def run_fit(
seed,
n_components_try_range,
n_components_try_rdpg,
n_block_try_range,
directed,
n_sims_sbm,
):
graph = load_drosophila_left()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
connected = is_fully_connected(graph)
if not connected:
heatmap(graph)
plt.show()
raise ValueError("input graph not connected")
np.random.seed(seed)
columns = columns = [
"n_params_gmm",
"n_params_sbm",
"rss",
"mse",
"score",
"n_components_try",
"n_block_try",
"sim_ind",
]
sbm_master_df = pd.DataFrame(columns=columns)
for i in range(n_sims_sbm):
sbm_df = select_sbm(graph,
n_components_try_range,
n_block_try_range,
directed=directed)
sbm_df["sim_ind"] = i
sbm_master_df = sbm_master_df.append(sbm_df,
ignore_index=True,
sort=True)
rdpg_df = select_rdpg(graph, n_components_try_rdpg, directed)
def metric(assignments, *args):
return -compute_mse_from_assignments(
assignments, graph, directed=directed)
tsbm_master_df = select_sbm(
graph,
n_components_try_range,
n_block_try_range,
directed=directed,
method="bc-metric",
metric=metric,
)
return (sbm_master_df, rdpg_df, tsbm_master_df)
def run_fit(
seed,
n_components_try_range,
n_components_try_rdpg,
n_block_try_range,
directed,
n_sims_sbm,
):
graph = load_drosophila_left()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
connected = is_fully_connected(graph)
if not connected:
heatmap(graph)
plt.show()
raise ValueError("input graph not connected")
np.random.seed(seed)
columns = columns = [
"n_params_gmm",
"n_params_sbm",
"rss",
"mse",
"score",
"n_components_try",
"n_block_try",
"sim_ind",
]
sbm_master_df = pd.DataFrame(columns=columns)
for i in range(n_sims_sbm):
sbm_df = select_sbm(
graph,
n_components_try_range,
n_block_try_range,
directed=directed,
rank="sweep",
)
sbm_df["sim_ind"] = i
sbm_master_df = sbm_master_df.append(sbm_df,
ignore_index=True,
sort=True)
save_obj(sbm_master_df, file_obs, "sbm_master_df")
return 0
def run_fit(seed, n_components_try_range, n_components_try_rdpg,
n_block_try_range, directed):
graph = load_drosophila_left()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
connected = is_fully_connected(graph)
if not connected:
heatmap(graph)
plt.show()
raise ValueError("input graph not connected")
np.random.seed(seed)
sbm_df = select_sbm(graph,
n_components_try_range,
n_block_try_range,
directed=directed)
rdpg_df = select_rdpg(graph, n_components_try_rdpg, directed)
return (sbm_df, rdpg_df)
# %% [markdown] # # Imports from src.graph import MetaGraph from src.data import load_networkx from graspy.utils import is_fully_connected # %% [markdown] # # Constants BRAIN_VERSION = "2019-12-18" GRAPH_TYPE = "Gad" # %% [markdown] # # Loads g = load_networkx(GRAPH_TYPE, BRAIN_VERSION) mg = MetaGraph(g) # %% [markdown] # # Show that getting LCC works print(is_fully_connected(mg.g)) print(mg.n_verts) print(mg.meta.shape) print() mg = mg.make_lcc() print(is_fully_connected(mg.g)) print(mg.n_verts) print(mg.meta.shape) # %% [markdown] # #
#%%
graph_types = ["Gaan", "Gadn", "Gdan", "Gddn"]
n_components = 4
# load the right side
use_graph = "Gn"
hemisphere = "right"
print(f"Using graph {use_graph}")
Gn = load_graph(use_graph)
Gn = get_subgraph(Gn, "Hemisphere", hemisphere)
n_verts_original = len(Gn)
print(f"Selected {hemisphere} side")
print("Checking if graph is fully connected")
print(is_fully_connected(Gn))
Gn, inds = get_lcc(Gn, return_inds=True)
num_removed = n_verts_original - len(Gn)
print(f"Removed {num_removed} node")
# select metadata
classes = meta_to_array(Gn, "Class")
simple_classes = to_simple_class(classes)
names = meta_to_array(Gn, "Name")
ids = meta_to_array(Gn, "ID")
# load adjacency and preprocess
Gn_adj = import_graph(Gn)
Gn_adj = remove_loops(Gn_adj)
# Gn = pass_to_ranks(Gn)
Gn_adj = binarize(Gn_adj)