def direct_neighbors(adj_matrix_file, node_labels_file, use_cuda, metadata):
mlflow.set_experiment("LOOCV")
with mlflow.start_run(run_name=RUN_NAME):
u_mlflow.add_metadata(metadata)
mlflow.set_tag("use_cuda", use_cuda)
mlflow.log_param("model", MODEL_NAME)
mlflow.log_param("merged_layers", True)
labels = data_loaders.load_labels(node_labels_file, use_cuda=use_cuda)
n_nodes = labels.size(0)
graph = data_loaders.load_graph(
[adj_matrix_file],
n_nodes,
self_loop=False,
normalization=None,
use_cuda=use_cuda,
)
print(RUN_NAME)
ranks_df = loocv.run(labels=labels,
model_class=DirectNeighbors,
graph=graph)
data_savers.save_ranks(ranks_df, n_nodes, RUN_NAME)
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_rgcn(adj_matrix_files, node_labels_file, node_features_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", len(adj_matrix_files) == 1)
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_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=features,
n_rels=len(adj_matrix_files),
**params)
data_savers.save_ranks(ranks_df, n_nodes, RUN_NAME, params)