Keep deeppurpose002 + predictions + runs

.gitattributes

@@ -1,3 +1,9 @@
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.tfevents.* filter=lfs diff=lfs merge=lfs -text
 *.tab filter=lfs diff=lfs merge=lfs -text
 *.stl filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text

deeppurpose002.py

@@ -0,0 +1,432 @@
+import os
+import json
+import math
+import time
+import argparse
+from datetime import datetime
+import matplotlib.pyplot as plt
+from datetime import datetime
+
+
+import numpy as np
+import pandas as pd
+
+from tdc.multi_pred import DTI
+from DeepPurpose import utils
+from DeepPurpose import DTI as dp_models
+
+
+# -------------------------
+# Metrics (régression)
+# -------------------------
+def mse(y_true, y_pred):
+    y_true = np.asarray(y_true, dtype=float).reshape(-1)
+    y_pred = np.asarray(y_pred, dtype=float).reshape(-1)
+    return float(np.mean((y_true - y_pred) ** 2))
+
+def mae(y_true, y_pred):
+    y_true = np.asarray(y_true, dtype=float).reshape(-1)
+    y_pred = np.asarray(y_pred, dtype=float).reshape(-1)
+    return float(np.mean(np.abs(y_true - y_pred)))
+
+def pearson(y_true, y_pred):
+    y_true = np.asarray(y_true, dtype=float).reshape(-1)
+    y_pred = np.asarray(y_pred, dtype=float).reshape(-1)
+    if y_true.size < 2 or np.std(y_true) == 0 or np.std(y_pred) == 0:
+        return float("nan")
+    return float(np.corrcoef(y_true, y_pred)[0, 1])
+
+def spearman(y_true, y_pred):
+    a = pd.Series(np.asarray(y_true, dtype=float).reshape(-1)).rank(method="average").to_numpy()
+    b = pd.Series(np.asarray(y_pred, dtype=float).reshape(-1)).rank(method="average").to_numpy()
+    return pearson(a, b)
+
+def concordance_index_approx(y_true, y_pred, max_n=2000, seed=0):
+    """
+    CI approximatif (échantillonné si trop grand).
+    """
+    y_true = np.asarray(y_true, dtype=float).reshape(-1)
+    y_pred = np.asarray(y_pred, dtype=float).reshape(-1)
+    n = len(y_true)
+    if n < 2:
+        return float("nan")
+
+    if n > max_n:
+        rng = np.random.default_rng(seed)
+        idx = rng.choice(n, size=max_n, replace=False)
+        y_true = y_true[idx]
+        y_pred = y_pred[idx]
+        n = max_n
+
+    conc = 0.0
+    total = 0.0
+    for i in range(n):
+        for j in range(i + 1, n):
+            if y_true[i] == y_true[j]:
+                continue
+            total += 1.0
+            dt = y_true[i] - y_true[j]
+            dp = y_pred[i] - y_pred[j]
+            prod = dt * dp
+            if prod > 0:
+                conc += 1.0
+            elif prod == 0:
+                conc += 0.5
+    if total == 0:
+        return float("nan")
+    return float(conc / total)
+
+
+# -------------------------
+# Utils
+# -------------------------
+def log_section(title):
+    bar = "=" * 70
+    print("\n" + bar)
+    print(title)
+    print(bar)
+
+def detect_cols(df):
+    cols = df.columns.tolist()
+    # TDC DTI: souvent Drug, Target, Y
+    drug_col = "Drug" if "Drug" in cols else None
+    target_col = "Target" if "Target" in cols else None
+    y_col = "Y" if "Y" in cols else None
+
+    # fallback "best effort"
+    if drug_col is None:
+        for c in cols:
+            if "smiles" in c.lower() or "drug" in c.lower():
+                drug_col = c
+                break
+    if target_col is None:
+        for c in cols:
+            if "sequence" in c.lower() or "target" in c.lower() or "protein" in c.lower():
+                target_col = c
+                break
+    if y_col is None:
+        for c in cols:
+            if c.lower() in {"y", "label", "labels", "affinity"}:
+                y_col = c
+                break
+
+    # dernier fallback: 0,1,2
+    if drug_col is None and len(cols) >= 1:
+        drug_col = cols[0]
+    if target_col is None and len(cols) >= 2:
+        target_col = cols[1]
+    if y_col is None and len(cols) >= 3:
+        y_col = cols[2]
+
+    return drug_col, target_col, y_col
+
+def label_transform(y, mode, dataset_name):
+    """
+    mode:
+      - none
+      - paffinity_nm : suppose y en nM -> p = 9 - log10(nM)
+      - auto : BindingDB_* -> paffinity_nm, sinon none
+    """
+    y = np.asarray(y, dtype=float)
+
+    if mode == "auto":
+        if dataset_name.startswith("BindingDB_"):
+            mode = "paffinity_nm"
+        else:
+            mode = "none"
+
+    if mode == "none":
+        return y, "none"
+
+    if mode == "paffinity_nm":
+        y = np.where(y <= 0, np.nan, y)
+        y = 9.0 - np.log10(y)
+        return y, "paffinity_nm"
+
+    raise ValueError(f"Unknown label_transform: {mode}")
+
+def make_run_dir(base_dir, dataset):
+    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+    run_id = f"{ts}_{dataset}"
+    run_dir = os.path.join(base_dir, run_id)
+    os.makedirs(run_dir, exist_ok=True)
+    return run_id, run_dir
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--dataset", default="DAVIS", help="DAVIS | KIBA | BindingDB_Kd | BindingDB_Ki | BindingDB_IC50")
+    ap.add_argument("--drug_enc", default="Morgan")
+    ap.add_argument("--target_enc", default="CNN")
+    ap.add_argument("--epochs", type=int, default=10)
+    ap.add_argument("--batch", type=int, default=256)
+    ap.add_argument("--lr", type=float, default=1e-4)
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--split", default="random", help="DeepPurpose split_method (random recommandé ici)")
+    ap.add_argument("--frac_train", type=float, default=0.8)
+    ap.add_argument("--frac_val", type=float, default=0.1)
+    ap.add_argument("--frac_test", type=float, default=0.1)
+    ap.add_argument("--label_transform", default="auto", help="auto | none | paffinity_nm")
+    ap.add_argument("--harmonize", default="none", help="none | mean | max_affinity (utile surtout BindingDB_*)")
+    ap.add_argument("--runs_dir", default="runs")
+    ap.add_argument("--dry_run", action="store_true", help="Charge dataset + prints info, sans training")
+    args = ap.parse_args()
+
+    np.random.seed(args.seed)
+
+    run_id, run_dir = make_run_dir(args.runs_dir, args.dataset)
+    pred_path = os.path.join(run_dir, "predictions_test.csv")
+    summary_path = os.path.join(run_dir, "run_summary.json")
+
+    # -------------------------
+    # 1) LOAD DATA (TDC)
+    # -------------------------
+    log_section("[1] LOAD DATASET (TDC)")
+    print(f"[RUN] run_id={run_id}")
+    print(f"[RUN] dataset={args.dataset}")
+    print(f"[RUN] cache=PyTDC (download auto si nécessaire)")
+
+    t0 = time.time()
+    data = DTI(name=args.dataset)
+
+    if args.harmonize != "none" and args.dataset.startswith("BindingDB_"):
+        mode = args.harmonize
+        print(f"[TDC] harmonize_affinities(mode='{mode}')")
+        data.harmonize_affinities(mode=mode)
+
+    # PyTDC renvoie un DataFrame via get_data() (selon version, get_data(format='df') existe aussi)
+    try:
+        df = data.get_data()
+    except TypeError:
+        df = data.get_data(format="df")
+
+    print(f"[INPUT] raw_shape={df.shape}")
+    drug_col, target_col, y_col = detect_cols(df)
+    print(f"[INPUT] detected_cols: drug='{drug_col}', target='{target_col}', label='{y_col}'")
+
+    # -------------------------
+    # 2) CLEAN + PREP
+    # -------------------------
+    log_section("[2] CLEAN + PREP (Input DeepPurpose)")
+    df = df[[drug_col, target_col, y_col]].copy()
+    df.columns = ["Drug", "Target", "Y"]
+
+    n0 = len(df)
+    df = df.dropna()
+    df["Drug"] = df["Drug"].astype(str).str.strip()
+    df["Target"] = df["Target"].astype(str).str.strip()
+    df = df[(df["Drug"] != "") & (df["Target"] != "")]
+    df["Y"] = pd.to_numeric(df["Y"], errors="coerce")
+    df = df.dropna()
+    n1 = len(df)
+
+    y_raw = df["Y"].astype(float).values
+    y_trans, used_transform = label_transform(y_raw, args.label_transform, args.dataset)
+    df["Y"] = y_trans
+    df = df.dropna()
+    n2 = len(df)
+
+    # drop duplicates (Drug,Target) -> keep first
+    df = df.drop_duplicates(subset=["Drug", "Target"], keep="first").reset_index(drop=True)
+    n3 = len(df)
+
+    print(f"[CLEAN] start={n0} -> after_na/parse={n1} -> after_transform={n2} -> after_dedup={n3}")
+    print(f"[LABEL] transform={used_transform}")
+    y = df["Y"].astype(float).values
+    print(f"[LABEL] stats: min={np.min(y):.6f} | mean={np.mean(y):.6f} | median={np.median(y):.6f} | max={np.max(y):.6f}")
+    print("[SAMPLE] first_rows:")
+    print(df.head(3).to_string(index=False))
+
+    if args.dry_run:
+        print(f"\n[DRY_RUN] stop ici. (Aucun training lancé)")
+        return
+
+    # -------------------------
+    # 3) SPLIT + ENCODE (DeepPurpose)
+    # -------------------------
+    log_section("[3] DATA_PROCESS (DeepPurpose)")
+    frac = [args.frac_train, args.frac_val, args.frac_test]
+    print(f"[SPLIT] method={args.split} | frac={frac} | seed={args.seed}")
+    X_drugs = df["Drug"].values
+    X_targets = df["Target"].values
+    y = df["Y"].values
+
+    train, val, test = utils.data_process(
+        X_drugs, X_targets, y,
+        drug_encoding=args.drug_enc,
+        target_encoding=args.target_enc,
+        split_method=args.split,
+        frac=frac,
+        random_seed=args.seed
+    )
+
+    # tailles (DeepPurpose objects ont souvent .Label)
+    try:
+        n_train, n_val, n_test = len(train.Label), len(val.Label), len(test.Label)
+    except Exception:
+        # fallback: len(obj)
+        n_train, n_val, n_test = len(train), len(val), len(test)
+
+    print(f"[SPLIT] sizes: train={n_train} | val={n_val} | test={n_test}")
+    print(f"[ENC] drug_encoding={args.drug_enc} | target_encoding={args.target_enc}")
+
+    # -------------------------
+    # 4) MODEL INIT + TRAIN
+    # -------------------------
+    log_section("[4] MODEL INIT + TRAIN")
+    config = utils.generate_config(
+        drug_encoding=args.drug_enc,
+        target_encoding=args.target_enc,
+        cls_hidden_dims=[1024, 1024, 512],
+        train_epoch=args.epochs,
+        batch_size=args.batch,
+        LR=args.lr,
+        result_folder=run_dir
+    )
+
+    print("[MODEL] config:")
+    print(f"  epochs={args.epochs} | batch={args.batch} | lr={args.lr}")
+    print(f"  hidden=[1024,1024,512] | result_dir={run_dir}")
+
+    model = dp_models.model_initialize(**config)
+
+    t_train0 = time.time()
+    try:
+        model.train(train, val, test)
+    except TypeError:
+        model.train(train, val)
+    t_train1 = time.time()
+    print(f"[TIME] train_seconds={t_train1 - t_train0:.1f}")
+
+    # -------------------------
+    # 5) EVAL + EXPORT
+    # -------------------------
+    log_section("[5] EVAL + EXPORT")
+    print("[PREDICT] predicting on test...")
+    y_true = np.asarray(test.Label.values, dtype=float).reshape(-1)
+    y_pred = np.asarray(model.predict(test), dtype=float).reshape(-1)
+
+    m_mse = mse(y_true, y_pred)
+    m_rmse = float(math.sqrt(m_mse))
+    m_mae = mae(y_true, y_pred)
+    m_p = pearson(y_true, y_pred)
+    m_s = spearman(y_true, y_pred)
+    m_ci = concordance_index_approx(y_true, y_pred, max_n=2000, seed=args.seed)
+
+    print("[METRICS] test:")
+    print(f"  MSE   = {m_mse:.6f}")
+    print(f"  RMSE  = {m_rmse:.6f}")
+    print(f"  MAE   = {m_mae:.6f}")
+    print(f"  Pearson  = {m_p:.6f}")
+    print(f"  Spearman = {m_s:.6f}")
+    print(f"  CI(approx) = {m_ci:.6f}")
+
+    out_pred = pd.DataFrame({"y_true": y_true, "y_pred": y_pred})
+    out_pred.to_csv(pred_path, index=False)
+    print(f"[FILE] saved predictions: {pred_path}")
+
+    summary = {
+        "run_id": run_id,
+        "dataset": args.dataset,
+        "drug_encoding": args.drug_enc,
+        "target_encoding": args.target_enc,
+        "epochs": args.epochs,
+        "batch": args.batch,
+        "lr": args.lr,
+        "seed": args.seed,
+        "split_method": args.split,
+        "frac": [args.frac_train, args.frac_val, args.frac_test],
+        "label_transform": used_transform,
+        "harmonize": args.harmonize,
+        "n_rows_after_clean": int(len(df)),
+        "metrics_test": {
+            "mse": m_mse,
+            "rmse": m_rmse,
+            "mae": m_mae,
+            "pearson": m_p,
+            "spearman": m_s,
+            "ci_approx": m_ci,
+        },
+        "files": {
+            "predictions_test_csv": pred_path,
+        },
+        "timing": {
+            "load_seconds": time.time() - t0,
+            "train_seconds": t_train1 - t_train0,
+        }
+    }
+    with open(summary_path, "w", encoding="utf-8") as f:
+        json.dump(summary, f, indent=2)
+    print(f"[FILE] saved summary: {summary_path}")
+
+    print("\n[DONE]")
+
+
+if __name__ == "__main__":
+    main()
+# =========================
+# VISUALISATION + EXPORTS
+# =========================
+
+# 0) Dossier de sortie (stable)
+BASE_DIR = os.path.dirname(__file__)
+RUNS_DIR = os.path.join(BASE_DIR, "runs")
+os.makedirs(RUNS_DIR, exist_ok=True)
+
+# si tu as déjà un run_dir dans ton code, il sera utilisé; sinon on en crée un
+try:
+    run_dir
+except NameError:
+    run_id = datetime.now().strftime("%Y%m%d_%H%M%S")
+    run_dir = os.path.join(RUNS_DIR, run_id)
+    os.makedirs(run_dir, exist_ok=True)
+
+# 1) Récupère y_true / y_pred (sans retraining)
+y_true = np.asarray(test.Label.values, dtype=float).reshape(-1)
+y_pred = np.asarray(model.predict(test), dtype=float).reshape(-1)
+
+# 2) Sauvegarde CSV predictions
+pred_csv = os.path.join(run_dir, "predictions_test.csv")
+pd.DataFrame({"y_true": y_true, "y_pred": y_pred}).to_csv(pred_csv, index=False)
+print("[FILE] saved predictions:", pred_csv)
+
+# 3) Scatter: y_true vs y_pred
+scatter_png = os.path.join(run_dir, "scatter.png")
+plt.figure()
+plt.scatter(y_true, y_pred, s=8)
+plt.xlabel("y_true")
+plt.ylabel("y_pred")
+plt.title("Test: y_true vs y_pred")
+plt.tight_layout()
+plt.savefig(scatter_png, dpi=200)
+plt.close()
+print("[PLOT] saved:", scatter_png)
+
+# 4) Courbes triées: y_true et y_pred (tri par y_true)
+curves_png = os.path.join(run_dir, "curves_sorted.png")
+order = np.argsort(y_true)
+plt.figure()
+plt.plot(y_true[order], label="y_true")
+plt.plot(y_pred[order], label="y_pred")
+plt.xlabel("samples (sorted by y_true)")
+plt.ylabel("value")
+plt.title("Test: curves (sorted)")
+plt.legend()
+plt.tight_layout()
+plt.savefig(curves_png, dpi=200)
+plt.close()
+print("[PLOT] saved:", curves_png)
+
+# 5) Résidus: (y_pred - y_true) vs y_true
+res_png = os.path.join(run_dir, "residuals.png")
+res = y_pred - y_true
+plt.figure()
+plt.scatter(y_true, res, s=8)
+plt.axhline(0)
+plt.xlabel("y_true")
+plt.ylabel("y_pred - y_true")
+plt.title("Test: residuals")
+plt.tight_layout()
+plt.savefig(res_png, dpi=200)
+plt.close()
+print("[PLOT] saved:", res_png)

predictions_test.csv

@@ -0,0 +1,3007 @@
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+|  MSE   | Pearson Correlation | with p-value | Concordance Index |
++--------+---------------------+--------------+-------------------+
+| 0.0008 |        0.4817       |    0.0000    |       0.6847      |
++--------+---------------------+--------------+-------------------+

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++---------+--------+---------------------+--------------+-------------------+
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+| epoch 9 | 0.0012 |        0.5242       |    0.0000    |       0.7023      |
++---------+--------+---------------------+--------------+-------------------+

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++--------+---------------------+--------------+-------------------+
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