{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":112509,"databundleVersionId":14254895,"sourceType":"competition"}],"dockerImageVersionId":31192,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# CSIRO Biomass Competition: **Ultimate Solution**  \n## Ratio-Based ResNet50 with State-Aware WA=0 & Gradient Accumulation \n\n---\n\n## **1️⃣ Overview**\n\nThis pipeline implements an **enhanced fusion model** combining **ResNet50 image features** with **engineered tabular features** to predict 5 biomass components. The solution incorporates **key competition insights** and fixes critical issues like **OOM errors** and **missing predictions**.\n\n** Key Innovations:**\n1. **Ratio-based Dead prediction** (Dead/Total ratio = 0.71 correlation from analysis)\n2. **State-specific handling** (WA samples always have Dead=0)\n3. **Gradient accumulation** to avoid CUDA OOM errors\n4. **Fixed test data processing** (5 → 25 predictions)\n5. **Multi-task learning** with Dead/Total ratio prediction\n6. **Enhanced post-processing** with biological constraints\n\n**Why ResNet50 over DINOv2?**\n| Factor | ResNet50 | DINOv2 | Choice |\n|--------|----------|--------|--------|\n| Memory | ~2GB | ~6GB | ✅ ResNet50 |\n| Training Speed | Fast | Slow | ✅ ResNet50 |\n| Data Requirements | 357 images | 1000s+ | ✅ ResNet50 |\n| Stability | Proven | Experimental | ✅ ResNet50 |\n\n---\n\n## **2️⃣ Configuration**\n\n```python\nclass Config:\n    IMG_ROOT = \"/kaggle/input/csiro-biomass/\"\n    TRAIN_CSV = \"/kaggle/input/csiro-biomass/train.csv\"\n    TEST_CSV = \"/kaggle/input/csiro-biomass/test.csv\"\n    \n    # Memory-optimized hyperparameters\n    BATCH_SIZE = 4           # Reduced from 8 to prevent OOM\n    ACCUMULATION_STEPS = 2   # Effective batch size = 8\n    IMG_SIZE = 224          # ResNet50 optimal size\n    EPOCHS = 15\n    LEARNING_RATE = 1e-4\n    HIDDEN_SIZE = 256\n    DROPOUT = 0.3\n    PATIENCE = 5\n    VAL_SPLIT = 0.2\n    SEED = 42\n    \n    # Competition-specific weights\n    TARGET_WEIGHTS = {\n        'Dry_Clover_g': 0.1,\n        'Dry_Dead_g': 0.1,\n        'Dry_Green_g': 0.1,\n        'GDM_g': 0.2,\n        'Dry_Total_g': 0.5  # Most important based on scoring\n    }\n```\n\n---\n\n## **3️⃣ Data Preparation with Advanced Feature Engineering**\n\n**Key Insights Applied:**\n1. **Dead/Total ratio** (0.71 correlation) → Strongest predictor\n2. **State information** → WA samples have Dead=0\n3. **Seasonal patterns** → Date features for temporal trends\n\n**Processing Steps:**\n1. Pivot train data from long to wide format\n2. Create critical ratio features:\n   ```python\n   train_data['Dead_to_Total_Ratio'] = train_data['Dry_Dead_g'] / (train_data['Dry_Total_g'] + 1e-8)\n   train_data['Dead_to_GDM_Ratio'] = train_data['Dry_Dead_g'] / (train_data['GDM_g'] + 1e-8)\n   ```\n3. Enhanced feature set (11 features total):\n   - **Numeric**: Pre_GSHH_NDVI, Height_Ave_cm, Year, Month, Day, Ratios\n   - **Categorical**: State, Species, Season (encoded)\n\n**Test Data Fix:** Processes all unique images → 25 predictions (5 images × 5 targets)\n\n---\n\n## **4️⃣ Enhanced Dataset Class**\n\n```python\nclass EnhancedBiomassDataset(Dataset):\n    # State-aware processing\n    if states[i] == 'WA':\n        pred[1] = 0.0  # Dead biomass = 0 for WA\n    \n    # Transformations:\n    # - Training: Random flips, rotation, color jitter\n    # - Testing: Only resize and normalize\n    # - Error handling: Black image for corrupted files\n```\n\n---\n\n## **5️⃣ Advanced Model Architecture**\n\n**Ratio-Based Prediction:**\n```python\nclass CompatibleRatioBasedModel(nn.Module):\n    # 1. ResNet50 backbone (frozen initially)\n    # 2. Fusion network: [2048 img features + 11 tab features] → 256\n    # 3. Two output heads:\n    #    - Main head: Predicts [Clover, Green, Total, GDM]\n    #    - Ratio head: Predicts Dead/Total ratio (0-1)\n    # 4. Reconstruct Dead = ratio × Total\n```\n\n**Multi-Task Learning:**\n- **Main loss**: Weighted Huber loss (Huber for Dead, MSE for others)\n- **Auxiliary loss**: BCE for Dead/Total ratio prediction\n- **Total loss**: `main_loss + 0.3 × ratio_loss`\n\n---\n\n## **6️⃣ Training with OOM Protection**\n\n**Gradient Accumulation:**\n```python\n# Simulates larger batch size without OOM\nfor batch_idx, batch in enumerate(train_loader):\n    loss = loss / accumulation_steps\n    loss.backward()\n    \n    if (batch_idx + 1) % accumulation_steps == 0:\n        optimizer.step()\n        optimizer.zero_grad()\n```\n\n**Memory Management:**\n1. Periodic cleanup: `torch.cuda.empty_cache()` every 10 batches\n2. Error handling: Skip batches on OOM, continue training\n3. Checkpointing: Save best model for resuming\n4. Early stopping: Stop if no improvement for 5 epochs\n\n**Training Results:**\n- Best validation loss: ~200.4 (epoch 11)\n- Dead/Total ratio: 0.187 (target: 0.2-0.4)\n- Training time: ~15 minutes for 15 epochs\n- Memory usage: ~2-3 GB on Tesla T4\n\n---\n\n## **7️⃣ Pipeline Workflow**\n![CIRO.png](attachment:c1ea3947-cece-4012-882a-e2855b2601e7.png)\n\n\n---\n\n## **8️⃣ Prediction with Biological Constraints**\n\n**Post-Processing Rules:**\n```python\n# 1. Ensure positivity\n# 2. Total >= sum of components (Clover + Green + Dead)\n# 3. Dead/Total ratio constraint (0-0.8)\n# 4. GDM proportional to Green\n# 5. State-specific: WA samples have Dead=0\n```\n\n**Prediction Statistics:**\n```\nDry_Clover_g:  mean=0.87  std=nan\nDry_Dead_g:    mean=0.49  std=nan  Dead/Total=0.187 ✓\nDry_Green_g:   mean=1.03  std=nan\nDry_Total_g:   mean=2.63  std=nan\nGDM_g:         mean=1.10  std=nan\n```\n```\n---\n","metadata":{},"attachments":{"c1ea3947-cece-4012-882a-e2855b2601e7.png":{"image/png":"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"}}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np \nimport pandas as pd \n\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = pd.read_csv(\"/kaggle/input/csiro-biomass/train.csv\")\ntrain_df.head()","metadata":{"execution":{"iopub.execute_input":"2025-12-19T15:11:19.015086Z","iopub.status.busy":"2025-12-19T15:11:19.014739Z","iopub.status.idle":"2025-12-19T15:11:19.077147Z","shell.execute_reply":"2025-12-19T15:11:19.075908Z","shell.execute_reply.started":"2025-12-19T15:11:19.015060Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==============================\n# 1️⃣ IMPORTS AND CONFIGURATION\n# ==============================\nimport os\nimport pandas as pd\nimport numpy as np\nfrom PIL import Image\nfrom tqdm import tqdm\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader, random_split\nimport torchvision.transforms as transforms\nimport torchvision.models as models\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Configuration optimisée\nclass Config:\n    # Paths\n    IMG_ROOT = \"/kaggle/input/csiro-biomass/\"\n    TRAIN_CSV = \"/kaggle/input/csiro-biomass/train.csv\"\n    TEST_CSV = \"/kaggle/input/csiro-biomass/test.csv\"\n    \n    # Hyperparameters optimisés pour éviter OOM\n    BATCH_SIZE = 4  # Réduit de 8 à 4\n    EPOCHS = 15\n    LEARNING_RATE = 1e-4\n    HIDDEN_SIZE = 256\n    DROPOUT = 0.3\n    IMG_SIZE = 224\n    PATIENCE = 5\n    VAL_SPLIT = 0.2\n    SEED = 42\n    \n    # Gradient accumulation pour simuler un batch plus grand\n    ACCUMULATION_STEPS = 2\n    \n    # Loss weights based on competition scoring\n    TARGET_WEIGHTS = {\n        'Dry_Clover_g': 0.1,\n        'Dry_Dead_g': 0.1,\n        'Dry_Green_g': 0.1,\n        'GDM_g': 0.2,\n        'Dry_Total_g': 0.5\n    }\n    \n    # Use ResNet (works offline)\n    USE_PRETRAINED = True\n\nconfig = Config()\n\n# Device\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Using device: {device}\")\n\n# Vérification mémoire GPU\nif torch.cuda.is_available():\n    print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n    print(f\"Mémoire totale: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")\n\n# Seed for reproducibility\ndef set_seed(seed=42):\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed(seed)\n        torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\nset_seed(config.SEED)\n\n# ==============================\n# 2️⃣ DATA PREPROCESSING CORRIGÉ\n# ==============================\ndef prepare_data_with_features_corrected(train_csv, test_csv):\n    \"\"\"Prepare data with corrected test data handling\"\"\"\n    print(\"📊 Preparing data with feature engineering...\")\n    \n    # Load data\n    train_df = pd.read_csv(train_csv)\n    test_df = pd.read_csv(test_csv)\n    \n    # Vérifier la structure des données de test\n    print(f\"Test data shape: {test_df.shape}\")\n    print(f\"Test columns: {test_df.columns.tolist()}\")\n    print(f\"Unique image_path in test: {test_df['image_path'].nunique()}\")\n    \n    # Pivot train data (long -> wide format)\n    train_pivot = train_df.pivot_table(\n        index=['image_path', 'Sampling_Date', 'State', 'Species', 'Pre_GSHH_NDVI', 'Height_Ave_cm'],\n        columns='target_name',\n        values='target',\n        aggfunc='first'\n    ).reset_index()\n    train_pivot.columns.name = None\n    \n    # Targets\n    target_names = [\"Dry_Clover_g\", \"Dry_Dead_g\", \"Dry_Green_g\", \"Dry_Total_g\", \"GDM_g\"]\n    for target in target_names:\n        if target not in train_pivot.columns:\n            train_pivot[target] = 0.0\n    \n    # Extract image_id\n    train_pivot['image_id'] = train_pivot['image_path'].apply(\n        lambda x: os.path.basename(x).replace('.jpg', '')\n    )\n    \n    # CORRECTION: Traitement correct des données de test\n    test_df = test_df.copy()\n    test_df['image_id'] = test_df['image_path'].apply(\n        lambda x: os.path.basename(x).replace('.jpg', '')\n    )\n    \n    print(f\"\\n📊 Test data analysis:\")\n    print(f\"   Total rows in test.csv: {len(test_df)}\")\n    print(f\"   Unique images in test: {test_df['image_path'].nunique()}\")\n    print(f\"   Expected predictions: {len(test_df)}\")\n    \n    # Feature engineering\n    def engineer_features(df, is_train=False):\n        df = df.copy()\n        \n        # Date features\n        if 'Sampling_Date' in df.columns:\n            df['Sampling_Date'] = pd.to_datetime(df['Sampling_Date'], errors='coerce')\n            df['Year'] = df['Sampling_Date'].dt.year.fillna(2015)\n            df['Month'] = df['Sampling_Date'].dt.month.fillna(6)\n            df['Day'] = df['Sampling_Date'].dt.day.fillna(15)\n            df['Season'] = df['Month'].apply(lambda x: \n                'Spring' if x in [9,10,11] else\n                'Summer' if x in [12,1,2] else\n                'Autumn' if x in [3,4,5] else 'Winter'\n            )\n        \n        # Encode categorical columns\n        categorical_cols = ['State', 'Species', 'Season']\n        for col in categorical_cols:\n            if col in df.columns:\n                le = LabelEncoder()\n                df[col] = df[col].fillna('Unknown')\n                df[f'{col}_enc'] = le.fit_transform(df[col])\n        \n        # Height features\n        if 'Height_Ave_cm' in df.columns:\n            df['Height_bin'] = pd.cut(df['Height_Ave_cm'], \n                                     bins=[0, 2, 5, 10, 20, 100],\n                                     labels=[0, 1, 2, 3, 4])\n            df['Height_bin'] = df['Height_bin'].cat.codes\n        \n        # NDVI features\n        if 'Pre_GSHH_NDVI' in df.columns:\n            df['NDVI_bin'] = pd.cut(df['Pre_GSHH_NDVI'],\n                                   bins=[-1, 0, 0.3, 0.5, 0.7, 1],\n                                   labels=[0, 1, 2, 3, 4])\n            df['NDVI_bin'] = df['NDVI_bin'].cat.codes\n        \n        return df\n    \n    train_data = engineer_features(train_pivot, is_train=True)\n    test_data = engineer_features(test_df, is_train=False)\n    \n    # Calculate derived features for training\n    if 'Dry_Total_g' in train_data.columns:\n        train_data['Dead_to_Total_Ratio'] = train_data['Dry_Dead_g'] / (train_data['Dry_Total_g'] + 1e-8)\n        train_data['Dead_to_Total_Ratio'] = train_data['Dead_to_Total_Ratio'].clip(0, 1)\n    \n    # Numeric features\n    numeric_features = []\n    for col in ['Pre_GSHH_NDVI', 'Height_Ave_cm', 'Year', 'Month', 'Day']:\n        if col in train_data.columns:\n            numeric_features.append(col)\n    \n    # Add ratio features if they exist\n    ratio_features = ['Dead_to_Total_Ratio']\n    for feat in ratio_features:\n        if feat in train_data.columns:\n            numeric_features.append(feat)\n    \n    # Normalize with StandardScaler\n    scaler = StandardScaler()\n    if numeric_features:\n        train_data[numeric_features] = scaler.fit_transform(train_data[numeric_features])\n        for feat in numeric_features:\n            if feat in test_data.columns:\n                if scaler.scale_[numeric_features.index(feat)] != 0:\n                    test_data[feat] = (test_data[feat] - scaler.mean_[numeric_features.index(feat)]) / scaler.scale_[numeric_features.index(feat)]\n                else:\n                    test_data[feat] = 0.0\n    \n    # Encoded categorical features\n    categorical_features = [col for col in train_data.columns if col.endswith('_enc')]\n    \n    # Combine all features\n    all_features = numeric_features + categorical_features\n    \n    # Fill NaNs\n    for col in all_features:\n        train_data[col] = train_data[col].fillna(0).astype(np.float32)\n        if col in test_data.columns:\n            test_data[col] = test_data[col].fillna(0).astype(np.float32)\n    \n    print(f\"\\n✅ Data prepared:\")\n    print(f\"   Train samples: {len(train_data)}\")\n    print(f\"   Test samples: {len(test_data)}\")\n    print(f\"   Features used: {len(all_features)}\")\n    \n    return train_data, test_data, all_features, target_names\n\n# ==============================\n# 3️⃣ DATASET WITH STATE-SPECIFIC HANDLING\n# ==============================\nclass EnhancedBiomassDataset(Dataset):\n    def __init__(self, df, img_root, features, targets=None, is_test=False):\n        self.df = df\n        self.img_root = img_root\n        self.features = features\n        self.targets = targets\n        self.is_test = is_test\n        \n        # Store state information for WA-specific handling\n        if 'State' in df.columns:\n            self.states = df['State'].values\n        else:\n            self.states = ['Unknown'] * len(df)\n        \n        # Transformations\n        if not is_test:\n            self.transform = transforms.Compose([\n                transforms.Resize((config.IMG_SIZE, config.IMG_SIZE)),\n                transforms.RandomHorizontalFlip(p=0.5),\n                transforms.RandomVerticalFlip(p=0.3),\n                transforms.RandomRotation(10),\n                transforms.ColorJitter(brightness=0.1, contrast=0.1),\n                transforms.ToTensor(),\n                transforms.Normalize(mean=[0.485, 0.456, 0.406], \n                                     std=[0.229, 0.224, 0.225]),\n            ])\n        else:\n            self.transform = transforms.Compose([\n                transforms.Resize((config.IMG_SIZE, config.IMG_SIZE)),\n                transforms.ToTensor(),\n                transforms.Normalize(mean=[0.485, 0.456, 0.406], \n                                     std=[0.229, 0.224, 0.225]),\n            ])\n    \n    def __len__(self):\n        return len(self.df)\n    \n    def __getitem__(self, idx):\n        row = self.df.iloc[idx]\n        state = self.states[idx]\n        \n        # Load image\n        img_path = os.path.join(self.img_root, row[\"image_path\"])\n        try:\n            img = Image.open(img_path).convert(\"RGB\")\n            img = self.transform(img)\n        except:\n            # Black image if error\n            img = torch.zeros(3, config.IMG_SIZE, config.IMG_SIZE)\n        \n        # Tabular features\n        tab_data = []\n        for feat in self.features:\n            if feat in row:\n                tab_data.append(float(row[feat]))\n            else:\n                tab_data.append(0.0)\n        tab_data = torch.tensor(tab_data, dtype=torch.float32)\n        \n        # Targets\n        if self.targets is not None and not self.is_test:\n            target_values = []\n            for target in self.targets:\n                if target in row:\n                    target_values.append(float(row[target]))\n                else:\n                    target_values.append(0.0)\n            \n            targets = torch.tensor(target_values, dtype=torch.float32)\n            \n            # Also return Dead/Total ratio\n            if 'Dry_Total_g' in row and 'Dry_Dead_g' in row:\n                total = float(row['Dry_Total_g'])\n                dead = float(row['Dry_Dead_g'])\n                dead_ratio = dead / (total + 1e-8) if total > 0 else 0.0\n                dead_ratio = torch.tensor([dead_ratio], dtype=torch.float32)\n            else:\n                dead_ratio = torch.tensor([0.0], dtype=torch.float32)\n            \n            return img, tab_data, targets, dead_ratio, state\n        else:\n            return img, tab_data, row.get('image_id', ''), state, row.get('target_name', '')\n\n# ==============================\n# 4️⃣ COMPATIBLE MODEL WITHOUT BATCHNORM ISSUE\n# ==============================\nclass CompatibleRatioBasedModel(nn.Module):\n    def __init__(self, tab_size=1, hidden_size=256, output_size=5, dropout=0.2):\n        super(CompatibleRatioBasedModel, self).__init__()\n        \n        # Load ResNet50\n        resnet = models.resnet50(pretrained=config.USE_PRETRAINED)\n        self.resnet_features = nn.Sequential(*list(resnet.children())[:-1])\n        \n        # Freeze ResNet layers initially\n        for param in self.resnet_features.parameters():\n            param.requires_grad = False\n        \n        # ResNet output features: 2048\n        resnet_out = 2048\n        \n        # Main fusion network - REPLACED BatchNorm1d with LayerNorm\n        self.fusion = nn.Sequential(\n            nn.Linear(resnet_out + tab_size, hidden_size),\n            nn.LayerNorm(hidden_size),  # Changed from BatchNorm1d\n            nn.ReLU(),\n            nn.Dropout(dropout),\n            nn.Linear(hidden_size, hidden_size // 2),\n            nn.LayerNorm(hidden_size // 2),  # Changed from BatchNorm1d\n            nn.ReLU(),\n            nn.Dropout(dropout),\n        )\n        \n        # Output heads for different targets\n        self.main_head = nn.Sequential(\n            nn.Linear(hidden_size // 2, hidden_size // 4),\n            nn.ReLU(),\n            nn.Linear(hidden_size // 4, output_size - 1),  # All except Dead\n            nn.Softplus()\n        )\n        \n        # Special head for Dead/Total ratio\n        self.ratio_head = nn.Sequential(\n            nn.Linear(hidden_size // 2, hidden_size // 4),\n            nn.ReLU(),\n            nn.Linear(hidden_size // 4, 1),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, img, tab_data):\n        # Extract ResNet features\n        img_features = self.resnet_features(img)\n        img_features = img_features.view(img_features.size(0), -1)\n        \n        # Fusion\n        combined = torch.cat([img_features, tab_data], dim=1)\n        fused = self.fusion(combined)\n        \n        # Get predictions\n        main_preds = self.main_head(fused)  # [Clover, Green, Total, GDM]\n        ratio_pred = self.ratio_head(fused)  # Dead/Total ratio\n        \n        # Reconstruct all 5 targets\n        total_pred = main_preds[:, 2:3]\n        dead_pred = ratio_pred * total_pred\n        \n        clover_pred = main_preds[:, 0:1]\n        green_pred = main_preds[:, 1:2]\n        gdm_pred = main_preds[:, 3:4]\n        \n        final_preds = torch.cat([clover_pred, dead_pred, green_pred, total_pred, gdm_pred], dim=1)\n        \n        return final_preds, ratio_pred\n\n# ==============================\n# 5️⃣ CUSTOM LOSS\n# ==============================\nclass WeightedHuberLoss(nn.Module):\n    def __init__(self, target_weights, beta=5.0):\n        super(WeightedHuberLoss, self).__init__()\n        self.target_weights = torch.tensor(list(target_weights.values())).float()\n        self.beta = beta\n        \n    def forward(self, predictions, targets):\n        weights = self.target_weights.to(predictions.device)\n        \n        losses = []\n        for i in range(predictions.shape[1]):\n            pred_i = predictions[:, i]\n            target_i = targets[:, i]\n            \n            if i == 1:  # Dead biomass\n                loss_i = F.smooth_l1_loss(pred_i, target_i, reduction='none', beta=self.beta)\n            else:\n                loss_i = F.mse_loss(pred_i, target_i, reduction='none')\n            \n            loss_i = loss_i * weights[i]\n            losses.append(loss_i)\n        \n        total_loss = torch.stack(losses).mean()\n        return total_loss\n\n# ==============================\n# 6️⃣ TRAINING FUNCTION WITH FIXED BATCHNORM ISSUE\n# ==============================\ndef train_ratio_model(model, train_loader, val_loader, device, epochs=10):\n    \"\"\"Train the ratio-based model\"\"\"\n    \n    main_criterion = WeightedHuberLoss(config.TARGET_WEIGHTS, beta=5.0)\n    ratio_criterion = nn.BCELoss()\n    \n    optimizer = torch.optim.AdamW(\n        model.parameters(),\n        lr=config.LEARNING_RATE,\n        weight_decay=1e-5\n    )\n    \n    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n        optimizer, mode='min', factor=0.5, patience=3, verbose=True\n    )\n    \n    best_val_loss = float('inf')\n    patience_counter = 0\n    \n    accumulation_steps = config.ACCUMULATION_STEPS\n    \n    for epoch in range(epochs):\n        model.train()\n        train_loss = 0\n        train_main_loss = 0\n        train_ratio_loss = 0\n        \n        optimizer.zero_grad()\n        \n        pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{epochs} [Train]\")\n        \n        for batch_idx, batch in enumerate(pbar):\n            try:\n                imgs, tab_data, targets, dead_ratios, states = batch\n                imgs, tab_data, targets, dead_ratios = (\n                    imgs.to(device), tab_data.to(device), \n                    targets.to(device), dead_ratios.to(device)\n                )\n                \n                predictions, ratio_predictions = model(imgs, tab_data)\n                \n                # Apply WA-specific handling\n                wa_mask = torch.tensor([1.0 if s == 'WA' else 0.0 for s in states], \n                                      device=device).unsqueeze(1)\n                predictions[:, 1:2] = predictions[:, 1:2] * (1 - wa_mask)\n                \n                main_loss = main_criterion(predictions, targets)\n                ratio_loss = ratio_criterion(ratio_predictions, dead_ratios)\n                \n                total_loss = (main_loss + 0.3 * ratio_loss) / accumulation_steps\n                \n                total_loss.backward()\n                \n                if (batch_idx + 1) % accumulation_steps == 0:\n                    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n                    optimizer.step()\n                    optimizer.zero_grad()\n                \n                train_loss += total_loss.item() * accumulation_steps\n                train_main_loss += main_loss.item()\n                train_ratio_loss += ratio_loss.item()\n                \n                if torch.cuda.is_available():\n                    memory_used = torch.cuda.memory_allocated() / 1e9\n                    pbar.set_postfix({\n                        \"loss\": f\"{total_loss.item() * accumulation_steps:.4f}\",\n                        \"mem\": f\"{memory_used:.2f}GB\"\n                    })\n                \n                if batch_idx % 10 == 0:\n                    torch.cuda.empty_cache()\n                    \n            except RuntimeError as e:\n                if \"CUDA out of memory\" in str(e):\n                    print(f\"\\n⚠️ CUDA OOM à batch {batch_idx}. Nettoyage...\")\n                    torch.cuda.empty_cache()\n                    optimizer.zero_grad()\n                    continue\n                else:\n                    raise e\n        \n        # Handle remaining gradients\n        if len(train_loader) % accumulation_steps != 0:\n            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n            optimizer.step()\n            optimizer.zero_grad()\n        \n        avg_train_loss = train_loss / len(train_loader)\n        avg_train_main = train_main_loss / len(train_loader)\n        avg_train_ratio = train_ratio_loss / len(train_loader)\n        \n        # Validation\n        model.eval()\n        val_loss = 0\n        val_samples = 0\n        \n        with torch.no_grad():\n            for batch in val_loader:\n                try:\n                    imgs, tab_data, targets, dead_ratios, states = batch\n                    imgs, tab_data, targets, dead_ratios = (\n                        imgs.to(device), tab_data.to(device), \n                        targets.to(device), dead_ratios.to(device)\n                    )\n                    \n                    predictions, ratio_predictions = model(imgs, tab_data)\n                    \n                    # Apply WA-specific handling\n                    wa_mask = torch.tensor([1.0 if s == 'WA' else 0.0 for s in states], \n                                          device=device).unsqueeze(1)\n                    predictions[:, 1:2] = predictions[:, 1:2] * (1 - wa_mask)\n                    \n                    main_loss = main_criterion(predictions, targets)\n                    ratio_loss = ratio_criterion(ratio_predictions, dead_ratios)\n                    total_loss = main_loss + 0.3 * ratio_loss\n                    \n                    val_loss += total_loss.item()\n                    val_samples += 1\n                    \n                except RuntimeError as e:\n                    if \"CUDA out of memory\" in str(e):\n                        print(\"⚠️ OOM pendant la validation, skip batch\")\n                        torch.cuda.empty_cache()\n                        continue\n        \n        avg_val_loss = val_loss / max(val_samples, 1)\n        \n        scheduler.step(avg_val_loss)\n        \n        # Save best model\n        if avg_val_loss < best_val_loss:\n            best_val_loss = avg_val_loss\n            patience_counter = 0\n            \n            torch.save({\n                'epoch': epoch,\n                'model_state_dict': model.state_dict(),\n                'optimizer_state_dict': optimizer.state_dict(),\n                'train_loss': avg_train_loss,\n                'val_loss': avg_val_loss,\n                'best_val_loss': best_val_loss,\n            }, 'best_compatible_model.pth')\n            \n            print(f\"✅ Modèle sauvegardé (val_loss: {avg_val_loss:.4f})\")\n        else:\n            patience_counter += 1\n            if patience_counter >= config.PATIENCE:\n                print(f\"🛑 Early stopping à epoch {epoch+1}\")\n                break\n        \n        current_lr = optimizer.param_groups[0]['lr']\n        print(f\"\\nEpoch {epoch+1}/{epochs}:\")\n        print(f\"  Train Loss: {avg_train_loss:.4f} (main={avg_train_main:.4f}, ratio={avg_train_ratio:.4f})\")\n        print(f\"  Val Loss: {avg_val_loss:.4f}\")\n        print(f\"  Best Val Loss: {best_val_loss:.4f}\")\n        print(f\"  Learning Rate: {current_lr:.2e}\")\n        print(f\"  Patience: {patience_counter}/{config.PATIENCE}\")\n        \n        torch.cuda.empty_cache()\n    \n    print(f\"\\n🎯 Entraînement terminé. Meilleure val_loss: {best_val_loss:.4f}\")\n    return model\n\n# ==============================\n# 7️⃣ PREDICTION AND POST-PROCESSING\n# ==============================\ndef predict_with_ratio_model(model, test_loader, device, target_names):\n    \"\"\"Make predictions using the ratio-based model\"\"\"\n    model.eval()\n    \n    all_predictions = []\n    all_image_ids = []\n    all_target_names = []\n    all_states = []\n    \n    with torch.no_grad():\n        for batch in tqdm(test_loader, desc=\"Predictions\"):\n            if len(batch) == 5:  # Test mode\n                imgs, tab_data, img_ids, states, target_names_batch = batch\n                imgs, tab_data = imgs.to(device), tab_data.to(device)\n                predictions, _ = model(imgs, tab_data)\n                \n                # Apply WA-specific handling\n                wa_mask = torch.tensor([1.0 if s == 'WA' else 0.0 for s in states], \n                                      device=device).unsqueeze(1)\n                predictions[:, 1:2] = predictions[:, 1:2] * (1 - wa_mask)\n                \n                all_predictions.append(predictions.cpu().numpy())\n                all_image_ids.extend(img_ids)\n                all_states.extend(states)\n                all_target_names.extend(target_names_batch)\n    \n    if len(all_predictions) > 0:\n        all_predictions = np.vstack(all_predictions)\n    else:\n        all_predictions = np.zeros((len(all_image_ids), len(target_names)))\n    \n    return all_predictions, all_image_ids, all_target_names\n\ndef enhanced_post_processing(predictions, states=None):\n    \"\"\"Enhanced post-processing with ratio constraints\"\"\"\n    if len(predictions) == 0:\n        return predictions\n    \n    processed = predictions.copy()\n    \n    # Ensure positivity\n    processed = np.maximum(processed, 0)\n    \n    # Apply ratio-based constraints\n    for i in range(len(processed)):\n        pred = processed[i]\n        \n        # 1. Ensure Total >= sum of components\n        components_sum = pred[0] + pred[2] + pred[1]\n        if pred[3] < components_sum:\n            pred[3] = components_sum * 1.1\n        \n        # 2. Apply Dead/Total ratio constraint\n        if pred[3] > 0:\n            dead_ratio = pred[1] / pred[3]\n            dead_ratio = np.clip(dead_ratio, 0, 0.8)\n            pred[1] = dead_ratio * pred[3]\n        \n        # 3. Ensure GDM is close to Green\n        if pred[2] > 0 and pred[4] > 0:\n            green_gdm_ratio = pred[4] / pred[2]\n            if green_gdm_ratio > 2 or green_gdm_ratio < 0.5:\n                pred[4] = pred[2] * np.random.uniform(0.8, 1.2)\n        \n        # 4. State-specific handling\n        if states and i < len(states):\n            if states[i] == 'WA':\n                pred[1] = 0.0\n        \n        processed[i] = pred\n    \n    return processed\n\n# ==============================\n# 8️⃣ MAIN PIPELINE SIMPLIFIÉ\n# ==============================\ndef main_simplified():\n    print(\"🚀 Starting SIMPLIFIED CSIRO Biomass pipeline...\")\n    \n    # 1. Data preparation\n    train_data, test_data, features, target_names = prepare_data_with_features_corrected(\n        config.TRAIN_CSV, config.TEST_CSV\n    )\n    \n    print(f\"\\n📊 Target names: {target_names}\")\n    print(f\"📊 Test data columns: {test_data.columns.tolist()}\")\n    \n    # 2. Create enhanced datasets\n    train_dataset = EnhancedBiomassDataset(\n        train_data, config.IMG_ROOT, features, target_names, is_test=False\n    )\n    \n    # Split train/val\n    train_size = int((1 - config.VAL_SPLIT) * len(train_dataset))\n    val_size = len(train_dataset) - train_size\n    train_dataset, val_dataset = random_split(\n        train_dataset, [train_size, val_size],\n        generator=torch.Generator().manual_seed(config.SEED)\n    )\n    \n    # Test dataset\n    test_dataset = EnhancedBiomassDataset(\n        test_data, config.IMG_ROOT, features, targets=None, is_test=True\n    )\n    \n    # 3. Create DataLoaders\n    train_loader = DataLoader(\n        train_dataset, batch_size=config.BATCH_SIZE, \n        shuffle=True, num_workers=0, pin_memory=True,\n        drop_last=True  # Avoid batches of size 1\n    )\n    val_loader = DataLoader(\n        val_dataset, batch_size=config.BATCH_SIZE,\n        shuffle=False, num_workers=0, pin_memory=True,\n        drop_last=False  # Keep all validation samples\n    )\n    test_loader = DataLoader(\n        test_dataset, batch_size=1, shuffle=False, num_workers=0\n    )\n    \n    print(f\"\\n📊 Dataset sizes:\")\n    print(f\"   Train={len(train_dataset)}, Val={len(val_dataset)}\")\n    print(f\"   Test={len(test_dataset)}\")\n    \n    # 4. Create model\n    print(\"\\n🧠 Creating compatible ratio-based model...\")\n    model = CompatibleRatioBasedModel(\n        tab_size=len(features),\n        hidden_size=config.HIDDEN_SIZE,\n        output_size=len(target_names),\n        dropout=config.DROPOUT\n    ).to(device)\n    \n    print(f\"📈 Total parameters: {sum(p.numel() for p in model.parameters()):,}\")\n    \n    # 5. Training\n    print(\"\\n🎯 Starting training...\")\n    model = train_ratio_model(\n        model=model,\n        train_loader=train_loader,\n        val_loader=val_loader,\n        device=device,\n        epochs=config.EPOCHS\n    )\n    \n    # 6. Predictions\n    print(f\"\\n🔮 Generating predictions for {len(test_dataset)} test samples...\")\n    test_predictions, test_image_ids, test_target_names = predict_with_ratio_model(\n        model=model,\n        test_loader=test_loader,\n        device=device,\n        target_names=target_names\n    )\n    \n    print(f\"\\n📊 Predictions generated: {len(test_predictions)} predictions\")\n    \n    # 7. Create submission\n    print(\"\\n📤 Creating submission file...\")\n    \n    submission_rows = []\n    \n    # Read original test.csv to maintain order\n    original_test = pd.read_csv(config.TEST_CSV)\n    \n    for idx, row in original_test.iterrows():\n        sample_id = row['sample_id']\n        \n        # Find matching prediction\n        pred_value = 0.0\n        \n        # Try to find by sample_id\n        for i, (img_id, target_name) in enumerate(zip(test_image_ids, test_target_names)):\n            # Check if this prediction matches the sample\n            if str(sample_id) in img_id or target_name in str(sample_id):\n                # Get the correct target index\n                if target_name in target_names:\n                    target_idx = target_names.index(target_name)\n                    pred_value = float(test_predictions[i, target_idx])\n                    break\n        \n        submission_rows.append({\n            'sample_id': sample_id,\n            'target': pred_value\n        })\n    \n    submission_df = pd.DataFrame(submission_rows)\n    \n    # Save submission\n    submission_df.to_csv('submission.csv', index=False)\n    \n    print(f\"\\n✅ Submission created with {len(submission_df)} predictions\")\n    print(f\"📊 Preview:\")\n    print(submission_df.head())\n    \n    # Show statistics\n    print(\"\\n📊 Prediction statistics:\")\n    for target in target_names:\n        target_preds = submission_df[submission_df['sample_id'].str.endswith(f'__{target}')]['target']\n        if len(target_preds) > 0:\n            print(f\"  {target}: mean={target_preds.mean():.3f}, min={target_preds.min():.3f}, max={target_preds.max():.3f}\")\n    \n    return model, submission_df\n\n# ==============================\n# 🚀 EXECUTION\n# ==============================\nif __name__ == \"__main__\":\n    try:\n        print(\"=\"*60)\n        print(\"🌱 CSIRO BIOMASS COMPETITION - SIMPLIFIED SOLUTION\")\n        print(\"=\"*60)\n        \n        # Run the simplified pipeline\n        model, submission = main_simplified()\n        \n        print(\"\\n🎉 Pipeline terminé avec succès!\")\n        print(f\"\\n📁 Submission saved to: submission.csv\")\n        \n        # Final check\n        print(f\"\\n📊 Final check:\")\n        print(f\"   Predictions in submission: {len(submission)}\")\n        print(f\"   Samples in test.csv: {len(pd.read_csv(config.TEST_CSV))}\")\n        \n    except Exception as e:\n        print(f\"\\n❌ Error in pipeline: {str(e)}\")\n        import traceback\n        traceback.print_exc()\n        \n        # Emergency fallback\n        print(\"\\n🔄 Creating emergency fallback submission...\")\n        \n        try:\n            test_df = pd.read_csv(config.TEST_CSV)\n            submission_rows = []\n            \n            for _, row in test_df.iterrows():\n                sample_id = row['sample_id']\n                \n                # Default values based on competition analysis\n                if 'Dry_Clover_g' in sample_id:\n                    target_value = 0.87\n                elif 'Dry_Dead_g' in sample_id:\n                    target_value = 0.49\n                elif 'Dry_Green_g' in sample_id:\n                    target_value = 1.03\n                elif 'Dry_Total_g' in sample_id:\n                    target_value = 2.63\n                elif 'GDM_g' in sample_id:\n                    target_value = 1.10\n                else:\n                    target_value = 0.0\n                \n                submission_rows.append({\n                    'sample_id': sample_id,\n                    'target': target_value\n                })\n            \n            submission_df = pd.DataFrame(submission_rows)\n            submission_df.to_csv('submission_emergency.csv', index=False)\n            print(f\"✅ Emergency submission created with {len(submission_df)} predictions\")\n            \n        except:\n            # Last resort\n            sample_ids = [f\"sample{i//5+1}__{['Dry_Clover_g', 'Dry_Dead_g', 'Dry_Green_g', 'Dry_Total_g', 'GDM_g'][i%5]}\" \n                         for i in range(25)]\n            submission_df = pd.DataFrame({\n                'sample_id': sample_ids,\n                'target': [1.0] * 25\n            })\n            submission_df.to_csv('submission_final.csv', index=False)\n            print(\"✅ Minimal submission created\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-06T20:53:02.568218Z","iopub.execute_input":"2026-01-06T20:53:02.568562Z","iopub.status.idle":"2026-01-06T20:58:23.839742Z","shell.execute_reply.started":"2026-01-06T20:53:02.568526Z","shell.execute_reply":"2026-01-06T20:58:23.838967Z"}},"outputs":[],"execution_count":null}]}