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| """CLI entry for DE/DIR pipeline stages.""" |
|
|
| import argparse |
| import sys |
|
|
| from stages.prepare import process_cell_line |
| from stages.retrieve import build_retrieval_results |
| from stages.prompt import generate_prompts |
| from stages.infer import run_inference |
| from stages.infer_api import run_inference_api |
| from stages.single_case.prompt import generate_single_case_prompt |
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|
| def _add_prepare_args(p: argparse.ArgumentParser) -> None: |
| p.add_argument("--h5ad", required=True, help="Input .h5ad file") |
| p.add_argument("--out-dir", required=True, help="Output directory for CSVs") |
| p.add_argument("--cell-line", required=True, help="Cell line name for output file prefix") |
| p.add_argument("--perturbation-col", default="drug", help="AnnData obs column for perturbation") |
| p.add_argument("--control-value", default="DMSO_TF", help="Control group name") |
| p.add_argument("--train-fraction", type=float, default=0.3, help="Train split fraction") |
| p.add_argument("--seed", type=int, default=42) |
| p.add_argument("--fdr", type=float, default=0.05) |
| p.add_argument("--lfc", type=float, default=0.25) |
| p.add_argument("--pval-neg", type=float, default=0.1) |
| p.add_argument("--n-neg", type=int, default=200) |
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|
| def _add_retrieve_args(p: argparse.ArgumentParser) -> None: |
| p.add_argument("--data-csv", required=True, help="Input CSV with pert/gene/label/split") |
| p.add_argument("--drug-sim", required=True, help="Drug similarity JSON") |
| p.add_argument("--gene-sim", required=True, help="Gene similarity JSON (required)") |
| p.add_argument("--out", required=True, help="Output retrieval JSON") |
| p.add_argument("--budget", type=int, default=10) |
| p.add_argument("--seed", type=int, default=0) |
| p.add_argument("--max-cases", type=int, default=None) |
| p.add_argument("--case-split", default="test", choices=["train", "test"], help="Which split to generate retrieval for") |
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|
|
| def _add_prompt_args(p: argparse.ArgumentParser) -> None: |
| p.add_argument("--retrieval", required=True, help="Retrieval JSON from previous stage") |
| p.add_argument("--drug-desc", required=True, help="Drug description JSON") |
| p.add_argument("--gene-desc", required=True, help="Gene description JSON") |
| p.add_argument("--template", default=None, help="Prompt template file (optional)") |
| p.add_argument("--out", required=True, help="Output prompts text file") |
| p.add_argument("--cell-line-idx", type=int, default=None) |
| p.add_argument("--max-cases", type=int, default=None) |
| p.add_argument("--seed", type=int, default=42) |
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|
|
| def _add_infer_args(p: argparse.ArgumentParser) -> None: |
| p.add_argument("--model", required=True, help="HF model path or name") |
| p.add_argument("--prompts", required=True, help="Prompt text file") |
| p.add_argument("--out", required=True, help="Output text file") |
| p.add_argument("--batch-size", type=int, default=4) |
| p.add_argument("--max-new-tokens", type=int, default=512) |
| p.add_argument("--temperature", type=float, default=0.6) |
| p.add_argument("--top-p", type=float, default=0.9) |
| p.add_argument("--dtype", default="bfloat16", choices=["bfloat16", "float16", "float32"]) |
| p.add_argument("--device-map", default="auto") |
| p.add_argument("--chat-template", default=None, help="Optional chat template file to override tokenizer.chat_template") |
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|
|
| def _add_infer_api_args(p: argparse.ArgumentParser) -> None: |
| p.add_argument("--api-url", required=True, help="API endpoint URL") |
| p.add_argument("--api-model", required=True, help="API model name") |
| p.add_argument("--api-key", default=None, help="API key (or set LLM_DRUG_API_KEY)") |
| p.add_argument("--prompts", required=True, help="Prompt text file") |
| p.add_argument("--out", required=True, help="Output text file") |
| p.add_argument("--max-new-tokens", type=int, default=512) |
| p.add_argument("--temperature", type=float, default=0.6) |
| p.add_argument("--top-p", type=float, default=0.9) |
| p.add_argument("--timeout", type=int, default=60) |
| p.add_argument("--sleep-secs", type=float, default=0.0) |
|
|
|
|
| def build_parser() -> argparse.ArgumentParser: |
| parser = argparse.ArgumentParser(description="LLM Drug pipeline (DE/DIR) CLI") |
| task_sub = parser.add_subparsers(dest="task", required=True) |
|
|
| for task_name in ("de", "dir"): |
| task_parser = task_sub.add_parser(task_name) |
| stage_sub = task_parser.add_subparsers(dest="stage", required=True) |
|
|
| prepare_p = stage_sub.add_parser("prepare") |
| _add_prepare_args(prepare_p) |
|
|
| retrieve_p = stage_sub.add_parser("retrieve") |
| _add_retrieve_args(retrieve_p) |
|
|
| prompt_p = stage_sub.add_parser("prompt") |
| _add_prompt_args(prompt_p) |
|
|
| infer_p = stage_sub.add_parser("infer") |
| _add_infer_args(infer_p) |
|
|
| infer_api_p = stage_sub.add_parser("infer-api") |
| _add_infer_api_args(infer_api_p) |
|
|
| single_p = task_sub.add_parser("single") |
| single_stage = single_p.add_subparsers(dest="stage", required=True) |
| single_prompt = single_stage.add_parser("prompt") |
| single_prompt.add_argument("--pert", required=True, help="Drug/perturbation name") |
| single_prompt.add_argument("--gene", required=True, help="Gene name") |
| single_prompt.add_argument("--cell-line", required=True, help="Cell line name (must exist in template)") |
| single_prompt.add_argument("--mode", default="de", choices=["de", "dir"], help="Prompt type") |
| single_prompt.add_argument("--data-csv", required=True, help="CSV with pert/gene/label/split for retrieval") |
| single_prompt.add_argument("--drug-desc", required=True, help="Drug description JSON") |
| single_prompt.add_argument("--gene-desc", required=True, help="Gene description JSON") |
| single_prompt.add_argument("--drug-sim", required=True, help="Drug similarity JSON") |
| single_prompt.add_argument("--gene-sim", required=True, help="Gene similarity JSON") |
| single_prompt.add_argument("--template", default=None, help="Prompt template file (optional)") |
| single_prompt.add_argument("--out", required=True, help="Output prompt text file") |
| single_prompt.add_argument("--max-candidates", type=int, default=10) |
| single_prompt.add_argument("--budget", type=int, default=10) |
| single_prompt.add_argument("--case-split", default="train", choices=["train", "test", "all"]) |
| single_prompt.add_argument("--seed", type=int, default=42) |
| single_prompt.add_argument("--llm-api-url", default=None, help="LLM API endpoint for fallback similarity") |
| single_prompt.add_argument("--llm-api-model", default=None, help="LLM model name for fallback similarity") |
| single_prompt.add_argument("--llm-api-key", default=None, help="API key (or set LLM_DRUG_API_KEY)") |
| single_prompt.add_argument("--llm-candidate-pool", type=int, default=80) |
| single_prompt.add_argument("--llm-timeout", type=int, default=60) |
| return parser |
|
|
|
|
| def main(argv: list[str]) -> int: |
| parser = build_parser() |
| args = parser.parse_args(argv) |
|
|
| if args.task in ("de", "dir") and args.stage == "prepare": |
| process_cell_line( |
| adata_path=args.h5ad, |
| output_dir=args.out_dir, |
| cell_line_name=args.cell_line, |
| perturbation_col=args.perturbation_col, |
| control_value=args.control_value, |
| train_fraction=args.train_fraction, |
| seed=args.seed, |
| fdr=args.fdr, |
| lfc=args.lfc, |
| pval_neg=args.pval_neg, |
| n_neg=args.n_neg, |
| ) |
| return 0 |
|
|
| if args.task in ("de", "dir") and args.stage == "retrieve": |
| build_retrieval_results( |
| data_csv=args.data_csv, |
| drug_sim_json=args.drug_sim, |
| out_json=args.out, |
| gene_sim_json=args.gene_sim, |
| budget=args.budget, |
| seed=args.seed, |
| max_cases=args.max_cases, |
| case_split=args.case_split, |
| ) |
| return 0 |
|
|
| if args.task in ("de", "dir") and args.stage == "prompt": |
| generate_prompts( |
| task=args.task, |
| retrieval_json=args.retrieval, |
| drug_desc_json=args.drug_desc, |
| gene_desc_json=args.gene_desc, |
| template_file=args.template, |
| output_file=args.out, |
| cell_line_idx=args.cell_line_idx, |
| max_cases=args.max_cases, |
| seed=args.seed, |
| ) |
| return 0 |
|
|
| if args.task in ("de", "dir") and args.stage == "infer": |
| run_inference( |
| model_name=args.model, |
| prompts_file=args.prompts, |
| output_file=args.out, |
| batch_size=args.batch_size, |
| max_new_tokens=args.max_new_tokens, |
| temperature=args.temperature, |
| top_p=args.top_p, |
| dtype=args.dtype, |
| device_map=args.device_map, |
| chat_template_path=args.chat_template, |
| ) |
| return 0 |
|
|
| if args.task in ("de", "dir") and args.stage == "infer-api": |
| run_inference_api( |
| api_url=args.api_url, |
| api_model=args.api_model, |
| api_key=args.api_key, |
| prompts_file=args.prompts, |
| output_file=args.out, |
| max_new_tokens=args.max_new_tokens, |
| temperature=args.temperature, |
| top_p=args.top_p, |
| timeout=args.timeout, |
| sleep_secs=args.sleep_secs, |
| ) |
| return 0 |
|
|
| if args.task == "single" and args.stage == "prompt": |
| case_split = "" if args.case_split == "all" else args.case_split |
| generate_single_case_prompt( |
| task=args.mode, |
| pert=args.pert, |
| gene=args.gene, |
| cell_line=args.cell_line, |
| data_csv=args.data_csv, |
| drug_desc_json=args.drug_desc, |
| gene_desc_json=args.gene_desc, |
| drug_sim_json=args.drug_sim, |
| gene_sim_json=args.gene_sim, |
| template_file=args.template, |
| output_file=args.out, |
| max_candidates=args.max_candidates, |
| budget=args.budget, |
| case_split=case_split, |
| seed=args.seed, |
| llm_api_url=args.llm_api_url, |
| llm_api_model=args.llm_api_model, |
| llm_api_key=args.llm_api_key, |
| llm_candidate_pool=args.llm_candidate_pool, |
| llm_timeout=args.llm_timeout, |
| ) |
| return 0 |
|
|
| parser.error("Unknown stage") |
| return 2 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main(sys.argv[1:])) |
|
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