def net_prop_with_restart(
run_name,
model_name,
normalization,
adj_matrix_file,
node_labels_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("LOOCV")
with mlflow.start_run(run_name=run_name):
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.log_param("model", model_name)
mlflow.set_tag("use_cuda", use_cuda)
mlflow.log_param("merged_layers", True)
labels = data_loaders.load_labels(node_labels_file, use_cuda=use_cuda)
n_nodes = labels.size(0)
adjacency_matrix = data_loaders.load_adj_matrices(
[adj_matrix_file], normalization=normalization,
use_cuda=use_cuda)[0]
print(run_name)
ranks_df = loocv.run(labels=labels,
model_class=NetPropWithRestart,
adjacency_matrix=adjacency_matrix,
**params)
data_savers.save_ranks(ranks_df, n_nodes, run_name, params)
def bagging_rgcn_embeddings(adj_matrix_files, node_labels_file,
embeddings_file, use_cuda, params, metadata):
mlflow.set_experiment("LOOCV")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
labels = data_loaders.load_labels(node_labels_file, use_cuda=use_cuda)
n_nodes = labels.size(0)
embeddings = data_loaders.load_embeddings(embeddings_file,
use_cuda=use_cuda)
graph = data_loaders.load_graph(
adj_matrix_files,
n_nodes,
add_edge_type=True,
add_node_ids=True,
normalization=NORMALIZATION,
use_cuda=use_cuda,
)
print(RUN_NAME)
ranks_df = loocv.run(labels=labels,
model_class=Bagging,
bagging_model=RGCN,
graph=graph,
features=embeddings,
n_rels=len(adj_matrix_files),
**params)
data_savers.save_ranks(ranks_df, n_nodes, RUN_NAME, params)
def rwr_m(adj_matrix_files, node_labels_file, use_cuda, params, metadata):
mlflow.set_experiment("LOOCV")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
labels = data_loaders.load_labels(node_labels_file, use_cuda=use_cuda)
n_nodes = labels.size(0)
print("Loading adjacency matrices")
adj_matrices = data_loaders.load_adj_matrices(adj_matrix_files,
normalization=None,
use_cuda=use_cuda)
adjacency_matrices = torch.stack(adj_matrices)
print(RUN_NAME)
ranks_df = loocv.run(labels=labels,
model_class=RwrM,
adjacency_matrices=adjacency_matrices,
**params)
data_savers.save_ranks(ranks_df, n_nodes, RUN_NAME, params)
def bagging_mlp(
embeddings_file,
train_node_labels_file,
test_node_labels_file,
node_features_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Predict")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(train_node_labels_file,
use_cuda=use_cuda)
test_labels = data_loaders.load_labels(test_node_labels_file,
use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
embeddings = None
node_features = None
if embeddings_file is not None:
mlflow.log_param("embeddings", True)
mlflow.log_artifact(embeddings_file, "inputs")
embeddings = data_loaders.load_embeddings(embeddings_file,
use_cuda=use_cuda)
else:
mlflow.log_param("embeddings", False)
if node_features_file is not None:
mlflow.log_param("node_features", True)
mlflow.log_artifact(node_features_file, "inputs")
node_features = data_loaders.load_node_features(
node_features_file, use_cuda)
else:
mlflow.log_param("node_features", False)
if embeddings is not None and node_features is not None:
in_features = torch.cat((embeddings, node_features), dim=1)
elif embeddings is not None:
in_features = embeddings
elif node_features is not None:
in_features = node_features
print(RUN_NAME)
ranks_df = predict.run(labels=labels,
model_class=Bagging,
bagging_model=MLP,
features=in_features,
**params)
u_mlflow.log_dataframe(ranks_df, "predictions", "results")
def bagging_rgcn(
adj_matrix_files,
train_node_labels_file,
test_node_labels_file,
node_features_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Test")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(
train_node_labels_file, use_cuda=use_cuda
)
test_labels = data_loaders.load_labels(test_node_labels_file, use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
train_mask = ~test_labels.byte()
n_nodes = labels.size(0)
if node_features_file is not None:
mlflow.log_param("node_features", True)
mlflow.log_artifact(node_features_file, "inputs")
features = data_loaders.load_node_features(node_features_file, use_cuda)
else:
mlflow.log_param("node_features", False)
features = None
graph = data_loaders.load_graph(
adj_matrix_files,
n_nodes,
add_edge_type=True,
add_node_ids=True,
normalization=NORMALIZATION,
use_cuda=use_cuda,
)
print(RUN_NAME)
ranks_df = test.run(
labels=labels,
train_mask=train_mask,
model_class=Bagging,
bagging_model=RGCN,
features=features,
graph=graph,
n_rels=len(adj_matrix_files),
**params
)
data_savers.save_ranks(ranks_df, n_nodes, RUN_NAME, params)
def bagging_gcn(
adj_matrix_file,
node_features_file,
train_node_labels_file,
test_node_labels_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Predict")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(train_node_labels_file,
use_cuda=use_cuda)
test_labels = data_loaders.load_labels(test_node_labels_file,
use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
n_nodes = labels.size(0)
if node_features_file is not None:
mlflow.log_param("node_features", True)
mlflow.log_artifact(node_features_file, "inputs")
features = data_loaders.load_node_features(node_features_file,
use_cuda)
else:
mlflow.log_param("node_features", False)
features = None
graph = data_loaders.load_graph(
[adj_matrix_file],
n_nodes,
self_loop=SELF_LOOP,
normalization=NORMALIZATION,
use_cuda=use_cuda,
)
print(RUN_NAME)
ranks_df = predict.run(labels=labels,
model_class=Bagging,
bagging_model=GCN,
graph=graph,
features=features,
**params)
u_mlflow.log_dataframe(ranks_df, "predictions", "results")
def bagging_rgcn_embeddings(
adj_matrix_files,
train_node_labels_file,
test_node_labels_file,
embeddings_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Predict")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(
train_node_labels_file, use_cuda=use_cuda
)
test_labels = data_loaders.load_labels(test_node_labels_file, use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
n_nodes = labels.size(0)
embeddings = data_loaders.load_embeddings(embeddings_file, use_cuda=use_cuda)
graph = data_loaders.load_graph(
adj_matrix_files,
n_nodes,
add_edge_type=True,
add_node_ids=True,
normalization=NORMALIZATION,
use_cuda=use_cuda,
)
print(RUN_NAME)
ranks_df = predict.run(
labels=labels,
model_class=Bagging,
bagging_model=RGCN,
features=embeddings,
graph=graph,
n_rels=len(adj_matrix_files),
**params
)
u_mlflow.log_dataframe(ranks_df, "predictions", "results")
def net_prop_with_restart(
run_name,
model_name,
normalization,
adj_matrix_file,
train_node_labels_file,
test_node_labels_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Test")
with mlflow.start_run(run_name=run_name):
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.log_param("model", model_name)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(
train_node_labels_file, use_cuda=use_cuda
)
test_labels = data_loaders.load_labels(test_node_labels_file, use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
train_mask = ~test_labels.byte()
n_nodes = labels.size(0)
adjacency_matrix = data_loaders.load_adj_matrices(
[adj_matrix_file], normalization=normalization, use_cuda=use_cuda
)[0]
print(run_name)
ranks_df = test.run(
labels=labels,
train_mask=train_mask,
model_class=NetPropWithRestart,
adjacency_matrix=adjacency_matrix,
**params
)
data_savers.save_ranks(ranks_df, n_nodes, run_name, params)
def bagging_gcn(adj_matrix_file, node_features_file, node_labels_file,
use_cuda, params, metadata):
mlflow.set_experiment("LOOCV")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
mlflow.log_param("merged_layers", True)
labels = data_loaders.load_labels(node_labels_file, use_cuda=use_cuda)
n_nodes = labels.size(0)
if node_features_file is not None:
mlflow.log_param("node_features", True)
mlflow.log_artifact(node_features_file, "inputs")
features = data_loaders.load_node_features(node_features_file,
use_cuda)
else:
mlflow.log_param("node_features", False)
features = None
graph = data_loaders.load_graph(
[adj_matrix_file],
n_nodes,
self_loop=SELF_LOOP,
normalization=NORMALIZATION,
use_cuda=use_cuda,
)
print(RUN_NAME)
ranks_df = loocv.run(labels=labels,
model_class=Bagging,
bagging_model=GCN,
graph=graph,
features=features,
**params)
data_savers.save_ranks(ranks_df, n_nodes, RUN_NAME, params)
def net_prop_with_restart(
run_name,
model_name,
normalization,
adj_matrix_file,
train_node_labels_file,
test_node_labels_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Predict")
with mlflow.start_run(run_name=run_name):
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.log_param("model", model_name)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(
train_node_labels_file, use_cuda=use_cuda
)
test_labels = data_loaders.load_labels(test_node_labels_file, use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
adjacency_matrix = data_loaders.load_adj_matrices(
[adj_matrix_file], normalization=normalization, use_cuda=use_cuda
)[0]
print(run_name)
ranks_df = predict.run(
labels=labels,
model_class=NetPropWithRestart,
adjacency_matrix=adjacency_matrix,
**params
)
u_mlflow.log_dataframe(ranks_df, "predictions", "results")
def rwr_m(
adj_matrix_files,
train_node_labels_file,
test_node_labels_file,
use_cuda,
params,
metadata,
):
mlflow.set_experiment("Predict")
with mlflow.start_run(run_name=RUN_NAME):
mlflow.log_param("model", MODEL_NAME)
u_mlflow.add_params(**params)
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
train_labels = data_loaders.load_labels(train_node_labels_file,
use_cuda=use_cuda)
test_labels = data_loaders.load_labels(test_node_labels_file,
use_cuda=use_cuda)
labels = (train_labels.byte() | test_labels.byte()).long()
print("Loading adjacency matrices")
adj_matrices = data_loaders.load_adj_matrices(adj_matrix_files,
normalization=None,
use_cuda=use_cuda)
adjacency_matrices = torch.stack(adj_matrices)
print(RUN_NAME)
ranks_df = predict.run(labels=labels,
model_class=RwrM,
adjacency_matrices=adjacency_matrices,
**params)
u_mlflow.log_dataframe(ranks_df, "predictions", "results")