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"source_sha256_matches": true + } + ] +} diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0a422d40f1d22f7771ef93ea9ef55930cbc3035b --- /dev/null +++ b/README.md @@ -0,0 +1,66 @@ +# 0.5B Pre-trained and F3-refined Model Delivery Package + +This is a compact, traceable delivery package containing the two previously reported +0.5B-class model checkpoints, their training datasets, data-preparation code, training +scripts, and evaluation evidence. + +## Delivered Models + +| Model | Path | Initialisation | Training data | Reported result | +|---|---|---|---|---| +| Stage-1 CPT | `models/pretrained/stage1_cpt/` | `Qwen/Qwen2.5-Coder-0.5B` | `data/pretrain/code_messages_pre_train_filtered_stage1.jsonl` (902,115 records) | Continued-pre-training deliverable | +| F3-refined SFT | `models/sft/sft_f3_refined_instruct/` | `Qwen/Qwen2.5-Coder-0.5B-Instruct` | `data/f3/output/scicode_sft_swift_F3_clean_v2.jsonl` (207,009 records) | pass@1 9.1%; pass@5 13.0% | + +Both directories are standard Hugging Face checkpoints. Model weights, tokenisers, +`args.json`, and `trainer_state.json` are included. Optimiser, scheduler, and random-number +generator states (approximately 2 GB per model) are excluded because this package is +intended for model delivery and inference rather than mid-run training resumption. + +## Data and F3 Filtering Chain + +`data/f3/` contains the input, principal intermediate artefacts, score output, and final +training dataset used for the reported F3-refined model: + +```text +473,465 00 input: scicode_sft_swift_F3_clean.jsonl +437,671 01 rule-based AST, structure, duplicate, and placeholder filtering +366,672 02 per-project caps and stratified sampling +362,367 03 assistant-response deduplication within projects +207,009 04 quality score >= 3: scicode_sft_swift_F3_clean_v2.jsonl +``` + +The corresponding scripts are in `code/f3_filtering/`. Stages 1–3 are local deterministic +processing stages. Stage 4 uses an external `Qwen3-Coder-30B-A3B-Instruct` scoring model; +the historical script retains its original absolute path, which must be replaced when the +stage is run in another environment. The saved scores and final dataset are included. + + +The continued-pre-training dataset and its preparation code are in `data/pretrain/` and +`code/pretrain_data_prep/`, respectively. + +## Training Scripts + +- `training_scripts/pretrain/stage1_short.sh` +- `training_scripts/sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh` + +These are the original historical launch scripts. They intentionally retain the GPU +selection, cache locations, ports, and absolute output paths used at the time. Review and +replace those paths before rerunning them on another system. The checkpoint `args.json` +files are the primary record of the effective training parameters. + +## Evaluation Evidence and Limitations + +`evidence/eval/f3_refined_9p1_summary.json` records the original evaluation: 200 tasks, +five samples per task, 1,000 attempts, pass@1 of 9.1%, and pass@5 of 13.0%. This result is +from the historical Runnable alignment benchmark and must not be conflated with the later +repository-level Core/Hold benchmark. + +The two delivered training runs do not have exact, run-specific `pip freeze` snapshots. +Training data and effective parameters are traceable, but bit-for-bit retraining is not +claimed. See `environment/KNOWN_LIMITATIONS.md` for details. + +## Integrity and Storage + +- `tools/build_manifest.py`: regenerates the manifest and checksums. +- `tools/smoke_test.py`: loads each checkpoint on CPU and checks finite, non-degenerate logits. + diff --git a/SHA256SUMS b/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..af6c2143dd4b0d3d281cdd607328a11a07d399af --- /dev/null +++ b/SHA256SUMS @@ -0,0 +1,59 @@ +c4dfa67956deb6eec305523de03d380a923cd70f9522d1e740846f57bd264fb8 MANIFEST.json +b9a66fd10a508e09e92335bf4b1f69124ec142805a04c70b4d9ba725d14c182f README.md +570dfb0cd8b5991714d7c52b2d8e65ebd22a874e9d85877ab8ad22162f778ed1 code/f3_filtering/build_F3_v3.py +5107add2818270eb0309219d5b73aedc43153a1da97165750b22dae80e7f047e code/f3_filtering/build_F3_v4.py +c93313faa9e23019bc87f6805d0fe1c83ab8fe4812cae1209f16d08f57e9f1a3 code/f3_filtering/clean_F3_stage1.py +2b3fa3632262f4dfe49b6a20899f0d7551523c040d904d70734f2f3b6312add2 code/f3_filtering/clean_F3_stage2.py +13b402f6fbce073507a2b51099e6b27ae266e238597db51566025b584fa35d19 code/f3_filtering/clean_F3_stage3.py +f6c72da037f62154fa02ef3a7187ac80debbd82bb2fdaa2f82166311983aded3 code/f3_filtering/clean_F3_stage4.py +b41cd23f44de05d1a79385a57482af2f166ddfd73788c1fe5d6e07568922e270 code/f3_filtering/clean_F3_stage4_calibrate.py +340895ba5021dac1d8e10a47f8d6cc9899c5c7a6cefa1815760e42a7660583b2 code/f3_filtering/reports/F3_stage1_report.json +f934470c04fbf3c4da8b94d7a0d582da454a8ffa0010f2ad49b851f5ad88e094 code/f3_filtering/reports/F3_stage2_report.json +197f96b66a39cb157d4b879ad7d04f19d381348625aba7fe1a8b5d5704da6012 code/f3_filtering/reports/F3_stage3_report.json +94427b4c24d0924f3c48837d49931273bd08a032bdc1c69167a8a4b6b7eef2ee code/f3_filtering/reports/F3_stage4_calibration.json +db2a7b2e19ea296bf1391a7c81f6f8313d5dee189f6410018deedce41a8fdbdc code/f3_filtering/reports/F3_stage4_report.json +8c358341e1934f6356b6268c84d37c7f20cef0733aacd0f362712847327adf8d 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models/sft/sft_f3_refined_instruct/config.json +87c51d61bd70a204993da36384296ccfeb56d46ee2cc7247929f917d0d8118f7 models/sft/sft_f3_refined_instruct/generation_config.json +05bca1c3ba20caad5a14ef0f450b6788aff7c9594bf90fe6deb5ecd2c5561847 models/sft/sft_f3_refined_instruct/model.safetensors +3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8 models/sft/sft_f3_refined_instruct/tokenizer.json +4d8ad3fe9729e37338fae50bac94ff03cb915071eb886b937077114e874dfa06 models/sft/sft_f3_refined_instruct/tokenizer_config.json +7259447195d9af3cad318bed168eb341c6ac2d171ccaddab7e195e173d033642 models/sft/sft_f3_refined_instruct/trainer_state.json +2be5dce6f73513de2b0ef0e5b41c9f3b5691dba0e8867193dae6d19f18518da6 tools/build_manifest.py +8219ffc3a307516000a0c9612ecade9d0c274d85347f23757b8b30f240f92740 tools/smoke_test.py +259bf953bb9254443f6a164db05f02c1dfdc54c3ec6ee57502fae910b0eb2165 training_scripts/README.md +f25a669388095d705cc190e3f0351aaf1d310759bd1c7f3f452b3601364a4005 training_scripts/pretrain/stage1_short.sh +8feeae24b837c7ebfa77c41057d4286eec4eae5d66c870c32a274f773ae76d39 training_scripts/sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh diff --git a/code/f3_filtering/build_F3_v3.py b/code/f3_filtering/build_F3_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc04029a7fb8946e4674ebd6e5af5dd47dc13d1 --- /dev/null +++ b/code/f3_filtering/build_F3_v3.py @@ -0,0 +1,184 @@ +"""Build F3_clean_v3 from F3_clean_v2 by removing residual repetition / dup problems. + +Rules (applied per sample, all must pass to keep): + R1: assistant length <= 8000 chars + R2: no line >=1 char (stripped) repeats >=10 times inside assistant + R3: no 5-gram (token-level) repeats >=20 times inside assistant + R4: cross-corpus exact-content dedup of assistant (keep first occurrence) + +Streaming, two-pass for R4 (first pass: build hash counter; second pass: keep +first occurrence of each hash). + +Output: scicode_sft_swift_F3_clean_v3.jsonl + report. +""" +from __future__ import annotations + +import collections +import hashlib +import json +import re +import time +from pathlib import Path + +SRC = Path("/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v2.jsonl") +DST = Path("/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl") +REPORT = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_v3_report.json") + +MAX_ASST_CHARS = 8000 +LINE_REPEAT_THRESH = 10 # any stripped line repeating >= 10 times +NGRAM_N = 5 +NGRAM_THRESH = 20 # 5-gram repeating >= 20 times +TOKEN_RE = re.compile(r"[A-Za-z_]\w*|[^\s\w]") + + +def get_assistant(o: dict) -> str | None: + for m in o.get("messages", []) or []: + if m.get("role") == "assistant": + return m.get("content", "") or "" + return None + + +def check_repetition(a: str) -> tuple[bool, bool, bool]: + """Return (line_rep_bad, ngram_rep_bad, too_long). + """ + too_long = len(a) > MAX_ASST_CHARS + lines = [ln.strip() for ln in a.split("\n") if ln.strip()] + line_rep_bad = False + if lines: + c = collections.Counter(lines) + if c.most_common(1)[0][1] >= LINE_REPEAT_THRESH: + line_rep_bad = True + toks = TOKEN_RE.findall(a) + ngram_rep_bad = False + if len(toks) >= NGRAM_N: + c2 = collections.Counter(tuple(toks[i:i + NGRAM_N]) for i in range(len(toks) - NGRAM_N + 1)) + if c2 and c2.most_common(1)[0][1] >= NGRAM_THRESH: + ngram_rep_bad = True + return line_rep_bad, ngram_rep_bad, too_long + + +def main() -> None: + print("=== F3_v3 cleaning pipeline ===", flush=True) + t0 = time.time() + + # ---------- Pass 1: scan + collect content hashes ---------- + n = 0 + bad_long = bad_line = bad_ngram = 0 + line_only = ngram_only = both = 0 + hash_counts: collections.Counter[str] = collections.Counter() + # we also remember whether each hash has any "good" representative to keep. + # cheaper: at pass2 we'll keep the first occurrence whose own per-row checks passed. + print(f"Pass 1: scanning {SRC}", flush=True) + with open(SRC, encoding="utf-8") as f: + for line in f: + try: + o = json.loads(line) + except Exception: + continue + n += 1 + a = get_assistant(o) + if a is None: + continue + ll, ng, tl = check_repetition(a) + if tl: + bad_long += 1 + if ll: + bad_line += 1 + if ng: + bad_ngram += 1 + if ll and ng: + both += 1 + elif ll: + line_only += 1 + elif ng: + ngram_only += 1 + # hash assistant content for dedup pass + h = hashlib.md5(a.encode("utf-8", errors="ignore")).hexdigest() + hash_counts[h] += 1 + if n % 50000 == 0: + print(f" [{n:,}] bad_long={bad_long} bad_line={bad_line} bad_ngram={bad_ngram} uniq_hashes={len(hash_counts):,}", flush=True) + t_p1 = time.time() - t0 + print(f"Pass 1 done in {t_p1:.0f}s. total samples = {n:,}", flush=True) + + dup_clusters = sum(1 for c in hash_counts.values() if c > 1) + dup_rows = sum(c for c in hash_counts.values() if c > 1) + print(f" exact-content dup clusters: {dup_clusters:,} covering {dup_rows:,} rows", flush=True) + + # ---------- Pass 2: write filtered output ---------- + seen_hashes: set[str] = set() + kept = 0 + dropped_long = dropped_line = dropped_ngram = dropped_dup = 0 + print(f"Pass 2: writing {DST}", flush=True) + DST.parent.mkdir(parents=True, exist_ok=True) + with open(SRC, encoding="utf-8") as fin, open(DST, "w", encoding="utf-8") as fout: + for line in fin: + try: + o = json.loads(line) + except Exception: + continue + a = get_assistant(o) + if a is None: + continue + # rule 1: length + if len(a) > MAX_ASST_CHARS: + dropped_long += 1 + continue + # rule 2/3: repetition + ll, ng, _ = check_repetition(a) + if ll: + dropped_line += 1 + continue + if ng: + dropped_ngram += 1 + continue + # rule 4: cross-corpus dedup (keep first occurrence) + h = hashlib.md5(a.encode("utf-8", errors="ignore")).hexdigest() + if h in seen_hashes: + dropped_dup += 1 + continue + seen_hashes.add(h) + fout.write(json.dumps(o, ensure_ascii=False) + "\n") + kept += 1 + if (kept + dropped_long + dropped_line + dropped_ngram + dropped_dup) % 50000 == 0: + so_far = kept + dropped_long + dropped_line + dropped_ngram + dropped_dup + print(f" [{so_far:,}] kept={kept} dropped: long={dropped_long}, line={dropped_line}, ngram={dropped_ngram}, dup={dropped_dup}", flush=True) + + elapsed = time.time() - t0 + report = { + "input": str(SRC), + "output": str(DST), + "input_size": n, + "final_size": kept, + "kept_rate": round(kept / max(1, n), 4), + "rules": [ + f"R1: drop if assistant > {MAX_ASST_CHARS} chars", + f"R2: drop if any stripped line repeats >= {LINE_REPEAT_THRESH} times in assistant", + f"R3: drop if any {NGRAM_N}-gram repeats >= {NGRAM_THRESH} times in assistant", + f"R4: drop cross-corpus exact-content duplicates (keep first occurrence)", + ], + "scan_stats_before_filter": { + "samples_with_long_assistant": bad_long, + "samples_with_line_repeat": bad_line, + "samples_with_ngram_repeat": bad_ngram, + "samples_with_both_line_and_ngram": both, + "samples_with_only_line": line_only, + "samples_with_only_ngram": ngram_only, + "exact_dup_clusters": dup_clusters, + "exact_dup_rows_total": dup_rows, + }, + "applied_drops": { + "R1_too_long": dropped_long, + "R2_line_repeat": dropped_line, + "R3_ngram_repeat": dropped_ngram, + "R4_dup": dropped_dup, + "total_dropped": dropped_long + dropped_line + dropped_ngram + dropped_dup, + }, + "elapsed_sec": round(elapsed, 1), + } + REPORT.write_text(json.dumps(report, ensure_ascii=False, indent=2)) + print(json.dumps(report, ensure_ascii=False, indent=2)) + print(f"\nWrote {DST} ({DST.stat().st_size / 1e9:.2f} GB, {kept:,} samples)") + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/build_F3_v4.py b/code/f3_filtering/build_F3_v4.py new file mode 100644 index 0000000000000000000000000000000000000000..ca0c37bf0732af1a029945b8c6e2ee9353fbdc73 --- /dev/null +++ b/code/f3_filtering/build_F3_v4.py @@ -0,0 +1,306 @@ +"""Build F3_clean_v4 from F3_clean_v3 by applying additional quality rules. + +Source data only has `messages` (no file/path), so S2 uses CONTENT heuristics: + - dunder __init__ whose body is ONLY attribute assignments (template ctor) + - main/setup/parse_args with trivial body + - one-line property getters/setters + +Rules applied: + S1: MinHash near-duplicate (bottom-128 shingles, Jaccard threshold 0.85) + S2: template-style functions (content-based substitute) + S3: trivial function body (< 3 non-trivial statements after stripping docstring) + S4: comment/docstring ratio extreme (> 0.7 or == 0 with >=10 code lines) + S6: import-only or docstring-only assistant + S8: placeholder residue (TODO/FIXME/pdb/breakpoint/NotImplementedError/...) + +Output: scicode_sft_swift_F3_clean_v4.jsonl + report. +""" +from __future__ import annotations + +import collections +import json +import re +import time +from pathlib import Path + +import xxhash + +SRC = Path("/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl") +DST = Path("/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v4.jsonl") +REPORT = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_v4_report.json") + +# ---------- knobs ---------- +SHINGLE_K = 8 # 8-token shingles +SIG_K = 128 # bottom-128 minhash sketch +LSH_BANDS = 16 # 16 bands × 8 rows +JACCARD_THRESH = 0.85 +TRIVIAL_STMT_THRESH = 3 # < 3 non-trivial lines = trivial +COMMENT_RATIO_HIGH = 0.70 +MIN_CODE_LINES_FOR_NO_COMMENT_CHECK = 10 +PLACEHOLDERS = ( + "TODO", "FIXME", "XXX", "HACK", + "pdb.set_trace", "breakpoint(", "import pdb", + "raise NotImplementedError", + "pass # placeholder", "pass # implement", +) + +CODE_FENCE_RE = re.compile(r"```(?:python|cpp|c|java|rust|go)?\s*\n([\s\S]*?)\n```", re.IGNORECASE) +TOKEN_RE = re.compile(r"[A-Za-z_]\w*|[^\s\w]") +COMMENT_RE = re.compile(r"^\s*(#|//|/\*|\*)") +DOCSTRING_TRIPLE = re.compile(r'(?s)"""[\s\S]*?"""|\'\'\'[\s\S]*?\'\'\'') +ATTR_ASSIGN_RE = re.compile(r"^\s*self\.\w+\s*=\s*\w+\s*$") +IMPORT_RE = re.compile(r"^\s*(?:from\s+\S+\s+import\s+|import\s+\S+)") + + +def get_messages(o: dict) -> tuple[str, str]: + u = a = "" + for m in o.get("messages", []) or []: + if m.get("role") == "user": + u = m.get("content", "") or "" + elif m.get("role") == "assistant": + a = m.get("content", "") or "" + return u, a + + +def extract_code(asst: str) -> str: + m = CODE_FENCE_RE.search(asst) + return m.group(1) if m else asst + + +def is_placeholder(code: str) -> bool: + for kw in PLACEHOLDERS: + if kw in code: + return True + return False + + +def is_import_or_docstring_only(code: str) -> bool: + stripped = DOCSTRING_TRIPLE.sub("", code) + lines = [ln.strip() for ln in stripped.split("\n") if ln.strip()] + if not lines: + return True + non_import_non_comment = [ln for ln in lines + if not IMPORT_RE.match(ln) and not COMMENT_RE.match(ln)] + return len(non_import_non_comment) < 3 + + +def is_trivial_body(code: str) -> bool: + # strip docstrings + comments, count statements + stripped = DOCSTRING_TRIPLE.sub("", code) + lines = [ln.strip() for ln in stripped.split("\n") if ln.strip()] + non_trivial = [ln for ln in lines + if not COMMENT_RE.match(ln) + and not IMPORT_RE.match(ln) + and not ln.startswith("def ") + and not ln.startswith("class ") + and not ln.startswith("@") + and ln not in ("pass", "...", "return", "return None")] + return len(non_trivial) < TRIVIAL_STMT_THRESH + + +def comment_ratio_bad(code: str) -> bool: + lines = [ln for ln in code.split("\n") if ln.strip()] + if not lines: + return True + # count comment lines + docstring approximate + in_doc = False + doc_lines = 0 + comment_lines = 0 + code_lines = 0 + for ln in lines: + s = ln.strip() + if in_doc: + doc_lines += 1 + if s.endswith('"""') or s.endswith("'''"): + in_doc = False + continue + if s.startswith('"""') or s.startswith("'''"): + doc_lines += 1 + if not (s.endswith('"""') and len(s) > 3) and not (s.endswith("'''") and len(s) > 3): + in_doc = True + continue + if COMMENT_RE.match(ln): + comment_lines += 1 + else: + code_lines += 1 + total = doc_lines + comment_lines + code_lines + if total == 0: + return True + doc_ratio = (doc_lines + comment_lines) / total + if doc_ratio > COMMENT_RATIO_HIGH: + return True + # also too few comments + lots of code: only penalize if NO comments AND >=10 code lines + if doc_lines + comment_lines == 0 and code_lines >= MIN_CODE_LINES_FOR_NO_COMMENT_CHECK: + return False # not punishing this; we keep it (only the >0.7 side is filtered) + return False + + +def is_template_func(asst: str, code: str) -> bool: + # detect ctor with only self.x=y body + if not re.search(r"def\s+__init__\s*\(", code): + pass + else: + body_lines = [ln.strip() for ln in code.split("\n") if ln.strip()] + body_lines = [ln for ln in body_lines + if not ln.startswith("def ") and not ln.startswith("@") + and not COMMENT_RE.match(ln) and not ln.startswith('"""') and not ln.startswith("'''") + and ln not in ("pass", "...")] + if body_lines and all(ATTR_ASSIGN_RE.match(ln) or ln == "" for ln in body_lines): + return True + # main/setup with trivial body + if re.search(r"def\s+(?:main|setup|parse_args|get_args)\s*\(", code) and is_trivial_body(code): + return True + return False + + +def code_shingles(code: str, k: int = SHINGLE_K) -> list[int]: + """Return shingled token n-gram hashes (deduplicated).""" + toks = TOKEN_RE.findall(code) + if len(toks) < k: + return [] + sh = set() + for i in range(len(toks) - k + 1): + h = xxhash.xxh64(" ".join(toks[i:i + k])).intdigest() + sh.add(h) + return sorted(sh)[:5000] # cap to bound mem for huge samples + + +def minhash_signature(shingles: list[int], sig_k: int = SIG_K) -> tuple[int, ...]: + """Bottom-K minhash.""" + if not shingles: + return tuple([0] * sig_k) + if len(shingles) >= sig_k: + return tuple(shingles[:sig_k]) + return tuple(shingles + [0] * (sig_k - len(shingles))) + + +def lsh_band_keys(sig: tuple[int, ...], bands: int = LSH_BANDS) -> list[bytes]: + rows_per_band = len(sig) // bands + keys = [] + for b in range(bands): + chunk = sig[b * rows_per_band:(b + 1) * rows_per_band] + keys.append(xxhash.xxh64(bytes(str(chunk), "utf-8")).digest()) + return keys + + +def jaccard(a: tuple, b: tuple) -> float: + sa, sb = set(a) - {0}, set(b) - {0} + if not sa or not sb: + return 0.0 + return len(sa & sb) / len(sa | sb) + + +def main() -> None: + print("=== F3_v4 cleaning pipeline ===", flush=True) + t0 = time.time() + + # ---------- Pass 1: scan, compute per-row features and signatures ---------- + rows: list[dict] = [] + n = 0 + print("Pass 1: scanning + computing features + minhash", flush=True) + with open(SRC, encoding="utf-8") as f: + for line in f: + try: + o = json.loads(line) + except Exception: + continue + n += 1 + _, a = get_messages(o) + code = extract_code(a) + features = { + "s8_placeholder": is_placeholder(code), + "s6_import_doc_only": is_import_or_docstring_only(code), + "s3_trivial": is_trivial_body(code), + "s4_comment_ratio": comment_ratio_bad(code), + "s2_template": is_template_func(a, code), + } + sig = minhash_signature(code_shingles(code)) + rows.append({"raw": line, "sig": sig, "features": features}) + if n % 20000 == 0: + print(f" [{n:,}] features computed", flush=True) + t_p1 = time.time() - t0 + print(f"Pass 1 done in {t_p1:.0f}s. total = {n:,}", flush=True) + + # ---------- Pass 2: LSH near-dup detection ---------- + print("Pass 2: LSH bucketing for near-dup", flush=True) + buckets: dict[bytes, list[int]] = collections.defaultdict(list) + for i, r in enumerate(rows): + for k in lsh_band_keys(r["sig"]): + buckets[k].append(i) + candidate_pairs: set[tuple[int, int]] = set() + for ids in buckets.values(): + if len(ids) < 2: + continue + if len(ids) > 50: # huge buckets are likely noise, cap + ids = ids[:50] + for i in range(len(ids)): + for j in range(i + 1, len(ids)): + candidate_pairs.add((ids[i], ids[j])) + print(f" candidate pairs: {len(candidate_pairs):,}", flush=True) + + near_dup: set[int] = set() + for a, b in candidate_pairs: + if a in near_dup or b in near_dup: + continue + if jaccard(rows[a]["sig"], rows[b]["sig"]) >= JACCARD_THRESH: + # keep the longer one (= more informative) + la = len(rows[a]["raw"]); lb = len(rows[b]["raw"]) + near_dup.add(b if la >= lb else a) + print(f" near-dup marked: {len(near_dup):,}", flush=True) + + # ---------- Pass 3: write filtered output ---------- + print(f"Pass 3: writing {DST}", flush=True) + DST.parent.mkdir(parents=True, exist_ok=True) + drops = collections.Counter() + kept = 0 + with open(DST, "w", encoding="utf-8") as fout: + for i, r in enumerate(rows): + f = r["features"] + # priority order — drop on first match for clean accounting + if i in near_dup: + drops["S1_near_dup"] += 1 + continue + if f["s2_template"]: + drops["S2_template"] += 1 + continue + if f["s3_trivial"]: + drops["S3_trivial"] += 1 + continue + if f["s4_comment_ratio"]: + drops["S4_comment_ratio"] += 1 + continue + if f["s6_import_doc_only"]: + drops["S6_import_doc_only"] += 1 + continue + if f["s8_placeholder"]: + drops["S8_placeholder"] += 1 + continue + fout.write(r["raw"]) + kept += 1 + + elapsed = time.time() - t0 + report = { + "input": str(SRC), + "output": str(DST), + "input_size": n, + "final_size": kept, + "kept_rate": round(kept / max(1, n), 4), + "rules": [ + f"S1: MinHash near-dup, Jaccard >= {JACCARD_THRESH}, bottom-{SIG_K} shingle-{SHINGLE_K}", + "S2: template ctor / setup / main with trivial body (content-based)", + f"S3: trivial body (< {TRIVIAL_STMT_THRESH} non-trivial statements)", + f"S4: comment+docstring ratio > {COMMENT_RATIO_HIGH}", + "S6: import-only / docstring-only assistant", + f"S8: placeholder residue ({', '.join(PLACEHOLDERS[:5])}, ...)", + ], + "applied_drops": dict(drops), + "total_dropped": sum(drops.values()), + "elapsed_sec": round(elapsed, 1), + } + REPORT.write_text(json.dumps(report, ensure_ascii=False, indent=2)) + print(json.dumps(report, ensure_ascii=False, indent=2)) + print(f"\nWrote {DST} ({DST.stat().st_size / 1e9:.2f} GB, {kept:,} samples)") + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/clean_F3_stage1.py b/code/f3_filtering/clean_F3_stage1.py new file mode 100644 index 0000000000000000000000000000000000000000..8792be7878a8276cf03c6ca0c22cd73491e34c4b --- /dev/null +++ b/code/f3_filtering/clean_F3_stage1.py @@ -0,0 +1,158 @@ +"""F3 → F3_v2 cleaning pipeline — Stage 1: structural filters. + +Drops samples whose assistant code block matches any of: + 1a AST cannot parse + 1b any line >=5 stripped chars repeats >=5 times + 1c function body is < 3 lines or > 500 lines + 1d contains hardcoded local paths (/home/, /Users/, C:\\, /tmp/, /mnt/) + 1e pure placeholder body (only pass / ... / raise NotImplementedError / return None) + 1f debug residue: breakpoint(), pdb.set_trace(), or >=3 TODO/FIXME markers +""" +from __future__ import annotations + +import ast +import json +import re +from collections import Counter +from pathlib import Path + +SRC = Path("/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean.jsonl") +DST = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage1.jsonl") +REPORT = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage1_report.json") + +CODE_FENCE_RE = re.compile(r"```(?:python|py)?\s*\n([\s\S]*?)```", re.IGNORECASE) +# Generic fence (any language) for language-aware filtering +GENERIC_FENCE_RE = re.compile(r"```(\S*)\s*\n([\s\S]*?)```", re.IGNORECASE) +HARDCODED_PATH_RE = re.compile( + r"(?:[\"'])(?:/home/[^/\s\"']+/|/Users/[^/\s\"']+/|/tmp/[A-Za-z0-9_-]+|/mnt/[^/\s\"']+/|/data/[A-Za-z0-9_-]+|[A-Z]:\\\\?[A-Za-z0-9_-]+\\\\)" +) +DEBUG_RE = re.compile(r"\b(?:breakpoint\(\)|pdb\.set_trace\(\)|ipdb\.set_trace\(\))") +TODO_RE = re.compile(r"#\s*(?:TODO|FIXME|XXX|HACK)\b", re.IGNORECASE) +PLACEHOLDER_RE = re.compile( + r"^\s*(?:pass|\.\.\.|raise\s+NotImplementedError(?:\(.*?\))?|return\s+None)\s*$", + re.MULTILINE, +) + + +def get_assistant_code_with_lang(obj: dict) -> tuple[str, str]: + """Return (code, fence_language). Language defaults to 'python' when fence omitted/empty.""" + for m in obj.get("messages", []): + if m.get("role") == "assistant": + text = m.get("content", "") + mfence = GENERIC_FENCE_RE.search(text) + if mfence: + lang = (mfence.group(1) or "").strip().lower() + code = mfence.group(2) + if not lang or lang == "py": + lang = "python" + return code, lang + return text, "python" + return "", "python" + + +def is_pure_placeholder(code: str) -> bool: + """True if the function body has only placeholder/no-op statements.""" + try: + tree = ast.parse(code) + except SyntaxError: + return False + has_real = False + for node in ast.walk(tree): + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): + body = node.body + # ignore docstring + if body and isinstance(body[0], ast.Expr) and isinstance(body[0].value, ast.Constant) and isinstance(body[0].value.value, str): + body = body[1:] + if not body: + continue + for stmt in body: + if isinstance(stmt, ast.Pass): + continue + if isinstance(stmt, ast.Expr) and isinstance(stmt.value, ast.Constant) and stmt.value.value is Ellipsis: + continue + if isinstance(stmt, ast.Raise) and stmt.exc and ( + (isinstance(stmt.exc, ast.Name) and stmt.exc.id == "NotImplementedError") + or (isinstance(stmt.exc, ast.Call) and isinstance(stmt.exc.func, ast.Name) and stmt.exc.func.id == "NotImplementedError") + ): + continue + if isinstance(stmt, ast.Return) and (stmt.value is None or (isinstance(stmt.value, ast.Constant) and stmt.value.value is None)): + continue + # any other stmt counts as real code + has_real = True + break + if has_real: + break + return not has_real # all defs are pure placeholder + + +def filter_sample(code: str, lang: str) -> tuple[bool, str]: + """Return (keep, reason_if_drop). `lang` decides whether to AST-parse Python.""" + if not code or not code.strip(): + return False, "1c_empty" + # 1c: length sanity + n_lines = len(code.splitlines()) + if n_lines < 3: + return False, "1c_too_short" + if n_lines > 500: + return False, "1c_too_long" + # 1a: AST parse — only enforced for python-fenced samples + if lang == "python": + try: + ast.parse(code) + except SyntaxError: + return False, "1a_ast_fail_python" + # 1b: line repeat >=5 (applies to all languages — repetition is universally bad signal) + qual_lines = [ln for ln in code.splitlines() if len(ln.strip()) >= 5] + if qual_lines: + max_rep = Counter(qual_lines).most_common(1)[0][1] + if max_rep >= 5: + return False, "1b_line_repeat" + # 1d: hardcoded local path + if HARDCODED_PATH_RE.search(code): + return False, "1d_hardcoded_path" + # 1f: debug residue (Python-specific markers; harmless to apply broadly) + if DEBUG_RE.search(code): + return False, "1f_debug_pdb" + if len(TODO_RE.findall(code)) >= 3: + return False, "1f_many_todo" + # 1e: pure placeholder (Python-only check; non-Python skipped) + if lang == "python" and is_pure_placeholder(code): + return False, "1e_placeholder" + return True, "" + + +def main() -> None: + DST.parent.mkdir(parents=True, exist_ok=True) + drop_reasons: Counter[str] = Counter() + lang_counts_kept: Counter[str] = Counter() + n_total = n_kept = 0 + with open(SRC) as fin, open(DST, "w", encoding="utf-8") as fout: + for line in fin: + n_total += 1 + obj = json.loads(line) + code, lang = get_assistant_code_with_lang(obj) + keep, reason = filter_sample(code, lang) + if not keep: + drop_reasons[reason] += 1 + continue + n_kept += 1 + lang_counts_kept[lang] += 1 + fout.write(line) + + n_dropped = n_total - n_kept + report = { + "src": str(SRC), + "dst": str(DST), + "n_total": n_total, + "n_kept": n_kept, + "n_dropped": n_dropped, + "drop_rate": round(n_dropped / max(n_total, 1), 4), + "drop_reasons": dict(drop_reasons.most_common()), + "kept_languages": dict(lang_counts_kept.most_common(20)), + } + REPORT.write_text(json.dumps(report, indent=2, ensure_ascii=False)) + print(json.dumps(report, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/clean_F3_stage2.py b/code/f3_filtering/clean_F3_stage2.py new file mode 100644 index 0000000000000000000000000000000000000000..1de0e9acf27dfd88eb8f179baee3d81b473dd0b6 --- /dev/null +++ b/code/f3_filtering/clean_F3_stage2.py @@ -0,0 +1,173 @@ +"""F3 → F3_v2 — Stage 2: per-project cap=300 with D1xD2 stratified sampling. + +D1 (code length): S<15, M1=15-39, M2=40-99, L>=100 +D2 (line repeat): norep<5, rep>=5 + +Per project (>300 samples): allocate 75 per D1 bucket; rep cap=5 per D1; reallocate shortfall to other D1 norep buckets. Random sample within bucket (seed=0). +""" +from __future__ import annotations + +import hashlib +import json +import random +import re +from collections import Counter, defaultdict +from pathlib import Path + +SRC = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage1.jsonl") +DST = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage2.jsonl") +REPORT = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage2_report.json") + +CAP = 300 +D1_TARGET = {"S": 75, "M1": 75, "M2": 75, "L": 75} +REP_LIMIT_PER_D1 = 5 +SEED = 0 + +CODE_FENCE_RE = re.compile(r"```(?:python|py)?\s*\n([\s\S]*?)```", re.IGNORECASE) + + +def get_assistant_code(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "assistant": + text = m.get("content", "") + mfence = CODE_FENCE_RE.search(text) + return mfence.group(1) if mfence else text + return "" + + +def get_user(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "user": + return m.get("content", "") + return "" + + +def d1_bucket(n_lines: int) -> str: + if n_lines < 15: return "S" + if n_lines < 40: return "M1" + if n_lines < 100: return "M2" + return "L" + + +def main() -> None: + DST.parent.mkdir(parents=True, exist_ok=True) + rng = random.Random(SEED) + + # PASS 1: bucket all samples by project, store sample with index for stable refs + proj_buckets: dict[str, dict[tuple[str, str], list[int]]] = defaultdict(lambda: defaultdict(list)) + rows: list[str] = [] + with open(SRC) as fin: + for idx, line in enumerate(fin): + obj = json.loads(line) + u = get_user(obj) + code = get_assistant_code(obj) + n_lines = len(code.splitlines()) + qual = [ln for ln in code.splitlines() if len(ln.strip()) >= 5] + max_rep = max(Counter(qual).values()) if qual else 0 + d1 = d1_bucket(n_lines) + d2 = "rep" if max_rep >= 5 else "norep" # should be 0 after stage1 + proj_h = hashlib.md5(u[:500].encode()).hexdigest() + proj_buckets[proj_h][(d1, d2)].append(idx) + rows.append(line) + print(f"loaded {len(rows)} samples from {len(proj_buckets)} projects") + + # PASS 2: per project sampling + keep_indices: set[int] = set() + n_proj_capped = 0 + n_proj_untouched = 0 + bucket_kept_counter: Counter[tuple[str, str]] = Counter() + + for proj_h, buckets in proj_buckets.items(): + n_in_proj = sum(len(v) for v in buckets.values()) + if n_in_proj <= CAP: + # untouched + for v in buckets.values(): + for idx in v: + keep_indices.add(idx) + # for stats only + n_proj_untouched += 1 + continue + + n_proj_capped += 1 + # allocate per D1 (target 75 each, 4 buckets = 300) + chosen: list[int] = [] + d1_remaining: dict[str, int] = dict(D1_TARGET) + unfilled: list[str] = [] # D1 that couldn't fill from norep + + for d1 in ["S", "M1", "M2", "L"]: + target = d1_remaining[d1] + norep_pool = list(buckets.get((d1, "norep"), [])) + rep_pool = list(buckets.get((d1, "rep"), [])) + rng.shuffle(norep_pool) + rng.shuffle(rep_pool) + + # take from norep first + take_norep = min(len(norep_pool), target) + chosen.extend(norep_pool[:take_norep]) + target -= take_norep + + # fill remaining from rep, capped by REP_LIMIT_PER_D1 + rep_take = min(target, REP_LIMIT_PER_D1, len(rep_pool)) + chosen.extend(rep_pool[:rep_take]) + target -= rep_take + + # update remaining: norep_pool[take_norep:] is leftover norep + norep_left = norep_pool[take_norep:] + buckets[(d1, "norep_leftover")] = norep_left + buckets[(d1, "rep_leftover")] = rep_pool[rep_take:] + + if target > 0: + unfilled.append(d1) + d1_remaining[d1] = target # how much D1 still needs + else: + d1_remaining[d1] = 0 + + # reallocate shortfall: pull norep_leftover from other D1 buckets + total_short = sum(d1_remaining.values()) + if total_short > 0: + extra_pool = [] + for d1 in ["S", "M1", "M2", "L"]: + extra_pool.extend(buckets.get((d1, "norep_leftover"), [])) + rng.shuffle(extra_pool) + take = min(total_short, len(extra_pool)) + chosen.extend(extra_pool[:take]) + + # final cap (in case of overshoot due to algorithm bug) + if len(chosen) > CAP: + chosen = chosen[:CAP] + + for idx in chosen: + keep_indices.add(idx) + + # stats: for each chosen, look up its bucket + # (skip detailed bucket stats per project; aggregate later) + + # write output + with open(DST, "w", encoding="utf-8") as fout: + for idx in sorted(keep_indices): + fout.write(rows[idx]) + + n_kept = len(keep_indices) + n_total = len(rows) + + report = { + "src": str(SRC), + "dst": str(DST), + "n_total": n_total, + "n_kept": n_kept, + "n_dropped": n_total - n_kept, + "drop_rate": round(1 - n_kept / max(n_total, 1), 4), + "n_projects": len(proj_buckets), + "n_projects_capped": n_proj_capped, + "n_projects_untouched": n_proj_untouched, + "cap": CAP, + "d1_target": D1_TARGET, + "rep_limit_per_d1": REP_LIMIT_PER_D1, + "seed": SEED, + } + REPORT.write_text(json.dumps(report, indent=2, ensure_ascii=False)) + print(json.dumps(report, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/clean_F3_stage3.py b/code/f3_filtering/clean_F3_stage3.py new file mode 100644 index 0000000000000000000000000000000000000000..489b1b08b8b4300c4e07497db997653d5a06b9f3 --- /dev/null +++ b/code/f3_filtering/clean_F3_stage3.py @@ -0,0 +1,101 @@ +"""F3 → F3_v2 — Stage 3: per-project assistant deduplication, K=3. + +For each project (identified by user-prompt-prefix hash), keep at most K samples +per unique assistant code. Sampling is random within over-represented groups +(seed=0). +""" +from __future__ import annotations + +import hashlib +import json +import random +import re +from collections import defaultdict +from pathlib import Path + +SRC = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage2.jsonl") +DST = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage3.jsonl") +REPORT = Path("/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage3_report.json") + +K = 3 +SEED = 0 + +CODE_FENCE_RE = re.compile(r"```(?:python|py)?\s*\n([\s\S]*?)```", re.IGNORECASE) + + +def get_assistant_code(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "assistant": + text = m.get("content", "") + mfence = CODE_FENCE_RE.search(text) + return mfence.group(1) if mfence else text + return "" + + +def get_user(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "user": + return m.get("content", "") + return "" + + +def md5(s: str) -> str: + return hashlib.md5(s.encode()).hexdigest() + + +def main() -> None: + DST.parent.mkdir(parents=True, exist_ok=True) + rng = random.Random(SEED) + + # group sample indices by (proj_hash, asst_hash) + groups: dict[tuple[str, str], list[int]] = defaultdict(list) + rows: list[str] = [] + with open(SRC) as fin: + for idx, line in enumerate(fin): + obj = json.loads(line) + u = get_user(obj) + a = get_assistant_code(obj) + proj_h = md5(u[:500]) + asst_h = md5(a) + groups[(proj_h, asst_h)].append(idx) + rows.append(line) + + n_total = len(rows) + keep: set[int] = set() + n_groups = len(groups) + n_groups_truncated = 0 + over_count = 0 + + for (proj_h, asst_h), idxs in groups.items(): + if len(idxs) <= K: + for i in idxs: keep.add(i) + else: + n_groups_truncated += 1 + over_count += len(idxs) - K + rng.shuffle(idxs) + for i in idxs[:K]: keep.add(i) + + with open(DST, "w", encoding="utf-8") as fout: + for idx in sorted(keep): + fout.write(rows[idx]) + + n_kept = len(keep) + report = { + "src": str(SRC), + "dst": str(DST), + "n_total": n_total, + "n_kept": n_kept, + "n_dropped": n_total - n_kept, + "drop_rate": round(1 - n_kept / max(n_total, 1), 4), + "n_unique_(proj,asst)_groups": n_groups, + "n_groups_truncated": n_groups_truncated, + "n_dups_dropped": over_count, + "K": K, + "seed": SEED, + } + REPORT.write_text(json.dumps(report, indent=2, ensure_ascii=False)) + print(json.dumps(report, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/clean_F3_stage4.py b/code/f3_filtering/clean_F3_stage4.py new file mode 100644 index 0000000000000000000000000000000000000000..4eb01174465e7f63b1fa148d7a91e6ca6d542f14 --- /dev/null +++ b/code/f3_filtering/clean_F3_stage4.py @@ -0,0 +1,186 @@ +"""F3 → F3_v2 — Stage 4 (full): Qwen3-Coder-30B-A3B-Instruct quality scoring. + +For every sample in F3_stage3.jsonl, ask the model to rate 1-5. Keep score >= 3. +Run with the skill conda env (vLLM).""" +from __future__ import annotations + +import argparse +import json +import re +import time +from collections import Counter +from pathlib import Path + +CODE_FENCE_RE = re.compile(r"```(?:python|py)?\s*\n([\s\S]*?)```", re.IGNORECASE) +GENERIC_FENCE_RE = re.compile(r"```(\S*)\s*\n([\s\S]*?)```", re.IGNORECASE) + +DEFAULT_MODEL = "/mnt/moon-data/jfjiang/swf/skillsecure/data/models/Qwen3-Coder-30B-A3B-Instruct" +DEFAULT_SRC = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage3.jsonl" +DEFAULT_DST = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4.jsonl" +DEFAULT_REPORT = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4_report.json" +DEFAULT_SCORES = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4_scores.jsonl" + +KEEP_THRESHOLD = 3 + +SCORE_PROMPT = """You are a senior Python code reviewer. Rate the quality of the following function on a scale of 1-5: + +1 = unusable (broken, placeholder, or nonsense) +2 = poor (works but low quality, bad style, or hardcoded values) +3 = ok (works, average style) +4 = good (clean, readable, reasonable structure) +5 = excellent (production-ready, well-documented, robust) + +Function description (from project context): +{user_brief} + +Code: +```python +{code} +``` + +Output ONLY a single digit from 1 to 5. No explanation.""" + + +def get_user_brief(u: str, max_chars: int = 1500) -> str: + return u[:max_chars] + + +def get_assistant_code(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "assistant": + text = m.get("content", "") + mfence = GENERIC_FENCE_RE.search(text) + return mfence.group(2) if mfence else text + return "" + + +def get_user(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "user": + return m.get("content", "") + return "" + + +def parse_score(text: str) -> int: + if not text: + return 0 + m = re.search(r"\b([1-5])\b", text) + return int(m.group(1)) if m else 0 + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--src", default=DEFAULT_SRC) + ap.add_argument("--dst", default=DEFAULT_DST) + ap.add_argument("--report", default=DEFAULT_REPORT) + ap.add_argument("--scores", default=DEFAULT_SCORES) + ap.add_argument("--model", default=DEFAULT_MODEL) + ap.add_argument("--threshold", type=int, default=KEEP_THRESHOLD) + ap.add_argument("--gpu-mem-util", type=float, default=0.85) + ap.add_argument("--max-model-len", type=int, default=8192) + ap.add_argument("--batch-chunk", type=int, default=20000, + help="how many samples to feed vLLM in one .generate() call") + args = ap.parse_args() + + src = Path(args.src) + rows: list[str] = [] + with open(src) as f: + for line in f: + rows.append(line) + print(f"loaded {len(rows)} samples") + + from vllm import LLM, SamplingParams + from transformers import AutoTokenizer + + tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True) + llm = LLM( + model=args.model, tokenizer=args.model, trust_remote_code=True, + max_model_len=args.max_model_len, gpu_memory_utilization=args.gpu_mem_util, + dtype="bfloat16", + ) + sp = SamplingParams(temperature=0.0, top_p=1.0, max_tokens=8) + + # Pre-build all chat prompts; if a prompt would exceed max_model_len after tokenize, skip (rare). + print("building chat prompts...") + chat_prompts: list[str] = [] + skip_indices: set[int] = set() + for idx, line in enumerate(rows): + obj = json.loads(line) + user_brief = get_user_brief(get_user(obj)) + code = get_assistant_code(obj) + prompt = SCORE_PROMPT.format(user_brief=user_brief, code=code) + msgs = [{"role": "user", "content": prompt}] + try: + chat_prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) + except Exception: + chat_prompt = prompt + # quick token-length check to avoid validation error + tok_ids = tok(chat_prompt, add_special_tokens=False)["input_ids"] + if len(tok_ids) >= args.max_model_len - 10: + skip_indices.add(idx) + chat_prompts.append("") # placeholder + else: + chat_prompts.append(chat_prompt) + print(f"too-long (skipped, default-keep): {len(skip_indices)}") + + scores: list[int] = [0] * len(rows) + n_chunk = (len(rows) + args.batch_chunk - 1) // args.batch_chunk + cumulative = 0 + t0 = time.time() + for c in range(n_chunk): + lo = c * args.batch_chunk + hi = min((c + 1) * args.batch_chunk, len(rows)) + batch_idx = [i for i in range(lo, hi) if i not in skip_indices] + batch_prompts = [chat_prompts[i] for i in batch_idx] + if not batch_prompts: + continue + print(f"chunk {c+1}/{n_chunk}: scoring {len(batch_prompts)} prompts (lo={lo})") + outs = llm.generate(batch_prompts, sp) + for i, o in zip(batch_idx, outs): + scores[i] = parse_score(o.outputs[0].text) + cumulative += len(batch_prompts) + elapsed = time.time() - t0 + rate = cumulative / max(elapsed, 1e-9) + eta = (len(rows) - len(skip_indices) - cumulative) / max(rate, 1e-9) + print(f" done {cumulative}; rate={rate:.1f}/s; eta={eta/60:.1f}min") + + # write scores + with open(args.scores, "w") as f: + for i, s in enumerate(scores): + f.write(json.dumps({"idx": i, "score": s}, ensure_ascii=False) + "\n") + + # filter and write + n_keep = 0 + score_dist: Counter[int] = Counter() + with open(args.dst, "w", encoding="utf-8") as fout: + for i, line in enumerate(rows): + s = scores[i] + score_dist[s] += 1 + if i in skip_indices or s >= args.threshold: + fout.write(line) + n_keep += 1 + + elapsed_total = time.time() - t0 + report = { + "src": str(src), + "dst": args.dst, + "model": args.model, + "threshold": args.threshold, + "n_total": len(rows), + "n_kept": n_keep, + "n_dropped": len(rows) - n_keep, + "drop_rate": round(1 - n_keep / max(len(rows), 1), 4), + "n_skipped_too_long_kept_default": len(skip_indices), + "score_distribution": dict(sorted(score_dist.items())), + "score_distribution_pct": {str(k): round(v / len(rows) * 100, 2) for k, v in sorted(score_dist.items())}, + "elapsed_sec": round(elapsed_total, 1), + } + Path(args.report).parent.mkdir(parents=True, exist_ok=True) + with open(args.report, "w") as f: + json.dump(report, f, indent=2, ensure_ascii=False) + print("\n=== summary ===") + print(json.dumps(report, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/clean_F3_stage4_calibrate.py b/code/f3_filtering/clean_F3_stage4_calibrate.py new file mode 100644 index 0000000000000000000000000000000000000000..2f48f05ee7d5b4734d2e940a1340df05a37aae8f --- /dev/null +++ b/code/f3_filtering/clean_F3_stage4_calibrate.py @@ -0,0 +1,156 @@ +"""F3 → F3_v2 — Stage 4 calibration: Qwen3-Coder-30B-A3B-Instruct 1-5 quality score on 500 sampled rows. + +Prints score distribution + a few low/high-score samples for sanity check. +Run with the skill conda env (has vLLM).""" +from __future__ import annotations + +import argparse +import json +import random +import re +import time +from collections import Counter +from pathlib import Path + +CODE_FENCE_RE = re.compile(r"```(?:python|py)?\s*\n([\s\S]*?)```", re.IGNORECASE) + +DEFAULT_MODEL = "/mnt/moon-data/jfjiang/swf/skillsecure/data/models/Qwen3-Coder-30B-A3B-Instruct" +DEFAULT_SRC = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage3.jsonl" +DEFAULT_OUT = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4_calibration.json" +DEFAULT_SCORES = "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4_calibration_scores.jsonl" + + +SCORE_PROMPT = """You are a senior Python code reviewer. Rate the quality of the following function on a scale of 1-5: + +1 = unusable (broken, placeholder, or nonsense) +2 = poor (works but low quality, bad style, or hardcoded values) +3 = ok (works, average style) +4 = good (clean, readable, reasonable structure) +5 = excellent (production-ready, well-documented, robust) + +Function description (from project context): +{user_brief} + +Code: +```python +{code} +``` + +Output ONLY a single digit from 1 to 5. No explanation.""" + + +def get_user_brief(u: str, max_chars: int = 1500) -> str: + """Trim user prompt to function-relevant section (skip Project Overview if too long).""" + return u[:max_chars] + + +def get_assistant_code(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "assistant": + text = m.get("content", "") + mfence = CODE_FENCE_RE.search(text) + return mfence.group(1) if mfence else text + return "" + + +def get_user(obj: dict) -> str: + for m in obj.get("messages", []): + if m.get("role") == "user": + return m.get("content", "") + return "" + + +def parse_score(text: str) -> int: + if not text: return 0 + m = re.search(r"\b([1-5])\b", text) + if m: return int(m.group(1)) + return 0 # unparseable + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--src", default=DEFAULT_SRC) + ap.add_argument("--model", default=DEFAULT_MODEL) + ap.add_argument("--n-sample", type=int, default=500) + ap.add_argument("--seed", type=int, default=0) + ap.add_argument("--out-summary", default=DEFAULT_OUT) + ap.add_argument("--out-scores", default=DEFAULT_SCORES) + ap.add_argument("--gpu-mem-util", type=float, default=0.85) + ap.add_argument("--max-model-len", type=int, default=8192) + args = ap.parse_args() + + rng = random.Random(args.seed) + src = Path(args.src) + + rows = [] + with open(src) as f: + for line in f: + rows.append(line) + print(f"loaded {len(rows)} samples from {src.name}") + + sample_idx = rng.sample(range(len(rows)), args.n_sample) + sampled = [(i, json.loads(rows[i])) for i in sample_idx] + + prompts = [] + for _, obj in sampled: + user_brief = get_user_brief(get_user(obj)) + code = get_assistant_code(obj) + prompts.append(SCORE_PROMPT.format(user_brief=user_brief, code=code)) + + from vllm import LLM, SamplingParams + from transformers import AutoTokenizer + + tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True) + llm = LLM( + model=args.model, tokenizer=args.model, trust_remote_code=True, + max_model_len=args.max_model_len, gpu_memory_utilization=args.gpu_mem_util, + dtype="bfloat16", + ) + sp = SamplingParams(temperature=0.0, top_p=1.0, max_tokens=8) + + chat_prompts = [] + for p in prompts: + msgs = [{"role": "user", "content": p}] + chat_prompts.append(tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)) + + print(f"scoring {len(chat_prompts)}...") + t0 = time.time() + outputs = llm.generate(chat_prompts, sp) + secs = time.time() - t0 + + scores = [] + raw_outs = [] + for o in outputs: + text = o.outputs[0].text + raw_outs.append(text) + scores.append(parse_score(text)) + + dist = Counter(scores) + summary = { + "n_sample": len(sampled), + "elapsed_sec": round(secs, 1), + "score_distribution": {str(k): dist.get(k, 0) for k in [0, 1, 2, 3, 4, 5]}, + "score_distribution_pct": {str(k): round(dist.get(k, 0) / len(sampled) * 100, 1) for k in [0, 1, 2, 3, 4, 5]}, + "ge3_count": sum(dist.get(k, 0) for k in [3, 4, 5]), + "ge3_pct": round(sum(dist.get(k, 0) for k in [3, 4, 5]) / len(sampled) * 100, 1), + "ge4_count": sum(dist.get(k, 0) for k in [4, 5]), + "ge4_pct": round(sum(dist.get(k, 0) for k in [4, 5]) / len(sampled) * 100, 1), + } + Path(args.out_summary).parent.mkdir(parents=True, exist_ok=True) + with open(args.out_summary, "w") as f: + json.dump(summary, f, indent=2, ensure_ascii=False) + with open(args.out_scores, "w") as f: + for (i, obj), s, raw in zip(sampled, scores, raw_outs): + code = get_assistant_code(obj) + f.write(json.dumps({ + "idx": i, + "score": s, + "raw_out": raw, + "code_head": code[:200], + }, ensure_ascii=False) + "\n") + print("\n=== summary ===") + print(json.dumps(summary, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/code/f3_filtering/reports/F3_stage1_report.json b/code/f3_filtering/reports/F3_stage1_report.json new file mode 100644 index 0000000000000000000000000000000000000000..ca38fd8731d70ae28a0fcdf59d05b1de30eef104 --- /dev/null +++ b/code/f3_filtering/reports/F3_stage1_report.json @@ -0,0 +1,26 @@ +{ + "src": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean.jsonl", + "dst": "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage1.jsonl", + "n_total": 473465, + "n_kept": 437671, + "n_dropped": 35794, + "drop_rate": 0.0756, + "drop_reasons": { + "1b_line_repeat": 13506, + "1c_too_short": 9295, + "1a_ast_fail_python": 5119, + "1e_placeholder": 2497, + "1d_hardcoded_path": 2396, + "1c_too_long": 1621, + "1f_debug_pdb": 962, + "1f_many_todo": 398 + }, + "kept_languages": { + "python": 400263, + "cpp": 16733, + "c": 10995, + "java": 6879, + "rust": 2577, + "go": 224 + } +} \ No newline at end of file diff --git a/code/f3_filtering/reports/F3_stage2_report.json b/code/f3_filtering/reports/F3_stage2_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2a2e589dfa9c5dfeaf7d55d8102431ce07392a9d --- /dev/null +++ b/code/f3_filtering/reports/F3_stage2_report.json @@ -0,0 +1,20 @@ +{ + "src": "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage1.jsonl", + "dst": "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage2.jsonl", + "n_total": 437671, + "n_kept": 366672, + "n_dropped": 70999, + "drop_rate": 0.1622, + "n_projects": 14408, + "n_projects_capped": 217, + "n_projects_untouched": 14191, + "cap": 300, + "d1_target": { + "S": 75, + "M1": 75, + "M2": 75, + "L": 75 + }, + "rep_limit_per_d1": 5, + "seed": 0 +} \ No newline at end of file diff --git a/code/f3_filtering/reports/F3_stage3_report.json b/code/f3_filtering/reports/F3_stage3_report.json new file mode 100644 index 0000000000000000000000000000000000000000..519f0807682826bebe9476226a205e79626d6e3d --- /dev/null +++ b/code/f3_filtering/reports/F3_stage3_report.json @@ -0,0 +1,13 @@ +{ + "src": "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage2.jsonl", + "dst": "/raid/data/weifeng/Datasets/Instruction-turning/F3_v2_pipeline/F3_stage3.jsonl", + "n_total": 366672, + "n_kept": 362367, + "n_dropped": 4305, + "drop_rate": 0.0117, + "n_unique_(proj,asst)_groups": 344696, + "n_groups_truncated": 1509, + "n_dups_dropped": 4305, + "K": 3, + "seed": 0 +} \ No newline at end of file diff --git a/code/f3_filtering/reports/F3_stage4_calibration.json b/code/f3_filtering/reports/F3_stage4_calibration.json new file mode 100644 index 0000000000000000000000000000000000000000..cc3b05f3e9ea1719b2601e8d9455b20c61d78524 --- /dev/null +++ b/code/f3_filtering/reports/F3_stage4_calibration.json @@ -0,0 +1,24 @@ +{ + "n_sample": 500, + "elapsed_sec": 9.3, + "score_distribution": { + "0": 0, + "1": 19, + "2": 180, + "3": 233, + "4": 66, + "5": 2 + }, + "score_distribution_pct": { + "0": 0.0, + "1": 3.8, + "2": 36.0, + "3": 46.6, + "4": 13.2, + "5": 0.4 + }, + "ge3_count": 301, + "ge3_pct": 60.2, + "ge4_count": 68, + "ge4_pct": 13.6 +} \ No newline at end of file diff --git a/code/f3_filtering/reports/F3_v3_build.log b/code/f3_filtering/reports/F3_v3_build.log new file mode 100644 index 0000000000000000000000000000000000000000..b0d07f312bddf4e580defd7e7f8f5c9f0e59beba --- /dev/null +++ b/code/f3_filtering/reports/F3_v3_build.log @@ -0,0 +1,45 @@ +=== F3_v3 cleaning pipeline === +Pass 1: scanning /raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v2.jsonl + [50,000] bad_long=827 bad_line=934 bad_ngram=1382 uniq_hashes=42,720 + [100,000] bad_long=1732 bad_line=1875 bad_ngram=2931 uniq_hashes=81,939 + [150,000] bad_long=2709 bad_line=3110 bad_ngram=4630 uniq_hashes=119,702 + [200,000] bad_long=3624 bad_line=4026 bad_ngram=6034 uniq_hashes=159,121 +Pass 1 done in 39s. total samples = 207,009 + exact-content dup clusters: 17,531 covering 61,156 rows +Pass 2: writing /raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl + [50,000] kept=40523 dropped: long=827, line=708, ngram=1046, dup=6896 + [100,000] kept=77467 dropped: long=1732, line=1400, ngram=2224, dup=17177 + [150,000] kept=112838 dropped: long=2709, line=2307, ngram=3474, dup=28672 +{ + "input": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v2.jsonl", + "output": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl", + "input_size": 207009, + "final_size": 154103, + "kept_rate": 0.7444, + "rules": [ + "R1: drop if assistant > 8000 chars", + "R2: drop if any stripped line repeats >= 10 times in assistant", + "R3: drop if any 5-gram repeats >= 20 times in assistant", + "R4: drop cross-corpus exact-content duplicates (keep first occurrence)" + ], + "scan_stats_before_filter": { + "samples_with_long_assistant": 3764, + "samples_with_line_repeat": 4140, + "samples_with_ngram_repeat": 6251, + "samples_with_both_line_and_ngram": 652, + "samples_with_only_line": 3488, + "samples_with_only_ngram": 5599, + "exact_dup_clusters": 17531, + "exact_dup_rows_total": 61156 + }, + "applied_drops": { + "R1_too_long": 3764, + "R2_line_repeat": 3046, + "R3_ngram_repeat": 4677, + "R4_dup": 41419, + "total_dropped": 52906 + }, + "elapsed_sec": 81.8 +} + +Wrote /raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl (0.79 GB, 154,103 samples) diff --git a/code/f3_filtering/reports/F3_v3_report.json b/code/f3_filtering/reports/F3_v3_report.json new file mode 100644 index 0000000000000000000000000000000000000000..89c1ae0594bb2659ff493080496f743e9370cd21 --- /dev/null +++ b/code/f3_filtering/reports/F3_v3_report.json @@ -0,0 +1,31 @@ +{ + "input": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v2.jsonl", + "output": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl", + "input_size": 207009, + "final_size": 154103, + "kept_rate": 0.7444, + "rules": [ + "R1: drop if assistant > 8000 chars", + "R2: drop if any stripped line repeats >= 10 times in assistant", + "R3: drop if any 5-gram repeats >= 20 times in assistant", + "R4: drop cross-corpus exact-content duplicates (keep first occurrence)" + ], + "scan_stats_before_filter": { + "samples_with_long_assistant": 3764, + "samples_with_line_repeat": 4140, + "samples_with_ngram_repeat": 6251, + "samples_with_both_line_and_ngram": 652, + "samples_with_only_line": 3488, + "samples_with_only_ngram": 5599, + "exact_dup_clusters": 17531, + "exact_dup_rows_total": 61156 + }, + "applied_drops": { + "R1_too_long": 3764, + "R2_line_repeat": 3046, + "R3_ngram_repeat": 4677, + "R4_dup": 41419, + "total_dropped": 52906 + }, + "elapsed_sec": 81.8 +} \ No newline at end of file diff --git a/code/f3_filtering/reports/F3_v4_build.log b/code/f3_filtering/reports/F3_v4_build.log new file mode 100644 index 0000000000000000000000000000000000000000..f78a6a6a693de7e00f0fe06095ae9a1f9e6a7965 --- /dev/null +++ b/code/f3_filtering/reports/F3_v4_build.log @@ -0,0 +1,40 @@ +=== F3_v4 cleaning pipeline === +Pass 1: scanning + computing features + minhash + [20,000] features computed + [40,000] features computed + [60,000] features computed + [80,000] features computed + [100,000] features computed + [120,000] features computed + [140,000] features computed +Pass 1 done in 95s. total = 154,103 +Pass 2: LSH bucketing for near-dup + candidate pairs: 28,732 + near-dup marked: 6,390 +Pass 3: writing /raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v4.jsonl +{ + "input": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v3.jsonl", + "output": "/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v4.jsonl", + "input_size": 154103, + "final_size": 134775, + "kept_rate": 0.8746, + "rules": [ + "S1: MinHash near-dup, Jaccard >= 0.85, bottom-128 shingle-8", + "S2: template ctor / setup / main with trivial body (content-based)", + "S3: trivial body (< 3 non-trivial statements)", + "S4: comment+docstring ratio > 0.7", + "S6: import-only / docstring-only assistant", + "S8: placeholder residue (TODO, FIXME, XXX, HACK, pdb.set_trace, ...)" + ], + "applied_drops": { + "S1_near_dup": 6390, + "S3_trivial": 6376, + "S8_placeholder": 3958, + "S4_comment_ratio": 2286, + "S2_template": 318 + }, + "total_dropped": 19328, + "elapsed_sec": 101.0 +} + +Wrote /raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v4.jsonl (0.70 GB, 134,775 samples) diff --git a/code/pretrain_data_prep/analyze_template_repetition.py b/code/pretrain_data_prep/analyze_template_repetition.py new file mode 100644 index 0000000000000000000000000000000000000000..61ad097954f7f21fed99f963b39e7e912a75b561 --- /dev/null +++ b/code/pretrain_data_prep/analyze_template_repetition.py @@ -0,0 +1,199 @@ +"""Scan filtered/filtered_*.jsonl, report: + 1. Top template-style basenames (counts + total bytes + duplicate ratio per file). + 2. Top high-frequency lines (stripped) across the whole corpus, with sample coverage. + 3. Top short-phrase n-grams (3-gram and 5-gram over tokens of stripped lines), sampled. + +Output: filtered_repetition_report.json + .md +Pure-stdlib, single-pass over disk, ~10GB. +""" +from __future__ import annotations + +import json +import os +import re +import sys +from collections import Counter, defaultdict +from glob import glob +from pathlib import Path + +SRC_DIR = Path("/raid/data/weifeng/Datasets/filtered") +OUT_JSON = Path("/raid/data/weifeng/Datasets/filtered_repetition_report.json") +OUT_MD = Path("/raid/data/weifeng/Datasets/filtered_repetition_report.md") + +TEMPLATE_BASENAMES = { + "__init__.py", "setup.py", "config.py", "conf.py", "settings.py", + "constants.py", "version.py", "_version.py", "manage.py", + "decorators.py", "exceptions.py", "errors.py", "utils.py", + "types.py", "schemas.py", "logging.py", "logger.py", + "__main__.py", "wsgi.py", "asgi.py", "urls.py", "apps.py", + "models.py", "admin.py", # not always template, but check +} + +# heuristic: line frequency we care about +TOP_LINES_KEEP = 2000 # keep top N lines for report +TOP_BASENAMES_KEEP = 500 +NGRAM_TOP_KEEP = 500 +NGRAM_SAMPLE_EVERY = 20 # only do n-gram counting on every Nth file to save memory + +# strip whitespace, ignore very short / very long lines for stats +MIN_LINE_LEN = 8 +MAX_LINE_LEN = 200 + +# token regex for n-grams over line text +TOKEN_RE = re.compile(r"[A-Za-z_][A-Za-z0-9_]*|[^\s\w]") + + +def main() -> None: + files = sorted(glob(str(SRC_DIR / "filtered_*.jsonl"))) + print(f"found {len(files)} jsonl files", flush=True) + + basename_counter: Counter[str] = Counter() + basename_bytes: Counter[str] = Counter() + basename_dup_lines: Counter[str] = Counter() # how many duplicate lines (within file) total per basename + line_counter: Counter[str] = Counter() + line_sample_coverage: Counter[str] = Counter() # how many distinct samples contain the line + ngram3_counter: Counter[tuple] = Counter() + ngram5_counter: Counter[tuple] = Counter() + + total_samples = 0 + total_bytes = 0 + py_samples = 0 + + for fi, fp in enumerate(files): + do_ngram = (fi % NGRAM_SAMPLE_EVERY == 0) + try: + with open(fp, encoding="utf-8") as f: + for line in f: + try: + o = json.loads(line) + except Exception: + continue + path = o.get("path", "") or "" + text = o.get("text", "") or "" + if not text: + continue + total_samples += 1 + total_bytes += len(text) + basename = os.path.basename(path) if path else "" + basename_counter[basename] += 1 + basename_bytes[basename] += len(text) + if path.endswith(".py"): + py_samples += 1 + + # within-file line duplication: how repetitive is THIS file? + file_lines = [ln.strip() for ln in text.split("\n")] + file_line_cnt = Counter(ln for ln in file_lines if MIN_LINE_LEN <= len(ln) <= MAX_LINE_LEN) + dup_here = sum(c - 1 for c in file_line_cnt.values() if c > 1) + basename_dup_lines[basename] += dup_here + + # cross-corpus line stats + seen_in_this_sample: set[str] = set() + for ln, c in file_line_cnt.items(): + line_counter[ln] += c + if ln not in seen_in_this_sample: + line_sample_coverage[ln] += 1 + seen_in_this_sample.add(ln) + + if do_ngram and len(file_lines) > 5: + # take a slice to bound memory + for ln in file_lines[:400]: + toks = TOKEN_RE.findall(ln) + if len(toks) < 3: + continue + for i in range(len(toks) - 2): + ngram3_counter[tuple(toks[i:i + 3])] += 1 + if len(toks) >= 5: + for i in range(len(toks) - 4): + ngram5_counter[tuple(toks[i:i + 5])] += 1 + + # prune to avoid OOM + if total_samples % 100000 == 0: + print(f" [{total_samples}] processed; " + f"line_counter={len(line_counter):,} ngram3={len(ngram3_counter):,}", + flush=True) + if len(line_counter) > 5_000_000: + # keep top half + cutoff = line_counter.most_common(2_000_000) + line_counter = Counter(dict(cutoff)) + line_sample_coverage = Counter({k: line_sample_coverage[k] for k, _ in cutoff}) + if len(ngram3_counter) > 3_000_000: + ngram3_counter = Counter(dict(ngram3_counter.most_common(1_000_000))) + if len(ngram5_counter) > 3_000_000: + ngram5_counter = Counter(dict(ngram5_counter.most_common(1_000_000))) + except Exception as e: + print(f" ERR {fp}: {e}", file=sys.stderr, flush=True) + + print(f"DONE: {total_samples:,} samples, {total_bytes/1e9:.2f} GB", flush=True) + + # ----- build report ----- + top_basenames = basename_counter.most_common(TOP_BASENAMES_KEEP) + top_lines = line_counter.most_common(TOP_LINES_KEEP) + top_ngram3 = ngram3_counter.most_common(NGRAM_TOP_KEEP) + top_ngram5 = ngram5_counter.most_common(NGRAM_TOP_KEEP) + + report = { + "total_samples": total_samples, + "total_bytes": total_bytes, + "py_samples": py_samples, + "n_files_scanned": len(files), + "top_basenames": [ + { + "basename": b, + "count": c, + "pct_of_corpus": round(c / total_samples * 100, 3), + "total_bytes": basename_bytes[b], + "dup_lines_within_files": basename_dup_lines[b], + } + for b, c in top_basenames + ], + "top_lines_corpus_wide": [ + { + "line": ln, + "occurrences": c, + "n_samples_containing": line_sample_coverage[ln], + "pct_samples": round(line_sample_coverage[ln] / total_samples * 100, 3), + } + for ln, c in top_lines[:500] + ], + "top_3grams": [{"tokens": list(t), "count": c} for t, c in top_ngram3], + "top_5grams": [{"tokens": list(t), "count": c} for t, c in top_ngram5], + "ngram_sampling_note": f"n-gram counted on every {NGRAM_SAMPLE_EVERY}th file, first 400 lines per sample", + } + OUT_JSON.write_text(json.dumps(report, ensure_ascii=False, indent=2)) + print(f"wrote {OUT_JSON} ({OUT_JSON.stat().st_size/1e6:.1f} MB)", flush=True) + + # ----- markdown summary ----- + md = [] + md.append(f"# Filtered pretrain repetition report\n") + md.append(f"- samples: **{total_samples:,}**, bytes: **{total_bytes/1e9:.2f} GB**, .py: {py_samples:,}\n") + md.append(f"- files scanned: {len(files)}\n\n") + + md.append("## Top template basenames (by sample count)\n\n") + md.append("| basename | count | % corpus | total bytes | dup lines (within-file) |\n|---|---:|---:|---:|---:|\n") + for b, c in top_basenames[:60]: + md.append(f"| `{b}` | {c} | {c/total_samples*100:.2f}% | {basename_bytes[b]/1e6:.1f} MB | {basename_dup_lines[b]} |\n") + + md.append("\n## Top corpus-wide lines (frequency)\n\n") + md.append("| line | occurrences | samples containing | % samples |\n|---|---:|---:|---:|\n") + for ln, c in top_lines[:80]: + disp = ln.replace("|", "\\|")[:120] + md.append(f"| `{disp}` | {c} | {line_sample_coverage[ln]} | {line_sample_coverage[ln]/total_samples*100:.2f}% |\n") + + md.append("\n## Top 5-grams (token-level)\n\n") + md.append("| 5-gram | count |\n|---|---:|\n") + for t, c in top_ngram5[:60]: + disp = " ".join(t).replace("|", "\\|")[:120] + md.append(f"| `{disp}` | {c} |\n") + + md.append("\n## Top 3-grams (token-level)\n\n") + md.append("| 3-gram | count |\n|---|---:|\n") + for t, c in top_ngram3[:60]: + disp = " ".join(t).replace("|", "\\|")[:120] + md.append(f"| `{disp}` | {c} |\n") + + OUT_MD.write_text("".join(md)) + print(f"wrote {OUT_MD}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/code/pretrain_data_prep/build_filtered_pretrain.py b/code/pretrain_data_prep/build_filtered_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..f8c3d14ae9e3d3e2f09629bf72b7d024d696c33c --- /dev/null +++ b/code/pretrain_data_prep/build_filtered_pretrain.py @@ -0,0 +1,76 @@ +"""Merge filtered/*.jsonl into swift pre-train format and shuffle. + +Output: code_messages_pre_train_filtered.jsonl +Format per line: {"messages": [{"role": "assistant", "content": }]} +""" +from __future__ import annotations + +import json +import random +from collections import Counter +from pathlib import Path + +SRC_DIR = Path("/raid/data/weifeng/Datasets/filtered") +DST = Path("/raid/data/weifeng/Datasets/code_messages_pre_train_filtered.jsonl") +REPORT = Path("/raid/data/weifeng/Datasets/code_messages_pre_train_filtered_report.json") +SEED = 0 + + +def main() -> None: + rng = random.Random(SEED) + files = sorted(SRC_DIR.glob("filtered_*.jsonl")) + print(f"found {len(files)} domain files") + + out_lines: list[str] = [] + by_domain: Counter[str] = Counter() + n_total = n_kept = n_empty = n_bad = 0 + bad_by_file: Counter[str] = Counter() + + for fp in files: + domain_kept = 0 + with open(fp, encoding="utf-8", errors="replace") as f: + for line_no, line in enumerate(f, 1): + n_total += 1 + try: + obj = json.loads(line) + except Exception: + n_bad += 1 + bad_by_file[fp.name] += 1 + continue + text = obj.get("text", "") + if not isinstance(text, str) or not text.strip(): + n_empty += 1 + continue + record = {"messages": [{"role": "assistant", "content": text}]} + out_lines.append(json.dumps(record, ensure_ascii=False)) + domain_kept += 1 + n_kept += 1 + by_domain[fp.stem.replace("filtered_", "")] = domain_kept + print(f" {fp.name}: kept {domain_kept} (bad rows: {bad_by_file[fp.name]})") + + print(f"\nshuffling {len(out_lines)} records (seed={SEED})...") + rng.shuffle(out_lines) + + print(f"writing → {DST}") + with open(DST, "w", encoding="utf-8") as fout: + for line in out_lines: + fout.write(line + "\n") + + report = { + "src_dir": str(SRC_DIR), + "dst": str(DST), + "seed": SEED, + "n_total_input": n_total, + "n_empty_dropped": n_empty, + "n_bad_rows_dropped": n_bad, + "bad_rows_by_file": dict(bad_by_file), + "n_kept": n_kept, + "per_domain_kept": dict(by_domain.most_common()), + "total_bytes": DST.stat().st_size, + } + REPORT.write_text(json.dumps(report, indent=2, ensure_ascii=False)) + print(json.dumps(report, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/code/pretrain_data_prep/dryrun_filter_repetition.py b/code/pretrain_data_prep/dryrun_filter_repetition.py new file mode 100644 index 0000000000000000000000000000000000000000..a85aa25a99581b3fcbb35459590b7a9317372d54 --- /dev/null +++ b/code/pretrain_data_prep/dryrun_filter_repetition.py @@ -0,0 +1,166 @@ +"""Dry-run apply the 5 filter rules over filtered/filtered_*.jsonl. Count only, +don't write output. + +Rules: + 1) drop by basename (CHANGELOG/NEWS/CONTRIBUTING/version.py/etc) + 2) cap per basename N=200 (random sample via reservoir) + 3) collapse long runs of same char inside a line (modifies text, doesn't drop) + 4) drop sample if any 5-token n-gram repeats >=30 times + 5) drop sample if any short line repeats >=20 times + +Reports: how many samples remain, how much each rule dropped. +""" +from __future__ import annotations + +import json +import os +import random +import re +from collections import Counter, defaultdict +from glob import glob +from pathlib import Path + +SRC_DIR = Path("/raid/data/weifeng/Datasets/filtered") +REPORT = Path("/raid/data/weifeng/Datasets/dryrun_filter_report.json") + +DROP_BASENAMES = { + "CHANGELOG.md", "NEWS.md", "CONTRIBUTING.md", "SKILL.md", + "package-info.java", "index.md", "version.py", "_version.py", +} +CAPPED_BASENAMES = { + "__init__.py", "utils.py", "setup.py", "conftest.py", "conf.py", + "config.py", "settings.py", "constants.py", "__main__.py", +} +CAP = 200 + +RUN_SAME_CHAR = re.compile(r"^(.)\1{40,}$") +RUN_PUNCT = re.compile(r"^([^\w\s])\1{10,}$") +TOKEN_RE = re.compile(r"[A-Za-z_][A-Za-z0-9_]*|[^\s\w]") + +NGRAM_N = 5 +NGRAM_THRESH = 30 +LINE_REP_THRESH = 20 +LINE_MAX_LEN = 80 +SEED = 0 + + +def main() -> None: + rng = random.Random(SEED) + files = sorted(glob(str(SRC_DIR / "filtered_*.jsonl"))) + + total = 0 + dropped_rule1 = 0 + dropped_rule2 = 0 + dropped_rule4 = 0 + dropped_rule5 = 0 + modified_rule3 = 0 + kept = 0 + bytes_kept = 0 + bytes_total = 0 + + # rule2 reservoir: for capped basenames, decide kept indices by reservoir sampling + # easier: stream twice OR random.random() < CAP/seen_count threshold. We do + # the simpler "see first then decide". Pass 1 counts. Pass 2 applies. To save + # time on dry-run we accept slight bias: simple random.random() keep_prob[bname]: + dropped_rule2 += 1 + drop_by_basename[bname] += 1 + continue + + # rule 3: in-place collapse + lines_in = text.split("\n") + modified = False + new_lines = [] + for ln in lines_in: + if RUN_SAME_CHAR.match(ln) or RUN_PUNCT.match(ln): + new_lines.append(ln[:5]) + modified = True + else: + new_lines.append(ln) + if modified: + modified_rule3 += 1 + text = "\n".join(new_lines) + + # rule 5: short line repeat + stripped = [ln.strip() for ln in new_lines] + cnt = Counter(ln for ln in stripped if 0 < len(ln) <= LINE_MAX_LEN) + if cnt and cnt.most_common(1)[0][1] >= LINE_REP_THRESH: + dropped_rule5 += 1 + continue + + # rule 4: 5-gram repeat (skip for very small samples) + tokens = TOKEN_RE.findall(text) + if len(tokens) >= 100: + # only check head + tail slices to bound cost + sample_toks = tokens[:5000] if len(tokens) > 5000 else tokens + ngrams = Counter() + for i in range(len(sample_toks) - NGRAM_N + 1): + ngrams[tuple(sample_toks[i:i + NGRAM_N])] += 1 + if ngrams and ngrams.most_common(1)[0][1] >= NGRAM_THRESH: + dropped_rule4 += 1 + continue + + kept += 1 + bytes_kept += len(text) + + if total % 100000 == 0: + print(f" [{total}] kept={kept} r1={dropped_rule1} r2={dropped_rule2} " + f"r4={dropped_rule4} r5={dropped_rule5} r3mod={modified_rule3}", flush=True) + + report = { + "total": total, + "kept": kept, + "kept_pct": round(kept / total * 100, 2), + "dropped_rule1_basename_drop": dropped_rule1, + "dropped_rule2_basename_cap": dropped_rule2, + "dropped_rule4_ngram_repeat": dropped_rule4, + "dropped_rule5_line_repeat": dropped_rule5, + "rule3_in_place_collapsed_samples": modified_rule3, + "bytes_total": bytes_total, + "bytes_kept": bytes_kept, + "bytes_kept_pct": round(bytes_kept / bytes_total * 100, 2), + "top_dropped_basenames": drop_by_basename.most_common(40), + "thresholds": { + "CAP": CAP, + "NGRAM_N": NGRAM_N, + "NGRAM_THRESH": NGRAM_THRESH, + "LINE_REP_THRESH": LINE_REP_THRESH, + "LINE_MAX_LEN": LINE_MAX_LEN, + }, + } + REPORT.write_text(json.dumps(report, ensure_ascii=False, indent=2)) + print(json.dumps(report, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/code/pretrain_data_prep/reports/code_messages_pre_train_filtered_report.json b/code/pretrain_data_prep/reports/code_messages_pre_train_filtered_report.json new file mode 100644 index 0000000000000000000000000000000000000000..47dc0c7e7ef74f6917a3d8db6ccf5352fdb7a074 --- /dev/null +++ b/code/pretrain_data_prep/reports/code_messages_pre_train_filtered_report.json @@ -0,0 +1,191 @@ +{ + "src_dir": "/raid/data/weifeng/Datasets/filtered", + "dst": "/raid/data/weifeng/Datasets/code_messages_pre_train_filtered.jsonl", + "seed": 0, + "n_total_input": 1656628, + "n_empty_dropped": 0, + "n_bad_rows_dropped": 0, + "bad_rows_by_file": {}, + "n_kept": 1656628, + "per_domain_kept": { + "Human": 241767, + "System_biology": 113218, + "Potential": 112912, + "Folding": 80374, + "3D": 69104, + "Activation": 68866, + "Networks": 67309, + "Single_cell": 54945, + "Generate": 47990, + "Imaging": 46115, + "Ligand": 41856, + "Protein": 39974, + "Bioinformatics": 36252, + "Gene_editing": 29170, + "Biomedical": 24735, + "Next_generation_sequencing": 24553, + "Quantum_mechanics": 24443, + "Score": 23368, + "Sequencing": 21806, + "Biologics": 20203, + "Modeling": 18449, + "Computational_Materials": 17899, + "Computational_Chemistry": 15152, + "Heterogeneity": 15150, + "Cycle": 14545, + "Integration": 14539, + "Mechanism": 14384, + "GAN": 13513, + "Molecular_biology": 13031, + "Kinetics": 12501, + "Ion": 12365, + "Diagnostics": 12150, + "Stochastic_modeling": 11911, + "Pharmacometrics": 11079, + "Design": 10790, + "Genotype": 10392, + "Screening": 10379, + "Genomics": 9262, + "DFT": 8520, + "Antibody": 7986, + "RNA": 7886, + "Proteomics": 7827, + "Coupled_cluster": 7771, + "Gene": 6981, + "High_throughput": 6934, + "Transformer": 6788, + "Dynamics": 6577, + "Protein_Protein_Interactions": 5893, + "Profiling": 5645, + "Cheminformatics": 5497, + "Chemistry": 5483, + "Diffusion": 5159, + "Crystal": 5120, + "Biology": 4777, + "Material": 4718, + "Evolution": 4649, + "Physiology": 4624, + "Hartree_Fock": 4540, + "Autoregressive": 4413, + "Electronic_structure": 3949, + "Flow_matching": 3815, + "Ab_initio": 3757, + "Lipid": 3718, + "Finite_element_method": 3697, + "Biotechnology": 3410, + "Phenotype": 3278, + "Monte_Carlo": 3244, + "Compartmental_model": 2834, + "Agent_based_model": 2744, + "Chemical_space": 2297, + "Microbial": 2198, + "Bio": 2191, + "Translation": 2157, + "Pathology": 2135, + "Metabolomics": 2090, + "Spectrometry": 2053, + "Spatial_Transcriptomics": 1951, + "Drug_repurpose": 1931, + "Pathway": 1798, + "De_novo": 1768, + "Reinforcement_learning": 1731, + "Drug": 1702, + "Immunology": 1676, + "Absorption": 1659, + "2D": 1573, + "Redox": 1537, + "Anomaly_detection": 1452, + "Pharmacology": 1374, + "Spatial_biology": 1357, + "Reproduction_number": 1336, + "First_principles_based_DFT": 1300, + "Antigen": 1293, + "DNA": 1277, + "AMR": 1124, + "Signaling": 1078, + "QM_MM": 974, + "Hydrophobic": 937, + "Prediction": 903, + "Free_energy_perturbation": 819, + "Pharmacokinetics": 777, + "Reaction": 751, + "Pharmaceutics": 742, + "Quantum_biology": 728, + "Multi_omics": 721, + "Lead_optimization": 719, + "Enzyme": 693, + "Metagenomics": 660, + "Generative": 653, + "Medicine": 617, + "Porous": 611, + "Rosettafold": 573, + "Phase_field_technique": 517, + "Transcriptomics": 459, + "Docking": 449, + "Biomarker": 439, + "Allele": 431, + "Discovery": 429, + "Microbiology": 411, + "Marker": 402, + "Lead_discovery": 390, + "Pharmacogenetics": 387, + "Pandemic": 386, + "Variational_autoencoders": 377, + "Transcription": 346, + "Nucleic_acids": 345, + "Pathogens": 314, + "Neurology": 313, + "Multi_scale_modeling": 310, + "Sensors": 301, + "CRISPR": 280, + "Glycomics": 270, + "Disease": 220, + "Quantum_Chemistry": 219, + "Substrate": 217, + "Assay": 201, + "Viral": 201, + "Biophysics": 200, + "VAE": 199, + "Omics": 198, + "Conformation": 194, + "Bio_foundation_model": 176, + "Ensemble": 170, + "Cell_biology": 169, + "Metabolic": 157, + "In_Silico": 150, + "Metamaterial": 144, + "Computational_Biochemistry": 137, + "Personalized": 137, + "Bioengineering": 132, + "AlphaFold": 99, + "Explainable_AI": 99, + "Genesis": 99, + "Nowcasting": 97, + "QSAR": 96, + "Pharmacogenomics": 93, + "Antagonist": 88, + "Codon": 76, + "ADMET": 75, + "Epigenetics": 71, + "Conjugate": 66, + "Toxicology": 43, + "RNN": 34, + "Vaccine": 33, + "PBPK_model": 31, + "Hydrophilic": 26, + "Protein_protein": 23, + "Hit_to_lead": 21, + "Homo_Lumo": 19, + "Homeostasis": 17, + "Receptor": 13, + "Biosensors": 9, + "Inhibition": 8, + "Organoid": 7, + "Lipidomics": 4, + "Biochemistry": 2, + "Digital_twin": 1, + "Cell_atlas": 0, + "Pseudotime_analysis": 0 + }, + "total_bytes": 9992235548 +} \ No newline at end of file diff --git a/code/pretrain_data_prep/reports/code_messages_pre_train_filtered_stages_report.json b/code/pretrain_data_prep/reports/code_messages_pre_train_filtered_stages_report.json new file mode 100644 index 0000000000000000000000000000000000000000..56e1a3da7bb648774120ad9c7696c4eb7d792af4 --- /dev/null +++ b/code/pretrain_data_prep/reports/code_messages_pre_train_filtered_stages_report.json @@ -0,0 +1,19 @@ +{ + "src": "/raid/data/weifeng/Datasets/code_messages_pre_train_filtered.jsonl", + "n_total": 1656628, + "stage1_path": "/raid/data/weifeng/Datasets/code_messages_pre_train_filtered_stage1.jsonl", + "stage1_count": 902115, + "stage1_pct": 0.5445489270976949, + "stage2_path": "/raid/data/weifeng/Datasets/code_messages_pre_train_filtered_stage2.jsonl", + "stage2_count": 605249, + "stage2_pct": 0.3653499759752944, + "stage3_path": "/raid/data/weifeng/Datasets/code_messages_pre_train_filtered_stage3.jsonl", + "stage3_count": 149264, + "stage3_pct": 0.09010109692701077, + "thresholds": [ + 1024, + 4096 + ], + "tokenizer": "Qwen/Qwen2.5-Coder-0.5B", + "elapsed_min": 10.5 +} \ No newline at end of file diff --git a/code/pretrain_data_prep/reports/dryrun_filter_report.json b/code/pretrain_data_prep/reports/dryrun_filter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..1c3dc3ba6a154f3fcbb1f3f6491f1017cb91c5c3 --- /dev/null +++ b/code/pretrain_data_prep/reports/dryrun_filter_report.json @@ -0,0 +1,90 @@ +{ + "total": 1656628, + "kept": 1046224, + "kept_pct": 63.15, + "dropped_rule1_basename_drop": 8994, + "dropped_rule2_basename_cap": 66713, + "dropped_rule4_ngram_repeat": 275296, + "dropped_rule5_line_repeat": 259401, + "rule3_in_place_collapsed_samples": 78338, + "bytes_total": 9490697379, + "bytes_kept": 3693108225, + "bytes_kept_pct": 38.91, + "top_dropped_basenames": [ + [ + "__init__.py", + 41168 + ], + [ + "utils.py", + 10610 + ], + [ + "setup.py", + 6890 + ], + [ + "index.md", + 2856 + ], + [ + "config.py", + 2122 + ], + [ + "conftest.py", + 1905 + ], + [ + "CHANGELOG.md", + 1790 + ], + [ + "conf.py", + 1781 + ], + [ + "CONTRIBUTING.md", + 1552 + ], + [ + "__main__.py", + 1076 + ], + [ + "constants.py", + 884 + ], + [ + "NEWS.md", + 878 + ], + [ + "package-info.java", + 609 + ], + [ + "version.py", + 561 + ], + [ + "SKILL.md", + 497 + ], + [ + "settings.py", + 277 + ], + [ + "_version.py", + 251 + ] + ], + "thresholds": { + "CAP": 200, + "NGRAM_N": 5, + "NGRAM_THRESH": 30, + "LINE_REP_THRESH": 20, + "LINE_MAX_LEN": 80 + } +} \ No newline at end of file diff --git a/code/pretrain_data_prep/split_filtered_by_token_length.py b/code/pretrain_data_prep/split_filtered_by_token_length.py new file mode 100644 index 0000000000000000000000000000000000000000..4a220e2cd6d4cc1d680ab1d251fa51b8eeacc768 --- /dev/null +++ b/code/pretrain_data_prep/split_filtered_by_token_length.py @@ -0,0 +1,86 @@ +"""Split code_messages_pre_train_filtered.jsonl into 3 token-length stages. + +Stage 1: <=1024 tokens +Stage 2: 1025-4096 +Stage 3: >4096 + +Tokenizer: Qwen2.5-Coder-0.5B. +Uses multiprocessing for speed. +""" +from __future__ import annotations + +import json +import multiprocessing as mp +from pathlib import Path + +SRC = Path("/raid/data/weifeng/Datasets/code_messages_pre_train_filtered.jsonl") +DST_DIR = Path("/raid/data/weifeng/Datasets") +DST_S1 = DST_DIR / "code_messages_pre_train_filtered_stage1.jsonl" # <=1024 +DST_S2 = DST_DIR / "code_messages_pre_train_filtered_stage2.jsonl" # 1025-4096 +DST_S3 = DST_DIR / "code_messages_pre_train_filtered_stage3.jsonl" # >4096 +REPORT = DST_DIR / "code_messages_pre_train_filtered_stages_report.json" + +T1 = 1024 +T2 = 4096 +N_WORKERS = 8 +CHUNK = 1000 + +# Worker setup +_tokenizer = None +def init_worker(): + global _tokenizer + from transformers import AutoTokenizer + _tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-0.5B", trust_remote_code=True) + +def count_tokens(line: str) -> tuple[int, str]: + """Return (n_tokens, original_line) so we can keep the line.""" + o = json.loads(line) + text = o["messages"][0]["content"] + n = len(_tokenizer.encode(text, add_special_tokens=False)) + return n, line + + +def main() -> None: + # Read all lines first (cheap, ~10GB) + print(f"loading {SRC} ...") + with open(SRC, "r", encoding="utf-8") as f: + lines = f.readlines() + print(f"loaded {len(lines):,} lines") + + out_s1 = open(DST_S1, "w", encoding="utf-8") + out_s2 = open(DST_S2, "w", encoding="utf-8") + out_s3 = open(DST_S3, "w", encoding="utf-8") + + n_s1 = n_s2 = n_s3 = 0 + import time + t0 = time.time() + with mp.Pool(N_WORKERS, initializer=init_worker) as pool: + for i, (n_tok, line) in enumerate(pool.imap_unordered(count_tokens, lines, chunksize=CHUNK)): + if n_tok <= T1: + out_s1.write(line); n_s1 += 1 + elif n_tok <= T2: + out_s2.write(line); n_s2 += 1 + else: + out_s3.write(line); n_s3 += 1 + if (i + 1) % 50000 == 0: + elapsed = time.time() - t0 + rate = (i + 1) / max(elapsed, 1e-9) + eta = (len(lines) - i - 1) / max(rate, 1e-9) + print(f" [{i+1:>8,}/{len(lines):,}] s1={n_s1:,} s2={n_s2:,} s3={n_s3:,} rate={rate:.0f}/s eta={eta/60:.1f}min", flush=True) + + out_s1.close(); out_s2.close(); out_s3.close() + report = { + "src": str(SRC), "n_total": len(lines), + "stage1_path": str(DST_S1), "stage1_count": n_s1, "stage1_pct": n_s1/len(lines), + "stage2_path": str(DST_S2), "stage2_count": n_s2, "stage2_pct": n_s2/len(lines), + "stage3_path": str(DST_S3), "stage3_count": n_s3, "stage3_pct": n_s3/len(lines), + "thresholds": [T1, T2], + "tokenizer": "Qwen/Qwen2.5-Coder-0.5B", + "elapsed_min": round((time.time() - t0)/60, 1), + } + REPORT.write_text(json.dumps(report, indent=2, ensure_ascii=False)) + print(json.dumps(report, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/data/f3/intermediate/F3_stage1.jsonl b/data/f3/intermediate/F3_stage1.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1fccc5b13c9e3621aec31adffd2ab1afc1f8dac7 --- /dev/null +++ b/data/f3/intermediate/F3_stage1.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:167d17cd03fbbdbca40e3ef289c81898120107ba13f975db2ce23e3d6516c1a3 +size 2276490009 diff --git a/data/f3/intermediate/F3_stage2.jsonl b/data/f3/intermediate/F3_stage2.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3ee589f3395a4548ac4e4dd6437d969a80286788 --- /dev/null +++ b/data/f3/intermediate/F3_stage2.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97353d440b3b0d292ff443cf2e705140ff5fa25ebbc2a65163d240b1eff03432 +size 1956575594 diff --git a/data/f3/intermediate/F3_stage3.jsonl b/data/f3/intermediate/F3_stage3.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ed07186a81d5c47cd7087831c441ad05308b80af --- /dev/null +++ b/data/f3/intermediate/F3_stage3.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c54871e7896bb83e49bed1f0fc07cef2021a8c7ddb5053c1f2c7d5eed8ddcb56 +size 1935884904 diff --git a/data/f3/intermediate/F3_stage4_calibration_scores.jsonl b/data/f3/intermediate/F3_stage4_calibration_scores.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2090496f9dc535f9429854b3e3a720db40ccd847 --- /dev/null +++ b/data/f3/intermediate/F3_stage4_calibration_scores.jsonl @@ -0,0 +1,500 @@ +{"idx": 201979, "score": 3, "raw_out": "3", "code_head": "def get_seq_onehot(seq):\n seq_onehot = np.zeros((len(seq), len(RESIDUE_TYPES)))\n total_p = 0\n total_l = 0\n # total_n = 0\n for i, res in enumerate(seq):\n # Check if the residue is "} +{"idx": 220500, "score": 3, "raw_out": "3", "code_head": "def test_seq_by_classes_invalid_fractions(self):\n \"\"\"Test sequence generation with invalid class fractions.\"\"\"\n # Test fraction > 1\n with pytest.raises(goose_exceptions.GooseInput"} +{"idx": 21225, "score": 3, "raw_out": "3", "code_head": "def filter_fasta(fasta_dict, HAVANA_IDs, region_lens):\n '''filters fasta'''\n \n filtered_dict = {}\n \n for transcript,seq in fasta_dict.items():\n if transcript[-5:] != 'PAR_Y':"} +{"idx": 135746, "score": 4, "raw_out": "4", "code_head": "def get_confidence_scores(\n batch: dict,\n outputs: dict,\n config: \"ConfigDict\",\n compute_per_sample: bool = False,\n) -> dict[str, torch.Tensor]:\n \"\"\"\n Wrapper for `_get_confidence_sc"} +{"idx": 268055, "score": 3, "raw_out": "3", "code_head": "def parse_input_data(path):\n file_names = os.listdir(path)\n for name in file_names:\n if not name.endswith(\".pdb\"):\n continue\n with open(f\"{path}/{name}\", \"rt\") as f:\n "} +{"idx": 254766, "score": 2, "raw_out": "2", "code_head": "def one_dataset(seed):\n if self.args.mode =='train':\n raw_dataset = LMDBDataset(self.args.data, split, epoch, self.args.max_epoch, self.lmdb_copy_thread)\n # sm"} +{"idx": 212302, "score": 3, "raw_out": "3", "code_head": "def build_fc(\n name: str,\n in_features: int,\n out_features: int,\n fc_kwargs: dict[str, Any],\n):\n kwargs = {\n 'in_features': in_features,\n 'out_features': out_features,\n "} +{"idx": 159023, "score": 3, "raw_out": "3", "code_head": "def sample_arm_natoms(self, arm_centers, protein_pos):\n pair_distance = torch.norm(arm_centers.view(-1, 1, 3) - protein_pos.view(1, -1, 3), p=2, dim=-1)\n p_natoms = torch.stack([(pair_di"} +{"idx": 249874, "score": 3, "raw_out": "3", "code_head": "def gen_peptides_ref_native_peptides(nat_peptides, sstype: str, standard=False):\n assert len(nat_peptides.shape) == 3\n length = nat_peptides.shape[0]\n nat_peptides_mass_center = np.mean(nat_p"} +{"idx": 187720, "score": 4, "raw_out": "4", "code_head": "def between_residue_clash_loss(\n pred_positions: geometry.Vec3Array, # (N, 14)\n atom_exists: jnp.ndarray, # (N, 14)\n atom_radius: jnp.ndarray, # (N, 14)\n residue_index: jnp.ndarray, # "} +{"idx": 305860, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n chain_id: Optional[str] = None,\n struct_oper_id: Optional[str] = None,\n label_seq_id: Optional[str] = None,\n exchanges: Optional[list] = None, "} +{"idx": 114526, "score": 3, "raw_out": "3", "code_head": "def run(self):\n\n # Check that there will not be git conflicts with inbuilt species\n # assert self.species not in ('Mmus', 'Hsap'), \\\n # \"Cannot overwrite inbuilt species. Pleas"} +{"idx": 264601, "score": 3, "raw_out": "3", "code_head": "def to_mol_conf(self, max_nuc):\n nucs = Nuclei.embed_to_masked(\n jax.device_put(self.coords),\n jax.device_put(self.charges),\n jax.device_put(self.species),\n "} +{"idx": 73019, "score": 3, "raw_out": "3", "code_head": "def realign_hit_to_structure(\n *,\n hit_sequence: str,\n hit_start_index: int,\n hit_end_index: int,\n full_length: int,\n structure_sequence: str,\n query_to_hit_mapping: Mapping[int, "} +{"idx": 147764, "score": 2, "raw_out": "2", "code_head": "def __init__(self, params, mol_1, mol_2 = None, _complex = None):\n \"\"\"\n Constructor for class APBSCmdToWebService\n params is APBSPrams instance\n mol_1,mol_2"} +{"idx": 73267, "score": 3, "raw_out": "3", "code_head": "def get_res_data_new(pdb_data: list):\n X = []\n Y = []\n\n for pdb in pdb_data:\n for i in range(min(len(pdb['poc_res_emb']), len(pdb['poc_labels']))): # iterate over pockets\n s"} +{"idx": 49718, "score": 4, "raw_out": "4", "code_head": "def sample_msa(protein, max_seq, keep_extra, seed=None):\n \"\"\"Sample MSA randomly, remaining sequences are stored are stored as `extra_*`.\"\"\" \n num_seq = protein[\"msa\"].shape[0]\n g = torch.Gen"} +{"idx": 324203, "score": 3, "raw_out": "3", "code_head": "def generate_sequences_post(\n self,\n num_seqs: int = 1,\n upper_bound: float = 10,\n nucleotide: bool = False,\n seed: Optional[\n int | np.random.Generator |"} +{"idx": 131337, "score": 3, "raw_out": "3", "code_head": "def process(self):\n processes = 10\n data_df = pd.read_csv(\n osp.join(self.raw_dir, self.raw_file_names),\n dtype=str,\n header=None,\n names=[\"sm"} +{"idx": 279216, "score": 3, "raw_out": "3", "code_head": "def __init__(self, ref_mols, output_dir, v3=False):\n self.ref_mols = ref_mols\n self.output_dir = output_dir\n self.v3 = v3\n"} +{"idx": 315570, "score": 3, "raw_out": "3", "code_head": "def main():\n set_seeds(2020)\n args = vars(parser.parse_args())\n\n alphabet = Protein()\n cfgs = []\n data_cfg = config.DataConfig(args[\"data_config\"]); cfgs.append(data_cfg)\n if args"} +{"idx": 77050, "score": 3, "raw_out": "3", "code_head": "def forward(self, \n m: torch.Tensor, \n mask: Optional[torch.Tensor] = None,\n chunk_size: Optional[int] = None\n ) -> torch.Tensor:\n \"\"\"\n Args:\n m:\n "} +{"idx": 162606, "score": 3, "raw_out": "3", "code_head": "def get_cnn_train_test_full_training_data_loader(training_data_name, comp_feature_list, tar_feature_list, batch_size=32, train_val_test=False, external_comp_feat_fl=None):\n import json\n\n trainin"} +{"idx": 51780, "score": 2, "raw_out": "2", "code_head": "def sample_one_seq(seq, passes=10):\n max_len = len(seq)\n adj_mat_tmp = np.zeros((max_len, max_len), dtype=np.int32)\n for i in range(max_len):\n if i != max_len - 1:\n adj_mat_"} +{"idx": 38662, "score": 2, "raw_out": "2", "code_head": "def tensorize(mol_batch, vocab):\n x = MolGraph.tensorize(mol_batch, vocab, common_atom_vocab)\n return to_numpy(x)\n"} +{"idx": 358604, "score": 3, "raw_out": "3", "code_head": "def coverage_wu(self, cov, beta=0.):\n \"\"\"GNMT coverage re-ranking score.\n\n See \"Google's Neural Machine Translation System\" :cite:`wu2016google`.\n ``cov`` is expected to be sized "} +{"idx": 173118, "score": 2, "raw_out": "2", "code_head": "def __init__(self, fp_names: List[str], fp_file: str = None, **kwargs):\n \"\"\"__init__\n\n Args:\n fp_names (List[str]): List of\n nbits (int): Number of bits\n "} +{"idx": 247538, "score": 4, "raw_out": "4", "code_head": "def step3_cluster(self, step2_df):\n \"\"\"Step 3: Final clustering with lower similarity threshold\"\"\"\n self.logger.info(\"Running Step 3: Final clustering\")\n \n valid_paths = st"} +{"idx": 293503, "score": 2, "raw_out": "2", "code_head": "def test_dockq_multichain_parity(test_system, r_chains, l_chains, expected, test_dir):\n dockq_directory = test_dir / test_system\n native = list(dockq_directory.glob(\"*.pdb\"))[0]\n models = lis"} +{"idx": 52796, "score": 3, "raw_out": "3", "code_head": "def get_db_proteins(\n class_filters,\n family_filters,\n kingdom_filters,\n taxonomy_filter_dict,\n ec_filters,\n connection,\n args,\n):\n \"\"\"Retrieve proteins from local CAZyme datab"} +{"idx": 185488, "score": 3, "raw_out": "3", "code_head": "def training_step(self, batch):\n pdb_id, data = batch\n pdb_id = pdb_id[0]\n loss_value, rna_recovery_rate, pred_rna_seq, one_hot = self.model_step(data)\n outputs = {\"loss\": "} +{"idx": 227631, "score": 2, "raw_out": "2", "code_head": "def test_process_pangenome_basic_function(self):\n \"\"\"tests basic functionality of the process_pangenome function\"\"\"\n tdir = tempfile.mkdtemp(prefix=\"filetest_\",)\n fpath = os.path."} +{"idx": 165778, "score": 3, "raw_out": "3", "code_head": "def _generate(\n self,\n token_exists_mask: Bool[Tensor, \"b n\"],\n atom_token_index: Int[Tensor, \"b a\"],\n atom_covalent_bond_indices: list[\n tuple[Int[Tensor, \"bond"} +{"idx": 320280, "score": 2, "raw_out": "2", "code_head": "def CLE_simulator_(self, level):\n\n self.calculate_half_response_(level)\n self.init_gene_bin_conc_(level)\n nReqSteps = self.calculate_required_steps_(level)\n sim_set = np.co"} +{"idx": 335766, "score": 1, "raw_out": "1", "code_head": "```c\nstatic void quantum_fourier_transform(QuantumState* state) {\n if (!state || !state->amplitudes) return;\n\n size_t n = state->num_qubits;\n size_t dim = 1ULL << n;\n\n // Allocate temporar"} +{"idx": 107205, "score": 3, "raw_out": "3", "code_head": "def parse(self, ref_fasta: Fasta, context_len: int):\n seq = ref_fasta.get_seq(\n self.chrom, self.center_posL-context_len, self.center_posR+context_len)\n offset = self.snp_pos "} +{"idx": 289681, "score": 2, "raw_out": "2", "code_head": "def add_rdkit_conf(data=None, mol=None):\n if mol is None:\n path = os.path.join('data', data.db, 'mols', data.data_id + '.sdf')\n mol = Chem.MolFromMolFile(path)\n else:\n asser"} +{"idx": 250090, "score": 3, "raw_out": "3", "code_head": "def get_pairs_sc_sc(name, fname, antigen_chains, antibody_chains, interface_angstroms=None):\n \"\"\"\n Define interaction by minimum distance between any side chain atoms\n\n :param name: Structure"} +{"idx": 232098, "score": 3, "raw_out": "3", "code_head": "def get_pwm(seqs) :\n pwm = np.zeros((len(seqs[0]), 4))\n \n for i in range(0, len(seqs)) :\n seq = seqs[i]\n for j in range(0, len(seq)) :\n if seq[j] == 'A' :\n "} +{"idx": 273339, "score": 2, "raw_out": "2", "code_head": "def build_sequence(sequences, seq_change):\n \"\"\"\n Construct the final DNA sequence by integrating original sequence segments with modifications.\n\n This function concatenates each segment from "} +{"idx": 136572, "score": 2, "raw_out": "2", "code_head": "def main(n_reactions: int):\n \"\"\" \"\"\"\n # Load SMILES data from the specified JSON file\n combined_data = {}\n\n cca = ChemGraph(\n model_name='gpt-4o-mini',\n workflow_type=\"single"} +{"idx": 32652, "score": 3, "raw_out": "3", "code_head": "def test_nested_loop_contexts_no_pseudoknot() -> None:\n structure = topology.RnaSecondaryStructureTopology(\"ACGU\", \"....\")\n contexts = structure.loop_contexts()\n assert len(contexts) == 1\n "} +{"idx": 287676, "score": 4, "raw_out": "4", "code_head": "def query(self, input_fasta_path: str) -> Mapping[str, Any]:\n \"\"\"Queries the database using HHblits.\"\"\"\n with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:\n a3m_path = os.path.joi"} +{"idx": 7361, "score": 3, "raw_out": "3", "code_head": "def violation_loss(\n violations: Dict[str, torch.Tensor],\n atom14_atom_exists: torch.Tensor,\n eps=1e-6,\n **kwargs,\n) -> torch.Tensor:\n num_atoms = torch.sum(atom14_atom_exists)\n l_cl"} +{"idx": 48901, "score": 3, "raw_out": "3", "code_head": "def get_ppi_dataset_setup(args, dataset_name=None):\n shared_arguments = dict(max_mask=args.masking_rate_max,\n min_mask=args.masking_rate_min,\n "} +{"idx": 209098, "score": 2, "raw_out": "2", "code_head": "def main(args):\n\n ### EXP peaks\n # Load experimental data and filter by benchmark genes\n path = args.exp_peaks_csv\n exp_df = pd.read_csv(path,index_col=0)\n # exp_df['fragment_parent_nam"} +{"idx": 350304, "score": 3, "raw_out": "3", "code_head": "def get_solution(\n self, t: ndarray, stacked: bool = True, with_keys: bool = False\n ) -> Union[Dict, ndarray]:\n \"\"\"Calculate solution of dynamics.\n\n Arguments\n ---------"} +{"idx": 327819, "score": 3, "raw_out": "3", "code_head": "def drawingConfigDisplaySelectedGenes(self, module):\n \"\"\" Will display DLOs for selected genes only.\n Similar to the selection of the 'Selected Genes' checkbox in the 'Display DLOs' sect"} +{"idx": 599, "score": 2, "raw_out": "2", "code_head": "def feature2seq(data) : \n A_onehot = np.array([1,0,0,0] , dtype=np.bool)\n C_onehot = np.array([0,1,0,0] , dtype=np.bool)\n G_onehot = np.array([0,0,1,0] , dtype=np.bool)\n T_onehot = np.a"} +{"idx": 320810, "score": 2, "raw_out": "2", "code_head": "```cpp\nchar *generate_mutated_sequence(char *seq, int len, double x, double d, int bw)\n{\n int i, j, wave = 0;\t\t\t/** wave is q-coordinate of the alignment path */\n char *mutated_seq;\n\n if(seq "} +{"idx": 258776, "score": 3, "raw_out": "3", "code_head": "def _features_from_obs(cls) -> Dict[GeneLocus, List[str]]:\n # Static read-only property:\n # What features do we want to extract from adata.obs?\n features_from_obs = {}\n for"} +{"idx": 174658, "score": 2, "raw_out": "2", "code_head": "def create_tensors_gene_exp_diff(\n control_idx,\n tumour_idx,\n original_idx,\n dataset,\n num_samples=20,\n):\n possible_pairs = np.array(list(itertools.product(control_idx, tumour_idx)))"} +{"idx": 127879, "score": 3, "raw_out": "3", "code_head": "def generate_motif_insertions(x, motif, flanks=(0, 0), masked_locations=None):\n \"\"\"Generate motif insertions in a sequence.\"\"\"\n _, L, A = x.shape\n start, end = 0, L\n x_mut = []\n motif_l"} +{"idx": 170500, "score": 3, "raw_out": "3", "code_head": "def __init__(self, args, pre_train_model, kg, max_path_len=3, state_pre_history=1):\n self.args = args\n self.max_num_nodes = max_path_len + 1\n self.kg = kg\n self.state_pre_h"} +{"idx": 33021, "score": 2, "raw_out": "2", "code_head": "def seq2vec(seq): # translate sequence to 0 for a.a. and 1 for gap\n\tvec = zeros(len(seq))\n\tfor i in range(len(seq)):\n\t\tif seq[i]==\"-\":\n\t\t\tvec[i]=1\n\treturn vec\n"} +{"idx": 100174, "score": 2, "raw_out": "2", "code_head": "def self_condition_seq(self):\n '''\n get previous logits and set at t1d template\n '''\n self.features['t1d'][:,:,:,:21] = self.features['logit_aa_s'][0,:21,:].permute(1,0"} +{"idx": 297539, "score": 3, "raw_out": "3", "code_head": "def __init__(self, scoring_function: str = 'ifd_composite'):\n self.scoring_function = scoring_function\n self.logger = logging.getLogger(\"pandadock.flex.phase4\")\n\n # Scoring weight"} +{"idx": 116236, "score": 3, "raw_out": "3", "code_head": "def _spatial_logits(self, R, t, x):\n N, L, _ = t.shape\n # Query\n proj_query_point = hk.Linear(self.num_query_points*self.num_heads*3, with_bias=self.bias)\n query_points = _"} +{"idx": 125103, "score": 3, "raw_out": "3", "code_head": "def generate_seq(self):\n np.random.seed(self.seed)\n seq = ''\n annotation_df = pd.DataFrame(columns=['start','end','motif','rep'])\n annotation_woMut_df = pd.DataFrame(column"} +{"idx": 74708, "score": 2, "raw_out": "2", "code_head": "def predict_step(self, batch: Any, batch_idx: int):\n\n if self.recalibrate == 'platt_va': \n x, y = batch # Platt-scaling\n y = y['Label']\n\n "} +{"idx": 284683, "score": 3, "raw_out": "3", "code_head": "def get_elongated_cds(cds: dict, sequence: dict, offset: int = bc.PSEUDOGENE_OFFSET) -> Dict[str, Union[int, str, bool]]:\n \"\"\"\n Elongate the given CDS sequence with the offset in upstream and do"} +{"idx": 234865, "score": 2, "raw_out": "2", "code_head": "def mol_transform(molecule, vocab, n_bonds, coord_std, rotate=True, zero_com=True):\n if coord_std != 1.0:\n molecule = molecule.scale(1.0 / coord_std)\n if rotate:\n rotation = tuple("} +{"idx": 47823, "score": 3, "raw_out": "3", "code_head": "def __init__(self, gap_score=-10):\n self.scorer = AlignmentScorer(gap_score)\n self.tokenizer = EsmTokenizer.from_pretrained(\"facebook/esm2_t6_8M_UR50D\")\n self.canonical_aa = set(\""} +{"idx": 42176, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n diffusion_model,\n folder,\n system,\n *,\n system_for_sample=None,\n ema_decay=0.995,\n op_number=4,\n train_batch_size=3"} +{"idx": 167800, "score": 3, "raw_out": "3", "code_head": "def fingerprint(\n self,\n bmg: BatchMolGraph,\n V_d: Tensor | None = None,\n E_d: Tensor | None = None,\n X_d: Tensor | None = None,\n ) -> tuple[Tensor | None, Tensor"} +{"idx": 266307, "score": 2, "raw_out": "2", "code_head": "def select_print_batch(rdict, i):\n return_dict_sub = {\n \"text\": rdict[\"text\"][i],\n \"input_instructions\": rdict[\"input_instructions\"][i],\n \"ground_truth_text\": rdict[\"ground_tru"} +{"idx": 256527, "score": 2, "raw_out": "2", "code_head": "def moment_1d_mp(data,n_job=1,k=2,n_sub=None,verbose=False,GC_true=None):\n \n\n n_gene,n_cell = data.shape[1],data.shape[0]\n if verbose: \n print('n_gene=%s, n_cell=%s, n_job=%s'%(str(n_g"} +{"idx": 57177, "score": 3, "raw_out": "3", "code_head": "def test_sample_multinom_sp_single_gene(self):\n # simulate a gene with 3 isoforms in 2 spots\n iso_ratio_expected = torch.tensor([[0.5, 0.3, 0.2], [0.4, 0.4, 0.2]])\n total_counts_e"} +{"idx": 158044, "score": 2, "raw_out": "2", "code_head": "def find_full_scope(self, folder_to_save=\"pathways\", name=None):\n \"\"\"\n Returns the scope of the compound: all pathways leading to the chassis.\n \"\"\"\n pile_to_treat = []\n "} +{"idx": 289022, "score": 2, "raw_out": "2", "code_head": "def AtomicSpins ( self, target, chargeModel = None ):\n \"\"\"Atomic spins.\"\"\"\n return target.scratch.dftbOutputData.get ( \"Spins\", None )\n"} +{"idx": 152615, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n *,\n pdb_csv_path,\n pdb_self_consistency_path,\n plddt_filter,\n tm_filter,\n "} +{"idx": 65436, "score": 3, "raw_out": "3", "code_head": "def forward(self, \n m: torch.Tensor, \n mask: Optional[torch.Tensor] = None, \n chunk_size: Optional[int] = None,\n use_deepspeed_evo_attention: bool = False,\n use_lma:"} +{"idx": 287016, "score": 3, "raw_out": "3", "code_head": "```rust\nfn test_multisequence_synthetic() {\n use synthetic_genomes::{generate_base_sequence, mutate_sequence};\n\n let temp_dir = TempDir::new().unwrap();\n let multi_fa = temp_dir.path().join(\""} +{"idx": 174457, "score": 2, "raw_out": "2", "code_head": "def calcCDSexpr(quantDict, CDSinfo):\n with open(CDSinfo, 'r') as f:\n dictionary = {}\n samplenum = len(list(quantDict[\"header\"])) - 1\n for l in f:\n line = l.rstrip('\\"} +{"idx": 283264, "score": 3, "raw_out": "3", "code_head": "def simulate(pos):\n integrator = openmm.LangevinMiddleIntegrator(500*unit.kelvin, 1/unit.picosecond, 0.001*unit.picosecond)\n simulation = app.Simulation(mmtop, system, integrator, openmm"} +{"idx": 106536, "score": 2, "raw_out": "2", "code_head": "def pdb_start_index(pdbfile, chain):\n with open(pdbfile) as f:\n for line in f:\n if line[0:4] == \"ATOM\" and line[21] == chain:\n long_aa = line[17:20]\n "} +{"idx": 316242, "score": 1, "raw_out": "1", "code_head": "```cpp\nvoid generate_haplotypes_naive_HiFi(haplotype_evdience_alloc* hap, overlap_region_alloc* overlap_list, double up, UC_Read* g_read, uint64_t multi_check, double st_rate, uint64_t st_max)\n{\n /"} +{"idx": 286906, "score": 3, "raw_out": "3", "code_head": "def _featurize(self, mol):\n \"\"\"\n Calculate Coulomb matrices for molecules. If extra randomized\n matrices are generated, they are treated as if they are features\n for additi"} +{"idx": 308080, "score": 4, "raw_out": "4", "code_head": "def _forward(self, \n m: torch.Tensor, \n mask: Optional[torch.Tensor] = None,\n chunk_size: Optional[int] = None,\n inplace_safe: bool = False,\n ) -> torch.Tensor:\n "} +{"idx": 150815, "score": 2, "raw_out": "2", "code_head": "def MSA(data_path, ID):\n os.system(\"{0} -db {1} -num_iterations 3 -num_alignments 1 -num_threads 4 -query {2}{3}.fa -out {2}{3}.bla -out_ascii_pssm {2}{3}.pssm\".format(PSIBLAST, UR90, data_path, ID"} +{"idx": 233302, "score": 2, "raw_out": "2", "code_head": "def render_genome(self, output_folder, output_file_name, extract_contigs=None):\n self.annotation_fasta = join(output_folder, 'sources', basename(self.gff_filename) +\n "} +{"idx": 48041, "score": 3, "raw_out": "3", "code_head": "def compute_sidechain_fape(\n pred_q: torch.Tensor, #[b,k,l,4]\n pred_t: torch.Tensor, #[b,k,l,3]\n pred_positions: torch.Tensor, #[b,k,l,14，3]\n target_ll_po"} +{"idx": 312626, "score": 1, "raw_out": "1", "code_head": "```c\nvoid ComputeNewParameters (Sequence *seq1, Sequence *seq2,\n\t\t\t const VF &forward, const VF &backward,\n\t\t\t VF &initDistribMat, VF &gapOpen,\n\t\t\t VF &gapExtend, VVF &emitPairs, VF "} +{"idx": 201799, "score": 3, "raw_out": "3", "code_head": "def _check_atom_mask_is_ideal(prot):\n \"\"\"Sanity-check the atom mask is ideal, up to a possible OXT.\"\"\"\n atom_mask = prot.atom_mask\n ideal_atom_mask = protein.ideal_atom_mask(prot)\n utils.a"} +{"idx": 166223, "score": 3, "raw_out": "3", "code_head": "def get_reactivity(smi: str, verbose: bool = False, n_procs: int = 1):\n \"\"\"Return fitness functions for the design of OPV molecules.\n\n :param smi: `str` representing molecule\n :param verbose:"} +{"idx": 301806, "score": 3, "raw_out": "3", "code_head": "def test_second_q_ops_without_transformers(self):\n \"\"\"Tests that the list of second quantized operators is created if no transformers\n provided.\"\"\"\n expected_num_of_sec_quant_ops "} +{"idx": 126933, "score": 3, "raw_out": "3", "code_head": "def __init__(self, label, output_dir, transcripts_metrics, WELL_FULLY_COVERAGE_THRESHOLDS):\n # get folders for separated reports:\n self.output_dir = UtilsPipeline.create_empty_folder(os."} +{"idx": 152216, "score": 2, "raw_out": "2", "code_head": "def rotate_molecule(path, target_path, count=10):\n # Load dataset\n mols = Chem.SDMolSupplier(path)\n rotated_mols = []\n\n print(\"Loaded {} Molecules from {}\".format(len(mols), path))\n\n pr"} +{"idx": 96400, "score": 3, "raw_out": "3", "code_head": "def __init__(self, model_id, chain_id, seq_id):\n super().__init__()\n self.model_id = model_id\n self.chain_id = chain_id\n self.seq_id = seq_id\n"} +{"idx": 99295, "score": 3, "raw_out": "3", "code_head": "def build_indices_should_be_counted_masks(\n encoded_msa: torch.Tensor, # [n_rows, n_tokens_across_chains] (int)\n mask_position: torch.Tensor, # [n_rows, n_tokens_across_chains] (bool)\n toke"} +{"idx": 97900, "score": 3, "raw_out": "3", "code_head": "def forward(self, msa, pair): # TODO: make this as tied-attention\n B, N, L = msa.shape[:3]\n #\n msa = self.norm_msa(msa)\n pair = self.norm_pair(pair)\n #\n seq_w"} +{"idx": 17287, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n fiber_in: Fiber,\n fiber_out: Fiber,\n fiber_edge: Fiber,\n pool: bool = True,\n use_layer_norm: bool = False,\n "} +{"idx": 321269, "score": 2, "raw_out": "2", "code_head": "```cpp\nvoid setMultiHitDbWorkflowDefaults(Parameters *p) {\n p->orfMinLength = 30;\n}\n```"} +{"idx": 344278, "score": 3, "raw_out": "3", "code_head": "def run_molecular_dynamics(prmtop: app.AmberPrmtopFile,\n inpcrd: app.AmberInpcrdFile,\n sim: Dict,\n saveFile: FilePat"} +{"idx": 136344, "score": 3, "raw_out": "3", "code_head": "def to_molecule(self, ignore_error=False):\n mols = super(CG22_PackedProtein, self).to_molecule(ignore_error)\n\n residue_type = self.residue_type.tolist()\n atom_name = self.atom_nam"} +{"idx": 249839, "score": 4, "raw_out": "4", "code_head": "def predicted_tm_score(\n logits: np.ndarray,\n breaks: np.ndarray,\n residue_weights: Optional[np.ndarray] = None,\n asym_id: Optional[np.ndarray] = None,\n interface: bool = False) -> np.n"} +{"idx": 36220, "score": 3, "raw_out": "3", "code_head": "def N_to_AA(x):\n # [[0,1,2,3]] -> [\"ARND\"]\n x = np.array(x)\n if x.ndim == 1:\n x = x[None]\n return [\"\".join([aa_N_1.get(a, \"-\") for a in y]) for y in x]\n"} +{"idx": 47093, "score": 2, "raw_out": "2", "code_head": "def connect_the_dots(mol, atoms, indicators, covalent_factor=1.3):\n '''Custom implementation of ConnectTheDots. This is similar to\n OpenBabel's version, but is more willing to make long bonds\n "} +{"idx": 355846, "score": 2, "raw_out": "2", "code_head": "def filter_helper(self):\n a = np.zeros(len(self.gene_list))\n n = 0\n for i, exp in self.exp_dict.items():\n n += exp.shape[0]\n exp[exp > 0] = 1\n for"} +{"idx": 68275, "score": 3, "raw_out": "3", "code_head": "def train(data, modeltype,rxn_keys=None,labels = None,validation_split=0.0,nuplo=30, min_con=0, max_con=0, min_for=0.05, max_for=0.3, con_p=None, del_p = None, nlayers=1, nnodes=256, nepochs=10, b_si"} +{"idx": 78407, "score": 3, "raw_out": "3", "code_head": "def generate_50_sequences():\n sequences = []\n base_seqs = PROTEIN_SEQUENCES * 5 # 5 × 10 = 50\n \n for i, seq in enumerate(base_seqs):\n # Add some variation to make them unique\n "} +{"idx": 20259, "score": 4, "raw_out": "4", "code_head": "def get_state(self) -> Dict[str, Any]:\n \"\"\"Get current membrane state\"\"\"\n return {\n 'lipids': self.lipids.copy(),\n 'membrane_fluidity': self.membrane_fluidity,\n "} +{"idx": 42073, "score": 3, "raw_out": "3", "code_head": "def process(self, state, inputs):\n \"\"\"filter a nblist on accelerator with jax and precomputed shapes\"\"\"\n graph_in = inputs[self.parent_graph]\n if state is None:\n # skin"} +{"idx": 283430, "score": 3, "raw_out": "3", "code_head": "def calculate_tf_activities(X: np.ndarray, gene_names: np.ndarray, net: pd.DataFrame, min_targets: int = 3) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Calculate TF activities using decoupler's ULM "} +{"idx": 358350, "score": 3, "raw_out": "3", "code_head": "def Qf(ff,gg,ee_p,ee_mu,ee_nu,mm,aa,bb,CC):\n out = np.exp(ff/ee_p) * np.sum(np.exp((gg.reshape(1,-1)-CC)/ee_p), axis=1) \\\n + np.exp((ff-mm)/ee_mu) - aa\n return out\n"} +{"idx": 205151, "score": 4, "raw_out": "4", "code_head": "def get_likelihood_scores(\n seqs: Sequence[str],\n model: vae.VAE,\n homolog_config: alignment.HomologConfig,\n num_posterior_samples: int = 1000,\n batch_size: int = 16,\n) -> list[float]:\n"} +{"idx": 275027, "score": 2, "raw_out": "2", "code_head": "def predict_profile(self, loader):\n \"\"\"predict profiles.\"\"\"\n\n test_size = 0\n # setup_seed(self.random_seed)\n self.eval()\n with torch.no_grad():\n for x1_da"} +{"idx": 144511, "score": 2, "raw_out": "2", "code_head": "def full_run(dataset, out_weights=\"weights.h5\"):\n cache_file = dataset.split(\"/\")[-1] + \".p\"\n dataset = open_dataset(dataset, dataset_cache=cache_file)\n cdrs, lbls, masks = dataset[\"cdrs\"], d"} +{"idx": 273568, "score": 3, "raw_out": "3", "code_head": "def launch_jobs(\n tsv_path: str,\n dry_run: bool = False\n) -> None:\n \"\"\"Launch multiple jobs sequentially from TSV file.\"\"\"\n # Read hyperparameters\n jobs = read_tsv_hyperparameters(tsv_p"} +{"idx": 123470, "score": 4, "raw_out": "4", "code_head": "def query(self, input_fasta_path: str) -> Mapping[str, Any]:\n \"\"\"Queries the database using HHblits.\"\"\"\n with utils.tmpdir_manager(base_dir=\"/tmp\") as query_tmp_dir:\n a3m_path"} +{"idx": 112827, "score": 2, "raw_out": "2", "code_head": "def pyrosetta_interface_energy(pdb_path, receptor_chains, ligand_chains, return_dict=False):\n pose = pyrosetta.pose_from_pdb(pdb_path)\n interface = ''.join(ligand_chains) + '_' + ''.join(recepto"} +{"idx": 356240, "score": 2, "raw_out": "2", "code_head": "def forward(self,x,kmer,mean,std,intense,dwell,base_quality):\n\n kmer_embedded=self.embed(kmer)\n mean = torch.reshape(mean, (-1, self.seq_len, 1)).float()\n std = torch.reshape(std,"} +{"idx": 309225, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n drug_layers,\n ligand_dim=2048,\n residue_dim=1280,\n drug_dim=128,\n proj_dim=256,\n nheads=32,\n activation=\"tanh\",\n co"} +{"idx": 219898, "score": 3, "raw_out": "3", "code_head": "def get_snp2gene(self, gene_embedding, snp_embedding, snp2gene_mask):\n \"\"\"Propagates information from SNP embeddings to gene embeddings.\"\"\"\n gene_embedding_input = self.dropout(self.gene"} +{"idx": 303927, "score": 2, "raw_out": "2", "code_head": "def expand_mutations(mutations):\n expanded = []\n for msite in mutations:\n for mut in msite.mutations:\n # Handle comma-separated mutation strings\n muts = mut.mutation"} +{"idx": 144289, "score": 3, "raw_out": "3", "code_head": "def setup_altrains_fname_generator(filters: bool, no_score: bool = False) -> Callable:\n if no_score:\n suffix = \"_noscore\"\n else:\n suffix = \"\"\n\n def prepare_altrains_fnames_wfilt"} +{"idx": 236224, "score": 2, "raw_out": "2", "code_head": "```c\nclass annotation_gene\n{\npublic:\n\tstring featureID;\n\tstring geneNm;\n\tstring chr;\n\tstring strand;\n\tlong range_start;\n\tlong range_end;\n\t//vector exonBoundary;\n\n\tannotation_gene() {range_start "} +{"idx": 258293, "score": 3, "raw_out": "3", "code_head": "def showNoGenomeWarning(dbInfo):\n if dbInfo==None:\n printNote('As there is no genome that can be used to get flanking sequence for your sequence, efficiency scores 50bp from the start or the"} +{"idx": 346158, "score": 3, "raw_out": "3", "code_head": "def mclust_R(adata, n_clusters, use_rep='SEDR', key_added='SEDR', random_seed=2023):\n \"\"\"\\\n Clustering using the mclust algorithm.\n The parameters are the same as those in the R package mclus"} +{"idx": 336171, "score": 3, "raw_out": "3", "code_head": "def get_train_smiles(cfg, train_dataloader, dataset_infos, evaluate_dataset=False):\n if evaluate_dataset:\n assert dataset_infos is not None, \"If wanting to evaluate dataset, need to pass dat"} +{"idx": 187363, "score": 2, "raw_out": "2", "code_head": "def GetAASumm(args):\n from collections import Counter\n\n AA = ['A','R','N','D','C','Q','E','G','H','I','L','K','M','F','P','S','T','W','Y','V']\n\n\n def clean(x):\n seq = x['seq'].upper()\n"} +{"idx": 43187, "score": 2, "raw_out": "2", "code_head": "def test_ideal_atom_mask(self):\n with open(\n os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR,\n '2rbg.pdb')) as f:\n pdb_string = f.read()\n prot = pr"} +{"idx": 170039, "score": 3, "raw_out": "3", "code_head": "def test_t4_all_ligands_explicit():\n \"\"\"\n Test that the all-binders to t4 lysozyme example works in explicit solvent\n This is a slow test\n \"\"\"\n example_dir = os.path.join('binding', 't4"} +{"idx": 321274, "score": 3, "raw_out": "3", "code_head": "```cpp\nOrf::Orf(const unsigned int requestedGenCode, bool useAllTableStarts) {\n TranslateNucl translateNucl(static_cast(requestedGenCode));\n std::vector codo"} +{"idx": 60476, "score": 3, "raw_out": "3", "code_head": "def process(self, x):\n \"\"\"Do all preprocessing steps\n\n Parameters:\n -----------\n x : list\n list of sequences from MSA\n\n Returns:\n --------\n "} +{"idx": 255036, "score": 2, "raw_out": "2", "code_head": "def process_imp(self):\n import scanpy as sc\n meta = pd.read_csv('atlas/MEL/GSE115978_tpm.csv', index_col=0).transpose()\n #meta2 = pd.read_csv('atlas/skin/GSE115978_counts.csv')\n "} +{"idx": 307796, "score": 3, "raw_out": "3", "code_head": "def insert(self, seq, type_insert):\n \"\"\"Performs n (mut_n, class variable) random point insertions. \n If type insert is 'aa' the new element will be an aminoacid.\n If type insert "} +{"idx": 330378, "score": 2, "raw_out": "2", "code_head": "def test_fff():\n fff_method = \"Full\"\n rmtree(\"SAVE/\" + mol_name.lower() + \"/\", ignore_errors=True)\n\n exps_appr, vars_appr = fm.compute_ev_var_all_ops(psi_appr, n_qubit, all_ops, trunc=True)\n "} +{"idx": 175778, "score": 2, "raw_out": "2", "code_head": "def fit_i(self, training_set):\n \n n,p = training_set.shape\n self.p_i = p\n covar = np.dot(training_set.T, training_set)/n\n eigen_values, eigen_vectors = np.linalg.eig"} +{"idx": 99813, "score": 4, "raw_out": "4", "code_head": "def gather_nearest_per_residue_atoms(\n self,\n per_residue_ligand_coords,\n per_residue_ligand_mask,\n per_residue_ligand_types,\n X_virtual_atoms,\n X_m_virtual_a"} +{"idx": 127420, "score": 3, "raw_out": "3", "code_head": "def forward(self, x):\n \"\"\"\n :param x: `torch.Tensor` corresponding to vector channels\n \"\"\"\n if not self.training:\n return x\n mask = torch.bernoulli(\n "} +{"idx": 8498, "score": 3, "raw_out": "3", "code_head": "def __init__ (self, vocab_size, n_layers, n_heads, model_depth, ff_depth, dropout):\n super(Transformer, self).__init__()\n self.model_depth = model_depth\n self.encoder = Encoder("} +{"idx": 142101, "score": 3, "raw_out": "3", "code_head": "```cpp\nSWIGINTERN PyObject *_wrap_PS_rna_plot_a_gquad(PyObject *SWIGUNUSEDPARM(self), PyObject *args, PyObject *kwargs) {\n PyObject *resultobj = 0;\n char *arg1 = (char *) 0 ;\n char *arg2 = (char *)"} +{"idx": 61412, "score": 3, "raw_out": "3", "code_head": "def _args(self, *, plist, mode, script):\n if script.endswith(\"run_multimer_jobs.py\"):\n return [\n sys.executable,\n str(self.script_multimer),\n "} +{"idx": 115584, "score": 3, "raw_out": "3", "code_head": "def compose_q_update_vec(self, \n q_update_vec: torch.Tensor, \n normalize_quats: bool = True\n ) -> Rotation:\n \"\"\"\n Returns a new quaternion Rotation after updating th"} +{"idx": 195066, "score": 2, "raw_out": "2", "code_head": "def graph_based_clustering(\n matrix: Union[str, ExpMatrix], output_file: str = None,\n num_components: int = 50,\n resolution: float = 0.8,\n umap_kwargs: dict = None,\n "} +{"idx": 89381, "score": 3, "raw_out": "3", "code_head": "def sample(self, h_V, edge_index, h_E, n_samples, temperature=0.1):\n '''\n Samples sequences autoregressively from the distribution\n learned by the model.\n \n :param h"} +{"idx": 174344, "score": 2, "raw_out": "2", "code_head": "def model1(q, params, full_id):\n \"\"\"\n Hdia = 0.5*k*x^2 \n Sdia = 1.0\n Ddia = 0.0\n\n \"\"\"\n\n Id = Cpp2Py(full_id)\n indx = Id[-1]\n\n Hdia = CMATRIX(1,1)\n Sdia = CMATRIX(1,1)\n "} +{"idx": 223415, "score": 3, "raw_out": "3", "code_head": "def __call__(self, tokens: Tokens | tuple[Tokens]) -> Dict[str, Embedding]:\n\n # Prepare outputs dict\n outs: Dict[str, Embedding] = {}\n\n # Compute embeddings\n\n if self._conf"} +{"idx": 32606, "score": 4, "raw_out": "4", "code_head": "def forward(\n self,\n input_ids: Tensor | NestedTensor | None = None,\n attention_mask: Tensor | None = None,\n position_ids: Tensor | None = None,\n head_mask: Tensor |"} +{"idx": 52747, "score": 4, "raw_out": "4", "code_head": "def read_StereoSeq(\n filepath: _PathLike,\n *,\n resolution: float | None = None,\n gene_name: bool = False,\n exon_count: bool = False,\n sep: str = \"\\t\",\n n_threads: int | None = Non"} +{"idx": 76733, "score": 3, "raw_out": "3", "code_head": "async def generate_query(state: AIRAState, config: RunnableConfig, writer: StreamWriter):\n \"\"\"\n Node for generating a research plan as a list of queries. \n Takes in a topic and desired report"} +{"idx": 114700, "score": 2, "raw_out": "2", "code_head": "def cast_seq_info(\n self,\n seq_profile,\n sequence=None,\n new_variable_name=\"new\",\n missing_aa_value=None,\n cast_color=True,\n max_or_fun_to_normalize_co"} +{"idx": 23715, "score": 3, "raw_out": "3", "code_head": "def test_identical_sequences_similarity(self, embedding_model):\n \"\"\"Test that identical sequences have similarity 1.0\"\"\"\n sequence = \"ATCGATCGATCG\"\n \n emb1 = embedding_mode"} +{"idx": 300871, "score": 2, "raw_out": "2", "code_head": "def mol_to_graph(mol):\n \"\"\"\n convert rdkit mol to graph\n \"\"\"\n if mol is None:\n # return empty graph if mol is None\n return edict.EasyDict(x=torch.zeros([0, 5], dtype=torch.lo"} +{"idx": 332506, "score": 3, "raw_out": "3", "code_head": "def test_aa_embedding(self):\n \"\"\" Tests whether different amino acid embeddings work. \"\"\"\n _ = self._test_ffn(model=\"linear\", chains=\"trb\", aa_embedding_dim=None, blosum=True)\n "} +{"idx": 280073, "score": 2, "raw_out": "2", "code_head": "def getAverageExpressionValues(filename,platform):\n \n \"\"\" This function imports two file sets: (A) The original raw input expression files and groups and (B) the DATASET file with annotations.\n "} +{"idx": 315710, "score": 3, "raw_out": "3", "code_head": "def tune_rabi(\n system,\n init_state,\n e_ops=None,\n mode_name=\"qubit\",\n pulse_name=None,\n amp_range=(-2, 2, 51),\n progbar=True,\n plot=True,\n ax=None,\n ylabel=None,\n upd"} +{"idx": 356824, "score": 2, "raw_out": "2", "code_head": "def generate_allowed_structure_site_structures(adsorbate_dir,sites,site_adjacency,nslab,max_dist=3.0,cut_off_num=None):\n allowed_structure_site_structures = []\n for ad in os.listdir(adsorbate_di"} +{"idx": 38795, "score": 3, "raw_out": "3", "code_head": "def stereo(self, mol_batch, mol_vec):\n stereo_cands,batch_idx = [],[]\n labels = []\n for i,mol_tree in enumerate(mol_batch):\n cands = mol_tree.stereo_cands\n i"} +{"idx": 13996, "score": 2, "raw_out": "2", "code_head": "def mol2graph(mol_batch):\n padding = torch.zeros(ATOM_FDIM + BOND_FDIM)\n fatoms,fbonds = [],[padding] #Ensure bond is 1-indexed\n in_bonds,all_bonds = [],[(-1,-1)] #Ensure bond is 1-indexed\n "} +{"idx": 65244, "score": 3, "raw_out": "3", "code_head": "def test_forward_dimensions(self):\n \"\"\"Test the output dimensions from the forward pass.\"\"\"\n token_repr = torch.randn(self.bs, self.n_seq, self.n_tokens, self.decoder.c_token)\n at"} +{"idx": 332922, "score": 3, "raw_out": "3", "code_head": "def __init__(self, matrix, cell_list, gene_list, source='arrays', normalized_matrix=None, calculate_statistics=True):\n \"\"\"A class for holding single-cell data with separate tables for expressio"} +{"idx": 98839, "score": 3, "raw_out": "3", "code_head": "def _update_rf_features_w_atomized_features(self, \\\n indep_deatomized,\n seq_atomize_all, \\\n "} +{"idx": 317895, "score": 2, "raw_out": "2", "code_head": "def get_gap_flank_seqs(self, ref_path, sf_gap_pos, frame_length, sf_fai, working_folder):\n m_scaffold_id={}\n l_scaffold_id=[]\n cnt=0##scaffold id, started from 0\n with open"} +{"idx": 301965, "score": 2, "raw_out": "2", "code_head": "def coverage_cmd(**kwargs):\n \"\"\"Calculate per-sample coverage of feature groups.\n \"\"\"\n from .tools import coverage_wf\n coverage_wf(**kwargs)\n"} +{"idx": 62754, "score": 2, "raw_out": "2", "code_head": "def select_target(self, tgt_X, tgt_h, tgt_A, tgt_pos):\n max_len = max([len(pos) for pos in tgt_pos])\n xlist = [tgt_X[i, pos] for i,pos in enumerate(tgt_pos)]\n hlist = [tgt_h[i, po"} +{"idx": 205106, "score": 4, "raw_out": "4", "code_head": "def make_state_matrix(signed_network: FermiNetLike, n: int) -> FermiNetLike:\n \"\"\"Construct a matrix-output ansatz which gives the Slater matrix of states.\n\n Let signed_network(params, pos, spins, op"} +{"idx": 47989, "score": 4, "raw_out": "4", "code_head": "def make_adata(transcripts_with_gt_and_main_nucleus_filtered):\n \"\"\"\n Create an AnnData object from the final assigned transcripts DataFrame.\n\n Parameters:\n transcripts_with_gt_and_main_nuc"} +{"idx": 194059, "score": 3, "raw_out": "3", "code_head": "def get_2d_gem(mol):\n # bond (i, j)\n edge_idx = []\n edge_feat = []\n for bond in mol.GetBonds():\n i_idx = bond.GetBeginAtomIdx()\n j_idx = bond.GetEndAtomIdx()\n edge_id"} +{"idx": 60841, "score": 4, "raw_out": "4", "code_head": "def predicted_tm_score(\n logits: np.ndarray,\n breaks: np.ndarray,\n residue_weights: Optional[np.ndarray] = None,\n asym_id: Optional[np.ndarray] = None,\n interface: bool = False) -> np.n"} +{"idx": 19079, "score": 2, "raw_out": "2", "code_head": "def run_sequence_prediction(\n sequence_input,\n nucleus_image_path,\n protein_image_path,\n model_ckpt_path,\n model_config_path,\n device\n):\n \"\"\"\n Run Celle model with provided inp"} +{"idx": 317458, "score": 2, "raw_out": "2", "code_head": "def vcf_binary_fast(ref, alt, model, window_size,batch_size=64):\n \"\"\"\n\n :param ref:\n :param alt:\n :param model:\n :param batch_size:\n :param layer:\n :return:\n \"\"\"\n if ref.sha"} +{"idx": 11343, "score": 4, "raw_out": "4", "code_head": "def train(self, model, loader, epoch):\n \"\"\"Executes a single training epoch with a multi-task self-supervised objective.\"\"\"\n model.train()\n total_loss, total_mse, total_topo, tota"} +{"idx": 102021, "score": 2, "raw_out": "2", "code_head": "def func(systems=None):\n n_atoms_max = None if systems is None else max([len(s) for s in systems])\n final_kwargs = {\n \"n_atoms_max\": n_atoms_max,\n \"permutation\": \"n"} +{"idx": 96978, "score": 2, "raw_out": "2", "code_head": "def batch_atoms(\n atoms: ase.Atoms | list[ase.Atoms],\n z_table: AtomicNumberTable,\n cutoff: float,\n device: str,\n) -> AtomicData:\n atomic_datas = convert_atoms_to_atomic_data(atoms, z_t"} +{"idx": 64956, "score": 2, "raw_out": "2", "code_head": "def make_data(seq, bi, tri, acc, acc20, ss, ss8, bio):\n\n x_seq = np.array(utils.pad_seq(seq, maxlen))\n x_bi = np.array(utils.pad_seq(bi, maxlen))\n x_tri = np.array(utils.pad_seq(tri, maxlen))"} +{"idx": 251256, "score": 3, "raw_out": "3", "code_head": "def probs(sequence, temperature = 37.0, lonelyPairs = True, pkStrategy = 'A', pkMinHairpin = 2, pkMaxKnot = None, shapeLevel = 2, discardStates = 0.0):\n '''\n Structure probabilities are strictly"} +{"idx": 110402, "score": 3, "raw_out": "3", "code_head": "def spatial_crop(\n self,\n interface_cropping: bool,\n n_res: int = 384,\n chain_1: str | None = None,\n chain_2: str | None = None,\n interface_distance_threshold"} +{"idx": 32026, "score": 3, "raw_out": "3", "code_head": "def makeSingleSpliceSite(iChrom, iPos, numSamples, iStrand, isStranded):\n\treturn(Site(\n\t\t\t\tchromosome = iChrom,\n\t\t\t\tpos = iPos,\n\t\t\t\tsamples = numSamples,\n\t\t\t\tstrand = iStrand,\n\t\t\t\tsource = '',\n\t\t\t\tisS"} +{"idx": 356173, "score": 3, "raw_out": "3", "code_head": "def get_pairwise_scaffold_matrix(smiles_1: List[str], smiles_2: List[str], radius: int = 2, nBits: int = 1024):\n \"\"\" Calculates a matrix of Tanimoto similarity scores for a list of SMILES string \"\""} +{"idx": 11944, "score": 4, "raw_out": "4", "code_head": "def fix_templates_aatype(protein):\n # Map one-hot to indices\n num_templates = protein[\"template_aatype\"].shape[0]\n if(num_templates > 0):\n protein[\"template_aatype\"] = torch.argmax(\n "} +{"idx": 285336, "score": 3, "raw_out": "3", "code_head": "def forward(self, h, pos, edge_index, is_mol):\n #NOTE in the encoder, the edge dose not change since the position of mol and protein is fixed\n # edge_index = radius_graph(pos, self.cutof"} +{"idx": 223146, "score": 4, "raw_out": "4", "code_head": "def __call__(self, x: jnp.ndarray) -> Dict[str, jnp.ndarray]:\n x = self._first_layer_norm(x)\n x = self._fc1(x)\n x = jax.nn.gelu(x, approximate=False)\n x = self._second_laye"} +{"idx": 325373, "score": 4, "raw_out": "4", "code_head": "def validation_step(model,dataloader,criterion,criterion_mse):\n # evaluation mode\n model.eval()\n\n batch_loss = [] # all\n batch_reconstruction = [] # reconstruction\n batch_kl "} +{"idx": 53215, "score": 1, "raw_out": "1", "code_head": "```java\npublic static void printDocs(){\n\t\tSystem.out.println(\"\\n\" +\n\t\t\t\t\"**************************************************************************************\\n\" +\n\t\t\t\t\"** "} +{"idx": 136272, "score": 3, "raw_out": "3", "code_head": "def protein_cropping(self, cropping_threshold=12, contact_threshold=6, compact=True):\n # this function is performed prior to the 'transform' functions\n aa_num_chain = [len(chain) for cha"} +{"idx": 36702, "score": 3, "raw_out": "3", "code_head": "def parse_protein(pdb_path: Path, remove_hydrogen=False) -> prody.AtomGroup:\n \"\"\"Convert pdb file to prody AtomGroup object.\n\n Args:\n pdb_path (Path): Path to receptor pdb file. \n\n Ret"} +{"idx": 115778, "score": 2, "raw_out": "2", "code_head": "def _concatenate_paired_and_unpaired_features(\n np_example: Mapping[str, np.ndarray]\n) -> dict[str, np.ndarray]:\n \"\"\"\n Concatenate paired and unpaired features\n\n Args:\n np_example ("} +{"idx": 37733, "score": 3, "raw_out": "3", "code_head": "def test_lowercase_ref_splice_site(self):\n reference = pkg_resources.resource_filename(\"Mikado.tests\", \"NC_037283.1.fa.gz\")\n fasta = pyfaidx.Fasta(reference)\n fai = pysam.FastaFil"} +{"idx": 339175, "score": 4, "raw_out": "4", "code_head": "def __init__(self, n_extra_block=4, n_main_block=8, n_ref_block=4,\\\n d_msa=256, d_msa_full=64, d_pair=128, d_templ=64,\n n_head_msa=8, n_head_pair=4, n_head_templ=4,\n "} +{"idx": 157842, "score": 2, "raw_out": "2", "code_head": "```java\nprotected DiploidSNPGenotypeLikelihoods calculateGenotypeLikelihoods(byte observedBase1, byte qualityScore1, byte observedBase2, byte qualityScore2) {\n double[] log10FourBaseLikelihoods"} +{"idx": 183654, "score": 2, "raw_out": "2", "code_head": "def get_consensus(vcf, ref, phased_mask, outdir, sample):\n consider_segment_str = get_contig_name_in_bed(phased_mask)\n cmd = f\"\"\"\n samtools faidx {ref} {consider_segment_str}| bcftools consen"} +{"idx": 228625, "score": 2, "raw_out": "2", "code_head": "def get_PhaseShift_quafu(self, qubits):\n '''\n Get corresponding qiskit object.\n '''\n\n qubit = qubits[0]\n self._qc.p(qubit)\n"} +{"idx": 94538, "score": 3, "raw_out": "3", "code_head": "def get_batch(self, data, drug_drug_batch):\n\n drug_1s = drug_drug_batch[0][:, 0] # Edge-tail drugs in the batch\n drug_2s = drug_drug_batch[0][:, 1] # Edge-head drugs in the batch\n "} +{"idx": 32003, "score": 2, "raw_out": "2", "code_head": "def plot_comparison(sim_data1, sim_data2):\n\tt_reg1 = sim_data1.process.transcription_regulation\n\tt_reg2 = sim_data2.process.transcription_regulation\n\ttf_to_gene_id = t_reg1.tf_to_gene_id\n\trna_to_gene_"} +{"idx": 264050, "score": 3, "raw_out": "3", "code_head": "def _compute_dG(fnc: np.ndarray, temperature: float, p_fold_thr: float, steepness: float) -> float:\n \"\"\"Compute dG from sigmoid of fraction of native contacts\"\"\"\n p_fold_from_stat = _foldedness_"} +{"idx": 244911, "score": 3, "raw_out": "3", "code_head": "def read_input_file(file: str, cname: str = \"sequence\", plmblastid: Optional[str] = None) -> pd.DataFrame:\n\t'''\n\tread sequence file in format (.csv, .p, .pkl, .fas, .fasta)\n\tArgs:\n\t\tfile (str): path t"} +{"idx": 20645, "score": 3, "raw_out": "3", "code_head": "def supervised_chi_loss(\n angles_sin_cos: torch.Tensor,\n unnormalized_angles_sin_cos: torch.Tensor,\n aatype: torch.Tensor,\n seq_mask: torch.Tensor,\n chi_mask: torch.Tensor,\n chi_angl"} +{"idx": 312729, "score": 2, "raw_out": "2", "code_head": "def search(data:GenData=Body()):\n #####FOR TEST####\n if data.numb_mol>100:\n data.numb_mol=100\n \"\"\"Function to init Alzheimer case generation.\n Args:\n "} +{"idx": 52911, "score": 2, "raw_out": "2", "code_head": "def extract_pocket_item_datasets(atlas: Atlas) -> Dict[str, PocketItemDataset]:\n \"\"\"Evaluates an atlas and returns up to 20 pocket item datasets, one for each residue type occurring in the atlas.\n "} +{"idx": 205141, "score": 3, "raw_out": "3", "code_head": "def load_msa_from_fasta(fasta_file: str) -> MSA:\n \"\"\"Loads an MSA from a fasta file where the first entry is the parent.\"\"\"\n msa_df = utils.read_fasta_as_df(fasta_file)\n parent = msa_df.sequence.il"} +{"idx": 104519, "score": 3, "raw_out": "3", "code_head": "```java\npublic static RepExOST repexMC(OrthogonalSpaceTempering orthogonalSpaceTempering,\n MonteCarloOST monteCarloOST, DynamicsOptions dynamicsOptions, OSTOptions ostOptions,\n CompositeConf"} +{"idx": 136384, "score": 2, "raw_out": "2", "code_head": "def seq_predict(self, graph, output, node_index):\n # node_index: absolute ids for masked residues in current batch, output2: conformer或者原始蛋白的经过encoder的atom-level embeddings (after corruption，同上"} +{"idx": 187986, "score": 4, "raw_out": "4", "code_head": "def __init__(self, num_letters, node_features, edge_features,\n hidden_dim, num_encoder_layers=3, num_decoder_layers=3, atom_context_num = 25,\n vocab=21, k_neighbors=64, augment_eps=0.05,"} +{"idx": 246528, "score": 4, "raw_out": "4", "code_head": "def _to_a3m(sequences: Sequence[str]) -> str:\n \"\"\"Converts sequences to an a3m file.\"\"\"\n names = [\"sequence %d\" % i for i in range(1, len(sequences) + 1)]\n a3m = []\n for sequence, name in "} +{"idx": 298722, "score": 3, "raw_out": "3", "code_head": "```cpp\nint main (int argc, char* argv[])\n{\n\n enum aminoAcid {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V, X};\n\n\tstring inputFileName = argv[1];\n\n \t// read in prot structure"} +{"idx": 88827, "score": 2, "raw_out": "2", "code_head": "```java\n@Test\n void nat2Sheet(TestInfo testInfo) throws Exception {\n // this file based on https://docs.google.com/spreadsheets/d/1XjvmKHP7pp2t1qoqLKByD4bGjvf2Swpe13DqD18Ws-w/\n Path tsvFile = P"} +{"idx": 352657, "score": 3, "raw_out": "3", "code_head": "def __getitem__(self, idx: int) -> dict:\n \"\"\"Get an item from the dataset.\n\n Returns\n -------\n Dict[str, Tensor]\n The sampled data features.\n\n \"\"\"\n "} +{"idx": 106647, "score": 2, "raw_out": "2", "code_head": "def decoder(feed_previous_bool):\n reuse = None if feed_previous_bool else True\n with variable_scope.variable_scope(\n variable_scope.get_variable_scope(), reuse=reuse):\n outpu"} +{"idx": 30435, "score": 3, "raw_out": "3", "code_head": "def setUp(self):\n self.tokens = ['chr1', '1', '20', 'A', '0', '-', '7', '16', '0,128,0', '4', '3,4,3,3', '0,5,12,16']\n self.t = Transcript(self.tokens)\n self.transcript_seq = 'AGG"} +{"idx": 354524, "score": 3, "raw_out": "3", "code_head": "def build(self, ptw_ids):\n \"\"\" Define modules of the model.\n \"\"\"\n\n self.ptw_ids = ptw_ids\n\n self.encoder = ExpEncoder(\n self.omc_size, self.hidden_dim_enc, self.dropout_rate,\n "} +{"idx": 82946, "score": 3, "raw_out": "3", "code_head": "def _get_conditions(\n self,\n model: torch.nn.Module,\n x_conds: torch.Tensor,\n specified_conds: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n # First prepare"} +{"idx": 84910, "score": 3, "raw_out": "3", "code_head": "def get_components(self) -> Tuple[torch.FloatTensor, torch.FloatTensor,\n torch.LongTensor, torch.LongTensor, torch.LongTensor,\n "} +{"idx": 179470, "score": 4, "raw_out": "4", "code_head": "def __init__(self,\n c_m: int,\n c_z: int,\n c_hidden_msa_att: int,\n c_hidden_opm: int,\n c_hidden_mul: int,\n c_hidden_pair_att: int,\n no_heads_msa: int,\n "} +{"idx": 277596, "score": 4, "raw_out": "4", "code_head": "def test_api_workflow(self, test_data_dir, temp_dir):\n \"\"\"Test the complete buscolite API workflow.\"\"\"\n # Set up paths\n genome_path = os.path.join(test_data_dir, \"test_genome.fast"} +{"idx": 131432, "score": 2, "raw_out": "2", "code_head": "def closure():\n optimizer.zero_grad()\n temp_R = construct_SO3(rot_v1, rot_v2)\n temp_model = torch.matmul(propose_rmcom, temp_R) + propose_com + trans_vec\n loss = -llgloss(\n"} +{"idx": 61453, "score": 2, "raw_out": "2", "code_head": "def test_3_extract_multimeric_template_features(self):\n single_hit_result = multimeric_template_utils.extract_multimeric_template_features_for_single_chain(self.monomer1.sequence,\n "} +{"idx": 312890, "score": 2, "raw_out": "2", "code_head": "def call_for_ic50(\n mol_list: Union[requests.models.Response, List[str], dict],\n url: str = f\"http://{models_ip_addr}/eva_altz\",\n) -> Tuple[requests.models.Response, dict]:\n \"\"\"Function that "} +{"idx": 231899, "score": 3, "raw_out": "3", "code_head": "def test_space_deep_lift_shap(X):\n\ttorch.manual_seed(0)\n\tmodel = SmallDeepSEA()\n\ty_before, y_after = space(model, X[:2], [\"ACGTC\", \"GAGA\"], [[1]], \n\t\tbatch_size=5, func=deep_lift_shap, device='cpu', n"} +{"idx": 348969, "score": 3, "raw_out": "3", "code_head": "def transform(data):\n x, adj = data\n # the last place is for virtual nodes\n # 6: C, 7: N, 8: O, 9: F, 15: P, 16: S, 17: Cl, 35: Br, 53: I\n GDSCv2_atomic_num_list = [5, 6, 7"} +{"idx": 91655, "score": 4, "raw_out": "4", "code_head": "def test_build_calculation_allows_different_structures(self, tmp_path):\n \"\"\"Test that build_calculation_from_qe_inputs no longer enforces single structure_id.\"\"\"\n # This test verifies th"} +{"idx": 6922, "score": 2, "raw_out": "2", "code_head": "def count_sequences(seqfile: PathLike) -> int:\n \"Utility for quickly counting sequences in a fasta/a3m file.\"\n num_seqs = subprocess.check_output(f'grep \"^>\" -c {seqfile}', shell=True)\n retur"} +{"idx": 247267, "score": 3, "raw_out": "3", "code_head": "def test_boxes(self):\n for seq1, seq2, matches in BOX_SAMPLES:\n align = read_alignment(\"> 1\\n%s\\n> 2\\n%s\\n\"%(seq1, seq2))\n recipe = RecipeMaker(align).recipe\n f"} +{"idx": 357187, "score": 4, "raw_out": "4", "code_head": "def __init__(self, pdb_block, ligand_rdmol, conda_env='adt', tmp_dir='./tmp', use_uff=True, center=None):\n super().__init__(pdb_block, ligand_rdmol)\n self.conda_env = conda_env\n s"} +{"idx": 214908, "score": 1, "raw_out": "1", "code_head": "```cpp\nvoid IRKE::build_graph(const string& fasta_filename, bool reassembleIworm, bool useKmers) {\n\t\n\tif (useKmers)\n\t\tpopulate_Kmers_from_kmers(fasta_filename);\n\telse\n\t\tpopulate_Kmers_from_fasta(fasta"} +{"idx": 298378, "score": 3, "raw_out": "3", "code_head": "def getROCE(predList,targetList,roceRate):\n p = sum(targetList)\n n = len(targetList) - p\n predList = [[index,x] for index,x in enumerate(predList)]\n predList = sorted(predList,key = lambda"} +{"idx": 266652, "score": 2, "raw_out": "2", "code_head": "def get_compound_data(self):\n return general_load(\"evebio_pharmone_v1_compound_table\", self.path,\n \"\\t\")\n"} +{"idx": 163287, "score": 3, "raw_out": "3", "code_head": "def removeEMBOSSpostfix(self): # remove pesky '_1' that EMBOSS adds to translated sequence\n for prot in self.fastaList:\n prot.removeEMBOSSpostfix()\n"} +{"idx": 340228, "score": 3, "raw_out": "3", "code_head": "```rust\nfn dnah11_db_snp_nm_001277115() -> Result<(), Error> {\n run_gxp_test(\"tests/data/mapper/gcp/DNAH11-dbSNP-NM_001277115.tsv\", false)\n }\n```"} +{"idx": 187250, "score": 4, "raw_out": "4", "code_head": "def max_tokens_per_msa_(self, value: int) -> None:\n \"\"\"The MSA Transformer automatically batches attention computations when\n gradients are disabled to allow you to pass in larger MSAs a"} +{"idx": 203760, "score": 2, "raw_out": "2", "code_head": "def validate_molecular_format(filename: str, content: bytes) -> None:\n \"\"\"\n Validate molecular file format using RDKit (if available) or basic checks\n \"\"\"\n try:\n from rdkit import C"} +{"idx": 344780, "score": 2, "raw_out": "2", "code_head": "def make_bind_batch(batch):\n mols = [entry['binder_mol'] for entry in batch]\n N = max([mol.GetNumAtoms() for mol in mols])\n M = max([len(entry['target_site']) for entry in batch])"} +{"idx": 131560, "score": 4, "raw_out": "4", "code_head": "def create_extra_msa_feature(batch):\n \"\"\"Expand extra_msa into 1hot and concat with other extra msa features.\n\n We do this as late as possible as the one_hot extra msa can be very large.\n\n Argument"} +{"idx": 80432, "score": 2, "raw_out": "2", "code_head": "def init_pyrosetta(init_string=None, silent=True):\n if not exists(init_string):\n init_string = '-use_input_sc -input_ab_scheme Chothia -ignore_unrecognized_res \\\n -ignore_zero_occupancy "} +{"idx": 293915, "score": 2, "raw_out": "2", "code_head": "def predict_crista_score(features_lst):\n \"\"\"\n :param features_df: dataframe: first col: rna, second: dna, the rest are features\n mode: either full, nogenomic or noflanking\n :return: featur"} +{"idx": 362190, "score": 2, "raw_out": "2", "code_head": "def get_match_score(phar_graphs, smiles_list, n_workers=8, timeout=20):\n \"\"\"\n meaning of return value:\n 0~1: normal match score;\n -1: invalid molecule;\n -2: timeout\n \"\"\"\n"} +{"idx": 6520, "score": 3, "raw_out": "3", "code_head": "def test_precompute_from_many_h5ad_with_tree(\n h5ad_input_path,\n records_fixture,\n obs_fixture,\n baseline_stats_fixture,\n gene_id_col_fixture,\n tmp_dir_fixtur"} +{"idx": 240099, "score": 3, "raw_out": "3", "code_head": "def forward(self,\n m: torch.Tensor,\n z: torch.Tensor,\n msa_mask: torch.Tensor,\n pair_mask: torch.Tensor,\n chunk_size: Optional[int] = None,\n _chunk_logits: Op"} +{"idx": 41457, "score": 2, "raw_out": "2", "code_head": "def run_seq(list_, folder1, folder2):\n os.chdir(\"./7XB0_RBD/\")\n for i in range(len(list_)):\n ilist = list_[i]\n folder_dir = f'{ilist[0]}_{ilist[3]}_{ilist[4]}_{ilist[5]}_{ilist[6]}"} +{"idx": 176117, "score": 3, "raw_out": "3", "code_head": "def test_step(self, batch):\n source = batch[1] # position 1 contains the electrostatic potential\n target = batch[2] # position 2 contains the tokenized smiles\n dec_input = targe"} +{"idx": 23958, "score": 1, "raw_out": "1", "code_head": "```cpp\nTranscript()\n : RefName(), RefLength(std::numeric_limits::max()),\n CompleteLength(std::numeric_limits::max()),\n EffectiveLength(-1.0), id(std::numeric_lim"} +{"idx": 285380, "score": 2, "raw_out": "2", "code_head": "def _get_test_mols(self):\n table = pd.read_csv(self.table_path)\n\n self.test_ligands = Chem.SDMolSupplier(self.ligands_path)\n self.test_fingers = [Chem.RDKFingerprint(mol) for mol "} +{"idx": 147263, "score": 2, "raw_out": "2", "code_head": "def molstar(target_mol, target_mol_id, starting_mols, expand_fn, value_fn,\n iterations, viz=False, viz_dir=None):\n mol_tree = MolTree(\n target_mol=target_mol,\n known_mols=s"} +{"idx": 70693, "score": 3, "raw_out": "3", "code_head": "def __getitem__(self, index):\n out_data = {\"ab_verts\": self.verts[\"cdr\"][index], \"ag_verts\": self.verts[\"ag\"][index],\n \"ab_labels\": self.labels[\"cdr\"][index], \"ag_labels\": se"} +{"idx": 125897, "score": 3, "raw_out": "3", "code_head": "def assign_covalent_radii_to_dataframe(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Assigns covalent radius\n (:const:`~graphein.protein.resi_atoms.COVALENT_RADII`) to each atom based\n on its b"} +{"idx": 252616, "score": 3, "raw_out": "3", "code_head": "def antiRNA(RNA):\n antiRNA = []\n for i in RNA:\n if i == 'A' or i == 'a':\n antiRNA.append('U')\n elif i == 'U' or i == 'u' or i == 'T' or i == 't':\n antiRNA.app"} +{"idx": 184667, "score": 1, "raw_out": "1", "code_head": "```cpp\nint main(int argc, char *argv[]) {\n // output arguments\n for (int i = 0; i < argc; i++)\n std::cout << argv[i] << \" \";\n std::cout << std::endl;\n\n // input parsing\n double p"} +{"idx": 319886, "score": 3, "raw_out": "3", "code_head": "def forward(self, x_dict, edge_index_dict, batch_size, genotype = None, return_h = False, \n return_attention_weights = False):\n \n x_dict['SNP'] = self.snp_feat_mlp(x_dict["} +{"idx": 150930, "score": 2, "raw_out": "2", "code_head": "def atom_features(atom, explicit_H=False, use_chirality=True):\n \"\"\"Generate atom features including atom symbol(10),degree(7),formal charge,\n radical electrons,hybridization(6),aromatic(1),Chira"} +{"idx": 353057, "score": 3, "raw_out": "3", "code_head": "def __call__(self, data: ProteinLigandData):\n data.ligand_bond_feature = F.one_hot(data.ligand_bond_type - 1, num_classes=4) # (1,2,3) to (0,1,2)-onehot\n return data\n"} +{"idx": 188324, "score": 4, "raw_out": "4", "code_head": "async def test_uniprot_integration_with_spike_protein(\n self,\n analyzer_with_default_uniprot: SequenceAnalyzer,\n spike_protein_uniprot_id: str,\n ) -> None:\n \"\"\"Integrati"} +{"idx": 309475, "score": 2, "raw_out": "2", "code_head": "def cli(*args, **kwargs):\n \"\"\" Define mutations and regions to filter reads and positions. \"\"\"\n return run(*args, **kwargs)\n"} +{"idx": 332266, "score": 2, "raw_out": "2", "code_head": "def pseudotime(adata, gene_list, ckey=\"velocity\", reverse=False):\n \"\"\"TODO.\"\"\"\n ixsort = adata.uns[\"principal_curve\"][\"ixsort\"]\n arclength = adata.uns[\"principal_curve\"][\"arclength\"]\n if r"} +{"idx": 325623, "score": 3, "raw_out": "3", "code_head": "```c\nstatic void mm_fix_bad_ends_splice(void *km, const mm_mapopt_t *opt, const mm_idx_t *mi, const mm_reg1_t *r, const int8_t mat[25], int qlen, uint8_t *qseq0[2], const mm128_t *a, int *as1, int *cn"} +{"idx": 69386, "score": 3, "raw_out": "3", "code_head": "def renumber(in_pdb, out_pdb, return_other_chains=False):\n parser = PDB.PDBParser(QUIET=True)\n structure = parser.get_structure(None, in_pdb)\n model = structure[0]\n model_new = Model.Model"} +{"idx": 162685, "score": 3, "raw_out": "3", "code_head": "def plot_similarities(self, gene, n_genes=10, save=None):\n df = self.compute_similarities(gene).head(n_genes)\n _, ax = plt.subplots(1, 1, figsize=(3, 6))\n sns.barplot(data=df, y=\""} +{"idx": 203432, "score": 2, "raw_out": "2", "code_head": "def initialize_rxn_from_smarts(reaction_smarts):\n # Initialize reaction\n rxn = AllChem.ReactionFromSmarts(reaction_smarts)\n rxn.Initialize()\n if rxn.Validate()[1] != 0:\n raise Value"} +{"idx": 217274, "score": 3, "raw_out": "3", "code_head": "```java\nprivate void restoreQualities(Read r1, Read r2, byte[] qual1, byte[] qual2){\n\t\t\tfinal int len1=r1.length(), len2=r2.length();\n\t\t\tr1.quality=qual1;\n\t\t\tr2.quality=qual2;\n\t\t\tfinal byte qf=Shared."} +{"idx": 341223, "score": 3, "raw_out": "3", "code_head": "def N_to_AA(x):\n # [[0,1,2,3]] -> [\"ARND\"]\n x = np.array(x);\n if x.ndim == 1: x = x[None]\n return [\"\".join([aa_N_1.get(a,\"X\") for a in y]) for y in x]\n"} +{"idx": 42322, "score": 3, "raw_out": "3", "code_head": "def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Arguments:\n x: Tensor, shape ``[batch_size, gate_color, space, time]``\n \"\"\"\n "} +{"idx": 799, "score": 2, "raw_out": "2", "code_head": "```java\npublic JSONObject expandedJSON(OrganismComposition src) {\n JsonHelper o = new JsonHelper(src);\n o.add(\"name\", super.standardName); // from BPElement\n if (components != null)\n for"} +{"idx": 311729, "score": 3, "raw_out": "3", "code_head": "def test_get_foldseek_onehot_protein_not_in_record(self):\n \"\"\"Test one-hot encoding when protein is not in fold_record.\"\"\"\n n0 = \"missing_protein\"\n size_n0 = 5\n fold_record"} +{"idx": 100825, "score": 5, "raw_out": "5", "code_head": "```cpp\nMichaelisMentenRxn::MichaelisMentenRxn(double kcat, double Km, SimpleSpecies* r, int rS, vector p, vector pS)\n\t: BioNetGenRxn(vector(1,r),vector(1,rS),"} +{"idx": 175322, "score": 2, "raw_out": "2", "code_head": "def calculate_fidelity_to_states(state, target_indices):\n \"\"\"Calculate fidelity to specific target states.\"\"\"\n # For states like (|00000⟩ + |11111⟩)/√2\n target_prob = 1.0 / len(target_indices"} +{"idx": 83927, "score": 2, "raw_out": "2", "code_head": "def main():\n ops = generate(\n molecule=\"LiH\", n_occ=4, n_spatial_orb=6, apply_HF_sym=True, apply_transform=True\n )\n\n print(len(ops))\n"} +{"idx": 125522, "score": 4, "raw_out": "4", "code_head": "def crop_chains(\n chains_list: List[pipeline.FeatureDict],\n msa_crop_size: int,\n pair_msa_sequences: bool,\n max_templates: int) -> List[pipeline.FeatureDict]:\n \"\"\"Crops the MSAs for a set"} +{"idx": 116967, "score": 2, "raw_out": "2", "code_head": "```c\nint\nmain(int argc, char **argv)\n{\n char *hmmfile = argv[1];\n char *seqfile = argv[2];\n ESL_ALPHABET *abc = NULL;\n P7_HMMFILE *hfp = NULL;\n P7_HMM *h"} +{"idx": 334180, "score": 2, "raw_out": "2", "code_head": "```cpp\nint FourXYZsGroupGetFourthAtom(FourXYZsGroup* pThis, double* icParam, XYZ* pAtomD){\n // calculate the fourth atom from three given atoms, please refer to the proda reference;\n double result[4"} +{"idx": 234951, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n ligand: GeometricMol,\n holo: Optional[ProteinPocket] = None,\n apo: Optional[ProteinPocket] = None,\n interactions: Optional[np.ndarray] = None,\n"} +{"idx": 198520, "score": 3, "raw_out": "3", "code_head": "def write_genbank(\n updated_cds_dict: Dict[str, Dict[str, SeqFeature]],\n non_cds_dict: Dict[str, Dict[str, SeqFeature]],\n source_dict: Dict[str, Dict[str, SeqFeature]],\n prefix: str,\n g"} +{"idx": 353252, "score": 3, "raw_out": "3", "code_head": "def model_of_yield_for_one_condition(index_in_df, pKa_COH2plus, K_tilde, k_forward_over_k_backward, k_backward):\n pKa_HBr = 6.6\n # 10**(-1*(pKa_carbocat - pKa_COH2plus)) * k_forward_over_k_backw"} +{"idx": 297874, "score": 2, "raw_out": "2", "code_head": "def saveInteractionsPDB(self, **kwargs):\n \"\"\"Save the number of potential interactions to PDB file in occupancy column.\n \n :arg filename: name of the PDB file which will be saved "} +{"idx": 217268, "score": 1, "raw_out": "1", "code_head": "```java\nprivate Overlap makeOverlapReverse(Unit u2, int start1, BandedAligner bandy, boolean earlyExit) {\n\t\t\tif(verbose){System.err.println(\"makeOverlapReverse(u1=\"+this.name()+\", u2=\"+u2.name()+\", st"} +{"idx": 16538, "score": 2, "raw_out": "2", "code_head": "def construct_networkx(self,edge_weight_matrix,gene_exp_matr):\n \"\"\"\n Constructs a network from an edge weight matrix.\n\n Args:\n edge_weight_matrix: A"} +{"idx": 210903, "score": 3, "raw_out": "3", "code_head": "def __init__(self):\n self._cdna_db = None\n self._cds_db = None\n self._protein_db = None\n"} +{"idx": 297518, "score": 3, "raw_out": "3", "code_head": "def _find_refined_receptor(self, pose: FlexiblePose, refined_complexes) -> Optional[str]:\n \"\"\"Find the refined receptor corresponding to a pose\"\"\"\n # Simplified - in production you'd tra"} +{"idx": 219271, "score": 2, "raw_out": "2", "code_head": "def blastn(self):\n try:\n global ref, query, blastdb, out, evalue, format, path\n ## toPlainText read path of files\n ref = self.textBrowser_2.toPlainText()\n "} +{"idx": 347181, "score": 3, "raw_out": "3", "code_head": "def process_token_features(\n data: Tokenized,\n max_tokens: Optional[int] = None,\n binder_pocket_conditioned_prop: Optional[float] = 0.0,\n binder_pocket_cutoff: Optional[float] = 6.0,\n b"} +{"idx": 24526, "score": 3, "raw_out": "3", "code_head": "def create_token_type_ids_from_sequences(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\n ) -> List[int]:\n \"\"\"\n Create a mask from the two sequences pas"} +{"idx": 86861, "score": 4, "raw_out": "4", "code_head": "def test_on_batch(self, x, y, sample_weight=None):\n \"\"\"Test the model on a single batch of samples.\n\n # Arguments\n x: Numpy array of test data,\n or list of Nump"} +{"idx": 233492, "score": 3, "raw_out": "3", "code_head": "def _load_splits(self, seed: int = 1):\n rng = random.Random(seed)\n raw_splits = torch.load(Path(self.raw_dir) / \"split_by_name.pt\")\n all_train_candidates = raw_splits[\"train\"]\n "} +{"idx": 33494, "score": 3, "raw_out": "3", "code_head": "def __init__(self, hard=True, soft=False, **template_kwargs):\n\n if hard:\n hard_filters = [\n MolWtFilter(160, 480),\n LogPFilter(-0.4, 5.6),\n "} +{"idx": 135909, "score": 3, "raw_out": "3", "code_head": "def test_structure_from_query(query: Query, ground_truth_file: Path):\n \"\"\"Tests that the generated structure and reference molecules matches gt.\"\"\"\n structure_with_ref_mols = structure_with_ref_"} +{"idx": 82673, "score": 3, "raw_out": "3", "code_head": "def _single_process_create(self, fingerprint: Union[Fingerprint, List[Fingerprint]], progress: bool = True,\n total: Optional[int] = None):\n \"\"\"Fill in the similari"} +{"idx": 234023, "score": 3, "raw_out": "3", "code_head": "def __init__(self, base_train_data: Dict[str, List], seed: int = 12345):\n \"\"\"\n Args:\n base_train_data: The base training dataset (always included)\n seed: Random see"} +{"idx": 276600, "score": 2, "raw_out": "2", "code_head": "def test_extract_domains_origin_spanning_domain_3(shared_datadir):\n \"\"\"\n Test read and write genbank files.\n \"\"\"\n with tempfile.TemporaryDirectory() as output_dir:\n # output_dir"} +{"idx": 255467, "score": 3, "raw_out": "3", "code_head": "def _sample_designs_pssm(\n self, design_state: DesignState, update_esm_bias: bool = True\n ) -> None:\n \"\"\"Sample design sequences using PSSM-based generation.\"\"\"\n if update_esm_"} +{"idx": 294337, "score": 3, "raw_out": "3", "code_head": "def __init__(self, emb_dim, feature_dims, sigma_embed_dim, lm_embedding_type= None):\n # first element of feature_dims tuple is a list with the lenght of each categorical feature and the second "} +{"idx": 316658, "score": 2, "raw_out": "2", "code_head": "def GetQuasiSequenceOrder1SW(ProteinSequence,maxlag=30,weight=0.1,distancematrix=_Distance1):\n\t\"\"\"\n\t###############################################################################\n\tComputing the first"} +{"idx": 36, "score": 3, "raw_out": "3", "code_head": "```c\nvoid create_wf_sys(qsystem qsys,qvec *new_wf){\n qvec temp = NULL;\n PetscInt n,Istart,Iend,i;\n\n /* Check to make sure some operators were created */\n if (qsys->num_subsystems==0){\n PetscPri"} +{"idx": 20400, "score": 2, "raw_out": "2", "code_head": "def run_dirmseq(prefix, workdir, threads, groups, batches, matrixFile, outDir, formulaMatrixFile):\n stderr = f\"{workdir}/{prefix}.txt\"\n try:\n with open(stderr, \"w\") as stderr_fh:\n "} +{"idx": 259303, "score": 4, "raw_out": "4", "code_head": "```cpp\nstd::string BNGLExporter::export_to_bngl(\n World* world_,\n const std::string& file_name,\n const API::BNGSimulationMethod simulation_method) {\n\n clear_temporaries();\n nfsim_export = s"} +{"idx": 170888, "score": 2, "raw_out": "2", "code_head": "def get_kmer(seq):\n ntarr = (\"A\",\"C\",\"G\",\"T\")\n kmerArray = []\n kmerre = []\n rst = []\n fst = 0\n total = 0.0\n pp = 0.0\n item = 0.0\n\n for n in range(4):\n kmerArray.appen"} +{"idx": 163608, "score": 1, "raw_out": "1", "code_head": "```c\nConstraint_list * seq2contacts (Sequence *S, Sequence *T,Constraint_list *CL, char *mode)\n{\n //Turns an RNA into a 2D maps (CL) using either Zuker or bona fide PDB\n //Does nothing for proteins\n"} +{"idx": 244785, "score": 3, "raw_out": "3", "code_head": "def get_families_to_write(\n pangenome: Pangenome,\n partition_filter: str = \"core\",\n soft_core: float = 0.95,\n dup_margin: float = 0.95,\n single_copy: bool = True,\n) -> Set[GeneFamily]:\n"} +{"idx": 26139, "score": 2, "raw_out": "2", "code_head": "def forward(self, batch):\n seq_t, antibody_len = batch['seq_t'], batch['anchor_flag'].shape[1]\n antibody_seq_t = batch['seq_t'][:,:antibody_len]\n\n str_seq_t = [self._make_one_anti"} +{"idx": 217651, "score": 3, "raw_out": "3", "code_head": "def forward(self, x, return_projection = True):\n embed = self.get_embedding(x)\n if not return_projection:\n return embed\n\n projector = self._get_projector(embed)\n "} +{"idx": 98566, "score": 3, "raw_out": "3", "code_head": "def merge_a3m_homo(msa_orig, ins_orig, nmer, mode=\"default\"):\n N, L = msa_orig.shape[:2]\n if mode == \"repeat\":\n\n # AAAAAA\n # AAAAAA\n\n msa = torch.tile(msa_orig,(1,nmer))\n "} +{"idx": 287604, "score": 4, "raw_out": "4", "code_head": "def add_constant_field(protein, key, value):\n protein[key] = tf.convert_to_tensor(value)\n return protein\n"} +{"idx": 331920, "score": 3, "raw_out": "3", "code_head": "def plot_uncertainty_dose(\n compert_api,\n cov,\n pert,\n N=11,\n metric='cosine',\n measured_points=None,\n cond_key='condition',\n log=False,\n min_dose=None,\n filename=None\n):"} +{"idx": 43760, "score": 2, "raw_out": "2", "code_head": "def crop_extra_msa(protein, max_extra_msa):\n \"\"\"MSA features are cropped so only `max_extra_msa` sequences are kept.\"\"\"\n num_seq = tf.shape(protein['extra_msa'])[0]\n num_sel = tf.minimum(max_extra_"} +{"idx": 68430, "score": 2, "raw_out": "2", "code_head": "def _dummy_fit(adata: AnnData):\n model = novae.Novae(adata, embedding_size=10)\n model.fit(adata, max_epochs=1)\n"} +{"idx": 7722, "score": 2, "raw_out": "2", "code_head": "def _process_cas9_dual_guide_sample(self, fastq_dir, sample_id, get_recombinant, trim_first_g, protospacer_A_length, protospacer_B_length, write, verbose=False):\n if verbose: print(green(sample"} +{"idx": 210667, "score": 1, "raw_out": "1", "code_head": "```c\nint scoring(float *weight, /* table containing the frequencies of the observed base \n\t\t\t at each position of the potential signal */\n\t int lsig, /* length of the signal */\n\t float"} +{"idx": 355737, "score": 3, "raw_out": "3", "code_head": "def get_lig_structure_graph(lig):\n coords = lig.GetConformer().GetPositions()\n weights = []\n for idx, atom in enumerate(lig.GetAtoms()):\n weights.append(atom.GetAtomicNum())\n weight"} +{"idx": 218882, "score": 2, "raw_out": "2", "code_head": "def can_react(state, rxns):\n \"\"\"\n Determines if two molecules can react using any of the input reactions.\n\n Args:\n state (np.ndarray): The current state in the synthetic tree.\n "} +{"idx": 165767, "score": 4, "raw_out": "4", "code_head": "def has_modifications(self) -> bool:\n \"\"\"\n Returns True if the entity has modifications; this only applies to polymers so\n is always False for ligands, waters, and unknowns.\n "} +{"idx": 1778, "score": 3, "raw_out": "3", "code_head": "def _forward(self, z, pos, batch):\n num_nodes = z.size(0)\n\n edge_index = radius_graph(pos, r=self.cutoff, batch=batch)\n j, i = edge_index\n\n vecs = pos[j] - pos[i]\n d"} +{"idx": 111948, "score": 3, "raw_out": "3", "code_head": "def maybe_fold_in(key, data):\n if key is not None:\n return jax.random.fold_in(key, data)\n else:\n return None\n"} +{"idx": 7492, "score": 3, "raw_out": "3", "code_head": "def tied_sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None"} +{"idx": 1236, "score": 4, "raw_out": "4", "code_head": "def _build_rollout(self, trust_remote_code=False):\n from torch.distributed.device_mesh import init_device_mesh\n\n # TODO(sgm): support FSDP hybrid shard for larger model\n infer_tp "} +{"idx": 354293, "score": 1, "raw_out": "1", "code_head": "```c\nint co_rvs2kmer_byreads( reverse_opt_val_t *opt_val ){\n dim_shuffle_t* shuf_arr = read_dim_shuffle_file(opt_val->shufile);\n int shuf_arr_len = 1LLU << (4 * shuf_arr->dim_shuffle_stat.subk) ;\n un"} +{"idx": 277005, "score": 1, "raw_out": "1", "code_head": "```cpp\nList compute_archetype_feature_specificity_bin(sp_mat &S, mat &H) {\n field res = ACTIONet::compute_feature_specificity_bin(S, H);\n\n List out_list;\n out_list[\"archetypes\"] = res(0);\n ou"} +{"idx": 320896, "score": 2, "raw_out": "2", "code_head": "```cpp\nvoid setCreatesetdbWorkflowDefaults(Parameters *p) {\n p->orfMinLength = 30;\n //TODO: for protein seq we have to force --shuffle 0 \n p->shuffleDatabase = 0;\n}\n```"} +{"idx": 51267, "score": 3, "raw_out": "3", "code_head": "def get_geometry_graph(lig):\n coords = lig.GetConformer().GetPositions()\n edges_src = []\n edges_dst = []\n for i, atom in enumerate(lig.GetAtoms()):\n src_idx = atom.GetIdx()\n "} +{"idx": 99855, "score": 3, "raw_out": "3", "code_head": "def test_write_fasta_to_handle() -> None:\n inference_output = _make_simple_inference_output()\n\n handle = StringIO()\n inference_output.write_fasta(handle=handle)\n contents = handle.getvalue"} +{"idx": 62339, "score": 4, "raw_out": "4", "code_head": "def forward(self, X, S, mask, chain_M, residue_idx, chain_encoding_all):\n \"\"\" Graph-conditioned sequence model \"\"\"\n device=X.device\n # Prepare node and edge embeddings\n\n if"} +{"idx": 318955, "score": 3, "raw_out": "3", "code_head": "def __init__(self, embed_dim, tgt, dropout, mean=False, binary=False):\n super(Generator3D, self).__init__()\n self.tgt = tgt\n self.mean = mean\n self.binary = binary\n "} +{"idx": 340402, "score": 3, "raw_out": "3", "code_head": "def readknead(fq_1, fq_2=None, outpath=None, fq_fname_out_r1=None, fq_fname_out_r2=None, fq_command_in=None, fq_command_out=None, quality_score=None, ops_r1=None, ops_r2=None, report_path=None, label="} +{"idx": 104079, "score": 2, "raw_out": "2", "code_head": "def canon_geo(elements,adj_mat,geo=None,bond_mat=None,dup=[],change_group_seq=True):\n \n # Initialize mass_dict (used for identifying the dihedral among a coincident set that will be explicitly s"} +{"idx": 158553, "score": 2, "raw_out": "2", "code_head": "def plot_hamming_distance_v_generation_violin(self, ax):\n \n #Retrieves the amino acid sequence of the ancestor protein\n with open(f\"{self.FOLDER_HOME}/parent/{self.WT}.seq\", \"r\") as f:\n "} +{"idx": 146791, "score": 2, "raw_out": "2", "code_head": "def three_frame_translation(self, input_fasta):\n \"\"\"\n This function translate a transcriptome into a 3'frame translation protein sequence database\n :param input_fasta: fasta input file\n "} +{"idx": 360993, "score": 2, "raw_out": "2", "code_head": "def loss_output(self, batch):\n # contrastive loss\n src_id = batch[\"src_input_ids\"].cuda()\n candidate_id = batch[\"candidate_ids\"].cuda()\n batch_size = src_id.size(0)\n "} +{"idx": 95536, "score": 3, "raw_out": "3", "code_head": "def atom37_to_frames(protein, eps=1e-8):\n # TODO: extract common part and put them into residue constants.\n aatype = protein[\"aatype\"]\n all_atom_positions = protein[\"all_atom_positions\"]\n "} +{"idx": 52523, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n dir_output: str,\n n_jobs: int,\n ) -> None:\n \"\"\"Constructor for the PrimerDesigner class.\"\"\"\n\n ##### create the output folder #####\n s"} +{"idx": 249354, "score": 3, "raw_out": "3", "code_head": "def predict_cage(\n model,\n chrom,start,end,\n dnase,\n fasta_extractor,\n cross_cell_type=False\n ):\n \"\"\"\n 1kb CAGE-seq prediction\n Args:\n model: EPCO"} +{"idx": 207983, "score": 3, "raw_out": "3", "code_head": "def __call__(self, data: ProteinLigandData):\n element = data.ligand_element.view(-1, 1) == self.atomic_numbers.view(1, -1) # (N_atoms, N_elements)\n # convert some features to one-hot v"} +{"idx": 329063, "score": 2, "raw_out": "2", "code_head": "def __init__(self, model, use_cuda, full_features=False):\n self.model = model\n self.use_cuda = use_cuda\n self.full_features = full_features\n if full_features:\n s"} +{"idx": 42648, "score": 2, "raw_out": "2", "code_head": "def beam_search(args, model, flow, frontiers_dict, graph_list):\n smiles_list = [frontier for frontiers in frontiers_dict.values() for frontier in frontiers]\n # print('frontiers', smiles_list)\n "} +{"idx": 11453, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n use_key: Optional[str] = None,\n filter_gene_by_counts: Union[int, bool] = False,\n filter_cell_by_counts: Union[int, bool] = False,\n normalize_t"} +{"idx": 144029, "score": 2, "raw_out": "2", "code_head": "```cpp\nvoid annotationLiftOverPversion(std::map >& refTranscriptHashSet,\n std::map& targetTranscriptHashMap,\n "} +{"idx": 237491, "score": 2, "raw_out": "2", "code_head": "def edit_pdb_file(\n pdb_file: str, target_res_name: str, target_res_numb: str\n) -> List[list]:\n \"\"\"Find the residue name to change, edit it and then store as a list to write out.\"\"\"\n exchange"} +{"idx": 60701, "score": 2, "raw_out": "2", "code_head": "def predict(self, input_path):\n \"\"\"\n Perform prediction with a pre-trained model\n :param input_path: prediction data path\n :return:\n \"\"\"\n # Load signature gen"} +{"idx": 134473, "score": 3, "raw_out": "3", "code_head": "```c\nvoid ampl_equations_1h2p_folded_triples_F3a()\n{\n dg_stack_pos_t pos = get_stack_pos();\n\n reorder(\"m3c\", \"r1\", \"345612\");\n mult(\"veff02\", \"r1\", \"r2\", 2);\n update(\"m3nw\", -0.5, \"r2\");\n\n"} +{"idx": 69950, "score": 3, "raw_out": "3", "code_head": "def forward(self, src, target, src_mask=None, mask_attend=None, checkpoint=False):\n \"\"\" Apply one Transformer update to TERM matches\n\n Args\n ----\n src: torch.Tensor\n "} +{"idx": 342657, "score": 2, "raw_out": "2", "code_head": "def crop_extra_msa(protein, max_extra_msa):\n \"\"\"MSA features are cropped so only `max_extra_msa` sequences are kept.\"\"\"\n num_seq = tf.shape(protein['extra_msa'])[0]\n num_sel = tf.minimum(max_extra_"} +{"idx": 273083, "score": 3, "raw_out": "3", "code_head": "def populate(self, compound_dict=None, x=1, y=1, z=1):\n \"\"\"Expand lattice and create compound from lattice.\n\n Expands lattice based on user input. The user must also\n pass in a di"} +{"idx": 341197, "score": 2, "raw_out": "2", "code_head": "def esm_refine(self, pred_ids, only_mask=False):\n \"\"\"Use ESM-1b to refine model predicted\"\"\"\n if not hasattr(self, 'esm'):\n import esm\n self.esm, self.esm_alphabet "} +{"idx": 338134, "score": 2, "raw_out": "2", "code_head": "```java\n@Test\n public void testComputeProteinStructure() {\n LOGGER.info(\"computeProteinStructure\");\n String pdbId = \"3tge\";\n \n PDB expResult = new PDB();\n expResu"} +{"idx": 181955, "score": 3, "raw_out": "3", "code_head": "def compare_models_maxcluster(experiments, predictions, norm_by_intersection=True,\n distance_cutoff=None, binary=\"maxcluster\"):\n \"\"\"\n Compare predicted models to a s"} +{"idx": 60350, "score": 2, "raw_out": "2", "code_head": "def test_FitnessPrediction(self):\n data = FitnessPrediction(normalize=False)\n task = SequenceRegression(\n backbone=protein_onehot,\n adapter=self.sequence_adapter_pa"} +{"idx": 80971, "score": 4, "raw_out": "4", "code_head": "def forward(\n self,\n x: T,\n seqs_cu_seqlens: Optional[Tensor] = None,\n seqs_cu_seqlens_cpu: Optional[Tensor] = None,\n src_mask: Optional[Tensor] = None,\n src_"} +{"idx": 145966, "score": 2, "raw_out": "2", "code_head": "def getFrontVals(self, letter):\n \"\"\"Get the energetic contributions based on the beginning of the\n sequence. These are based on the 'init_X/Y' values stored in the\n "} +{"idx": 9738, "score": 3, "raw_out": "3", "code_head": "```cpp\nassemble::ProteinModel FusionProtein::CreateFusedModel\n (\n const biol::AASequence &FUSED_SEQUENCE,\n const assemble::ProteinModel &SCAFFOLD\n )\n {\n const char scaffold_cha"} +{"idx": 22176, "score": 2, "raw_out": "2", "code_head": "def get_refmfc(X,params):\n \"\"\"\n **Caclulates median profile across all spectra in a dataset, **median fold change inter-sample normalization.**\n Args:\n X: MSI dataset (number of "} +{"idx": 21319, "score": 3, "raw_out": "3", "code_head": "def __init__(self, fragment, features, **kwargs):\n CHICMolecule.__init__(self, fragment, **kwargs)\n FeatureAnnotatedMolecule.__init__(self, fragment, features, **kwargs)\n"} +{"idx": 107864, "score": 2, "raw_out": "2", "code_head": "def evaluate_fold(fold):\n\n #load data\n #opts=get_args()\n df=pd.read_csv(opts.path)\n\n sequences=np.asarray(df.sequence)\n labels=np.asarray(df.label)\n\n train_indices, val_indices, test"} +{"idx": 357038, "score": 2, "raw_out": "2", "code_head": "def create_pdb_line(self, index):\n output = \"ATOM \"\n output = (\n output\n + r_just(str(index), 6)\n + r_just(self.atom_name, 5)\n + r_just(self.r"} +{"idx": 136141, "score": 3, "raw_out": "3", "code_head": "def load_rnaseq_data(disease_code,\n with_clinical=False,\n wait_time=30):\n # Fetch RNA data\n archive_path = prefetch_rnaseq_data(disease_code)\n\n result_"} +{"idx": 292739, "score": 3, "raw_out": "3", "code_head": "def match_non_acc_fmt_genbank_protein_seqs(candidate_protein_seq_strs, genbank_protein_info_set,\n non_acc_fmt_protein_ids):\n if type(candidate_protein_seq_"} +{"idx": 165000, "score": 2, "raw_out": "2", "code_head": "def __call__(self, track):\n data = []\n upfold = self.mMinFoldChange\n downfold = 1.0 / upfold\n data.append((\"> %5.2f fold\" % upfold, self.getValue(\n \"SELECT COUNT"} +{"idx": 192358, "score": 3, "raw_out": "3", "code_head": "def test_uniform_rank_based_frequent_flyer_transformation(spark, sample_matrix):\n \"\"\"\n Given a sample matrix, when the frequent flyer transformation is applied,\n then the transformed matrix s"} +{"idx": 297516, "score": 3, "raw_out": "3", "code_head": "def generate(self, receptor_file: str, center: Tuple[float, float, float],\n dimensions: Tuple[float, float, float], spacing: float = 0.375, **kwargs) -> List[GridBox]:\n \"\"\"Gener"} +{"idx": 22022, "score": 3, "raw_out": "3", "code_head": "def build_center_conv_graph(self, data):\n # builds the filter and edges for the convolution generating translational and rotational scores\n edge_index = torch.cat([data['ligand'].batch.u"} +{"idx": 318555, "score": 3, "raw_out": "3", "code_head": "def __getitem__(self, idx):\n \"\"\"\n Get a sample from the dataset.\n\n Args:\n idx (int): Index of the sample.\n\n Returns:\n dict: Dictionary containing 'ref"} +{"idx": 343598, "score": 4, "raw_out": "4", "code_head": "def get_transition_transversion_ratio(fasta):\n '''\n TRAN Transitions and Transversions\n\n For DNA strings s1 and s2 having the same length, their\n transition/transversion ratio R(s1,s2) is "} +{"idx": 259252, "score": 2, "raw_out": "2", "code_head": "def run_mcell4(args_str_w_opts):\n args_str = args_str_w_opts[0]\n opts = args_str_w_opts[1]\n \n cmd_str = sys.executable + ' ' + opts.main_model_file + ' '\n cmd_str += args_str\n \n p"} +{"idx": 337716, "score": 3, "raw_out": "3", "code_head": "def _generate_for_selection(self, label_name: str):\n tables, figures = [], []\n for assessment_index, assessment_state in enumerate(self.state.assessment_states):\n selection_st"} +{"idx": 240472, "score": 3, "raw_out": "3", "code_head": "def get_moosavi_mof_data() -> pd.DataFrame:\n \"\"\"Return the data and features used in [MoosaviDiversity]_.\n\n You can find the original datasets on `MaterialsCloud archive Mapping[str, _FastaChain]:\n \"\"\"Makes a mappi"} +{"idx": 195270, "score": 3, "raw_out": "3", "code_head": "def model_f_score(self, model_name):\n r\"\"\"\n Get the F score of a candidate model.\n\n :param str model_name: name of candidate model\n :returns float f_score: F score, between"} +{"idx": 282029, "score": 2, "raw_out": "2", "code_head": "```java\n@PublicApi\n @PremiumPublicApi\n @ApiOperation(value = \"\", notes = \"Annotate mutation by HGVSg.\", response = IndicatorQueryResp.class)\n @ApiResponses(value = {\n @ApiResponse(code"} +{"idx": 93474, "score": 1, "raw_out": "1", "code_head": "```rust\nfn test_qasm_generation() {\n let backend = IBMBackend::new();\n let circuit = HardwareCircuit {\n num_qubits: 2,\n gates: vec![\n HardwareGate {\n"} +{"idx": 108970, "score": 3, "raw_out": "3", "code_head": "def calculate_sequence_similarity(seq1, seq2):\n \"\"\"\n Calculate sequence similarity between two protein sequences using global alignment.\n Returns percentage similarity (0-100).\n \"\"\"\n # "} +{"idx": 196911, "score": 2, "raw_out": "2", "code_head": "def renumber_to_mcs(mol, tuple_order_list):\n \"\"\"\n This renumbers the indexes of the atoms in a lig to that of the MCS and\n returns a renumbered atom.\n\n Inputs:\n :param rdkit.Chem.rdchem"} +{"idx": 307822, "score": 2, "raw_out": "2", "code_head": "def QEDScore(smi):\n try:\n mol = Chem.MolFromSmiles(smi)\n return QED.qed(mol)\n except:\n return np.nan\n"} +{"idx": 152586, "score": 2, "raw_out": "2", "code_head": "def format_redesign_positions(chain_id, res_nums):\n \"\"\"Format redesign positions for a chain into a string.\"\"\"\n ranges = get_residue_ranges(res_nums)\n return ';'.join(f\"{chain_id}{r}\" for r i"} +{"idx": 4664, "score": 2, "raw_out": "2", "code_head": "def test_modify_sequence_modifications_included():\n test_seq = \"C(=O)N[C@H]C(=O)N[C@H]C\"\n mod_seq, mods = modify_sequence(\n test_seq, do_methylate=True, do_pegylate=True, nmeth_residues=0"} +{"idx": 72591, "score": 2, "raw_out": "2", "code_head": "def get_ranks(y, thresh=0.8, prefix=\"\", flip=False, col_name='score'):\n \"\"\"\n y should be a DataFrame with one column\n thresh is the threshold at which to call it a knock-down or not\n col_n"} +{"idx": 79179, "score": 2, "raw_out": "2", "code_head": "def compute_fold(ARG): \n cv_train_idx, cv_val_idx = ARG \n train = slice_data(data_stuff, cv_train_idx, N_inputs)\n val = slice_data(data_stuff, cv_val_idx, N_inputs)\n model, metric"} +{"idx": 142277, "score": 2, "raw_out": "2", "code_head": "def test_up_exhaustive(self):\n \"\"\"Unpaired probabilties for short RNAs are identical to what we get from exhaustive enumeration\"\"\"\n RNA.init_rand()\n randseq_length = 20\n ul"} +{"idx": 174802, "score": 1, "raw_out": "1", "code_head": "```cpp\nint main()\n{\n std::vector geom = {\n 1.49574345397488, 1.16281825573451, 0.28849847338774,\n -0.49945625222174, -2.53040659067913, -0.53071797893969,\n 0.1619464810"} +{"idx": 176953, "score": 2, "raw_out": "2", "code_head": "def main(opts):\n df = pd.read_table(opts['input'])\n feature_df = df.drop_duplicates(subset=['Gene_ID']).copy()\n\n # setup class label\n y = feature_df['Modal Bin'].apply(lambda x: 1 if x<2.5"} +{"idx": 192519, "score": 3, "raw_out": "3", "code_head": "def calculate_non_prompt_sequence_identity(\n input_aa: str, reference_aa: str, prompt_dna: str, mafft_path: str = \"mafft\"\n) -> float:\n \"\"\"Calculate sequence identity for the non-prompt portion u"} +{"idx": 49129, "score": 1, "raw_out": "1", "code_head": "```c\nstatic int __pyx_pw_13_rnaseq_utils_9BAMobject_1__init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n struct __pyx_obj_13_rnaseq_utils_RNAseqDataset *__pyx_v_dataset = "} +{"idx": 177343, "score": 3, "raw_out": "3", "code_head": "def xtb_mo_gen(in_file, calc):\n local_folder = os.path.join(CALCUS_SCR_HOME, str(calc.id))\n\n ret = launch_xtb_calc(in_file, calc, [\"calc.out\", \"molden.input\", \"xtbout.json\"])\n\n if ret != Erro"} +{"idx": 325340, "score": 2, "raw_out": "2", "code_head": "def pocketCode(self, pdbPath, poc_pos, pocket_contact, lig_pos,center=None, rrot=None, rdkit_pkt=None):\n\n names, names1, reses, positions = self.loadMacroPDBInfo(pdbPath, rdkit_pkt)\n\n co"} +{"idx": 18698, "score": 3, "raw_out": "3", "code_head": "def het_plot(\n adata: AnnData,\n # plotting param\n title: str | None = None,\n figsize: tuple[float, float] | None = None,\n cmap: str = \"Spectral_r\",\n use_label: str | None = None,\n "} +{"idx": 21602, "score": 3, "raw_out": "3", "code_head": "def _forward(self, h_V, h_P, P_idx, batch_id, S=None, AT_test = False, mask_bw = None, mask_fw = None, decoding_order= None, return_logit=False, return_feats=False):\n t1 = time.time()\n h"} +{"idx": 141359, "score": 3, "raw_out": "3", "code_head": "def get_decision_sequence(self, mol, atom_order):\n \"\"\"Extract a decision sequence with which DGMG can generate the\n molecule with a specified atom order.\n\n Parameters\n ----"} +{"idx": 85910, "score": 3, "raw_out": "3", "code_head": "def mol_to_graph_data(mol):\n \"\"\"\n mol_to_graph_data\n\n Args:\n atom_features: Atom features.\n edge_features: Edge features.\n morgan_fingerprint: Morgan fingerprint.\n "} +{"idx": 78337, "score": 4, "raw_out": "4", "code_head": "def _auto_detect_tax_id_mode(a3m_files: Dict[str, Path]) -> bool:\n \"\"\"\n Auto-detect whether to use TaxID or UniRef ID pairing based on A3M file contents.\n \n This implements ColabFold's def"} +{"idx": 305896, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n structure_search_type: StructEntryType = \"entry_id\",\n backbone_distance_tolerance: StructMotifTolerance = 1,\n side_chain_distance_tolerance: StructMot"} +{"idx": 151797, "score": 2, "raw_out": "2", "code_head": "```cpp\nvoid compute_tandem()\n{\n cout<<\"Detecting tandem arrays...\"< Dict[str, Any]:\n \"\"\"BLAT a nucleotide or amino acid sequence against any BLAT UCSC"} +{"idx": 125675, "score": 4, "raw_out": "4", "code_head": "def generate_feature_dataframe(\n G: nx.Graph,\n funcs: List[Callable],\n return_array=False,\n) -> pd.DataFrame:\n \"\"\"\n Return a pandas DataFrame representation of node metadata.\n\n ``fun"} +{"idx": 25300, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n parent,\n batch_size=13,\n seq_length=7,\n is_training=True,\n use_input_mask=True,\n vocab_size=99,\n hidden_size=32,\n n"} +{"idx": 161417, "score": 2, "raw_out": "2", "code_head": "def plot_seqlogo(ax, seq_1hot, sat_score_ti, pseudo_pct=0.05):\n \"\"\" Plot a sequence logo for the loss/gain scores.\n\n Args:\n ax (Axis): matplotlib axis to plot to.\n seq_1hot (Lx4 arra"} +{"idx": 94155, "score": 3, "raw_out": "3", "code_head": "def test_openqasm3_version():\n \"\"\"测试 OpenQASM 3.0 版本声明 (标准格式)\"\"\"\n from tyxonq.core.ir.circuit import Circuit\n from tyxonq.compiler.api import compile\n \n print(\"\\n\" + \"=\" * 70)\n print"} +{"idx": 274228, "score": 3, "raw_out": "3", "code_head": "def partial_transform(self, sequence, mode='clip'):\n if self.n_macrostates is None:\n raise RuntimeError('n_macrostates must not be None to transform')\n trimmed_sequence = supe"} +{"idx": 37152, "score": 3, "raw_out": "3", "code_head": "def __call__(self, data):\n context_idx = data.ligand.context_idx\n data.ligand.frontier = data.ligand_context.num_neighbors < data.ligand.num_neighbors[context_idx]\n\n # Positive sa"} +{"idx": 158686, "score": 2, "raw_out": "2", "code_head": "def run_kma(fastq, basename, identity, coverage, reads):\n \"\"\"Run kma on fastq(s) and return sequences\"\"\"\n if is_database_indexing(basename) is False:\n raise exceptions.PyMLSTError('Dabata"} +{"idx": 211376, "score": 2, "raw_out": "2", "code_head": "def calculate_fold_change(read_count_file, output_label, control_columns, min_reads, pseudo_count):\n \"\"\"\n \\b\n Calculate fold changes from read count data outputting a fold change column:\n\n "} +{"idx": 172239, "score": 4, "raw_out": "4", "code_head": "def __init__(self):\n\n self.position=['&']\n self.num_atom=8\n #self.vl=['\\n', '&', 'C', '(', 'c', '1', 'o', '=', 'O', 'N', 'F', '[C@@H]',\n #'n', '-', '#', 'S', 'Cl', '[O-]', "} +{"idx": 156880, "score": 3, "raw_out": "3", "code_head": "def get_multiplicity_likelihoods(self):\n \"\"\"\n Likelihood = binomial probability of alt count from purity, copy number, and multiplicity\n \"\"\"\n mult_lik_dict = {}\n n ="} +{"idx": 217433, "score": 2, "raw_out": "2", "code_head": "def run(self, prediction, reference):\n \"\"\"\"\"\"\n # - input shape should be (?, ?, ?)\n print(f\"reference: {reference}\")\n print(f\"prediction: {prediction}\")\n\n reference "} +{"idx": 56964, "score": 4, "raw_out": "4", "code_head": "def run_latent_to_gene(config: LatentToGeneConfig):\n logger.info(\"------Loading the spatial data...\")\n adata = sc.read_h5ad(config.hdf5_with_latent_path)\n logger.info(f\"Loaded spatial data wi"} +{"idx": 52114, "score": 3, "raw_out": "3", "code_head": "def scan_sequences(\n seqs: List[str],\n motifs: Union[Motif, List[Motif], str],\n names: Optional[List[str]] = None,\n bg=None,\n seq_ids: Optional[List[str]] = None,\n pthresh: float = 1"} +{"idx": 294036, "score": 4, "raw_out": "4", "code_head": "def p_sample_guided(self, x, classes, t, t_index, context_mask, cond_weight):\n # adapted from: https://openreview.net/pdf?id=qw8AKxfYbI\n batch_size = x.shape[0]\n device = self.dev"} +{"idx": 252272, "score": 3, "raw_out": "3", "code_head": "def calc_weights_for_df(\n indices_df: pd.DataFrame, beta_dict: dict[str, Any], alpha_dict: dict[str, Any]\n) -> pd.DataFrame:\n \"\"\"\n Calculate weights for each example in the dataframe.\n\n Ar"} +{"idx": 248514, "score": 2, "raw_out": "2", "code_head": "def apply_make_generation_fast_(module, prefix):\n if len(prefix) > 0:\n prefix += \".\"\n\n base_func = BaseFairseqModel.make_generation_fast_\n for n, m in m"} +{"idx": 176727, "score": 3, "raw_out": "3", "code_head": "```cpp\nint\nCGOAssocs::GenGONumGenes(void)\n{\nint Idx;\nint AssocIdx;\ntsGOAssoc *pAssoc;\ntsGOGene *pGene;\nint BIOcnt;\nint MOLcnt;\nint CELcnt;\n\nif(m_pGenes == NULL || !m_GOGenesCnt || m_pAssocs == NULL ||"} +{"idx": 180172, "score": 2, "raw_out": "2", "code_head": "def test_rmsd():\n \"\"\"Test RMSD calculation.\"\"\"\n p = Protein(fk)\n q = p.translate([3.0,4.0,0.0], inplace=False)\n assert np.all(np.isclose(q.center()-p.center(), np.array([3.,4.,0.]))), \"rms"} +{"idx": 65182, "score": 3, "raw_out": "3", "code_head": "def random_crop_to_size(\n protein,\n crop_size,\n max_templates,\n shape_schema,\n spatial_crop_prob,\n interface_threshold,\n subsample_templates=False,\n "} +{"idx": 251179, "score": 1, "raw_out": "1", "code_head": "```c\nPUBLIC int\nvrna_BT_ext_loop_f5(vrna_fold_compound_t *fc,\n int *k,\n int *i,\n int *j,"} +{"idx": 60821, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n *,\n binary_path: str,\n databases: Sequence[str],\n n_cpu: int = 4,\n n_iter: int = 3,\n e_value: float = 0.001,\n maxseq: int "} +{"idx": 260956, "score": 4, "raw_out": "4", "code_head": "def from_pdb_string(pdb_str: str, chain_id: Optional[str] = None) -> Protein:\n \"\"\"Takes a PDB string and constructs a Protein object.\n\n WARNING: All non-standard residue types will be converted "} +{"idx": 223627, "score": 2, "raw_out": "2", "code_head": "def tm_score_from_pdbs(\n filepath_pdb: str,\n filepath_reference_pdb: str,\n) -> Tuple[bool, str, Optional[Dict[str, float]]]:\n \"\"\"Computes the TM-score, RMSD and GDT metrics from prediction/ta"} +{"idx": 19830, "score": 3, "raw_out": "3", "code_head": "def get_selected_batch_counts(data, batch_names, \n include_ref: bool=False, verbose: bool=False):\n \"\"\" For each batch, get the set of genes which were significantly\n "} +{"idx": 158284, "score": 2, "raw_out": "2", "code_head": "def predict_structures_plfold(input_file, output_file):\n handle_in = get_handle(input_file, \"rt\")\n handle_out = get_handle(output_file, \"wt\")\n for header, seq in parse_fasta(handle_in):\n "} +{"idx": 175679, "score": 4, "raw_out": "4", "code_head": "def __init__(\n self,\n templates: dict[str, mRNA] | None = None,\n filters: dict[str, Callable[[Any], str]] | None = None,\n strict: bool = False,\n silent: bool = False"} +{"idx": 360243, "score": 2, "raw_out": "2", "code_head": "def main(fa: str, output: str, blast: str, blastdb: str):\n if which(blast) is None:\n raise ValueError(f'{blast} was not found.')\n sys.exit(0)\n\n fa_pool = defaultdict(str)\n cid ="} +{"idx": 81612, "score": 3, "raw_out": "3", "code_head": "def add_dummy_atoms_3d(mol, anchor_indices):\n '''\n Add dummy atoms to a molecule based on the anchor indices. We adopted a trick to create 3D dummy atoms by replacing the hydrogen atoms.\n '''"} +{"idx": 87323, "score": 2, "raw_out": "2", "code_head": "def get_comparable_res(proteins, score, score_lfq, label, const, logFC, real_logFC, organisms, in_files):\n proteins_all = pd.read_csv(in_files['all_candidate'], header=0, sep='\\t') # ['Protein'].v"} +{"idx": 328593, "score": 2, "raw_out": "2", "code_head": "def mitosomatic(args):\n \"\"\"\n %prog mitosomatic t.piledriver\n\n Find mito mosaic somatic mutations in piledriver results.\n \"\"\"\n import pandas as pd\n\n p = OptionParser(mitosomatic.__doc"} +{"idx": 295953, "score": 4, "raw_out": "4", "code_head": "def test(rank, test_dataloader, model, params):\n model.eval()\n\n int_log = defaultdict(int)\n list_log = defaultdict(list)\n for batch_idx, batch in enumerate(tqdm(test_dataloader, disable=(r"} +{"idx": 196907, "score": 3, "raw_out": "3", "code_head": "def replace_core_mol_dummy_atoms(mol, mcs, replace_core_mol):\n \"\"\"\n This function will replace the dummy atoms (*) with the isotope label from\n the core atoms. example:\n mol = Chem.Mol"} +{"idx": 334956, "score": 3, "raw_out": "3", "code_head": "def _fetch_pae_from_database_if_possible(self, model):\n from chimerax.alphafold.database import uniprot_id_from_filename\n structure_path = getattr(model, 'filename', None)\n if str"} +{"idx": 45572, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n smi_file,\n population_size: int,\n n_mutations: int,\n n_jobs: int,\n random_start: bool,\n gene_size: int,\n generations: int,"} +{"idx": 34516, "score": 2, "raw_out": "2", "code_head": "def read_haddock(self,fname):\n \"\"\"Read the utput file of a haddock calculation.\n\n Args:\n fname (str): file name containing the data\n \"\"\"\n\n with open(fname,'r') a"} +{"idx": 44391, "score": 3, "raw_out": "3", "code_head": "def get_gene_download_summary_by_id(self, gene_ids: Union[int, List[int]]):\n \"\"\"\n Get gene download summary by GeneID.\n \n Args:\n gene_ids: A single Gene ID or a "} +{"idx": 103830, "score": 4, "raw_out": "4", "code_head": "def germinal_design(\n design_name: str,\n run_settings: dict,\n target_settings: dict,\n io: IO,\n seed: int = None,\n):\n \"\"\"\n Conduct binder hallucination with ColabDesign AF2 model.\n"} +{"idx": 115898, "score": 2, "raw_out": "2", "code_head": "def get_correct_reactant_bags_batch_error_analysis(decoded_dict: dict, answer_dict: dict, dtype, device):\n max_step = len(decoded_dict)\n decoded_result = torch.empty(size=(max_step, decoded_dict"} +{"idx": 32063, "score": 2, "raw_out": "2", "code_head": "def processSites(inBAM, qChrom, isStranded, strandedType,isbeta2Cryptic, sample = 0, numsamples = 1):\n\tprint('Processing sample '+ str(int(sample)+1)+' out of '+str(numsamples))\n\tfor c in chrom_index:"} +{"idx": 201752, "score": 3, "raw_out": "3", "code_head": "def chain_center_of_mass_loss(\n all_atom_pred_pos: torch.Tensor,\n all_atom_positions: torch.Tensor,\n all_atom_mask: torch.Tensor,\n asym_id: torch.Tensor,\n clamp_distance: float = -4.0,\n"} +{"idx": 4116, "score": 2, "raw_out": "2", "code_head": "def predict(self, react, top_cand_bonds, top_cand_scores=[], scores=True, top_n=100, atommap=False):\n '''react: atom mapped reactant smiles\n top_cand_bonds: list of strings \"ai-aj-bo\"'''"} +{"idx": 51420, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n design_neighborhood_sizes: List[int] = [10],\n substructure_neighborhood_sizes: List[int] = [10],\n distance_noise_std: float = 1,\n run_selection"} +{"idx": 206468, "score": 3, "raw_out": "3", "code_head": "def test_gene_get_snps_cov_snp_list_different_chromosomes():\n g1_snps = [\n \"chr13_45120003_C_T_b38\",\n \"chr13_49493306_T_C_b38\",\n \"chr12_25064415_T_C_b38\",\n ]\n\n with pytes"} +{"idx": 291752, "score": 2, "raw_out": "2", "code_head": "def dihedral_angle(a: Vec3Array, b: Vec3Array, c: Vec3Array,\n d: Vec3Array) -> Float:\n \"\"\"Computes torsion angle for a quadruple of points.\n\n For points (a, b, c, d), this is the a"} +{"idx": 272107, "score": 2, "raw_out": "2", "code_head": "def _calculate_potential_OOD_exceedance_ratio(self):\n\n \"\"\"\n CellOracle does not intend to simulate out-of-distribution simulation.\n This function evaluates how the simulated gene "} +{"idx": 151949, "score": 4, "raw_out": "4", "code_head": "def make_sequence_features(\n sequence: str, description: str, num_res: int\n) -> FeatureDict:\n \"\"\"Construct a feature dict of sequence features.\"\"\"\n features = {}\n features[\"aatype\"] = resi"} +{"idx": 235166, "score": 4, "raw_out": "4", "code_head": "def filter_mol_type(df, keep_covalent=True):\n \"\"\"Returns df with non small molecule ligand types removed\"\"\"\n\n filter = (\n df[\"ligand_is_fragment\"]\n | df[\"ligand_is_ion\"]\n | "} +{"idx": 256176, "score": 2, "raw_out": "2", "code_head": "def run(args):\n import os\n\n import esm\n import pytorch_lightning as pl\n import torch\n import pandas as pd\n from transformers import EsmTokenizer, EsmForMaskedLM\n from trill.utils."} +{"idx": 306650, "score": 3, "raw_out": "3", "code_head": "```cpp\nauto defaultChemicalPotentialModel(Species const& species, ChemicalSystem const& system) -> Fn\n{\n const auto numspecies = system.species().size();\n const auto "} +{"idx": 356150, "score": 2, "raw_out": "2", "code_head": "def mp_simulate_ancestral_process(tau, psi, Us):\n mp.dps = 50 # set decimal places for high precision\n \n tau = mp.mpf(tau)\n psi = mp.mpf(psi)\n Us = [mp.mpf(U) for U in Us]\n \n sta"} +{"idx": 113903, "score": 3, "raw_out": "3", "code_head": "def _get_projection(self, positions_nm: np.ndarray) -> np.ndarray:\n \"\"\"Convert positions to Å and evaluate the projection function.\"\"\"\n positions_angstrom = self.NM_TO_ANGSTROM * positio"} +{"idx": 221793, "score": 3, "raw_out": "3", "code_head": "def mol_to_fp(self, mol):\n if mol is None:\n return np.zeros((self.FP_len,), dtype=np.float32)\n return np.array(\n AllChem.GetMorganFi"} +{"idx": 43863, "score": 2, "raw_out": "2", "code_head": "def CF_PAEs():\n all_models_pae = []\n jobname = FLAGS.input_path\n preds_to_plot = extract_top_predictions()\n \n pkl_dir = jobname\n light_pkl = f'{jobname}/light_pkl'\n if os.path.isdir(light_pkl) "} +{"idx": 193111, "score": 3, "raw_out": "3", "code_head": "def __call__(self, batch, regularize: Optional[bool] = None):\n if regularize is None:\n regularize = self.default_regularize\n\n # rename/add required input fields for fairchem m"} +{"idx": 115415, "score": 2, "raw_out": "2", "code_head": "def _load_blosum(self):\n \"\"\"Return the blosum matrix as a dataframe\"\"\"\n with open(self.blosum_dir) as source:\n d = json.load(source)\n retval = pd.DataFrame(d)\n "} +{"idx": 136795, "score": 2, "raw_out": "2", "code_head": "def MFPT(X_input: np.ndarray, F: Callable, D: float = 0.1, lambda_: float = 1) -> float:\n \"\"\"Calculate the MFPT (mean first passage time) to convert from one cell state to another cell state by tak"} +{"idx": 306828, "score": 4, "raw_out": "4", "code_head": "def all_are_tanimoto_different(thresh, fp, mode_fps, delta=16):\n \"\"\"\n Equivalent to `all(DataStructs.BulkTanimotoSimilarity(fp, mode_fps) < thresh)` but much faster.\n \"\"\"\n assert delta > 0"} +{"idx": 87336, "score": 3, "raw_out": "3", "code_head": "def load_model(checkpoint_fp, sample_structure, qubits, tf_arch, gcn_arch, gcn_features,\n dropout, device):\n # initialize graph encoder\n encoder = get_graph_encoder(\n gcn_ar"} +{"idx": 226084, "score": 2, "raw_out": "2", "code_head": "def _init_load_reference_conformer_and_clusters(self):\n \"\"\"\n Loads everything I need to memory for generating fuzzballs\n :return:\n \"\"\"\n # Load reference conformer as"} +{"idx": 100634, "score": 4, "raw_out": "4", "code_head": "def __generate_cons_matrix(\n self, conservation_scores, mutation_gen: utils.MutationGenerator\n ):\n vector = np.zeros((mutation_gen.total_mut_num, NUM_MUTATION_SCORES))\n idx = 0"} +{"idx": 187967, "score": 4, "raw_out": "4", "code_head": "def forward(self, h_V, h_E, mask_V=None, mask_attend=None):\n \"\"\" Parallel computation of full transformer layer \"\"\"\n\n # Concatenate h_V_i to h_E_ij\n h_V_expand = h_V.unsqueeze(-2)"} +{"idx": 60299, "score": 4, "raw_out": "4", "code_head": "def dp_dt(self, p_part_vec):\n \"\"\" Calculate the time derivative of the probability of each (mRNA, protein) state\n\n Args:\n p_part_vec (:obj:`numpy.array`): vector of probabilit"} +{"idx": 33479, "score": 3, "raw_out": "3", "code_head": "def __init__(self,\n drop_scale=1.,\n base_update=0.97,\n base_update_iter=5,\n base_model=True,\n opt_kwargs={},\n "} +{"idx": 14480, "score": 2, "raw_out": "2", "code_head": "def amino3to1(self, record=\"ATOM\", residue_col=\"residue_name\", fillna=\"?\"):\n \"\"\"Creates 1-letter amino acid codes from DataFrame\n\n Non-canonical amino-acids are converted as follows:\n "} +{"idx": 275678, "score": 3, "raw_out": "3", "code_head": "def positional_features_all(positions: tf.Tensor,\n feature_size: int,\n seq_length: Optional[int] = None,\n bin_size: Opt"} +{"idx": 236774, "score": 3, "raw_out": "3", "code_head": "def __init__(self, model_save_dir, graph_build_scheme_dir=None, graph_build_scheme=None, device = 'cuda',\n **kwargs):\n Calculator.__init__(self, **kwargs)\n\n self.model_sa"} +{"idx": 354901, "score": 3, "raw_out": "3", "code_head": "def __getitem__(self, idx, mask_chain_id = None) -> aap.StructureData:\n try:\n if self.use_cache:\n data = self.cache_data.get(self.structs[idx])\n else:\n "} +{"idx": 105742, "score": 3, "raw_out": "3", "code_head": "def compute_obj_properties(self, mols: list[RDMol]) -> tuple[ObjectProperties, Tensor]:\n graphs = [bengio2021flow.mol2graph(i) for i in mols]\n assert len(graphs) == len(mols)\n is_"} +{"idx": 62343, "score": 3, "raw_out": "3", "code_head": "def loader_pdb(item,params):\n\n pdbid,chid = item[0].split('_')\n PREFIX = \"%s/pdb/%s/%s\"%(params['DIR'],pdbid[1:3],pdbid)\n \n # load metadata\n if not os.path.isfile(PREFIX+\".pt\"):\n "} +{"idx": 260616, "score": 2, "raw_out": "2", "code_head": "def redesign_primer(instruction,stage):\n if stage == 1:\n stage += 1\n response = '''The current stage is the primer redesign stage. You need to provide the following three files. We wi"} +{"idx": 208642, "score": 4, "raw_out": "4", "code_head": "def make_transcript_patches(\n sdata: SpatialData,\n patch_width: float | int | None = 2000,\n patch_overlap: int = 50,\n points_key: str | None = None,\n prior_shapes_key: Literal[\"auto\"] |"} +{"idx": 134469, "score": 2, "raw_out": "2", "code_head": "```c\nvoid ampl_equations_2h1p_folded_F10()\n{\n dg_stack_pos_t pos = get_stack_pos();\n reorder(\"veff21\", \"r1\", \"134562\");\n mult(\"r1\", \"e1c\", \"r2\", 2);\n tmplt(\"r3\", \"hphh\", \"0111\", \"1234\", NO"} +{"idx": 108649, "score": 2, "raw_out": "2", "code_head": "def __init__(self, pdb_file, dry=False):\n super().__init__(pdb_file, dry)\n self._cg_names = None\n self._bead_names = None\n"} +{"idx": 336067, "score": 3, "raw_out": "3", "code_head": "def generate_ligands(\n self,\n pocket_data,\n num_graphs,\n inner_verbose,\n save_traj,\n ddpm,\n eta_ddim,\n relax_mol=False,\n max_relax_iter=2"} +{"idx": 22073, "score": 2, "raw_out": "2", "code_head": "def preprocessor(ligand_dir,data_dir):\n \"\"\"\n get pocket from docking result and save to file:(m1,m2)\n\n input:\n docking_result_sdf_fn: docking result sdf file, one ligand in sdf file wi"} +{"idx": 113190, "score": 2, "raw_out": "2", "code_head": "def mupexi(\n self, l_mut, l_wt, mut_not_in_prot, no_mismatch, transcript_gene_expr, vaf_tumor\n ):\n priority_score = (l_mut * vaf_tumor * math.tanh(transcript_gene_expr)) * (\n "} +{"idx": 326849, "score": 3, "raw_out": "3", "code_head": "def load_igor_genomic_data(self, params_file_name, V_anchor_pos_file, J_anchor_pos_file):\n \"\"\"Set attributes by loading in genomic data from IGoR parameter file.\n \n Sets attribute"} +{"idx": 76731, "score": 3, "raw_out": "3", "code_head": "async def test_generate_summary_no_web(workflow_builder):\n \"\"\"Test summary generation without web research.\"\"\"\n async for builder in workflow_builder:\n workflow = builder.build(entry_func"} +{"idx": 54856, "score": 1, "raw_out": "1", "code_head": "```cpp\nMotifsWithQualWeight_t getAllMotifsWithQualWeight(\n const locus::AlignmentBufferData& alignmentData, const std::vector& binaryQuals,\n boost::optional Tuple[int, List[str]]:\n \"\"\"\n Identify mutations outside CDR regions"} +{"idx": 240337, "score": 2, "raw_out": "2", "code_head": "def get_roi_from_genome_annotation(genome_annotation: pd.DataFrame):\n \"\"\"\n Get region-of-interest for APARENT from some genome annotation\n :param genome_annotation: Pandas dataframe with the "} +{"idx": 198197, "score": 2, "raw_out": "2", "code_head": "def gmst2Hints(gmst, tseq, ggtf, complete=False, lorf=False, extend=False,\n subset=set()):\n\n if len(subset) != 0:\n gmst = selectSubsetRawGmst(gmst, subset)\n\n gtfOut = temp(\""} +{"idx": 189604, "score": 3, "raw_out": "3", "code_head": "def genes_regulating_gene(self, gene):\n \"\"\"\n Description\n Return all genes regulating the given gene by means of a\n transcription factor. \n Parms\n gene\n "} +{"idx": 286466, "score": 3, "raw_out": "3", "code_head": "def get_model_log_path(log_path, molecule, method, basis, suffix):\n \"\"\"\n Returns the path to a log file for this molecule, method, and basis.\n\n Args:\n log_path - Local path "} +{"idx": 79342, "score": 3, "raw_out": "3", "code_head": "def run_on_sequence(\n self,\n header: str,\n query_sequence: str,\n scheme: Scheme = Scheme.IMGT,\n extend_alignment: bool = True,\n ) -> AirrRearrangementEntryAA | li"} +{"idx": 54917, "score": 2, "raw_out": "2", "code_head": "def combine_phenotypes_by_prefix(ukb_phenotypes,\n prefixes,\n phenotype_name=None,\n vcfdata=None,\n "} +{"idx": 312601, "score": 1, "raw_out": "1", "code_head": "```cpp\nvoid\nMcCaskillHomModel::\ncalculate_posterior(const TH& th)\n{\n const std::string& seq = th.first;\n const std::vector& hom = th.second;\n\n typedef const CONTRALIGN::SparseMatrixEnt"} +{"idx": 255800, "score": 2, "raw_out": "2", "code_head": "def sequence_align_util(query_fasta, target_fasta, input_search_result, cpu_num=56):\n with open(input_search_result, \"r\") as f:\n pairs = f.readlines()\n\n pool = multiprocessing.Pool(proces"} +{"idx": 77805, "score": 3, "raw_out": "3", "code_head": "def test_geneformer_checkpoint_loss(model_variant, input_data):\n \"\"\"Test that the TE model can process input data and produce valid loss outputs.\"\"\"\n\n set_seed(42)\n\n model_name, _ = model_var"} +{"idx": 295648, "score": 5, "raw_out": "5", "code_head": "```cpp\nFixBackbone::FixBackbone(LAMMPS *lmp, int narg, char **arg) :\n Fix(lmp, narg, arg)\n{\n if (narg != 7) error->all(\"Illegal fix backbone command\");\n\t\n efile = fopen(\"energy.log\", \"w\");\n\t\n char"} +{"idx": 212812, "score": 2, "raw_out": "2", "code_head": "def get_SS_from_PDB_by_RNAVIEW(pdb_path):\n '''\n will generate files in data_dir, awful\n '''\n pattern = r'pair-type\\s+\\d+\\s+(?P\\d+)\\s+(?P\\d+) [\\-\\+][\\-\\+]c'\n\n length_pat"} +{"idx": 334686, "score": 4, "raw_out": "4", "code_head": "def forward(\n self,\n z: Tensor,\n pos: Tensor,\n batch: Tensor,\n box: Optional[Tensor] = None,\n q: Optional[Tensor] = None,\n s: Optional[Tensor] = None,\n"} +{"idx": 356478, "score": 2, "raw_out": "2", "code_head": "def binary_metrics_multi_target_nan(y_true, y_score, y_pred=None, nan_fill=-1, threshod=0.5):\n '''\n y_true and y_score should be `(N, T)` where N = number of GLAMs, and T = number of targets\n"} +{"idx": 221899, "score": 2, "raw_out": "2", "code_head": "def train(self):\n\n scope = \"train/\"\n tf.reset_default_graph()\n\n # Get numpy arrays with data (training and testing)\n all_train_data, all_train_lengths, all_train_labels, al"} +{"idx": 273273, "score": 2, "raw_out": "2", "code_head": "```cpp\nvoid ScaffoldGenome()\n\t{\n\t\tint i, j ;\n\t\tint bscafCnt ;\n\t\tcontigCnt = 0 ;\n\n\t\tstd::sort( blockScaffolds.begin(), blockScaffolds.end(), CompScaffold ) ;\t\t\t\t\t\n\t\t// The sort and mapping can make sca"} +{"idx": 259735, "score": 2, "raw_out": "2", "code_head": "def run_metrics(adatas, cell_type_key, stage_key):\n '''\n Evaluation metrics.\n\n Parameters\n ----------\n adatas : AnnData object\n Annotated data matrix.\n cell_type_key : str\n "} +{"idx": 356158, "score": 3, "raw_out": "3", "code_head": "def find_cliffs(self, similarity: float = 0.9, potency_fold: float = 10, in_log10: bool = True,\n custom_cliff_function: Callable = None, mmp: bool = False, reverse: bool = False):\n "} +{"idx": 169062, "score": 2, "raw_out": "2", "code_head": "```cpp\nint init_genome_cut(uint16_t* methy, uint16_t* total, uint16_t* correct_methy, uint16_t* correct_total,\n\tchar* ref_genome, bitmapper_bs_iter length,\n\tbitmapper_bs_iter index, bitmapper_bs_iter "} +{"idx": 261326, "score": 2, "raw_out": "2", "code_head": "def TranslateRC(DNA, table=1):\n\t'''Translate the reverse complement of DNA'''\n\tassert type(DNA) == str or type(DNA) == str, 'Error, input sequence must be a string or unicode'\n\tDNA = RC(DNA)\n\treturn T"} +{"idx": 261434, "score": 2, "raw_out": "2", "code_head": "def compounds(self, input_protein: pd.DataFrame) -> tuple[pd.DataFrame, str, pd.DataFrame]:\n \"\"\"\n Retrieves compounds from OTP database interacting with proteins passed as input.\n\n "} +{"idx": 332956, "score": 3, "raw_out": "3", "code_head": "def load_multiplexed(matrix, barcodes, metafile, controlstring=None, genes=None, outputfolder='output', existing_obj=None, only=[], col=None, value=None):\n\t'''\n\tLoad data from a screen experiment and "} +{"idx": 351308, "score": 2, "raw_out": "2", "code_head": "def main(ratio_file, chroms, blacklist_chroms, pcs=50):\n\n dic_pop, fout = {}, {}\n try: open(ratio_file)\n except:\n sys.stderr.write(\"Can't find %s..exiting\\n\"%(ratio_file))\n retu"} +{"idx": 105901, "score": 3, "raw_out": "3", "code_head": "def sample_conditional_information(self, n: int, train_it: int) -> Dict[str, Tensor]:\n cond_info = super().sample_conditional_information(n, train_it)\n pref_ci = self.pref_cond.sample(n)"} +{"idx": 284588, "score": 3, "raw_out": "3", "code_head": "def filter(obj, normalization=None, clust_alg=None, thres=0.01, frac=0.8,\n retx=False):\n \"\"\"Filters combined tests according to percent of cells expressing\n a gene\n\n Parameters\n "} +{"idx": 319695, "score": 4, "raw_out": "4", "code_head": "def simulate_microbial_population_dynamics(\n initial_populations,\n growth_rates,\n clearance_rates,\n carrying_capacities,\n max_time=100,\n num_simulations=100,\n time_points=100,\n):\n"} +{"idx": 5088, "score": 3, "raw_out": "3", "code_head": "def collate_fn(cls, arr):\n \"\"\"Function to take a list of encoded sequences and turn them into a batch.\"\"\"\n max_length = max([seq.size(0) for seq in arr])\n collated_arr = torch.one"} +{"idx": 178373, "score": 3, "raw_out": "3", "code_head": "def fp_morgan_mp(mol, i, radius, features, chiral, nBits):\n fp = Chem.GetMorganFingerprintAsBitVect(mol, radius, nBits=nBits, useFeatures=features,\n useCh"} +{"idx": 166859, "score": 3, "raw_out": "3", "code_head": "def get_quadruplets(idx_s, idx_t, adj_matrix, edge_ids, idx_int_s, idx_int_t):\n \"\"\"\n c -> a - b <- d where D_ab <= int_cutoff; D_ca & D_db <= cutoff\n \"\"\"\n # Number of incoming edges to tar"} +{"idx": 168728, "score": 3, "raw_out": "3", "code_head": "def get_violation_metrics(prot: protein.Protein):\n \"\"\"Computes violation and alignment metrics.\"\"\"\n structural_violations, struct_metrics = find_violations(prot)\n violation_idx = np.flatnonzero(\n "} +{"idx": 18595, "score": 3, "raw_out": "3", "code_head": "def main(allow_downloads: bool, input_file: str, output_file_dir: str, verbose: bool):\n\n if allow_downloads:\n gene_list = get_symbol_list(GENE_SYMBOL_URL)\n else:\n with open(input_f"} +{"idx": 275307, "score": 2, "raw_out": "2", "code_head": "def mutate(pose, idx, res, pack_radius=5.0, fixed_idx=[], var_idx=[], repack_rotamers=0):\n if idx in fixed_idx:\n return pose\n elif len(var_idx) > 0 and idx not in var_idx:\n return "} +{"idx": 77786, "score": 3, "raw_out": "3", "code_head": "def merge_qkv_concat(ctx: TransformCTX, qkv: torch.Tensor):\n \"\"\"Merge naively concatenated q, k, v to interleave-concatenated qkv.\n\n Example: import HF qkv to layer linear_qkv\n \"\""} +{"idx": 134680, "score": 3, "raw_out": "3", "code_head": "def to_hgvs(cls, sequence_name: str, gene_id: Optional[str], variant: str) -> str:\n return NucleotideMutationTranslater.to_hgvs_id(sequence_name=sequence_name,\n "} +{"idx": 315928, "score": 3, "raw_out": "3", "code_head": "def __init__(\n self,\n latent_dim: int = 50,\n highly_variable: bool = True,\n use_mod_weight: bool = False,\n h_regularization: float = {\n \"rna\": 1e-2,\n "} +{"idx": 81739, "score": 4, "raw_out": "4", "code_head": "def compose_context(h_protein, h_ligand, pos_protein, pos_ligand, batch_protein, batch_ligand):\n batch_ctx = torch.cat([batch_protein, batch_ligand], dim=0)\n sort_idx = batch_ctx.argsort()\n\n "} +{"idx": 198707, "score": 2, "raw_out": "2", "code_head": "def get_coords(prody_pdb):\n resindices = sorted(set(prody_pdb.ca.getResindices()))\n coords = np.full((len(resindices), 14, 3), np.nan)\n for i, resind in enumerate(resindices):\n sel = p"} +{"idx": 305632, "score": 2, "raw_out": "2", "code_head": "```cpp\nTYPED_TEST(LabeledAlignerTest, CanonicalTangleGraph) {\n size_t k = 5;\n /* B B AB AB\n TTAGT-TAGTC TCGAA-CGAAA\n \\ BC ABC /\n "} diff --git a/data/f3/intermediate/F3_stage4_scores.jsonl b/data/f3/intermediate/F3_stage4_scores.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..90dd11db35a1b998eab7bddbcdaf219093c501b8 --- /dev/null +++ b/data/f3/intermediate/F3_stage4_scores.jsonl @@ -0,0 +1,362367 @@ +{"idx": 0, "score": 2} +{"idx": 1, "score": 2} +{"idx": 2, "score": 2} +{"idx": 3, "score": 2} +{"idx": 4, "score": 2} +{"idx": 5, "score": 2} +{"idx": 6, "score": 2} +{"idx": 7, "score": 2} +{"idx": 8, "score": 2} +{"idx": 9, "score": 2} +{"idx": 10, "score": 2} +{"idx": 11, "score": 4} +{"idx": 12, "score": 3} +{"idx": 13, "score": 3} +{"idx": 14, "score": 2} +{"idx": 15, "score": 3} +{"idx": 16, "score": 3} +{"idx": 17, "score": 2} +{"idx": 18, "score": 2} +{"idx": 19, "score": 2} +{"idx": 20, "score": 2} +{"idx": 21, "score": 2} +{"idx": 22, "score": 2} +{"idx": 23, "score": 3} +{"idx": 24, "score": 2} +{"idx": 25, "score": 2} +{"idx": 26, "score": 2} +{"idx": 27, "score": 2} +{"idx": 28, "score": 1} +{"idx": 29, "score": 2} +{"idx": 30, "score": 2} +{"idx": 31, "score": 1} +{"idx": 32, "score": 2} +{"idx": 33, "score": 2} +{"idx": 34, "score": 2} +{"idx": 35, "score": 2} +{"idx": 36, "score": 2} +{"idx": 37, "score": 1} +{"idx": 38, "score": 2} +{"idx": 39, "score": 3} +{"idx": 40, "score": 2} +{"idx": 41, "score": 1} +{"idx": 42, "score": 2} +{"idx": 43, "score": 2} +{"idx": 44, "score": 1} +{"idx": 45, "score": 1} +{"idx": 46, "score": 2} +{"idx": 47, "score": 3} +{"idx": 48, "score": 2} +{"idx": 49, "score": 2} +{"idx": 50, "score": 2} +{"idx": 51, "score": 2} +{"idx": 52, "score": 2} +{"idx": 53, "score": 2} +{"idx": 54, "score": 4} +{"idx": 55, "score": 4} +{"idx": 56, "score": 2} +{"idx": 57, "score": 4} +{"idx": 58, "score": 2} +{"idx": 59, "score": 4} +{"idx": 60, "score": 3} +{"idx": 61, "score": 2} +{"idx": 62, "score": 2} +{"idx": 63, "score": 3} +{"idx": 64, "score": 3} +{"idx": 65, "score": 3} +{"idx": 66, "score": 3} +{"idx": 67, "score": 3} +{"idx": 68, "score": 3} +{"idx": 69, "score": 3} +{"idx": 70, "score": 3} +{"idx": 71, "score": 3} +{"idx": 72, "score": 4} +{"idx": 73, "score": 3} +{"idx": 74, "score": 4} +{"idx": 75, "score": 3} +{"idx": 76, "score": 2} +{"idx": 77, "score": 4} +{"idx": 78, "score": 4} +{"idx": 79, "score": 4} +{"idx": 80, "score": 3} +{"idx": 81, "score": 3} +{"idx": 82, "score": 4} +{"idx": 83, "score": 3} +{"idx": 84, "score": 3} +{"idx": 85, "score": 2} +{"idx": 86, "score": 3} +{"idx": 87, "score": 3} +{"idx": 88, "score": 5} +{"idx": 89, "score": 4} +{"idx": 90, "score": 3} +{"idx": 91, "score": 3} +{"idx": 92, "score": 2} +{"idx": 93, "score": 3} +{"idx": 94, "score": 3} +{"idx": 95, "score": 2} +{"idx": 96, "score": 4} +{"idx": 97, "score": 3} +{"idx": 98, "score": 3} +{"idx": 99, "score": 3} +{"idx": 100, "score": 3} +{"idx": 101, "score": 3} +{"idx": 102, "score": 3} +{"idx": 103, "score": 3} +{"idx": 104, "score": 4} +{"idx": 105, "score": 3} +{"idx": 106, "score": 3} +{"idx": 107, "score": 3} +{"idx": 108, "score": 3} +{"idx": 109, "score": 2} +{"idx": 110, "score": 3} +{"idx": 111, "score": 3} +{"idx": 112, "score": 4} +{"idx": 113, "score": 3} +{"idx": 114, "score": 2} +{"idx": 115, "score": 4} +{"idx": 116, "score": 3} +{"idx": 117, "score": 3} +{"idx": 118, "score": 2} +{"idx": 119, "score": 3} +{"idx": 120, "score": 3} +{"idx": 121, "score": 3} +{"idx": 122, "score": 3} +{"idx": 123, "score": 3} +{"idx": 124, "score": 3} +{"idx": 125, "score": 2} +{"idx": 126, "score": 4} +{"idx": 127, "score": 2} +{"idx": 128, "score": 3} +{"idx": 129, "score": 3} +{"idx": 130, "score": 3} +{"idx": 131, 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"score": 4} diff --git a/environment/KNOWN_LIMITATIONS.md b/environment/KNOWN_LIMITATIONS.md new file mode 100644 index 0000000000000000000000000000000000000000..755b3dd28c5b51f9432112b88b1a8ff16ca31a4d --- /dev/null +++ b/environment/KNOWN_LIMITATIONS.md @@ -0,0 +1,54 @@ +# Environment Record and Known Reproducibility Limitations + +## Best Available Training-Environment Evidence + +The only nearby training run for which a complete environment snapshot was captured was a +text-only continued-pre-training run on 13 April 2026. It used the following key versions: + +| Component | Version | +|---|---| +| ms-swift | 4.0.2 | +| PyTorch | 2.5.1+cu121 | +| Transformers | 5.3.0 | +| Accelerate | 1.13.0 | +| Datasets | 3.6.0 | +| PEFT | 0.18.1 | +| DeepSpeed | 0.18.8 | +| FlashAttention | 2.8.3 (CUDA 12 / PyTorch 2.5 / CPython 3.11 wheel) | +| CUDA / driver | 12.2 / 535.183.06 | + +This environment is contextual evidence only. It is not a run-specific snapshot for either +checkpoint in this package. + +## Known Gap + +The following delivered runs were launched directly and do not have their own `pip freeze` +or complete system snapshot: + +- `models/pretrained/stage1_cpt` (14–15 May 2026) +- `models/sft/sft_f3_refined_instruct` (8 May 2026) + +Each checkpoint includes `args.json`, which records the effective ms-swift training +configuration, and `trainer_state.json`, which records the observed steps and loss history. +The exact datasets and historical launch scripts are also included. These artefacts support +procedural reproduction, but not a claim of bit-for-bit numerical reproduction. + +The April environment is the best known approximation because the training container was +not intentionally changed between the April and May runs. This is an inference rather than +a contemporaneous record and must not be presented as confirmed run-specific evidence. + +## Delivery Validation Environment + +Both delivered checkpoints were loaded and executed successfully using: + +| Component | Version | +|---|---| +| Python | 3.12 | +| PyTorch | 2.10.0+cu128 | +| Transformers | 5.7.0 | +| Safetensors | 0.7.0 | + +`tools/smoke_test.py` confirmed that both checkpoints produce finite, non-degenerate logits. +The weights can therefore be loaded with the newer delivery environment. Rerunning the +historical training scripts requires a compatible ms-swift environment; version 4.0.2 is +the best available reference. diff --git a/evidence/eval/f3_refined_9p1_summary.json b/evidence/eval/f3_refined_9p1_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b757f2a5d2a3b586790c650ada9ed686c9912a5f --- /dev/null +++ b/evidence/eval/f3_refined_9p1_summary.json @@ -0,0 +1,27 @@ +{ + "dataset": "/raid/data/weifeng/Runable_Benchmark/dataset/alignment_humaneval_runnable_benchmark_reviewed_primary.jsonl", + "provider": "hf", + "model": "/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu/v0-20260508-164342/checkpoint-6435", + "num_tasks": 200, + "samples_per_task": 5, + "pass@1": 0.09100000000000001, + "pass@5": 0.13, + "reasons": { + "test_runtime_error": 721, + "ok": 91, + "solution_syntax_error": 105, + "timeout": 2, + "solution_exec_error": 62, + "entry_point_not_found": 19 + }, + "elapsed_sec": 2452.127, + "attempts_file": "/raid/data/weifeng/Runable_Benchmark/outputs/attempts_hf__raid_data_weifeng_Pre_train_output_user_qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu_v0-20260508-164342_checkpoint-6435_20260508_191037.jsonl", + "generation_limit": { + "reasoning_model": false, + "max_new_tokens": 2048, + "batch_size": 5, + "temperature": 0.8 + }, + "num_attempts": 1000, + "resumed": true +} \ No newline at end of file diff --git a/evidence/instruction_recipe_results.md b/evidence/instruction_recipe_results.md new file mode 100644 index 0000000000000000000000000000000000000000..f324a747f8c8a9587f8fbf7b8a04b517a4ebb7a7 --- /dev/null +++ b/evidence/instruction_recipe_results.md @@ -0,0 +1,43 @@ +# Instruction Tuning Recipe Results + +Evaluation protocol for the main HF results: +- Dataset: runnable alignment benchmark, 200 tasks +- Samples per task: 5 +- Attempts: 1000 +- Metric: pass@1 / pass@5 + +## Same Instruct Initialization: F3 / F4 Data Recipe + +| Setup | Data Recipe | Approx. Train Samples | pass@1 | pass@5 | Notes | +|---|---|---:|---:|---:|---| +| Qwen2.5-Coder-0.5B-Instruct + SFT | F3 clean | 473K | 9.1% | 13.0% | Strongest F3 baseline | +| Qwen2.5-Coder-0.5B-Instruct + SFT | F3 refined | 207K | 9.1% | 13.0% | Similar performance with fewer samples | +| Qwen2.5-Coder-0.5B-Instruct + SFT | F3 high-quality | 135K | 8.3% | 13.0% | Slight pass@1 drop after stronger filtering | +| Qwen2.5-Coder-0.5B-Instruct + SFT | F3 + F4 clean | 150K | 5.7% | 11.5% | Mixed recipe underperforms F3-only | +| Qwen2.5-Coder-0.5B-Instruct + SFT | F4 clean | 17.5K | 0.9% | 2.5% | Too small / narrow for this benchmark | + +## Evol / Sequential Instruction Tuning + +| Setup | Initialization | Data Recipe | Approx. Train Samples | pass@1 | pass@5 | Notes | +|---|---|---|---:|---:|---:|---| +| Stage2 CPT + SFT | Stage2 CPT | Evol + self-OSS | 80.7K | 1.7% | 5.5% | General instruction data alone is weak for the target benchmark | +| Stage2 CPT + SFT + SFT | Stage2 CPT + Evol/self-OSS SFT | F3 refined | 207K | 1.9% | 6.5% | F3 recovery is limited after weak Evol-stage alignment | +| Magicoder-style SFT | Qwen2.5-Coder Base | F3 refined, 3 epochs | 207K | 0.6% | 2.5% | F3 alone from base is weak under this setup | +| Magicoder-style SFT | Qwen2.5-Coder Base | Evol, 3 epochs | 30K | 1.7% | 5.0% | Evol improves over F3-only base setup | +| Magicoder-style SFT | Qwen2.5-Coder Base + Evol SFT | F3 refined, 1 epoch | 207K | 4.1% | 10.5% | Sequential Evol -> F3 gives the best result in this group | + +## Initialization Reference + +| Setup | Initialization | Data Recipe | pass@1 | pass@5 | Notes | +|---|---|---|---:|---:|---| +| Base + SFT | Qwen2.5-Coder-0.5B Base | F3 high-quality | 0.4% | 2.0% | Raw base lacks instruction-following prior | +| CPT + SFT | Continued-pretrained checkpoint | F3 clean | 2.8% | 8.5% | CPT helps but does not match instruct initialization | +| Stage1 CPT + SFT | Stage1 CPT | F3 high-quality | 4.9% | 10.5% | Stage1 CPT is better than raw base | +| Instruct + SFT | Qwen2.5-Coder-0.5B-Instruct | F3 high-quality | 8.3% | 13.0% | Instruct initialization remains strongest | + +## PPT Takeaways + +1. F3-style instruction data is the most reliable recipe for the runnable benchmark. +2. Stronger cleaning can reduce data size substantially with only small pass@1 loss, but overly small F4-style data loses coverage. +3. Evol-style general instruction data helps in sequential settings, especially Evol -> F3, but Evol alone does not solve the target benchmark. +4. Model initialization is a major confound: instruction-aligned initialization is much stronger than raw base initialization. diff --git a/evidence/smoke_test_results.json b/evidence/smoke_test_results.json new file mode 100644 index 0000000000000000000000000000000000000000..2a3c95abb90577a23286f1a31d217625668e80f5 --- /dev/null +++ b/evidence/smoke_test_results.json @@ -0,0 +1,38 @@ +[ + { + "model": "pretrained/stage1_cpt", + "path": "models/pretrained/stage1_cpt", + "ok": true, + "architecture": "Qwen2ForCausalLM", + "hidden_size": 896, + "layers": 24, + "vocab_size": 151936, + "tokenizer": "Qwen2Tokenizer", + "parameters_million": 494.0, + "logits_shape": [ + 1, + 14, + 151936 + ], + "finite": true, + "top1_zscore": 13.02 + }, + { + "model": "sft/sft_f3_refined_instruct", + "path": "models/sft/sft_f3_refined_instruct", + "ok": true, + "architecture": "Qwen2ForCausalLM", + "hidden_size": 896, + "layers": 24, + "vocab_size": 151936, + "tokenizer": "Qwen2Tokenizer", + "parameters_million": 494.0, + "logits_shape": [ + 1, + 14, + 151936 + ], + "finite": true, + "top1_zscore": 12.84 + } +] diff --git a/models/pretrained/stage1_cpt/args.json b/models/pretrained/stage1_cpt/args.json new file mode 100644 index 0000000000000000000000000000000000000000..3e37ce542a96c6e401674cbfa0324984161514f9 --- /dev/null +++ b/models/pretrained/stage1_cpt/args.json @@ -0,0 +1,344 @@ +{ + "output_dir": "/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_curriculum_filtered_stage1/v1-20260514-232114", + "per_device_train_batch_size": 8, + "num_train_epochs": 1.0, + "max_steps": -1, + "learning_rate": 5e-05, + "lr_scheduler_type": "cosine", + "lr_scheduler_kwargs": null, + "warmup_steps": 0, + "optim": "adamw_torch_fused", + "optim_args": null, + "weight_decay": 0.1, + "adam_beta1": 0.9, + "adam_beta2": 0.95, + "adam_epsilon": 1e-08, + "optim_target_modules": null, + "gradient_accumulation_steps": 4, + "average_tokens_across_devices": true, + "max_grad_norm": 1.0, 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requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='Qwen/QwQ-32B', hf_model_id='Qwen/QwQ-32B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/QwQ-32B-AWQ', hf_model_id='Qwen/QwQ-32B-AWQ', model_path=None, ms_revision=None, hf_revision=None)], template='qwq', ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='iic/QwenLong-L1-32B', hf_model_id='Tongyi-Zhiwen/QwenLong-L1-32B', model_path=None, ms_revision=None, hf_revision=None)], template='deepseek_r1', ignore_patterns=None, requires=None, tags=[])], loader=, template='qwen2_5', model_arch=ModelKeys(arch_name='llama', embedding='model.embed_tokens', module_list='model.layers', lm_head='lm_head', q_proj='model.layers.{}.self_attn.q_proj', k_proj='model.layers.{}.self_attn.k_proj', v_proj='model.layers.{}.self_attn.v_proj', o_proj='model.layers.{}.self_attn.o_proj', attention='model.layers.{}.self_attn', mlp='model.layers.{}.mlp', down_proj='model.layers.{}.mlp.down_proj', qkv_proj=None, qk_proj=None, qa_proj=None, qb_proj=None, kv_proj=None, kva_proj=None, kvb_proj=None), mcore_model_type=None, architectures=['Qwen2ForCausalLM'], additional_saved_files=[], torch_dtype=None, is_multimodal=False, is_reward=False, task_type=None, ignore_patterns=None, requires=['transformers>=4.37'], tags=['coding'])", + "model_dir": "/home/weifeng/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-0.5B/snapshots/8123ea2e9354afb7ffcc6c8641d1b2f5ecf18301", + "template_meta": "Qwen2_5TemplateMeta(template_type='qwen2_5', prefix=[], prompt=['<|im_start|>user\\n{{QUERY}}<|im_end|>\\n<|im_start|>assistant\\n'], chat_sep=['<|im_end|>\\n'], suffix=['<|im_end|>\\n'], template_cls=, system_prefix=['<|im_start|>system\\n{{SYSTEM}}<|im_end|>\\n'], default_system='You are Qwen, created by Alibaba Cloud. You are a helpful assistant.', auto_add_bos=False, stop_words=['<|endoftext|>'], agent_template='hermes', is_thinking=False, thinking_prefix='', non_thinking_prefix='', history_thinking_prefix='')", + "_val_dataset_exists": false, + "hub": "", + "evaluation_strategy": "steps", + "training_args": "Seq2SeqTrainingArguments(output_dir='/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_curriculum_filtered_stage1/v1-20260514-232114', per_device_train_batch_size=8, num_train_epochs=1.0, max_steps=-1, learning_rate=5e-05, lr_scheduler_type=, lr_scheduler_kwargs=None, warmup_steps=0.03, optim=, optim_args=None, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, optim_target_modules=None, gradient_accumulation_steps=4, average_tokens_across_devices=None, max_grad_norm=1.0, label_smoothing_factor=0.0, bf16=True, fp16=False, bf16_full_eval=False, fp16_full_eval=False, tf32=None, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, use_liger_kernel=False, liger_kernel_config=None, use_cache=False, neftune_noise_alpha=None, torch_empty_cache_steps=None, auto_find_batch_size=False, logging_strategy=, logging_steps=50, logging_first_step=True, log_on_each_node=True, logging_nan_inf_filter=True, include_num_input_tokens_seen=None, log_level='passive', log_level_replica='warning', disable_tqdm=False, report_to=['tensorboard'], run_name='/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_curriculum_filtered_stage1/v1-20260514-232114', project='huggingface', trackio_space_id=None, trackio_bucket_id=None, trackio_static_space_id=None, eval_strategy=, eval_steps=2000.0, eval_delay=0, per_device_eval_batch_size=1, prediction_loss_only=False, eval_on_start=False, eval_do_concat_batches=True, eval_use_gather_object=False, eval_accumulation_steps=None, include_for_metrics=[], batch_eval_metrics=False, save_only_model=False, save_strategy=, save_steps=2000, save_on_each_node=False, save_total_limit=3, enable_jit_checkpoint=False, push_to_hub=False, hub_token=None, hub_private_repo=None, hub_model_id=None, hub_strategy=, hub_always_push=False, hub_revision=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, restore_callback_states_from_checkpoint=False, full_determinism=False, seed=42, data_seed=42, use_cpu=False, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), parallelism_config=None, dataloader_drop_last=False, dataloader_num_workers=8, dataloader_pin_memory=True, dataloader_persistent_workers=False, dataloader_prefetch_factor=2, remove_unused_columns=False, label_names=None, train_sampling_strategy='random', length_column_name='length', ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, ddp_static_graph=None, ddp_backend=None, ddp_timeout=18000000, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, deepspeed=None, debug=[], skip_memory_metrics=True, do_train=False, do_eval=False, do_predict=False, resume_from_checkpoint=None, warmup_ratio=0.03, logging_dir='/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_curriculum_filtered_stage1/v1-20260514-232114/runs', local_rank=0, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, tuner_backend='peft', vit_gradient_checkpointing=False, router_aux_loss_coef=0.0, enable_dft_loss=False, enable_channel_loss=False, safe_serialization=True, max_shard_size='5GB', check_model=True, acc_strategy='token', train_dataloader_shuffle=True, group_by_length=False, max_epochs=None, aligner_lr=None, vit_lr=None, use_logits_to_keep=None, ds3_gather_for_generation=True, resume_only_model=False, optimizer=None, loss_type=None, eval_metric=None, callbacks=[], early_stop_interval=None, eval_use_evalscope=False, eval_dataset=[], eval_dataset_args=None, eval_limit=None, eval_generation_config=None, extra_eval_args=None, tuner_type='full', use_galore=False, galore_target_modules=None, galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)" +} \ No newline at end of file diff --git a/models/pretrained/stage1_cpt/chat_template.jinja b/models/pretrained/stage1_cpt/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..28028c056af412405debd878cdda0171e35fa5d1 --- /dev/null +++ b/models/pretrained/stage1_cpt/chat_template.jinja @@ -0,0 +1,54 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0]['role'] == 'system' %} + {{- messages[0]['content'] }} + {%- else %} + {{- 'You are a helpful assistant.' }} + {%- endif %} + {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0]['role'] == 'system' %} + {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role }} + {%- if message.content %} + {{- '\n' + message.content }} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} +{%- endif %} diff --git a/models/pretrained/stage1_cpt/config.json b/models/pretrained/stage1_cpt/config.json new file mode 100644 index 0000000000000000000000000000000000000000..1a7d31a0fa1ff86b6a9659f7813def5b38d51775 --- /dev/null +++ b/models/pretrained/stage1_cpt/config.json @@ -0,0 +1,57 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151643, + "hidden_act": "silu", + "hidden_size": 896, + "initializer_range": 0.02, + "intermediate_size": 4864, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + 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Model(ms_model_id='Qwen/Qwen2.5-14B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-14B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-0.5B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-0.5B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-1.5B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-1.5B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-3B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-3B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-7B-Instruct-AWQ', 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Model(ms_model_id='Qwen/Qwen2.5-Coder-7B', hf_model_id='Qwen/Qwen2.5-Coder-7B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-14B', hf_model_id='Qwen/Qwen2.5-Coder-14B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-32B', hf_model_id='Qwen/Qwen2.5-Coder-32B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-0.5B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-Coder-0.5B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-1.5B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-Coder-1.5B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-3B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-Coder-3B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-7B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-Coder-7B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-14B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-Coder-14B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-3B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-3B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-3B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-3B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-14B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-14B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-14B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-14B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None)], template='qwen2_5', ignore_patterns=None, requires=None, tags=['coding']), ModelGroup(models=[Model(ms_model_id='moonshotai/Kimi-Dev-72B', hf_model_id='moonshotai/Kimi-Dev-72B', model_path=None, ms_revision=None, hf_revision=None)], template='qwen2_5', ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='Qwen/Qwen2.5-Math-1.5B-Instruct', hf_model_id='Qwen/Qwen2.5-Math-1.5B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Math-7B-Instruct', hf_model_id='Qwen/Qwen2.5-Math-7B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Math-72B-Instruct', hf_model_id='Qwen/Qwen2.5-Math-72B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Math-1.5B', hf_model_id='Qwen/Qwen2.5-Math-1.5B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Math-7B', hf_model_id='Qwen/Qwen2.5-Math-7B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Math-72B', hf_model_id='Qwen/Qwen2.5-Math-72B', model_path=None, ms_revision=None, hf_revision=None)], template='qwen2_5_math', ignore_patterns=None, requires=None, tags=['math']), ModelGroup(models=[Model(ms_model_id='AIDC-AI/Marco-o1', hf_model_id='AIDC-AI/Marco-o1', model_path=None, ms_revision=None, hf_revision=None)], template='marco_o1', ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='Qwen/QwQ-32B-Preview', hf_model_id='Qwen/QwQ-32B-Preview', model_path=None, ms_revision=None, hf_revision=None)], template='qwq_preview', ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='Qwen/QwQ-32B', hf_model_id='Qwen/QwQ-32B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/QwQ-32B-AWQ', hf_model_id='Qwen/QwQ-32B-AWQ', model_path=None, ms_revision=None, hf_revision=None)], template='qwq', ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', hf_model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='iic/QwenLong-L1-32B', hf_model_id='Tongyi-Zhiwen/QwenLong-L1-32B', model_path=None, ms_revision=None, hf_revision=None)], template='deepseek_r1', ignore_patterns=None, requires=None, tags=[])], loader=, template='qwen2_5', model_arch=ModelKeys(arch_name='llama', embedding='model.embed_tokens', module_list='model.layers', lm_head='lm_head', q_proj='model.layers.{}.self_attn.q_proj', k_proj='model.layers.{}.self_attn.k_proj', v_proj='model.layers.{}.self_attn.v_proj', o_proj='model.layers.{}.self_attn.o_proj', attention='model.layers.{}.self_attn', mlp='model.layers.{}.mlp', down_proj='model.layers.{}.mlp.down_proj', qkv_proj=None, qk_proj=None, qa_proj=None, qb_proj=None, kv_proj=None, kva_proj=None, kvb_proj=None), mcore_model_type=None, architectures=['Qwen2ForCausalLM'], additional_saved_files=[], torch_dtype=None, is_multimodal=False, is_reward=False, task_type=None, ignore_patterns=None, requires=['transformers>=4.37'], tags=['coding'])", + "model_dir": "/home/weifeng/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-0.5B-Instruct/snapshots/ea3f2471cf1b1f0db85067f1ef93848e38e88c25", + "template_meta": "Qwen2_5TemplateMeta(template_type='qwen2_5', prefix=[], prompt=['<|im_start|>user\\n{{QUERY}}<|im_end|>\\n<|im_start|>assistant\\n'], chat_sep=['<|im_end|>\\n'], suffix=['<|im_end|>\\n'], template_cls=, system_prefix=['<|im_start|>system\\n{{SYSTEM}}<|im_end|>\\n'], default_system='You are Qwen, created by Alibaba Cloud. You are a helpful assistant.', auto_add_bos=False, stop_words=['<|endoftext|>'], agent_template='hermes', is_thinking=False, thinking_prefix='', non_thinking_prefix='', history_thinking_prefix='')", + "_val_dataset_exists": false, + "hub": "", + "evaluation_strategy": "steps", + "training_args": "Seq2SeqTrainingArguments(output_dir='/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu/v0-20260508-164342', per_device_train_batch_size=4, num_train_epochs=1.0, max_steps=-1, learning_rate=2e-05, lr_scheduler_type=, lr_scheduler_kwargs=None, warmup_steps=0.03, optim=, optim_args=None, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, optim_target_modules=None, gradient_accumulation_steps=8, average_tokens_across_devices=None, max_grad_norm=1.0, label_smoothing_factor=0.0, bf16=True, fp16=False, bf16_full_eval=False, fp16_full_eval=False, tf32=None, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, use_liger_kernel=False, liger_kernel_config=None, use_cache=False, neftune_noise_alpha=None, torch_empty_cache_steps=None, auto_find_batch_size=False, logging_strategy=, logging_steps=10, logging_first_step=True, log_on_each_node=True, logging_nan_inf_filter=True, include_num_input_tokens_seen=None, log_level='passive', log_level_replica='warning', disable_tqdm=False, report_to=['tensorboard'], run_name='/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu/v0-20260508-164342', project='huggingface', trackio_space_id=None, trackio_bucket_id=None, trackio_static_space_id=None, eval_strategy=, eval_steps=500.0, eval_delay=0, per_device_eval_batch_size=1, prediction_loss_only=False, eval_on_start=False, eval_do_concat_batches=True, eval_use_gather_object=False, eval_accumulation_steps=None, include_for_metrics=[], batch_eval_metrics=False, save_only_model=False, save_strategy=, save_steps=500, save_on_each_node=False, save_total_limit=5, enable_jit_checkpoint=False, push_to_hub=False, hub_token=None, hub_private_repo=None, hub_model_id=None, hub_strategy=, hub_always_push=False, hub_revision=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, restore_callback_states_from_checkpoint=False, full_determinism=False, seed=42, data_seed=42, use_cpu=False, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), parallelism_config=None, dataloader_drop_last=False, dataloader_num_workers=8, dataloader_pin_memory=True, dataloader_persistent_workers=False, dataloader_prefetch_factor=2, remove_unused_columns=False, label_names=None, train_sampling_strategy='random', length_column_name='length', ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, ddp_static_graph=None, ddp_backend=None, ddp_timeout=18000000, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, deepspeed=None, debug=[], skip_memory_metrics=True, do_train=False, do_eval=False, do_predict=False, resume_from_checkpoint=None, warmup_ratio=0.03, logging_dir='/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu/v0-20260508-164342/runs', local_rank=0, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, tuner_backend='peft', vit_gradient_checkpointing=False, router_aux_loss_coef=0.0, enable_dft_loss=False, enable_channel_loss=False, safe_serialization=True, max_shard_size='5GB', check_model=True, acc_strategy='token', train_dataloader_shuffle=True, group_by_length=False, max_epochs=None, aligner_lr=None, vit_lr=None, use_logits_to_keep=None, ds3_gather_for_generation=True, resume_only_model=False, optimizer=None, loss_type=None, eval_metric=None, callbacks=[], early_stop_interval=None, eval_use_evalscope=False, eval_dataset=[], eval_dataset_args=None, eval_limit=None, eval_generation_config=None, extra_eval_args=None, tuner_type='full', use_galore=False, galore_target_modules=None, galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)" +} \ No newline at end of file diff --git a/models/sft/sft_f3_refined_instruct/chat_template.jinja b/models/sft/sft_f3_refined_instruct/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..bdf7919a96cfe43d50914a007b9c0877bd0ec27e --- /dev/null +++ b/models/sft/sft_f3_refined_instruct/chat_template.jinja @@ -0,0 +1,54 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0]['role'] == 'system' %} + {{- messages[0]['content'] }} + {%- else %} + {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }} + {%- endif %} + {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0]['role'] == 'system' %} + {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role }} + {%- if message.content %} + {{- '\n' + message.content }} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} +{%- endif %} diff --git a/models/sft/sft_f3_refined_instruct/config.json b/models/sft/sft_f3_refined_instruct/config.json new file mode 100644 index 0000000000000000000000000000000000000000..cc4e70e4a1c691a28523c55665037ce307862db4 --- /dev/null +++ b/models/sft/sft_f3_refined_instruct/config.json @@ -0,0 +1,57 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": null, + "dtype": "bfloat16", + "eos_token_id": 151645, + "hidden_act": "silu", + "hidden_size": 896, + "initializer_range": 0.02, + "intermediate_size": 4864, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + 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"save_steps": 500, + "stateful_callbacks": { + "TrainerControl": { + "args": { + "should_epoch_stop": false, + "should_evaluate": false, + "should_log": false, + "should_save": true, + "should_training_stop": true + }, + "attributes": {} + } + }, + "total_flos": 8.083905125946491e+17, + "train_batch_size": 4, + "trial_name": null, + "trial_params": null +} diff --git a/tools/build_manifest.py b/tools/build_manifest.py new file mode 100644 index 0000000000000000000000000000000000000000..74a035a42e95bdfdbe1c24eb8c0f2ad3dc661dc5 --- /dev/null +++ b/tools/build_manifest.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python3 +"""Generate a provenance manifest and SHA256SUMS for this delivery directory.""" + +from __future__ import annotations + +import hashlib +import json +import os +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WORKSPACE = Path(os.environ.get("DELIVERY_SOURCE_ROOT", "/raid/data/weifeng")) + +SOURCE_PREFIXES = { + "models/pretrained/stage1_cpt": WORKSPACE / "Pre_train/output_user/qwen25_coder_0p5b_curriculum_filtered_stage1/v1-20260514-232114/checkpoint-28175", + "models/sft/sft_f3_refined_instruct": WORKSPACE / "Pre_train/output_user/qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu/v0-20260508-164342/checkpoint-6435", + "code/f3_filtering": WORKSPACE / "Datasets/Instruction-turning/F3_v2_pipeline", + "code/pretrain_data_prep": WORKSPACE / "Datasets", +} + +SOURCE_FILES = { + "data/pretrain/code_messages_pre_train_filtered_stage1.jsonl": WORKSPACE / "Datasets/code_messages_pre_train_filtered_stage1.jsonl", + "data/f3/input/scicode_sft_swift_F3_clean.jsonl": WORKSPACE / "Datasets/Instruction-turning/scicode_sft_swift_F3_clean.jsonl", + "data/f3/intermediate/F3_stage1.jsonl": WORKSPACE / "Datasets/Instruction-turning/F3_v2_pipeline/F3_stage1.jsonl", + "data/f3/intermediate/F3_stage2.jsonl": WORKSPACE / "Datasets/Instruction-turning/F3_v2_pipeline/F3_stage2.jsonl", + "data/f3/intermediate/F3_stage3.jsonl": WORKSPACE / "Datasets/Instruction-turning/F3_v2_pipeline/F3_stage3.jsonl", + "data/f3/intermediate/F3_stage4_scores.jsonl": WORKSPACE / "Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4_scores.jsonl", + "data/f3/intermediate/F3_stage4_calibration_scores.jsonl": WORKSPACE / "Datasets/Instruction-turning/F3_v2_pipeline/F3_stage4_calibration_scores.jsonl", + "data/f3/output/scicode_sft_swift_F3_clean_v2.jsonl": WORKSPACE / "Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v2.jsonl", + "training_scripts/pretrain/stage1_short.sh": WORKSPACE / "Pre_train/swift_runs_curriculum_filtered_0p5b/stage1_short.sh", + "training_scripts/sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh": WORKSPACE / "Pre_train/swift_runs_F3_v2_0p5b_instruct/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh", + "evidence/eval/f3_refined_9p1_summary.json": WORKSPACE / "DELIVERY_0p5B_CPT_SFT/eval/summaries/summary_hf__raid_data_weifeng_Pre_train_output_user_qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu_v0-20260508-164342_checkpoint-6435_20260508_191037.json", + "evidence/instruction_recipe_results.md": WORKSPACE / "DELIVERY_0p5B_CPT_SFT/reports/instruction_recipe_results.md", +} + + +def source_for(relative: str) -> Path | None: + if relative in SOURCE_FILES: + return SOURCE_FILES[relative] + # Reports were grouped into a delivery-only ``reports/`` directory. In + # the historical source trees they live beside the corresponding scripts. + if relative.startswith("code/f3_filtering/reports/"): + return ( + WORKSPACE + / "Datasets/Instruction-turning/F3_v2_pipeline" + / Path(relative).name + ) + if relative.startswith("code/pretrain_data_prep/reports/"): + return WORKSPACE / "Datasets" / Path(relative).name + for prefix, source_root in sorted(SOURCE_PREFIXES.items(), key=lambda item: -len(item[0])): + if relative == prefix or relative.startswith(prefix + "/"): + return source_root / Path(relative).relative_to(prefix) + return None + + +def hash_and_count(path: Path) -> tuple[str, int | None]: + digest = hashlib.sha256() + lines = 0 if path.suffix == ".jsonl" else None + with path.open("rb") as handle: + while chunk := handle.read(8 * 1024 * 1024): + digest.update(chunk) + if lines is not None: + lines += chunk.count(b"\n") + return digest.hexdigest(), lines + + +def role_for(relative: str) -> str: + return relative.split("/", 1)[0] + + +def main() -> None: + excluded = {"MANIFEST.json", "SHA256SUMS"} + paths = sorted( + path for path in ROOT.rglob("*") + if path.is_file() and path.relative_to(ROOT).as_posix() not in excluded + ) + files = [] + copied_bytes = 0 + hardlinked_bytes = 0 + + for path in paths: + relative = path.relative_to(ROOT).as_posix() + source = source_for(relative) + sha256, records = hash_and_count(path) + mode = "authored" + source_sha256_matches = None + if source is not None: + if not source.is_file(): + raise FileNotFoundError(f"missing provenance source: {source}") + same_inode = os.path.samefile(path, source) + mode = "hardlink" if same_inode else "copy" + source_sha256, _ = hash_and_count(source) if not same_inode else (sha256, None) + source_sha256_matches = source_sha256 == sha256 + if not source_sha256_matches: + raise RuntimeError(f"delivery/source mismatch: {relative}") + + size = path.stat().st_size + if mode == "hardlink": + hardlinked_bytes += size + else: + copied_bytes += size + record = { + "path": relative, + "role": role_for(relative), + "bytes": size, + "sha256": sha256, + "mode": mode, + "source": str(source) if source is not None else None, + "source_sha256_matches": source_sha256_matches, + } + if records is not None: + record["records"] = records + files.append(record) + + manifest = { + "schema_version": "delivery-0p5b-pretrain-f3refine-v1", + "generated_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"), + "delivery_root": str(ROOT), + "headline": { + "pretrained_model": "models/pretrained/stage1_cpt", + "instruction_tuned_model": "models/sft/sft_f3_refined_instruct", + "reported_pass_at_1_pct": 9.1, + "reported_pass_at_5_pct": 13.0, + "evaluation": "Runnable alignment benchmark; 200 tasks; 5 samples/task; 1000 attempts", + }, + "exclusions": [ + "optimizer.pt, scheduler.pt, rng_state.pth and training_args.bin are not included", + "upstream Qwen base/instruct checkpoints are not included", + "the exact May training environment snapshot was not captured", + ], + "storage": { + "hardlinked_bytes": hardlinked_bytes, + "copied_or_authored_bytes": copied_bytes, + "note": "Hard links save local disk; tar/zip/upload reads complete file bytes.", + }, + "file_count": len(files), + "files": files, + } + manifest_path = ROOT / "MANIFEST.json" + manifest_path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") + + checksum_paths = sorted(path for path in ROOT.rglob("*") if path.is_file() and path.name != "SHA256SUMS") + checksum_lines = [] + for path in checksum_paths: + digest, _ = hash_and_count(path) + checksum_lines.append(f"{digest} {path.relative_to(ROOT).as_posix()}") + (ROOT / "SHA256SUMS").write_text("\n".join(checksum_lines) + "\n", encoding="utf-8") + + print(json.dumps({ + "root": str(ROOT), + "files": len(files), + "hardlinked_bytes": hardlinked_bytes, + "copied_or_authored_bytes": copied_bytes, + }, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/tools/smoke_test.py b/tools/smoke_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5e9bd4f9437d6aff99a56c909e87092357363044 --- /dev/null +++ b/tools/smoke_test.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +"""Load each delivered checkpoint on CPU and verify finite logits.""" + +from __future__ import annotations + +import json +import os +import sys +import traceback +from pathlib import Path + + +THREADS = "4" +for variable in ( + "OMP_NUM_THREADS", + "MKL_NUM_THREADS", + "OPENBLAS_NUM_THREADS", + "NUMEXPR_NUM_THREADS", + "VECLIB_MAXIMUM_THREADS", + "BLIS_NUM_THREADS", +): + os.environ.setdefault(variable, THREADS) +os.environ.setdefault("CUDA_VISIBLE_DEVICES", "") + +import torch # noqa: E402 +from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +PROMPT = 'def rmsd(a, b):\n """Root-mean-square deviation."""\n' +TARGETS = { + "pretrained/stage1_cpt": ROOT / "models/pretrained/stage1_cpt", + "sft/sft_f3_refined_instruct": ROOT / "models/sft/sft_f3_refined_instruct", +} + + +def check(name: str, path: Path) -> dict: + result = {"model": name, "path": str(path.relative_to(ROOT)), "ok": False} + try: + config = AutoConfig.from_pretrained(path) + result.update( + architecture=(config.architectures or [type(config).__name__])[0], + hidden_size=getattr(config, "hidden_size", None), + layers=getattr(config, "num_hidden_layers", None), + vocab_size=getattr(config, "vocab_size", None), + ) + tokenizer = AutoTokenizer.from_pretrained(path) + model = AutoModelForCausalLM.from_pretrained(path, dtype=torch.float32) + model.eval() + inputs = tokenizer(PROMPT, return_tensors="pt") + with torch.no_grad(): + logits = model(**inputs).logits + last = logits[0, -1].float() + result.update( + tokenizer=type(tokenizer).__name__, + parameters_million=round(sum(p.numel() for p in model.parameters()) / 1e6, 1), + logits_shape=list(logits.shape), + finite=bool(torch.isfinite(logits).all()), + top1_zscore=round(((last.max() - last.mean()) / last.std()).item(), 2), + ) + result["ok"] = result["finite"] and result["top1_zscore"] > 3.0 + del model + except Exception as error: # noqa: BLE001 + result["error"] = f"{type(error).__name__}: {error}" + result["traceback"] = traceback.format_exc(limit=3) + return result + + +def main() -> int: + torch.set_num_threads(int(THREADS)) + results = [check(name, path) for name, path in TARGETS.items()] + output = ROOT / "evidence/smoke_test_results.json" + output.write_text(json.dumps(results, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") + for result in results: + status = "PASS" if result["ok"] else "FAIL" + detail = f"{result.get('parameters_million', '?')}M, z={result.get('top1_zscore', '?')}" + print(f"{status}: {result['model']} ({detail})") + passed = sum(result["ok"] for result in results) + print(f"{passed}/{len(results)} checkpoints passed; results: {output}") + return 0 if passed == len(results) else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/training_scripts/README.md b/training_scripts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5aa4274a8ee18d10086fc08ef75c1a3b48307ca6 --- /dev/null +++ b/training_scripts/README.md @@ -0,0 +1,11 @@ +# Historical Training Scripts + +This directory contains the original launch scripts for the two principal training runs. +Machine-specific absolute paths have intentionally not been rewritten. + +- `pretrain/stage1_short.sh`: Stage-1 short-sequence continued pre-training. +- `sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh`: full-parameter F3-refined SFT. + +Before rerunning either script, review `CUDA_VISIBLE_DEVICES`, `DATASET`, `OUTPUT_DIR`, cache +directories, ports, and the FlashAttention/ms-swift environment. Use each checkpoint's +`args.json` to verify the effective parameters item by item. diff --git a/training_scripts/pretrain/stage1_short.sh b/training_scripts/pretrain/stage1_short.sh new file mode 100644 index 0000000000000000000000000000000000000000..5e6ccd723002f59f695c62abc0a1bd5197690699 --- /dev/null +++ b/training_scripts/pretrain/stage1_short.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Curriculum Stage 1 (short, token <= 1024) +# Start: Qwen2.5-Coder-0.5B (base) +# Data: 902,115 samples +# lr: 5e-5, cosine + +export CUDA_VISIBLE_DEVICES=3 +export NPROC_PER_NODE=1 +export NNODES=1 +export NODE_RANK=0 +export MASTER_ADDR=127.0.0.1 +export MASTER_PORT=29540 + +export USE_HF=1 +export HF_HOME=/home/weifeng/.cache/huggingface +export HUGGINGFACE_HUB_CACHE=/home/weifeng/.cache/huggingface/hub +export TRANSFORMERS_CACHE=/home/weifeng/.cache/huggingface/transformers +export MODELSCOPE_CACHE=/home/weifeng/.cache/modelscope + +mkdir -p "${HF_HOME}" "${HUGGINGFACE_HUB_CACHE}" "${TRANSFORMERS_CACHE}" "${MODELSCOPE_CACHE}" + +DATASET="/raid/data/weifeng/Datasets/code_messages_pre_train_filtered_stage1.jsonl" +OUTPUT_DIR="/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_curriculum_filtered_stage1" + +swift pt \ + --model Qwen/Qwen2.5-Coder-0.5B \ + --tuner_type full \ + --dataset "${DATASET}" \ + --torch_dtype bfloat16 \ + --attn_impl flash_attn \ + --per_device_train_batch_size 8 \ + --learning_rate 5e-5 \ + --gradient_accumulation_steps 4 \ + --save_steps 2000 \ + --save_total_limit 3 \ + --logging_steps 50 \ + --max_length 1024 \ + --num_train_epochs 1 \ + --warmup_ratio 0.03 \ + --lr_scheduler_type cosine \ + --dataloader_num_workers 8 \ + --dataset_num_proc 8 \ + --use_hf true \ + --save_only_model false \ + --output_dir "${OUTPUT_DIR}" diff --git a/training_scripts/sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh b/training_scripts/sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh new file mode 100644 index 0000000000000000000000000000000000000000..4746b25724501b91b4cc481a377ed751a3ddb281 --- /dev/null +++ b/training_scripts/sft/run_qwen25_coder_0p5b_instruct_F3_v2_1gpu.sh @@ -0,0 +1,44 @@ +#!/usr/bin/env bash +set -euo pipefail + +# F3_v2 (cleaned: structural + cap+stratify + asst dedup + Qwen3-Coder-30B quality >=3) → Instruct SFT +# Apples-to-apples comparison vs F3 baseline (pass@1=0.091): same hyperparams, only data differs. + +export CUDA_VISIBLE_DEVICES=4 +export NPROC_PER_NODE=1 +export NNODES=1 +export NODE_RANK=0 +export MASTER_ADDR=127.0.0.1 +export MASTER_PORT=29538 + +export USE_HF=1 +export HF_HOME=/home/weifeng/.cache/huggingface +export HUGGINGFACE_HUB_CACHE=/home/weifeng/.cache/huggingface/hub +export TRANSFORMERS_CACHE=/home/weifeng/.cache/huggingface/transformers +export MODELSCOPE_CACHE=/home/weifeng/.cache/modelscope + +mkdir -p "${HF_HOME}" "${HUGGINGFACE_HUB_CACHE}" "${TRANSFORMERS_CACHE}" "${MODELSCOPE_CACHE}" + +DATASET="/raid/data/weifeng/Datasets/Instruction-turning/scicode_sft_swift_F3_clean_v2.jsonl" +OUTPUT_DIR="/raid/data/weifeng/Pre_train/output_user/qwen25_coder_0p5b_instruct_F3_v2_1epoch_1gpu" + +swift sft \ + --model Qwen/Qwen2.5-Coder-0.5B-Instruct \ + --tuner_type full \ + --dataset "${DATASET}" \ + --torch_dtype bfloat16 \ + --attn_impl flash_attn \ + --per_device_train_batch_size 4 \ + --learning_rate 2e-5 \ + --gradient_accumulation_steps 8 \ + --save_steps 500 \ + --save_total_limit 5 \ + --logging_steps 10 \ + --max_length 4096 \ + --num_train_epochs 1 \ + --warmup_ratio 0.03 \ + --dataloader_num_workers 8 \ + --dataset_num_proc 8 \ + --use_hf true \ + --save_only_model false \ + --output_dir "${OUTPUT_DIR}"