Datasets:

integMET
/

edge-ML-metabolomics-graph

"""Build an undirected PyG graph from the edge_ML_expected_ge5 DuckDB table."""

import duckdb
import torch
from torch_geometric.data import Data
from torch_geometric.utils import contains_self_loops, is_undirected, to_undirected

from paths import DB_PATH, GRAPH_PATH

DB = DB_PATH
TABLE = "edge_ML_expected_ge5"
OUT = GRAPH_PATH

con = duckdb.connect(DB, read_only=True)

# Map every id appearing in either column to a contiguous 0..N-1 index,
# ordered lexicographically so the mapping is reproducible.
con.execute(f"""
CREATE OR REPLACE TEMP TABLE node_map AS
SELECT id, (row_number() OVER (ORDER BY id) - 1)::BIGINT AS idx
FROM (
    SELECT DISTINCT id FROM (
        SELECT start_id AS id FROM {TABLE}
        UNION ALL
        SELECT end_id   AS id FROM {TABLE}
    )
)
""")

node_ids = [r[0] for r in con.execute("SELECT id FROM node_map ORDER BY idx").fetchall()]
num_nodes = len(node_ids)

pairs = con.execute(f"""
SELECT s.idx AS src, t.idx AS dst,
       e.OddsRatio_log2, e.ChiTestsPValue
FROM {TABLE} e
JOIN node_map s ON s.id = e.start_id
JOIN node_map t ON t.id = e.end_id
""").fetchnumpy()

src = torch.from_numpy(pairs["src"]).to(torch.long)
dst = torch.from_numpy(pairs["dst"]).to(torch.long)
directed_edge_index = torch.stack([src, dst], dim=0)

# float64: ChiTestsPValue reaches ~1.8e-79, below the float32 normal range.
directed_edge_attr = torch.stack(
    [
        torch.from_numpy(pairs["OddsRatio_log2"]).to(torch.float64),
        torch.from_numpy(pairs["ChiTestsPValue"]).to(torch.float64),
    ],
    dim=1,
)

# to_undirected() adds the reverse of every edge and coalesces duplicates,
# so a pair present as both (a,b) and (b,a) in the table yields one edge pair.
# Both attributes are invariant under transposing the 2x2 table -- ad/(bc) and
# the chi-square statistic are unchanged -- so the reverse edge carries the
# same values, and the reduce= for any collapsed duplicate is immaterial.
edge_index, edge_attr = to_undirected(
    directed_edge_index, directed_edge_attr, num_nodes=num_nodes, reduce="mean"
)

data = Data(edge_index=edge_index, edge_attr=edge_attr, num_nodes=num_nodes)
data.node_id = node_ids  # index -> original string id
data.edge_attr_names = ["OddsRatio_log2", "ChiTestsPValue"]

torch.save(data, OUT)

print(f"rows in table          : {directed_edge_index.size(1):,}")
print(f"nodes                  : {num_nodes:,}")
print(f"edge_index columns     : {edge_index.size(1):,}  (= 2 x undirected edges)")
print(f"undirected edges       : {edge_index.size(1) // 2:,}")
print(f"is_undirected          : {is_undirected(edge_index, num_nodes=num_nodes)}")
print(f"contains_self_loops    : {contains_self_loops(edge_index)}")
print(f"isolated nodes         : {num_nodes - torch.unique(edge_index[0]).numel():,}")
print(f"edge_attr              : {tuple(edge_attr.shape)} {edge_attr.dtype} {data.edge_attr_names}")
print(f"  OddsRatio_log2 range : {edge_attr[:, 0].min():.4f} .. {edge_attr[:, 0].max():.4f}")
print(f"  ChiTestsPValue range : {edge_attr[:, 1].min():.6g} .. {edge_attr[:, 1].max():.6g}")
print(f"saved to               : {OUT}")
print(data)

	"""Build an undirected PyG graph from the edge_ML_expected_ge5 DuckDB table."""

	import duckdb
	import torch
	from torch_geometric.data import Data
	from torch_geometric.utils import contains_self_loops, is_undirected, to_undirected

	from paths import DB_PATH, GRAPH_PATH

	DB = DB_PATH
	TABLE = "edge_ML_expected_ge5"
	OUT = GRAPH_PATH

	con = duckdb.connect(DB, read_only=True)

	# Map every id appearing in either column to a contiguous 0..N-1 index,
	# ordered lexicographically so the mapping is reproducible.
	con.execute(f"""
	CREATE OR REPLACE TEMP TABLE node_map AS
	SELECT id, (row_number() OVER (ORDER BY id) - 1)::BIGINT AS idx
	FROM (
	SELECT DISTINCT id FROM (
	SELECT start_id AS id FROM {TABLE}
	UNION ALL
	SELECT end_id AS id FROM {TABLE}
	)
	)
	""")

	node_ids = [r[0] for r in con.execute("SELECT id FROM node_map ORDER BY idx").fetchall()]
	num_nodes = len(node_ids)

	pairs = con.execute(f"""
	SELECT s.idx AS src, t.idx AS dst,
	e.OddsRatio_log2, e.ChiTestsPValue
	FROM {TABLE} e
	JOIN node_map s ON s.id = e.start_id
	JOIN node_map t ON t.id = e.end_id
	""").fetchnumpy()

	src = torch.from_numpy(pairs["src"]).to(torch.long)
	dst = torch.from_numpy(pairs["dst"]).to(torch.long)
	directed_edge_index = torch.stack([src, dst], dim=0)

	# float64: ChiTestsPValue reaches ~1.8e-79, below the float32 normal range.
	directed_edge_attr = torch.stack(
	[
	torch.from_numpy(pairs["OddsRatio_log2"]).to(torch.float64),
	torch.from_numpy(pairs["ChiTestsPValue"]).to(torch.float64),
	],
	dim=1,
	)

	# to_undirected() adds the reverse of every edge and coalesces duplicates,
	# so a pair present as both (a,b) and (b,a) in the table yields one edge pair.
	# Both attributes are invariant under transposing the 2x2 table -- ad/(bc) and
	# the chi-square statistic are unchanged -- so the reverse edge carries the
	# same values, and the reduce= for any collapsed duplicate is immaterial.
	edge_index, edge_attr = to_undirected(
	directed_edge_index, directed_edge_attr, num_nodes=num_nodes, reduce="mean"
	)

	data = Data(edge_index=edge_index, edge_attr=edge_attr, num_nodes=num_nodes)
	data.node_id = node_ids # index -> original string id
	data.edge_attr_names = ["OddsRatio_log2", "ChiTestsPValue"]

	torch.save(data, OUT)

	print(f"rows in table : {directed_edge_index.size(1):,}")
	print(f"nodes : {num_nodes:,}")
	print(f"edge_index columns : {edge_index.size(1):,} (= 2 x undirected edges)")
	print(f"undirected edges : {edge_index.size(1) // 2:,}")
	print(f"is_undirected : {is_undirected(edge_index, num_nodes=num_nodes)}")
	print(f"contains_self_loops : {contains_self_loops(edge_index)}")
	print(f"isolated nodes : {num_nodes - torch.unique(edge_index[0]).numel():,}")
	print(f"edge_attr : {tuple(edge_attr.shape)} {edge_attr.dtype} {data.edge_attr_names}")
	print(f" OddsRatio_log2 range : {edge_attr[:, 0].min():.4f} .. {edge_attr[:, 0].max():.4f}")
	print(f" ChiTestsPValue range : {edge_attr[:, 1].min():.6g} .. {edge_attr[:, 1].max():.6g}")
	print(f"saved to : {OUT}")
	print(data)