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c87881a | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
import torch
from bgc_retrieval.artifacts import create_run_directory, sha256_file, write_json_immutable
from bgc_retrieval.baselines import cosine_scores, pfam_jaccard_scores
from bgc_retrieval.checkpoints import load_checkpoint
from bgc_retrieval.config import load_config
from bgc_retrieval.data import BGCEmbeddingDataset
from bgc_retrieval.evaluation import evaluate_retrieval
from bgc_retrieval.model import ModelConfig
from bgc_retrieval.reporting import write_paper_outputs
from bgc_retrieval.residual import (
ResidualGeneWeightingEncoder,
RetrievalScoreCache,
encode_residual_components,
select_validation_weights,
validation_grid,
)
from bgc_retrieval.splits import load_split
from bgc_retrieval.training import choose_device
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="configs/residual_griseus.yaml")
parser.add_argument("--checkpoint", action="append", required=True)
parser.add_argument("--run-id", required=True)
parser.add_argument("--split", default="data/manifests/silver_split.csv")
args = parser.parse_args()
config = load_config(args.config)
values = config.values
if values["scope"]["organism"] != "Streptomyces griseus":
raise ValueError("Residual evaluation is locked to Streptomyces griseus")
split_path = Path(args.split).resolve()
assignments = load_split(split_path)
atlas_path = config.resolve_path("data", "atlas_csv")
embeddings_path = config.resolve_path("data", "embeddings_h5")
model_config = ModelConfig.from_dict(values["model"])
dataset = BGCEmbeddingDataset(
embeddings_path, atlas_path, assignments, model_config.esm_dimension
)
device = choose_device()
raw_embeddings: dict[str, torch.Tensor] | None = None
learned_models: list[dict[str, torch.Tensor]] = []
for checkpoint in args.checkpoint:
model = ResidualGeneWeightingEncoder(model_config)
load_checkpoint(checkpoint, model, split_path)
model.to(device)
raw, learned = encode_residual_components(
model,
dataset,
device,
int(values["training"]["num_workers"]),
)
if raw_embeddings is None:
raw_embeddings = raw
learned_models.append(learned)
if raw_embeddings is None:
raise RuntimeError("No residual checkpoints were loaded")
atlas = pd.read_csv(atlas_path, usecols=["bgc_id", "pfam_ids"])
pfam_sets = {
str(row.bgc_id): set(str(row.pfam_ids).split(";"))
if pd.notna(row.pfam_ids)
else set()
for row in atlas.itertuples(index=False)
}
evaluation = values["evaluation"]
residual = values["residual"]
evaluation_seed = int(evaluation["seed"])
alphas = [float(value) for value in residual["alpha_grid"]]
betas = [float(value) for value in residual["pfam_beta_grid"]]
validation_cache = RetrievalScoreCache(raw_embeddings, learned_models, pfam_sets)
validation = validation_grid(
assignments,
validation_cache,
alphas,
betas,
int(evaluation["reference_size"]),
int(evaluation["query_draws"]),
evaluation_seed,
evaluation["recall_at"],
evaluation["ndcg_at"],
)
metric = str(evaluation["primary_metric"])
residual_alpha, _ = select_validation_weights(validation, metric, "residual_")
hybrid_alpha, hybrid_beta = select_validation_weights(validation, metric, "hybrid_")
if hybrid_beta is None:
raise RuntimeError("Hybrid validation did not select beta")
test_cache = RetrievalScoreCache(raw_embeddings, learned_models, pfam_sets)
def raw_score(candidates: list[str], references: list[str]) -> dict[str, float]:
return cosine_scores(candidates, references, raw_embeddings, "mean")
def learned_score(candidates: list[str], references: list[str]) -> dict[str, float]:
_, learned, _ = test_cache.components(candidates, references)
return learned
def pfam_score(candidates: list[str], references: list[str]) -> dict[str, float]:
return pfam_jaccard_scores(candidates, references, pfam_sets, "max")
methods: dict[str, Any] = {
"pfam_jaccard_max": pfam_score,
"raw_esm_mean": raw_score,
"weighted_gene_esm": learned_score,
f"residual_validation_alpha_{residual_alpha:g}": (
lambda candidates, references: test_cache.residual(
candidates, references, residual_alpha
)
),
f"residual_pfam_validation_a{hybrid_alpha:g}_b{hybrid_beta:g}": (
lambda candidates, references: test_cache.hybrid(
candidates, references, hybrid_alpha, hybrid_beta
)
),
}
test_results = evaluate_retrieval(
assignments,
"test",
methods,
int(evaluation["reference_size"]),
int(evaluation["query_draws"]),
evaluation_seed,
evaluation["recall_at"],
evaluation["ndcg_at"],
)
run_dir = create_run_directory(
config.resolve_path("project", "run_root"),
args.run_id,
)
metadata = {
"schema_version": 1,
"organism_scope": values["scope"]["organism"],
"task_scope": values["scope"]["task"],
"analysis_status": "post_hoc_redesign_pilot",
"selected_residual_alpha": residual_alpha,
"selected_hybrid_alpha": hybrid_alpha,
"selected_pfam_beta": hybrid_beta,
"weights_selected_on": "validation_only",
"checkpoint_sha256": {
str(path): sha256_file(path) for path in args.checkpoint
},
"split_sha256": sha256_file(split_path),
"label_tier": "silver",
"publication_eligible": False,
"scope_warning": (
"The atlas provenance states Streptomyces griseus, but the atlas table "
"does not contain a machine-verifiable species column."
),
}
write_paper_outputs(
run_dir,
test_results,
[metric, "mrr", "map", "ndcg@50", "tie_fraction"],
int(evaluation["bootstrap_samples"]),
float(evaluation["confidence_level"]),
evaluation_seed,
metadata,
)
validation.to_csv(run_dir / "validation_weight_search.csv", index=False)
write_json_immutable(run_dir / "config.json", values)
print(json.dumps(metadata, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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