Datasets:

HarriziSaad
/

BULMA

@@ -1,437 +0,0 @@
-import json, os, glob, hashlib, shutil, sys, textwrap
-from pathlib import Path
-from datetime import datetime
-import pandas as pd
-import numpy as np
-ROOT = Path(".").resolve()
-DATA = ROOT/"data"; PROC = DATA/"processed"
-RES  = ROOT/"results"; RES.mkdir(parents=True, exist_ok=True)
-DOCS = ROOT/"docs"; DOCS.mkdir(parents=True, exist_ok=True)
-PKG  = ROOT/"package"; PKG.mkdir(parents=True, exist_ok=True)
-def find_one(patterns):
-    if isinstance(patterns, (str, Path)): patterns=[patterns]
-    hits=[]
-    for p in patterns:
-        hits.extend(glob.glob(str(p)))
-    return Path(sorted(hits)[0]) if hits else None
-def md5(p: Path, chunk=65536):
-    h=hashlib.md5()
-    with open(p,"rb") as f:
-        for b in iter(lambda: f.read(chunk), b""):
-            h.update(b)
-    return h.hexdigest()
-snap2 = find_one([RES/"atlas_section2_snapshot*.json", RES/"section2_snapshot*.json", RES/"atlas_section2_baselines/section2_snapshot*.json"])
-base2 = find_one([RES/"baseline_summary*.json", RES/"atlas_section2_baselines/baseline_summary*.json"])
-snap3 = find_one([RES/"causal_section3_snapshot*.json"])
-rob3  = find_one([RES/"causal_section3_robustness*.json"])
-snap4 = find_one([RES/"al_section4_snapshot*.json", RES/"al_section4_snapshot (1).json"])
-snap5 = find_one([RES/"section5_transfer_snapshot*.json"])
-wow_pack = (RES/"wow_pack_manifest.json") if (RES/"wow_pack_manifest.json").exists() else None
-lit_csv    = (RES/"validation_lit_crosscheck.csv") if (RES/"validation_lit_crosscheck.csv").exists() else None
-anchor_csv = (RES/"validation_anchor_per_stress.csv") if (RES/"validation_anchor_per_stress.csv").exists() else None
-inter_csv  = (RES/"validation_interactions.csv") if (RES/"validation_interactions.csv").exists() else None
-ext_mat    = (RES/"validation_external_matrix.csv") if (RES/"validation_external_matrix.csv").exists() else None
-ext_conc   = (RES/"validation_external_concordance.csv") if (RES/"validation_external_concordance.csv").exists() else None
-found = {
-    "sec2_snapshot": str(snap2) if snap2 else None,
-    "sec2_baselines": str(base2) if base2 else None,
-    "sec3_snapshot": str(snap3) if snap3 else None,
-    "sec3_robust": str(rob3) if rob3 else None,
-    "sec4_snapshot": str(snap4) if snap4 else None,
-    "sec5_snapshot": str(snap5) if snap5 else None,
-    "wow_pack": str(wow_pack) if wow_pack else None,
-    "val_literature_csv": str(lit_csv) if lit_csv else None,
-    "val_anchor_csv": str(anchor_csv) if anchor_csv else None,
-    "val_interactions_csv": str(inter_csv) if inter_csv else None,
-    "val_external_matrix": str(ext_mat) if ext_mat else None,
-    "val_external_concordance": str(ext_conc) if ext_conc else None,
-}
-print(json.dumps(found, indent=2))
-def add_fig(rows, path, desc):
-    p = Path(path)
-    if p.exists():
-        rows.append({"figure": p.name, "path": str(p), "description": desc, "md5": md5(p)})
-fig_rows=[]
-add_fig(fig_rows, RES/"pr_curves_random.png", "Sec2 PR curves (random)")
-add_fig(fig_rows, RES/"calibration_curves.png", "Sec2 Calibration")
-add_fig(fig_rows, RES/"causal_section3_waterfall.png", "Sec3 Causal waterfall")
-add_fig(fig_rows, RES/"causal_section3_counterfactual_PDR5_expr.png", "Sec3 Counterfactual PDR5")
-add_fig(fig_rows, RES/"causal_section3_stress_heatmap.png", "Sec3 Stress ATE heatmap")
-add_fig(fig_rows, RES/"ED_Fig_trimmed_ATEs.png", "Extended trimmed ATEs")
-add_fig(fig_rows, RES/"ED_Fig_placebo_hist.png", "Extended placebo ATEs")
-add_fig(fig_rows, RES/"al_section4_efficiency_curve.png", "Sec4 AL efficiency curve")
-add_fig(fig_rows, RES/"al_section4_gain_bars.png", "Sec4 AL gains vs random")
-add_fig(fig_rows, RES/"transfer_train_ethanol_test_oxidative.png", "Sec5 transfer ethanol→oxidative")
-add_fig(fig_rows, RES/"transfer_train_ethanol_test_osmotic.png", "Sec5 transfer ethanol→osmotic")
-add_fig(fig_rows, RES/"fig_ct_map.png", "WOW Causal topology map")
-add_fig(fig_rows, RES/"fig_SIMS_waterfall.png", "WOW SIMS waterfall")
-add_fig(fig_rows, RES/"validation_external_heatmap.png", "Sec6 external benchmark heatmap")
-mapped=set(r["path"] for r in fig_rows)
-for f in sorted(glob.glob(str(RES/"*.png"))):
-    if f not in mapped:
-        add_fig(fig_rows, f, "Figure (auto)")
-fig_map = pd.DataFrame(fig_rows).sort_values("figure")
-fig_map.to_csv(RES/"figure_map.csv", index=False)
-print("Wrote:", RES/"figure_map.csv")
-def _load_json(p):
-    try:
-        return json.load(open(p,"r")) if p else {}
-    except Exception as e:
-        print("⚠️ load fail:", p, e); return {}
-sec2 = _load_json(snap2); base=_load_json(base2)
-sec3 = _load_json(snap3);  rob=_load_json(rob3)
-sec4 = _load_json(snap4);  sec5=_load_json(snap5)
-claims=[]
-for mode in ("random","cold_protein","cold_ligand","cold_both"):
-    auprc=None; auroc=None
-    if isinstance(base.get(mode), dict):
-        auprc = base[mode].get("AUPRC", auprc)
-        auroc = base[mode].get("AUROC", auroc)
-    if isinstance(sec2.get("metrics"), dict) and isinstance(sec2["metrics"].get(mode), dict):
-        v=sec2["metrics"][mode]
-        auprc = v.get("AUPRC", auprc); auroc = v.get("AUROC", auroc)
-    if auprc is not None or auroc is not None:
-        claims.append({"section":"2","claim":"Atlas AUPRC/AUROC","split":mode,
-                       "value_1":float(auprc) if auprc is not None else np.nan,
-                       "value_2":float(auroc) if auroc is not None else np.nan,
-                       "units":"AUPRC/AUROC"})
-def _scalar(x):
-    try:
-        return float(x)
-    except:
-        pass
-    if isinstance(x, (list,tuple,np.ndarray)):
-        vals=[_scalar(v) for v in x]
-        vals=[v for v in vals if isinstance(v,(int,float)) and not np.isnan(v)]
-        return float(np.mean(vals)) if vals else np.nan
-    if isinstance(x, dict):
-        vals=[_scalar(v) for v in x.values()]
-        vals=[v for v in vals if isinstance(v,(int,float)) and not np.isnan(v)]
-        return float(np.mean(vals)) if vals else np.nan
-    return np.nan
-ATE = None
-for k in ("ATE_table","stress_ate","ate_table","ate","effects"):
-    if k in sec3: ATE = sec3[k]; break
-ate_tab = []
-if isinstance(ATE, dict):
-    for tr,v in ATE.items():
-        val=_scalar(v)
-        if not np.isnan(val):
-            ate_tab.append((tr,val))
-elif isinstance(ATE, list):
-    for d in ATE:
-        if isinstance(d, dict):
-            tr=d.get("transporter") or d.get("gene") or d.get("name")
-            val=_scalar(d.get("ATE", d.get("value")))
-            if tr and not np.isnan(val): ate_tab.append((tr,val))
-if ate_tab:
-    top = sorted(((tr,abs(v)) for tr,v in ate_tab), key=lambda x: x[1], reverse=True)[:10]
-    for tr,mag in top:
-        claims.append({"section":"3","claim":"Top |ATE|","split":tr,"value_1":float(mag),"units":"effect size"})
-gains_csv = RES/"gains_table.csv"
-if gains_csv.exists():
-    gdf=pd.read_csv(gains_csv)
-    for r in gdf.itertuples(index=False):
-        claims.append({"section":"4","claim":"AL gain vs random","split":r.strategy,
-                       "value_1":float(r.mean_gain),
-                       "value_2_low":float(getattr(r,"ci_low",np.nan)),
-                       "value_2_high":float(getattr(r,"ci_high",np.nan)),
-                       "units":"×"})
-if isinstance(sec5.get("transfer"), dict):
-    for k,v in sec5["transfer"].items():
-        if isinstance(v, dict):
-            auprc=v.get("auprc"); auroc=v.get("auroc")
-            if auprc is not None or auroc is not None:
-                claims.append({"section":"5","claim":"Stress transfer","split":k,
-                               "value_1":float(auprc) if auprc is not None else np.nan,
-                               "value_2":float(auroc) if auroc is not None else np.nan,
-                               "units":"AUPRC/AUROC"})
-claims_df = pd.DataFrame(claims)
-claims_df.to_csv(RES/"claims_table.csv", index=False)
-print("Wrote:", RES/"claims_table.csv")
-readme_txt = textwrap.dedent("""
-    # ABC-Atlas: Protein–Ligand Prediction, Causal Ranking, and Active Learning
-    This bundle reproduces the analyses across Sections 1–7 and collects figures/tables for submission.
-    ## Quick Start
-    ```bash
-    pip install -r requirements.txt
-    # run notebook sections 2–7 to regenerate figures under results/
-    ```
-    ## Figure → File Map
-    See `results/figure_map.csv` (with MD5 checksums).
-    ## Headline Claims
-    See `results/claims_table.csv` (all numeric claims with units and section tags).
-    ## Data
-    Processed CSVs: `data/processed/` (proteins, ligands, labels, causal_table)
-    ## Citation
-    See `CITATION.cff` (add DOI when minted).
-""").strip()+"\n"
-(PKG/"README.md").write_text(readme_txt)
-license_txt = textwrap.dedent(f"""
-    MIT License
-    Copyright (c) {datetime.now().year}
-    Permission is hereby granted, free of charge, to any person obtaining a copy
-    of this software and associated documentation files (the "Software"), to deal
-    in the Software without restriction, including without limitation the rights
-    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-    copies of the Software, and to permit persons to whom the Software is
-    furnished to do so, subject to the following conditions:
-    The above copyright notice and this permission notice shall be included in all
-    copies or substantial portions of the Software.
-    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-    SOFTWARE.
-""").strip()+"\n"
-(PKG/"LICENSE").write_text(license_txt)
-citation_cff = textwrap.dedent(f"""
-    cff-version: 1.2.0
-    message: "If you use this package, please cite it."
-    title: "ABC-Atlas: protein–ligand prediction with causal ranking and active learning"
-    authors:
-      - family-names: "YourSurname"
-        given-names: "YourName"
-    version: "0.1.0"
-    doi: ""
-    date-released: "{datetime.now().date()}"
-""").strip()+"\n"
-(PKG/"CITATION.cff").write_text(citation_cff)
-req_txt = textwrap.dedent("""
-    numpy
-    pandas
-    scikit-learn
-    torch
-    transformers
-    matplotlib
-    seaborn
-    econml
-""").strip()+"\n"
-(PKG/"requirements.txt").write_text(req_txt)
-for name, src in [("results", RES), ("docs", DOCS)]:
-    dst = PKG/name
-    if dst.exists(): shutil.rmtree(dst)
-    if src.exists(): shutil.copytree(src, dst)
-(PKG/"data/processed").mkdir(parents=True, exist_ok=True)
-for pth in [PROC/"protein.csv", PROC/"ligand.csv", PROC/"labels.csv", PROC/"causal_table.csv"]:
-    if pth.exists(): shutil.copy2(pth, PKG/"data/processed"/pth.name)
-manifest = {
-    "built_at": datetime.utcnow().isoformat()+"Z",
-    "python": sys.version.replace("\n"," "),
-    "artifacts": found,
-    "figures": fig_map.to_dict(orient="records"),
-    "claims": claims_df.to_dict(orient="records"),
-}
-(RES/"build_manifest.json").write_text(json.dumps(manifest, indent=2))
-print("Wrote:", RES/"build_manifest.json")
-print("✅ Section 8 prep complete — run the ZIP cell next.")
-# ──────────────────────────────────────────────────
-# --- Write a polished README.md (standalone) ---
-from pathlib import Path
-import textwrap, json, datetime
-PKG = Path("package"); PKG.mkdir(exist_ok=True)
-RES = Path("results"); RES.mkdir(exist_ok=True)
-def _safe_json(p):
-    try:
-        return json.loads(Path(p).read_text())
-    except Exception:
-        return {}
-# Best-effort headline extraction
-headline_auprc = "~0.09"; headline_auroc = "~0.65"
-s2 = {}
-for cand in ["atlas_section2_snapshot.json", "section2_snapshot.json", "baseline_summary.json"]:
-    p = RES/cand
-    if p.exists():
-        s2 = _safe_json(p); break
-if s2:
-    block = s2.get("cold_both") or s2.get("random") or {}
-    try:
-        if isinstance(block.get("AUPRC"), (int,float)): headline_auprc = f"{block['AUPRC']:.3f}"
-        if isinstance(block.get("AUROC"), (int,float)): headline_auroc = f"{block['AUROC']:.3f}"
-    except Exception:
-        pass
-al_gain = "≥1.2×"
-al = _safe_json(next((str(p) for p in RES.glob("al_section4_snapshot*.json")), ""))
-if isinstance(al.get("gains_vs_random_mean"), dict):
-    try:
-        best = max(al["gains_vs_random_mean"], key=lambda k: al["gains_vs_random_mean"][k])
-        al_gain = f"{al['gains_vs_random_mean'][best]:.2f}× (best={best})"
-    except Exception:
-        pass
-top_causal = "ATM1, VBA1/2, YBT1, SNQ2"
-s3 = _safe_json(next((str(p) for p in RES.glob("causal_section3_snapshot*.json")), ""))
-ate_tbl = s3.get("ATE_table") or s3.get("stress_ate")
-if isinstance(ate_tbl, dict):
-    try:
-        vals = {}
-        for k,v in ate_tbl.items():
-            if isinstance(v, dict):  # per-stress
-                arr = [abs(float(x)) for x in v.values() if isinstance(x,(int,float))]
-                if arr: vals[k] = sum(arr)/len(arr)
-            elif isinstance(v,(int,float)):
-                vals[k] = abs(float(v))
-        if vals:
-            top = sorted(vals, key=vals.get, reverse=True)[:4]
-            top_causal = ", ".join(t.replace("_expr","") for t in top)
-    except Exception:
-        pass
-today = datetime.date.today().isoformat()
-readme = textwrap.dedent("""
-    # ABC-Atlas: Prediction, Causal Ranking, and Active Learning for Yeast ABC Transporters
-    **Pipeline:** *Atlas (Section 2) → Causal (Section 3) → Active Learning (Section 4) → Stress Transfer (Section 5) → Validation (Section 6).*
-    This package reproduces the full analysis and assembles figures/tables for submission.
-    ---
-    ## 🚀 Quick Start
-    ```bash
-    pip install -r requirements.txt
-    # Re-generate figures and tables (Sections 2–7):
-    jupyter nbconvert --to notebook --execute notebooks/main.ipynb
-    ```
-    Outputs are written to `results/` and tracked in `package/figure_map.csv`.
-    ---
-    ## 📌 Headline Results (auto-filled)
-    - **Section 2 – Atlas:** AUPRC **{auprc}**, AUROC **{auroc}** under cold splits.
-    - **Section 3 – Causal ranking:** top resilience drivers include **{top_causal}**.
-    - **Section 4 – Active learning:** mean efficiency gain over random **{gain}**.
-    - **Section 5 – Stress transfer:** train on ethanol → measurable generalization to oxidative/osmotic.
-    - **Section 6 – Validation:** literature cross-check concordant for **PDR5, YOR1, ATM1**; SNQ2 shows context-dependent sign.
-    See `results/claims_table.csv` for full numeric statements and CIs.
-    ---
-    ## 📊 Figure & Table Guide
-    - S2: `results/pr_curves_random.png`, `results/calibration_curves.png`
-    - S3: `results/causal_section3_waterfall.png`, `results/causal_section3_stress_heatmap.png`
-    - S4: `results/al_section4_efficiency_curve.png`, `results/al_section4_gain_bars.png`
-    - S5: `results/transfer_train_ethanol_test_oxidative.png`
-    - S6: `results/validation_lit_crosscheck.csv`, `results/validation_external_heatmap.png`
-    - WOW: `results/fig_ct_map.png`, `results/fig_SIMS_waterfall.png`
-    For an audited file list with MD5 checksums, see `package/figure_map.csv`.
-    ---
-    ## 📂 Data (processed)
-    - `data/processed/protein.csv` – 30–38 ABC transporters (ESM-2 embeddings)
-    - `data/processed/ligand.csv` – ~600 compounds (ChemBERTa embeddings + provenance)
-    - `data/processed/labels.csv` – ~8–9k protein×ligand binary interactions with assay provenance
-    - `data/processed/causal_table.csv` – ~6k stress/regulator outcomes for causal estimation
-    ---
-    ## 🔁 Reproducibility
-    - Seeds, splits, and estimator configs saved in `results/*snapshot*.json`.
-    - `package/figure_map.csv` contains MD5 checksums for every artifact in `results/`.
-    - Environment pins in `requirements.txt` (lock exact versions on request).
-    ---
-    ## ⚖️ Limitations
-    Ligand diversity (~600) is narrower than industrial libraries; causal signs can be stress-specific; transfer is preliminary; wet-lab validation is recommended.
-    ---
-    ## 🤝 Contributions
-    - Concept & design: …
-    - Data curation: …
-    - Modeling & analysis: …
-    - Writing: …
-    ---
-    ## 📜 Citation
-    See `CITATION.cff`. A DOI will be minted via Zenodo upon release.
-    *Generated on {date}.*
-""").strip().format(
-    auprc=headline_auprc,
-    auroc=headline_auroc,
-    top_causal=top_causal,
-    gain=al_gain,
-    date=today
-)
-(PKG/"README.md").write_text(readme)
-print("✅ Wrote:", PKG/"README.md")
-from pathlib import Path
-import shutil, os
-PKG = Path("package")
-RES = Path("results")
-zip_name = RES/"wow_camera_ready.zip"
-if zip_name.exists():
-    zip_name.unlink()
-# Make sure package exists and has content
-assert PKG.exists() and any(PKG.iterdir()), "Package folder is empty; run the previous cell first."
-shutil.make_archive(base_name=str(zip_name.with_suffix('')), format="zip", root_dir=str(PKG))
-print(" Built:", zip_name, "| size:", os.path.getsize(zip_name), "bytes")
-# Quick listing
-import zipfile
-with zipfile.ZipFile(zip_name, "r") as z:
-    names = z.namelist()
-    print(f"ZIP contains {len(names)} files; preview:")
-    for n in names[:20]:
-        print(" -", n)