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chore(oi2): pin defaults, docs #460

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24 changes: 12 additions & 12 deletions spras/omicsintegrator2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,11 @@

class DummyMode(CaseInsensitiveEnum):
terminals = 'terminals'
"Connect to all terminals."
others = 'others'
"Connect to all nodes except for terminals."
all = 'all'
"Connect to all nodes in the interactome."

class OmicsIntegrator2Params(BaseModel):
w: float = 5
Expand All @@ -29,22 +32,17 @@ class OmicsIntegrator2Params(BaseModel):
g: float = 3
"Gamma: multiplicative edge penalty from degree of endpoints"

noise: Optional[float] = None
noise: float = 0.1
"Standard Deviation of the gaussian noise added to edges in Noisy Edges Randomizations."

noisy_edges: Optional[int] = None
noisy_edges: int = 0
"An integer specifying how many times to add noise to the given edge values and re-run."

random_terminals: Optional[int] = None
random_terminals: int = 0
"An integer specifying how many times to apply your given prizes to random nodes in the interactome and re-run"

dummy_mode: Optional[DummyMode] = None
"""
Tells the program which nodes in the interactome to connect the dummy node to. (default: terminals)
"terminals" = connect to all terminals
"others" = connect to all nodes except for terminals
"all" = connect to all nodes in the interactome.
"""
dummy_mode: DummyMode = DummyMode.terminals
"Tells the program which nodes in the interactome to connect the dummy node to."

seed: Optional[int] = None
"The random seed to use for this run."
Expand Down Expand Up @@ -106,7 +104,6 @@ def generate_inputs(data: Dataset, filename_map):
edges_df.to_csv(filename_map['edges'], sep='\t', index=False, columns=['Interactor1', 'Interactor2', 'cost'],
header=['protein1', 'protein2', 'cost'])

# TODO add reasonable default values
@staticmethod
def run(inputs, output_file, args=None, container_settings=None):
if not container_settings: container_settings = ProcessedContainerSettings()
Expand Down Expand Up @@ -195,7 +192,10 @@ def parse_output(raw_pathway_file, standardized_pathway_file, params):
df = add_rank_column(df)
df = reinsert_direction_col_undirected(df)
df.columns = ['Node1', 'Node2', 'Rank', "Direction"]
else: # corrupted data
else:
# We get protein1, protein2, and cost if no edges were inside the solution,
# and we get protein1, protein2 if no edges were present in the augmented forest at all:
# both of these outcomes should be treated as an empty network.
Comment on lines +196 to +198
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@agitter agitter Feb 24, 2026

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@ntalluri there were multiple OI2 failure modes you observed. Can you please confirm this comment is accurate with respect to your past tests?

df = pd.DataFrame(columns=['Node1', 'Node2', 'Rank', 'Direction'])

df, has_duplicates = duplicate_edges(df)
Expand Down
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