feat: benchmark multiple models and validate improved results

.gitignore

@@ -0,0 +1 @@
+.venv

data/davis.tab

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d4c6809dcb7c5da2b91a32d594d6935b75484940bde4d18055eb5e1059262f4
+size 21376712

deeppurpose002.py

@@ -5,8 +5,7 @@ import time
 import argparse
 from datetime import datetime
 import matplotlib.pyplot as plt
-from datetime import datetime
-
+import torch
 
 import numpy as np
 import pandas as pd
@@ -121,16 +120,11 @@ def detect_cols(df):
     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)
 
+    # Force auto to paffinity_nm for standard datasets
     if mode == "auto":
-        if dataset_name.startswith("BindingDB_"):
+        if dataset_name.lower() in ["davis", "kiba"] or dataset_name.startswith("BindingDB"):
             mode = "paffinity_nm"
         else:
             mode = "none"
@@ -139,7 +133,11 @@ def label_transform(y, mode, dataset_name):
         return y, "none"
 
     if mode == "paffinity_nm":
-        y = np.where(y <= 0, np.nan, y)
+        # SAFETY CHECK: Clip values to avoid log(0) or log(negative)
+        y = np.where(y < 1e-9, 1e-9, y) 
+        # Convert nM to pM ( -log10( Molar ) )
+        # Value 100 nM = 100e-9 M = 1e-7 M -> -log10(1e-7) = 7.0
+        # Formula: 9 - log10(nM)
         y = 9.0 - np.log10(y)
         return y, "paffinity_nm"
 
@@ -152,8 +150,24 @@ def make_run_dir(base_dir, dataset):
     os.makedirs(run_dir, exist_ok=True)
     return run_id, run_dir
 
+def check_gpu():
+    """Check GPU availability and return CUDA ID for DeepPurpose."""
+    if torch.cuda.is_available():
+        device_name = torch.cuda.get_device_name(0)
+        gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
+        print(f"\n[SYSTEM] ✅ CUDA GPU Detected: {device_name}")
+        print(f"[SYSTEM]    Memory: {gpu_memory:.1f} GB")
+        print(f"[SYSTEM]    CUDA Version: {torch.version.cuda}")
+        return 0  # Use GPU 0
+    else:
+        print("\n[SYSTEM] ⚠️ No GPU detected. Running on CPU (will be slow).")
+        return -1  # Use CPU
+
 
 def main():
+    # --- GPU DETECTION FIRST ---
+    use_cuda = check_gpu()
+    
     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")
@@ -166,12 +180,18 @@ def main():
     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("--label_transform", default="paffinity_nm", help="Force log transform! (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")
+    ap.add_argument("--gpu", type=int, default=None, help="Override GPU ID (default: auto-detect)")
     args = ap.parse_args()
 
+    # Override GPU if specified
+    if args.gpu is not None:
+        use_cuda = args.gpu
+        print(f"[SYSTEM] Using specified GPU: {use_cuda}")
+
     np.random.seed(args.seed)
 
     run_id, run_dir = make_run_dir(args.runs_dir, args.dataset)
@@ -271,7 +291,7 @@ def main():
     print(f"[ENC] drug_encoding={args.drug_enc} | target_encoding={args.target_enc}")
 
     # -------------------------
-    # 4) MODEL INIT + TRAIN
+    # 4) MODEL INIT + TRAIN (WITH GPU CONFIG)
     # -------------------------
     log_section("[4] MODEL INIT + TRAIN")
     config = utils.generate_config(
@@ -281,14 +301,21 @@ def main():
         train_epoch=args.epochs,
         batch_size=args.batch,
         LR=args.lr,
-        result_folder=run_dir
+        result_folder=run_dir,
+        cuda_id=use_cuda  # <<< GPU CONFIG HERE
     )
 
     print("[MODEL] config:")
     print(f"  epochs={args.epochs} | batch={args.batch} | lr={args.lr}")
     print(f"  hidden=[1024,1024,512] | result_dir={run_dir}")
+    print(f"  cuda_id={use_cuda} {'(GPU)' if use_cuda >= 0 else '(CPU)'}")
 
     model = dp_models.model_initialize(**config)
+    
+    # Verify device placement
+    if use_cuda >= 0:
+        device = next(model.model.parameters()).device
+        print(f"[MODEL] ✓ Model loaded on device: {device}")
 
     t_train0 = time.time()
     try:
@@ -303,8 +330,19 @@ def main():
     # -------------------------
     log_section("[5] EVAL + EXPORT")
     print("[PREDICT] predicting on test...")
+    
+    # [FIX] Reset index to ensure alignment between DataFrame and Model Output
+    test = test.reset_index(drop=True)
+    
     y_true = np.asarray(test.Label.values, dtype=float).reshape(-1)
-    y_pred = np.asarray(model.predict(test), dtype=float).reshape(-1)
+    
+    # [DEBUG] Check what the model is actually predicting
+    raw_pred = model.predict(test)
+    y_pred = np.asarray(raw_pred, dtype=float).reshape(-1)
+
+    # [DEBUG] Print first 5 comparisons to verify scaling matches
+    print(f"[DEBUG] First 5 True: {y_true[:5]}")
+    print(f"[DEBUG] First 5 Pred: {y_pred[:5]}")
 
     m_mse = mse(y_true, y_pred)
     m_rmse = float(math.sqrt(m_mse))
@@ -339,6 +377,9 @@ def main():
         "label_transform": used_transform,
         "harmonize": args.harmonize,
         "n_rows_after_clean": int(len(df)),
+        "cuda_id": use_cuda,
+        "gpu_used": use_cuda >= 0,
+        "gpu_name": torch.cuda.get_device_name(0) if use_cuda >= 0 else "CPU",
         "metrics_test": {
             "mse": m_mse,
             "rmse": m_rmse,
@@ -359,74 +400,56 @@ def main():
         json.dump(summary, f, indent=2)
     print(f"[FILE] saved summary: {summary_path}")
 
+    # -------------------------
+    # 6) VISUALISATION
+    # -------------------------
+    log_section("[6] VISUALISATION")
+    
+    # Scatter plot
+    scatter_png = os.path.join(run_dir, "scatter.png")
+    plt.figure(figsize=(8, 6))
+    plt.scatter(y_true, y_pred, s=8, alpha=0.5)
+    plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', label='Perfect fit')
+    plt.xlabel("y_true")
+    plt.ylabel("y_pred")
+    plt.title("Test: y_true vs y_pred")
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(scatter_png, dpi=200)
+    plt.close()
+    print(f"[PLOT] saved: {scatter_png}")
+
+    # Sorted curves
+    curves_png = os.path.join(run_dir, "curves_sorted.png")
+    order = np.argsort(y_true)
+    plt.figure(figsize=(10, 6))
+    plt.plot(y_true[order], label="y_true", alpha=0.7)
+    plt.plot(y_pred[order], label="y_pred", alpha=0.7)
+    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(f"[PLOT] saved: {curves_png}")
+
+    # Residuals
+    res_png = os.path.join(run_dir, "residuals.png")
+    res = y_pred - y_true
+    plt.figure(figsize=(8, 6))
+    plt.scatter(y_true, res, s=8, alpha=0.5)
+    plt.axhline(0, color='r', linestyle='--')
+    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(f"[PLOT] saved: {res_png}")
+
     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)
+    main()

runs/{Jan24_21-29-31_hamza-sammoud/events.out.tfevents.1769286571.hamza-sammoud.18280.0 → 20260126_153020_DAVIS/Morgan_CNN_logits.npy}

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+size 20744

runs/20260126_153020_DAVIS/loss_curve_iter.pkl

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runs/20260126_153020_DAVIS/test_markdowntable.txt

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++---------------+---------------------+--------------+-------------------+
+|      MSE      | Pearson Correlation | with p-value | Concordance Index |
++---------------+---------------------+--------------+-------------------+
+| 73974003.5008 |       -0.0242       |    0.2194    |       0.4867      |
++---------------+---------------------+--------------+-------------------+

runs/20260126_153020_DAVIS/valid_markdowntable.txt

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++---------+---------------+---------------------+--------------+-------------------+
+| # epoch |      MSE      | Pearson Correlation | with p-value | Concordance Index |
++---------+---------------+---------------------+--------------+-------------------+
+| epoch 0 | 11222237.5076 |        0.5493       |    0.0000    |       0.7836      |
+| epoch 1 | 11268550.4673 |        0.5596       |    0.0000    |       0.7869      |
+| epoch 2 | 10935933.6634 |        0.5698       |    0.0000    |       0.7924      |
+| epoch 3 | 10767720.9830 |        0.5822       |    0.0000    |       0.7985      |
+| epoch 4 | 10760275.3041 |        0.5891       |    0.0000    |       0.8025      |
+| epoch 5 | 10299218.9160 |        0.6010       |    0.0000    |       0.8093      |
+| epoch 6 | 10213825.8110 |        0.6086       |    0.0000    |       0.8132      |
+| epoch 7 | 10005538.3511 |        0.6172       |    0.0000    |       0.8199      |
+| epoch 8 |  9869431.4575 |        0.6207       |    0.0000    |       0.8219      |
+| epoch 9 |  9812799.9596 |        0.6245       |    0.0000    |       0.8244      |
++---------+---------------+---------------------+--------------+-------------------+

runs/20260126_154351_DAVIS/Morgan_CNN_logits.npy

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runs/20260126_154351_DAVIS/run_summary.json

@@ -0,0 +1,37 @@
+{
+  "run_id": "20260126_154351_DAVIS",
+  "dataset": "DAVIS",
+  "drug_encoding": "Morgan",
+  "target_encoding": "CNN",
+  "epochs": 10,
+  "batch": 256,
+  "lr": 0.0001,
+  "seed": 1,
+  "split_method": "random",
+  "frac": [
+    0.8,
+    0.1,
+    0.1
+  ],
+  "label_transform": "none",
+  "harmonize": "none",
+  "n_rows_after_clean": 25772,
+  "cuda_id": 0,
+  "gpu_used": true,
+  "gpu_name": "NVIDIA GeForce RTX 3070 Laptop GPU",
+  "metrics_test": {
+    "mse": 73974003.50081368,
+    "rmse": 8600.814118489812,
+    "mae": 7643.865212462178,
+    "pearson": -0.024202168836860823,
+    "spearman": -0.02411776290508354,
+    "ci_approx": 0.4922038187527615
+  },
+  "files": {
+    "predictions_test_csv": "runs\\20260126_154351_DAVIS\\predictions_test.csv"
+  },
+  "timing": {
+    "load_seconds": 545.3709628582001,
+    "train_seconds": 537.8021245002747
+  }
+}

runs/20260126_154351_DAVIS/test_markdowntable.txt

@@ -0,0 +1,5 @@
++---------------+---------------------+--------------+-------------------+
+|      MSE      | Pearson Correlation | with p-value | Concordance Index |
++---------------+---------------------+--------------+-------------------+
+| 73974003.5008 |       -0.0242       |    0.2194    |       0.4867      |
++---------------+---------------------+--------------+-------------------+

runs/20260126_154351_DAVIS/valid_markdowntable.txt

@@ -0,0 +1,14 @@
++---------+---------------+---------------------+--------------+-------------------+
+| # epoch |      MSE      | Pearson Correlation | with p-value | Concordance Index |
++---------+---------------+---------------------+--------------+-------------------+
+| epoch 0 | 18396109.4621 |       -0.0600       |    0.0023    |       0.4606      |
+| epoch 1 | 12896225.6144 |        0.4845       |    0.0000    |       0.7501      |
+| epoch 2 | 11418881.9174 |        0.5436       |    0.0000    |       0.7804      |
+| epoch 3 | 11164989.3404 |        0.5549       |    0.0000    |       0.7856      |
+| epoch 4 | 11044041.3782 |        0.5587       |    0.0000    |       0.7876      |
+| epoch 5 | 11052461.8699 |        0.5608       |    0.0000    |       0.7881      |
+| epoch 6 | 10984461.8219 |        0.5624       |    0.0000    |       0.7888      |
+| epoch 7 | 10958531.1202 |        0.5636       |    0.0000    |       0.7897      |
+| epoch 8 | 10944600.7693 |        0.5651       |    0.0000    |       0.7898      |
+| epoch 9 | 10927414.8391 |        0.5653       |    0.0000    |       0.7896      |
++---------+---------------+---------------------+--------------+-------------------+

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runs/20260126_160009_DAVIS/test_markdowntable.txt

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++--------+---------------------+--------------+-------------------+
+|  MSE   | Pearson Correlation | with p-value | Concordance Index |
++--------+---------------------+--------------+-------------------+
+| 0.4684 |        0.5446       |    0.0000    |       0.7914      |
++--------+---------------------+--------------+-------------------+

runs/20260126_160009_DAVIS/valid_markdowntable.txt

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++---------+--------+---------------------+--------------+-------------------+
+| # epoch |  MSE   | Pearson Correlation | with p-value | Concordance Index |
++---------+--------+---------------------+--------------+-------------------+
+| epoch 0 | 0.5955 |        0.4549       |    0.0000    |       0.7321      |
+| epoch 1 | 0.5085 |        0.5353       |    0.0000    |       0.7816      |
+| epoch 2 | 0.4964 |        0.5558       |    0.0000    |       0.7882      |
+| epoch 3 | 0.4866 |        0.5639       |    0.0000    |       0.7920      |
+| epoch 4 | 0.4834 |        0.5651       |    0.0000    |       0.7921      |
+| epoch 5 | 0.4785 |        0.5731       |    0.0000    |       0.7970      |
+| epoch 6 | 0.4875 |        0.5734       |    0.0000    |       0.7975      |
+| epoch 7 | 0.4730 |        0.5797       |    0.0000    |       0.8017      |
+| epoch 8 | 0.4690 |        0.5853       |    0.0000    |       0.8044      |
+| epoch 9 | 0.4651 |        0.5885       |    0.0000    |       0.8074      |
++---------+--------+---------------------+--------------+-------------------+

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runs/20260126_161739_DAVIS/run_summary.json

@@ -0,0 +1,37 @@
+{
+  "run_id": "20260126_161739_DAVIS",
+  "dataset": "DAVIS",
+  "drug_encoding": "Morgan",
+  "target_encoding": "CNN",
+  "epochs": 10,
+  "batch": 256,
+  "lr": 0.0001,
+  "seed": 1,
+  "split_method": "random",
+  "frac": [
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+    0.1,
+    0.1
+  ],
+  "label_transform": "paffinity_nm",
+  "harmonize": "none",
+  "n_rows_after_clean": 25772,
+  "cuda_id": 0,
+  "gpu_used": true,
+  "gpu_name": "NVIDIA GeForce RTX 3070 Laptop GPU",
+  "metrics_test": {
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+    "rmse": 0.6844016586276546,
+    "mae": 0.4135942073549742,
+    "pearson": 0.544594927541983,
+    "spearman": 0.5194291749084063,
+    "ci_approx": 0.7863682932361785
+  },
+  "files": {
+    "predictions_test_csv": "runs\\20260126_161739_DAVIS\\predictions_test.csv"
+  },
+  "timing": {
+    "load_seconds": 568.5627524852753,
+    "train_seconds": 560.3927385807037
+  }
+}

runs/20260126_161739_DAVIS/test_markdowntable.txt

@@ -0,0 +1,5 @@
++--------+---------------------+--------------+-------------------+
+|  MSE   | Pearson Correlation | with p-value | Concordance Index |
++--------+---------------------+--------------+-------------------+
+| 0.4684 |        0.5446       |    0.0000    |       0.7914      |
++--------+---------------------+--------------+-------------------+

runs/20260126_161739_DAVIS/valid_markdowntable.txt

@@ -0,0 +1,14 @@
++---------+--------+---------------------+--------------+-------------------+
+| # epoch |  MSE   | Pearson Correlation | with p-value | Concordance Index |
++---------+--------+---------------------+--------------+-------------------+
+| epoch 0 | 0.5955 |        0.4549       |    0.0000    |       0.7321      |
+| epoch 1 | 0.5085 |        0.5353       |    0.0000    |       0.7816      |
+| epoch 2 | 0.4964 |        0.5558       |    0.0000    |       0.7882      |
+| epoch 3 | 0.4866 |        0.5639       |    0.0000    |       0.7920      |
+| epoch 4 | 0.4834 |        0.5651       |    0.0000    |       0.7921      |
+| epoch 5 | 0.4785 |        0.5731       |    0.0000    |       0.7970      |
+| epoch 6 | 0.4875 |        0.5734       |    0.0000    |       0.7975      |
+| epoch 7 | 0.4730 |        0.5797       |    0.0000    |       0.8017      |
+| epoch 8 | 0.4690 |        0.5853       |    0.0000    |       0.8044      |
+| epoch 9 | 0.4651 |        0.5885       |    0.0000    |       0.8074      |
++---------+--------+---------------------+--------------+-------------------+

setup_venv.txt

@@ -0,0 +1,62 @@
+# Setup Guide for DeepPurpose (Windows/CUDA 11.8)
+
+This guide reproduces the current working environment for `deeppurpose002.py` on Windows 10/11 with an NVIDIA GPU.
+
+## 1. Prerequisites
+- **Python 3.10.x** (Required for compatibility with DGL and DeepPurpose)
+- **NVIDIA GPU** with drivers installed.
+
+## 2. Create Virtual Environment
+Open PowerShell/Terminal in your project folder:
+```powershell
+python -3.10 -m venv .venv
+.\.venv\Scripts\Activate
+```
+
+## 3. Install PyTorch (CUDA 11.8)
+Install the specific CUDA-enabled version of PyTorch:
+```powershell
+pip install torch==2.7.1+cu118 torchvision==0.22.1+cu118 torchaudio==2.7.1+cu118 --index-url https://download.pytorch.org/whl/cu118
+```
+*(Note: If 2.7.1+cu118 is not available in the future, check pytorch.org for the compatible LTS version matching DGL)*
+
+## 4. Install Deep Graph Library (DGL)
+DGL must match the CUDA version:
+```powershell
+pip install dgl==2.2.1+cu118 -f https://data.dgl.ai/wheels/cu118/repo.html
+pip install dgllife
+```
+
+## 5. Install Core Dependencies
+DeepPurpose and PyTDC (Therapeutics Data Commons):
+```powershell
+pip install DeepPurpose PyTDC
+pip install git+https://github.com/bp-kelley/descriptastorus
+pip install pandas numpy matplotlib prettytable tensorboard lifelines scikit-learn
+```
+
+## 6. Windows-Specific Fixes (Crucial)
+
+### Fix A: PyTDC Dependency Issues (`tiledbsoma`, `cellxgene_census`)
+PyTDC tries to install `cellxgene_census`, which depends on `tiledbsoma`. On Windows, `tiledbsoma` often fails to build.
+**Solution:** If `pip install PyTDC` fails, manually install dependencies excluding the problematic ones, or create mock files if the code doesn't actually use them (PyTDC generally works without them for standard datasets like DAVIS/KIBA).
+
+### Fix B: DGL GraphBolt DLL Error
+DGL 2.x includes "GraphBolt" which requires C++ DLLs that may be missing or incompatible on Windows.
+**Error:** `FileNotFoundError: ... graphbolt_pytorch_X.X.X.dll`
+**Solution:**
+1.  **Code Fix (Recommended):** Add this line to the **very top** of your Python script (before `import torch` or `import dgl`):
+    ```python
+    import os
+    os.environ["DGL_DISABLE_GRAPHBOLT"] = "1"
+    ```
+2.  **Library Fix (Manual):** If the error persists, edit `.venv\Lib\site-packages\dgl\graphbolt\__init__.py` and comment out all imports to prevent it from loading.
+
+## 7. Running the Script
+```powershell
+python deeppurpose002.py
+```
+To force usage of a specific GPU encoding:
+```powershell
+python deeppurpose002.py --drug_enc DGL_GCN --target_enc CNN --epochs 10
+```