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12fea4a | 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 | #!/usr/bin/env python3
"""
Run evaluate_val-style evaluation multiple times and report per-run metric lists.
Stochastic steps (sampling from the test set, flow time / noise, generation) can make
scalar metrics differ between runs. This script calls run_evaluate_val() repeatedly and
prints every value plus simple mean / std / min / max summaries.
Seeding:
--base_seed unset: same as legacy evaluate_val (no explicit RNG seeding; variation across runs).
--base_seed K: run i uses seed K + i before each replicate (reproducible multi-run bracket).
"""
import argparse
import json
import numpy as np
from cas9.evaluate_val import get_evaluate_val_argument_parser, run_evaluate_val, print_evaluation_results
def _get_nested(d, *keys):
for k in keys:
d = d[k]
return d
def _stats(vals):
arr = np.array([v for v in vals if v is not None], dtype=float)
if arr.size == 0:
return None
return {
"mean": float(arr.mean()),
"std": float(arr.std()),
"min": float(arr.min()),
"max": float(arr.max()),
}
def _fmt_list(vals, prec=4):
parts = []
for v in vals:
if v is None:
parts.append("None")
elif isinstance(v, float):
parts.append(f"{v:.{prec}f}")
elif isinstance(v, int):
parts.append(str(v))
else:
parts.append(str(v))
return "[" + ", ".join(parts) + "]"
def _print_scalar_block(title, rows, all_metrics):
print(f"\n{title}")
print("-" * len(title))
for label, path in rows:
vals = [_get_nested(m, *path) for m in all_metrics]
print(f" {label}")
print(f" values: {_fmt_list(vals)}")
st = _stats(vals)
if st:
print(f" mean={st['mean']:.6f} std={st['std']:.6f} min={st['min']:.6f} max={st['max']:.6f}")
else:
print(" (no numeric values)")
def print_multi_run_summary(all_metrics, args):
n = len(all_metrics)
print("\n" + "=" * 70)
print(f"MULTI-RUN SUMMARY ({n} runs)")
print("=" * 70)
seeds = [m["run_seed"] for m in all_metrics]
print(f"Per-run seeds: {seeds}")
loss_rows = [
("Train avg (full train; None if not computed)", ("loss", "train_avg")),
("Decoded avg (full test/val)", ("loss", "decoded_full_avg")),
("Decoded avg (sampled)", ("loss", "decoded_sampled_avg")),
("Generated avg", ("loss", "generated_avg")),
]
_print_scalar_block("SEQUENCE LOSS (val_unweighted_total_loss)", loss_rows, all_metrics)
if all_metrics[0]["cas9"] is not None:
cas9_rows = [
("Decoded validity rate", ("cas9", "decoded_validity_rate")),
("Decoded avg Cas9 score", ("cas9", "decoded_avg_score")),
("Generated validity rate", ("cas9", "generated_validity_rate")),
("Generated avg Cas9 score", ("cas9", "generated_avg_score")),
]
_print_scalar_block("CAS9 SCORES", cas9_rows, all_metrics)
div_dec = [
("unique_count", ("diversity_decoded", "unique_count")),
("uniqueness_ratio", ("diversity_decoded", "uniqueness_ratio")),
("kmer_diversity", ("diversity_decoded", "kmer_diversity")),
("kmer_avg_similarity", ("diversity_decoded", "kmer_avg_similarity")),
("levenshtein_diversity", ("diversity_decoded", "levenshtein_diversity")),
("levenshtein_avg_similarity", ("diversity_decoded", "levenshtein_avg_similarity")),
]
_print_scalar_block("DIVERSITY — decoded (sampled)", div_dec, all_metrics)
div_gen = [
("unique_count", ("diversity_generated", "unique_count")),
("uniqueness_ratio", ("diversity_generated", "uniqueness_ratio")),
("kmer_diversity", ("diversity_generated", "kmer_diversity")),
("kmer_avg_similarity", ("diversity_generated", "kmer_avg_similarity")),
("levenshtein_diversity", ("diversity_generated", "levenshtein_diversity")),
("levenshtein_avg_similarity", ("diversity_generated", "levenshtein_avg_similarity")),
]
_print_scalar_block("DIVERSITY — generated", div_gen, all_metrics)
if any(m.get("plddt") is not None for m in all_metrics):
p_rows = [
("Decoded mean pLDDT", ("plddt", "decoded_mean")),
("Decoded std pLDDT", ("plddt", "decoded_std")),
("Generated mean pLDDT", ("plddt", "generated_mean")),
("Generated std pLDDT", ("plddt", "generated_std")),
]
_print_scalar_block("PLDDT", p_rows, all_metrics)
print("=" * 70)
def main():
parser = get_evaluate_val_argument_parser()
parser.add_argument(
"--n_runs",
type=int,
default=3,
help="Number of independent evaluate_val runs (default: 3)",
)
parser.add_argument(
"--base_seed",
type=int,
default=None,
help="If set, run i uses RNG seed (base_seed + i). If unset, do not seed (like default evaluate_val).",
)
parser.add_argument(
"--save_fasta",
action="store_true",
help="Write generated_sequences_run{i}.fasta per run under output_dir",
)
parser.add_argument(
"--print_each_run",
action="store_true",
help="Print the full EVALUATION RESULTS block after every replicate",
)
parser.add_argument(
"--json_out",
type=str,
default=None,
help="Optional path to write a JSON list of per-run metric dicts",
)
args = parser.parse_args()
if args.n_runs < 1:
raise ValueError("--n_runs must be >= 1")
all_metrics = []
for i in range(args.n_runs):
print("\n" + "#" * 70)
print(f"RUN {i + 1} / {args.n_runs}")
print("#" * 70)
run_seed = (args.base_seed + i) if args.base_seed is not None else None
if run_seed is not None:
print(f"(run_seed={run_seed})")
fasta_tag = f"run{i + 1}" if args.save_fasta else None
metrics = run_evaluate_val(
args,
run_seed=run_seed,
save_fasta=args.save_fasta,
fasta_tag=fasta_tag,
)
all_metrics.append(metrics)
if args.print_each_run:
print_evaluation_results(metrics, args)
print_multi_run_summary(all_metrics, args)
if args.json_out:
with open(args.json_out, "w") as f:
json.dump(all_metrics, f, indent=2)
print(f"\nWrote per-run metrics to {args.json_out}")
if __name__ == "__main__":
main()
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