File size: 6,491 Bytes
f28d994 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | """Data loading and audit helpers for figures_v2."""
from __future__ import annotations
import csv
import re
from pathlib import Path
import numpy as np
import pandas as pd
def path_exists(path: Path) -> bool:
"""Explicit existence helper used by the audit and loaders."""
return Path(path).exists()
def read_table(path: Path, **kwargs) -> pd.DataFrame | None:
if not path_exists(path):
return None
return pd.read_csv(path, **kwargs)
def read_edges(path: Path) -> pd.DataFrame | None:
if not path_exists(path):
return None
return pd.read_csv(path, sep=r"\s+", header=None, engine="python")
def load_dataset_degrees(root: Path) -> dict[str, np.ndarray] | None:
dd = root / "data_and_docs"
co = read_edges(dd / "author_file_ann.txt")
cite = read_edges(dd / "paper_file_ann.txt")
read = read_edges(dd / "bipartite_train_ann.txt")
if co is None or cite is None or read is None:
return None
co_deg = np.bincount(np.concatenate([co[0].to_numpy(), co[1].to_numpy()]), minlength=6611)
citation_in = np.bincount(cite[1].to_numpy(), minlength=79937)
paper_read = np.bincount(read[1].to_numpy(), minlength=79937)
author_read = np.bincount(read[0].to_numpy(), minlength=6611)
return {
"coauthor_degree": co_deg,
"citation_indegree": citation_in,
"paper_read_degree": paper_read,
"author_read_degree": author_read,
}
def load_manual_metrics(root: Path) -> pd.DataFrame:
path = root / "figures_v2" / "data" / "manual_metrics.csv"
if path_exists(path):
return pd.read_csv(path)
rows = [
(0, "gnn_baseline", "GNN\nbaseline", 0.8850, np.nan, "reports"),
(1, "lightgcn_ensemble", "LightGCN\nensemble", 0.938576, 0.93044, "validation_runs/dynamic_summary.csv; README"),
(2, "graph_stack", "+ graph/meta-path\nstack", 0.95599, 0.95760, "validation_runs/stack_threshold_summary.csv; README"),
(3, "content_bpr", "+ content\n+ BPR-MF", 0.95930, 0.95996, "reports; README"),
(4, "deepwalk_node2vec", "+ DeepWalk\n/ Node2Vec", 0.96213, 0.96252, "error_group_calibration anchor; README"),
(5, "rw7", "+ 7 RW\nblocks", 0.964947, np.nan, "high_order validation_summary.csv"),
(6, "highorder_directed", "+ high-order\ncitation", 0.966874, 0.96626, "high_order validation_summary.csv; README"),
]
return pd.DataFrame(rows, columns=["order", "stage", "label", "val_f1", "public_f1", "source"])
def high_order_summary(root: Path) -> pd.DataFrame:
path = root / "validation_runs" / "dynamic_seed202" / "high_order_graph_stack" / "validation_summary.csv"
df = read_table(path)
if df is not None:
return df
return pd.DataFrame(
[
("base_highorder", 0.9642697338013148, 0.4554775357246399, 0.994052111749616, 0.9653815084086069, 0.9631605169836758, 108),
("rich_rw7", 0.9649474248055991, 0.49044686555862427, 0.9945549026665483, 0.9663869251458722, 0.9635122065590106, 190),
("rich_rw7_highorder", 0.9665557233547776, 0.46933943033218384, 0.9948903494937357, 0.9671087377501321, 0.9660033410509656, 214),
("rich_rw7_highorder_directed", 0.966873736337297, 0.46173080801963806, 0.9949182985645343, 0.9667037764040665, 0.9670437560446645, 259),
],
columns=["stage", "validation_f1", "threshold", "auc", "precision", "recall", "n_features"],
)
def rw_ensemble_metrics(root: Path) -> tuple[list[int], list[float], list[str]]:
rw = root / "validation_runs" / "dynamic_seed202" / "randomwalk_systematic"
sources: list[str] = []
single_df = read_table(rw / "small_ablation_table.csv")
e5_df = read_table(rw / "ensemble_5_ablation.csv")
e7_df = read_table(rw / "ensemble_7_ablation.csv")
if single_df is None or e5_df is None or e7_df is None:
return [1, 5, 7], [0.96310, 0.96393, 0.96492], ["reported fallback values"]
sources = [str(rw / "small_ablation_table.csv"), str(rw / "ensemble_5_ablation.csv"), str(rw / "ensemble_7_ablation.csv")]
return [1, 5, 7], [
float(single_df["validation_F1"].max()),
float(e5_df["validation_F1"].iloc[0]),
float(e7_df["validation_F1"].iloc[0]),
], sources
def load_npy(path: Path) -> np.ndarray | None:
if not path_exists(path):
return None
return np.load(path)
def pr_curve(y_true: np.ndarray, scores: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
order = np.argsort(-scores)
y = y_true[order].astype(float)
tp = np.cumsum(y)
fp = np.cumsum(1.0 - y)
precision = tp / np.maximum(tp + fp, 1.0)
recall = tp / max(float(y.sum()), 1.0)
precision = np.r_[1.0, precision]
recall = np.r_[0.0, recall]
ap = float(np.sum((recall[1:] - recall[:-1]) * precision[1:]))
return recall, precision, ap
def numeric_bucket_key(bucket: str) -> float:
toks = re.findall(r"-?inf|-?\d+(?:\.\d+)?(?:e-?\d+)?", str(bucket))
if not toks:
return 0.0
val = toks[0]
if val == "-inf":
return -1e18
if val == "inf":
return 1e18
return float(val)
def inventory_files(root: Path, out_csv: Path) -> pd.DataFrame:
watched = [
"README.md",
"CLAUDE.md",
"WORKSPACE_STATUS.md",
"reports/final_report.md",
"reports/exploration_summary.md",
"reports/preliminary_report.md",
"notes/experiment_history.md",
"data_and_docs",
"validation_runs",
"cached_scores",
"submissions",
"code",
"figures_paper",
]
rows = []
for rel in watched:
p = root / rel
if not path_exists(p):
rows.append({"path": rel, "exists": False, "kind": "missing", "size_bytes": ""})
continue
if p.is_file():
rows.append({"path": rel, "exists": True, "kind": "file", "size_bytes": p.stat().st_size})
else:
for child in p.rglob("*"):
if child.is_file():
rows.append(
{
"path": child.relative_to(root).as_posix(),
"exists": True,
"kind": "file",
"size_bytes": child.stat().st_size,
}
)
df = pd.DataFrame(rows)
out_csv.parent.mkdir(parents=True, exist_ok=True)
df.to_csv(out_csv, index=False, quoting=csv.QUOTE_MINIMAL)
return df
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