels[i].obs[cell_type_key] = data.obs[
cell_type_key].values
sc.pp.neighbors(latent_with_fake_labels[i])
sc.tl.umap(latent_with_fake_labels[i])
sc.pl.umap(
latent_with_fake_labels[i],
color=color,
save=f"_{data_name}_{cell_type}_latent_with_fake_labels_{i}",
show=False,
wspace=0.15,
frameon=False)
mmd_latent_with_true_labels = sc.AnnData(X=mmd_latent_with_true_labels)
mmd_latent_with_true_labels.obs[condition_key] = data.obs[
condition_key].values
mmd_latent_with_true_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
mmd_latent_with_fake_labels = [
sc.AnnData(X=mmd_latent_with_fake_labels[i])
for i in range(n_conditions)
]
for i in range(n_conditions):
mmd_latent_with_fake_labels[i].obs[condition_key] = data.obs[
condition_key].values
mmd_latent_with_fake_labels[i].obs[cell_type_key] = data.obs[
cell_type_key].values
sc.pp.neighbors(mmd_latent_with_fake_labels[i])
sc.tl.umap(mmd_latent_with_fake_labels[i])
sc.pl.umap(mmd_latent_with_fake_labels[i],
color=color,
save=f"_{data_name}_latent_with_fake_labels_{i}",
show=False,
wspace=0.15,
frameon=False)
sc.pp.neighbors(train_data)
sc.tl.umap(train_data)
sc.pl.umap(train_data,
color=color,
save=f'_{data_name}_{cell_type}_train_data',
show=False,
wspace=0.15,
frameon=False)
sc.pp.neighbors(latent_with_true_labels)
sc.tl.umap(latent_with_true_labels)
sc.pl.umap(latent_with_true_labels,
color=color,
save=f"_{data_name}_{cell_type}_latent_with_true_labels",
show=False,
wspace=0.15,
frameon=False)
sc.pp.neighbors(mmd_latent_with_true_labels)
sc.tl.umap(mmd_latent_with_true_labels)
sc.pl.umap(
mmd_latent_with_true_labels,
color=color,
save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels",
show=False,
wspace=0.15,
frameon=False)
if gene_list is not None:
for target_condition in target_keys:
pred_adata = pred_adatas[pred_adatas.obs[condition_key].str.
endswith(target_condition)]
violin_adata = cell_type_adata.concatenate(pred_adata)
for gene in gene_list[:3]:
sc.pl.violin(
violin_adata,
keys=gene,
groupby=condition_key,
save=
f"_{data_name}_{cell_type}_{gene}_{target_condition}.pdf",
show=False,
wspace=0.2,
rotation=90,
frameon=False)
plt.close("all")
def visualize_batch_correction(data_dict, z_dim=100, mmd_dimension=128):
plt.close("all")
data_name = data_dict['name']
source_keys = data_dict.get("source_conditions")
target_keys = data_dict.get("target_conditions")
cell_type_key = data_dict.get("cell_type", None)
need_merge = data_dict.get('need_merge', False)
label_encoder = data_dict.get('label_encoder', None)
condition_key = data_dict.get('condition', 'condition')
if need_merge:
data, _ = merge_data(data_dict)
else:
data = sc.read(f"../data/{data_name}/train_{data_name}.h5ad")
cell_types = data.obs[cell_type_key].unique().tolist()
spec_cell_type = data_dict.get("spec_cell_types", None)
if spec_cell_type:
cell_types = spec_cell_type
for cell_type in cell_types:
path_to_save = f"../results/RCVAEMulti/{data_name}/{cell_type}/{z_dim}/Visualizations/"
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
train_data = data.copy()[~(
(data.obs[condition_key].isin(target_keys)) &
(data.obs[cell_type_key] == cell_type))]
cell_type_adata = data[data.obs[cell_type_key] == cell_type]
network = trvae.trVAEMulti(
x_dimension=data.shape[1],
z_dimension=z_dim,
n_conditions=len(source_keys),
mmd_dimension=mmd_dimension,
model_path=f"../models/RCVAEMulti/{data_name}/{cell_type}/{z_dim}/",
)
network.restore_model()
if sparse.issparse(data.X):
data.X = data.X.A
feed_data = data.X
train_labels, _ = trvae.label_encoder(data, label_encoder,
condition_key)
mmd_latent_with_true_labels = network.to_mmd_layer(network,
feed_data,
train_labels,
feed_fake=0)
latent_with_true_labels = network.to_z_latent(feed_data, train_labels)
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
color = [condition_key]
mmd_latent_with_true_labels = sc.AnnData(X=mmd_latent_with_true_labels)
mmd_latent_with_true_labels.obs[condition_key] = data.obs[
condition_key].values
mmd_latent_with_true_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
latent_with_true_labels = sc.AnnData(X=latent_with_true_labels)
latent_with_true_labels.obs[condition_key] = data.obs[
condition_key].values
latent_with_true_labels.obs[cell_type_key] = data.obs[
cell_type_key].values
sc.pp.neighbors(train_data)
sc.tl.umap(train_data)
sc.pl.umap(train_data,
color=color,
save=f'_{data_name}_{cell_type}_train_data',
show=False,
wspace=0.15,
frameon=False)
sc.pp.neighbors(mmd_latent_with_true_labels)
sc.tl.umap(mmd_latent_with_true_labels)
sc.pl.umap(
mmd_latent_with_true_labels,
color=color,
save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels",
show=False,
wspace=0.15,
frameon=False)
sc.pp.neighbors(latent_with_true_labels)
sc.tl.umap(latent_with_true_labels)
sc.pl.umap(latent_with_true_labels,
color=color,
save=f"_{data_name}_{cell_type}_latent_with_true_labels",
show=False,
wspace=0.15,
frameon=False)
# mmd_latent_with_true_labels.obs['mmd'] = 'others'
# mmd_latent_with_true_labels.obs['mmd'] = mmd_latent_with_true_labels.obs.mmd.astype(str)
# mmd_latent_with_true_labels.obs['mmd'].cat.add_categories([f'alpha-{target_keys[0]}'], inplace=True)
# mmd_latent_with_true_labels.obs['mmd'].cat.add_categories([f'alpha-others'], inplace=True)
# print(mmd_latent_with_true_labels.obs['mmd'].cat.categories)
# mmd_latent_with_true_labels.obs.loc[((mmd_latent_with_true_labels.obs[condition_key] == target_keys[0]) &
# mmd_latent_with_true_labels.obs[
# cell_type_key] == cell_type), 'mmd'] = f'alpha-{target_keys[0]}'
# mmd_latent_with_true_labels.obs.loc[((mmd_latent_with_true_labels.obs[condition_key] != target_keys[0]) &
# mmd_latent_with_true_labels.obs[
# cell_type_key] == cell_type), 'mmd'] = f'alpha-others'
#
# sc.pl.umap(mmd_latent_with_true_labels, color='mmd',
# save=f"_{data_name}_{cell_type}_mmd_latent_with_true_labels_cell_comparison",
# show=False,
# wspace=0.15,
# frameon=False)
mmd_latent_with_true_labels.write_h5ad('../data/mmd.h5ad')
latent_with_true_labels.write_h5ad('../data/latent.h5ad')
def visualize_trained_network_results(data_dict,
z_dim=100,
arch_style=1,
preprocess=True,
max_size=80000):
plt.close("all")
data_name = data_dict.get('name', None)
source_key = data_dict.get('source_key', None)
target_key = data_dict.get('target_key', None)
img_width = data_dict.get('width', None)
img_height = data_dict.get('height', None)
n_channels = data_dict.get('n_channels', None)
train_digits = data_dict.get('train_digits', None)
test_digits = data_dict.get('test_digits', None)
attribute = data_dict.get('attribute', None)
path_to_save = f"../results/RCCVAE/{data_name}-{img_width}x{img_height}-{preprocess}/{arch_style}-{z_dim}/{source_key} to {target_key}/UMAPs/"
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
if data_name == "celeba":
gender = data_dict.get('gender', None)
data = trvae.prepare_and_load_celeba(
file_path="../data/celeba/img_align_celeba.zip",
attr_path="../data/celeba/list_attr_celeba.txt",
landmark_path="../data/celeba/list_landmarks_align_celeba.txt",
gender=gender,
attribute=attribute,
max_n_images=max_size,
img_width=img_width,
img_height=img_height,
restore=True,
save=False)
if sparse.issparse(data.X):
data.X = data.X.A
train_images = data.X
train_data = anndata.AnnData(X=data)
train_data.obs['condition'] = data.obs['condition'].values
train_data.obs.loc[train_data.obs['condition'] == 1,
'condition'] = f'with {attribute}'
train_data.obs.loc[train_data.obs['condition'] == -1,
'condition'] = f'without {attribute}'
train_data.obs['labels'] = data.obs['labels'].values
train_data.obs.loc[train_data.obs['labels'] == 1, 'labels'] = f'Male'
train_data.obs.loc[train_data.obs['labels'] == -1,
'labels'] = f'Female'
if preprocess:
train_images /= 255.0
else:
train_data = sc.read(f"../data/{data_name}/{data_name}.h5ad")
train_images = np.reshape(train_data.X,
(-1, img_width, img_height, n_channels))
if preprocess:
train_images /= 255.0
train_labels, _ = trvae.label_encoder(train_data)
fake_labels = np.ones(train_labels.shape)
network = trvae.DCtrVAE(
x_dimension=(img_width, img_height, n_channels),
z_dimension=z_dim,
arch_style=arch_style,
model_path=
f"../models/RCCVAE/{data_name}-{img_width}x{img_height}-{preprocess}/{arch_style}-{z_dim}/",
)
network.restore_model()
train_data_feed = np.reshape(train_images,
(-1, img_width, img_height, n_channels))
latent_with_true_labels = network.to_z_latent(train_data_feed,
train_labels)
latent_with_fake_labels = network.to_z_latent(train_data_feed, fake_labels)
mmd_latent_with_true_labels = network.to_mmd_layer(network,
train_data_feed,
train_labels,
feed_fake=False)
mmd_latent_with_fake_labels = network.to_mmd_layer(network,
train_data_feed,
train_labels,
feed_fake=True)
latent_with_true_labels = sc.AnnData(X=latent_with_true_labels)
latent_with_true_labels.obs['condition'] = pd.Categorical(
train_data.obs['condition'].values)
latent_with_fake_labels = sc.AnnData(X=latent_with_fake_labels)
latent_with_fake_labels.obs['condition'] = pd.Categorical(
train_data.obs['condition'].values)
mmd_latent_with_true_labels = sc.AnnData(X=mmd_latent_with_true_labels)
mmd_latent_with_true_labels.obs['condition'] = train_data.obs[
'condition'].values
mmd_latent_with_fake_labels = sc.AnnData(X=mmd_latent_with_fake_labels)
mmd_latent_with_fake_labels.obs['condition'] = train_data.obs[
'condition'].values
if data_name.__contains__("mnist") or data_name == "celeba":
latent_with_true_labels.obs['labels'] = pd.Categorical(
train_data.obs['labels'].values)
latent_with_fake_labels.obs['labels'] = pd.Categorical(
train_data.obs['labels'].values)
mmd_latent_with_true_labels.obs['labels'] = pd.Categorical(
train_data.obs['labels'].values)
mmd_latent_with_fake_labels.obs['labels'] = pd.Categorical(
train_data.obs['labels'].values)
color = ['condition', 'labels']
else:
color = ['condition']
if train_digits is not None:
train_data.obs.loc[(train_data.obs['condition'] == source_key) &
(train_data.obs['labels'].isin(train_digits)),
'type'] = 'training'
train_data.obs.loc[(train_data.obs['condition'] == source_key) &
(train_data.obs['labels'].isin(test_digits)),
'type'] = 'training'
train_data.obs.loc[(train_data.obs['condition'] == target_key) &
(train_data.obs['labels'].isin(train_digits)),
'type'] = 'training'
train_data.obs.loc[(train_data.obs['condition'] == target_key) &
(train_data.obs['labels'].isin(test_digits)),
'type'] = 'heldout'
sc.pp.neighbors(train_data)
sc.tl.umap(train_data)
sc.pl.umap(train_data,
color=color,
save=f'_{data_name}_train_data.png',
show=False,
wspace=0.5)
if train_digits is not None:
sc.tl.umap(train_data)
sc.pl.umap(train_data,
color=['type', 'labels'],
save=f'_{data_name}_data_type.png',
show=False)
sc.pp.neighbors(latent_with_true_labels)
sc.tl.umap(latent_with_true_labels)
sc.pl.umap(latent_with_true_labels,
color=color,
save=f"_{data_name}_latent_with_true_labels.png",
wspace=0.5,
show=False)
sc.pp.neighbors(latent_with_fake_labels)
sc.tl.umap(latent_with_fake_labels)
sc.pl.umap(latent_with_fake_labels,
color=color,
save=f"_{data_name}_latent_with_fake_labels.png",
wspace=0.5,
show=False)
sc.pp.neighbors(mmd_latent_with_true_labels)
sc.tl.umap(mmd_latent_with_true_labels)
sc.pl.umap(mmd_latent_with_true_labels,
color=color,
save=f"_{data_name}_mmd_latent_with_true_labels.png",
wspace=0.5,
show=False)
sc.pp.neighbors(mmd_latent_with_fake_labels)
sc.tl.umap(mmd_latent_with_fake_labels)
sc.pl.umap(mmd_latent_with_fake_labels,
color=color,
save=f"_{data_name}_mmd_latent_with_fake_labels.png",
wspace=0.5,
show=False)
plt.close("all")
