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dbc6675 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | """
Experiment result tracking and comparison table generation.
Logs results from all tokenizer × domain × model × mode × split combinations
and generates CSV / LaTeX tables for the paper.
"""
import csv
import json
import os
from collections import defaultdict
class ExperimentTracker:
"""
Track and store experimental results across all configurations.
Results are stored in a nested dict:
results[domain][tokenizer][model][mode][split] = metrics_dict
"""
def __init__(self, output_dir: str = "results"):
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
self.results: dict = defaultdict(
lambda: defaultdict(
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))
)
)
def log_result(
self,
domain: str,
tokenizer: str,
model: str,
mode: str,
split: str,
metrics: dict,
) -> None:
"""
Log a single experimental result.
Args:
domain: e.g. 'blocks', 'gripper'
tokenizer: e.g. 'wl', 'simhash', 'shortest_path', 'graphbpe', 'random'
model: e.g. 'lstm', 'xgboost'
mode: 'state' or 'delta'
split: 'validation', 'test-interpolation', 'test-extrapolation'
metrics: dict with keys like 'solved_rate', 'exec_rate', 'vocab_size'
"""
self.results[domain][tokenizer][model][mode][split] = metrics
print(
f" Logged: {domain}/{tokenizer}/{model}/{mode}/{split} → "
f"{metrics.get('solved_rate', 'N/A')}"
)
def generate_comparison_table(self) -> str:
"""
Generate a comparison table across all methods.
Returns: CSV string
"""
rows = []
header = [
"Domain",
"Tokenizer",
"Model",
"Mode",
"Val Solved",
"Interp Solved",
"Extrap Solved",
"Val Exec",
"Interp Exec",
"Extrap Exec",
"Vocab Size",
]
rows.append(header)
for domain in sorted(self.results.keys()):
for tokenizer in sorted(self.results[domain].keys()):
for model in sorted(self.results[domain][tokenizer].keys()):
for mode in sorted(
self.results[domain][tokenizer][model].keys()
):
splits = self.results[domain][tokenizer][model][mode]
row = [domain, tokenizer, model, mode]
for split in [
"validation",
"test-interpolation",
"test-extrapolation",
]:
m = splits.get(split, {})
row.append(f"{m.get('solved_rate', 0):.2%}")
for split in [
"validation",
"test-interpolation",
"test-extrapolation",
]:
m = splits.get(split, {})
row.append(f"{m.get('exec_rate', 0):.2%}")
# Vocab size (same for all splits)
any_m = next(iter(splits.values()), {})
row.append(str(any_m.get("vocab_size", "N/A")))
rows.append(row)
# Write CSV
csv_path = os.path.join(self.output_dir, "tokenization_comparison.csv")
with open(csv_path, "w", newline="") as f:
writer = csv.writer(f)
writer.writerows(rows)
print(f"Saved comparison table to {csv_path}")
# Generate LaTeX
self._generate_latex_table(rows)
return csv_path
def _generate_latex_table(self, rows: list[list]) -> None:
"""Generate LaTeX table from rows."""
header = rows[0]
data = rows[1:]
n_cols = len(header)
col_spec = "l" * n_cols
lines = [
r"\begin{table}[ht]",
r"\centering",
r"\caption{Comparison of tokenization strategies}",
r"\label{tab:tokenization_comparison}",
f"\\begin{{tabular}}{{{col_spec}}}",
r"\toprule",
" & ".join(header) + r" \\",
r"\midrule",
]
for row in data:
lines.append(" & ".join(str(x) for x in row) + r" \\")
lines.extend(
[
r"\bottomrule",
r"\end{tabular}",
r"\end{table}",
]
)
tex_path = os.path.join(self.output_dir, "tokenization_comparison.tex")
with open(tex_path, "w") as f:
f.write("\n".join(lines))
print(f"Saved LaTeX table to {tex_path}")
def save_results(self, filepath: str | None = None) -> None:
"""Save all results to JSON."""
if filepath is None:
filepath = os.path.join(self.output_dir, "full_results.json")
# Convert defaultdicts to regular dicts for JSON serialization
def to_dict(d):
if isinstance(d, defaultdict):
return {k: to_dict(v) for k, v in d.items()}
return d
with open(filepath, "w") as f:
json.dump(to_dict(self.results), f, indent=2)
print(f"Saved full results to {filepath}")
def load_results(self, filepath: str) -> None:
"""Load results from JSON."""
with open(filepath, "r") as f:
data = json.load(f)
# Populate results
for domain, tok_data in data.items():
for tok, model_data in tok_data.items():
for model, mode_data in model_data.items():
for mode, split_data in mode_data.items():
for split, metrics in split_data.items():
self.results[domain][tok][model][mode][split] = metrics
def generate_vocabulary_stats(self) -> None:
"""Generate vocabulary size stats per tokenizer per domain."""
stats = {}
for domain in self.results:
for tok in self.results[domain]:
for model in self.results[domain][tok]:
for mode in self.results[domain][tok][model]:
for split, m in self.results[domain][tok][model][
mode
].items():
if "vocab_size" in m:
stats.setdefault(tok, {})[domain] = m["vocab_size"]
stats_path = os.path.join(self.output_dir, "vocabulary_stats.json")
with open(stats_path, "w") as f:
json.dump(stats, f, indent=2)
print(f"Saved vocabulary stats to {stats_path}")
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