{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":102335,"databundleVersionId":12518947,"isSourceIdPinned":false,"sourceType":"competition"},{"sourceId":242954653,"sourceType":"kernelVersion"}],"dockerImageVersionId":31041,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**Please follow R^2 negative https://www.kaggle.com/code/rktqwe/lb-0-76-imu-thm-tof-tf-bilstm-gru-attention\n\n\n**用于学习\n\n**我的添加：我自己读代码过程的中文注释+生成训练过程图，可以在输出文件中找到类似\n![image.png](attachment:3c9c79b5-7feb-4d47-8bde-582fcaf1c814.png)![image.png](attachment:0f3b8add-c2ad-48a6-8d55-e4c6f39f8423.png)\n\n**This solution was developed by building upon the foundational work of two key contributors:**\n\n- **CMI25 | IMU+THM/TOF |TF BiLSTM+GRU+Attention|LB.75** by [yukiZ](https://www.kaggle.com/code/hideyukizushi/cmi25-imu-thm-tof-tf-bilstm-gru-attention-lb-75)\n- **imu-tof** by [Erich von Mainstein](https://www.kaggle.com/code/vonmainstein/imu-tof)\n\n**Special recognition for the exceptional exploratory data analysis:**\n\n- **Sensor Pulse🧠| Viz & EDA for BFRB Detection** by [Tarun Mishra](https://www.kaggle.com/code/tarundirector/sensor-pulse-viz-eda-for-bfrb-detection)\n\n### Implemented Enhancements\n\n#### 1. IMU Signal Processing Optimization (Core Improvement)\n\n**Implemented Features:**\n- Derived kinematic metrics:\n  - Acceleration vector magnitude (`acc_mag`)\n  - Scalar rotation angle (`rot_angle`)\n  - Acceleration derivative (`acc_mag_jerk`)\n  - Angular velocity (`rot_angle_vel`)\n- Initial learning rate reduction from `1e-3` to `5e-4`\n- Implemented cosine decay scheduling:\n  - Parameters: `first_decay_steps = 15 × steps_per_epoch`\n\n**Unsuccessful Experiments (Reverted):**\n- Further LR Reduction: `LR_INIT = 2-e4` worsened score.\n- Dropout Rate Adjustments: No improvement over baseline dropout.\n- TOF/THEM Branch Architecture Change: Replacing simpler CNNs with `residual_se_cnn_blocks` was detrimental.\n\n**If you support the notebook, please upvote—I’d really appreciate it!**  \n**Also, if you have any questions, feel free to ask—I’ll be happy to answer. 😊**","metadata":{},"attachments":{"3c9c79b5-7feb-4d47-8bde-582fcaf1c814.png":{"image/png":"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"},"0f3b8add-c2ad-48a6-8d55-e4c6f39f8423.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### 》》》**Importing the necessary Libraries**\n##这边是库的加载，具体库下一block有中文解释","metadata":{}},{"cell_type":"code","source":"import os, json, joblib, numpy as np, pandas as pd\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nimport warnings \nwarnings.filterwarnings(\"ignore\")\n\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.utils.class_weight import compute_class_weight\n\nfrom tensorflow.keras.utils import Sequence, to_categorical, pad_sequences\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras.layers import (\n    Input, Conv1D, BatchNormalization, Activation, add, MaxPooling1D, Dropout,\n    Bidirectional, LSTM, GlobalAveragePooling1D, Dense, Multiply, Reshape,\n    Lambda, Concatenate, GRU, GaussianNoise\n)\nfrom tensorflow.keras.regularizers import l2\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras import backend as K\nimport tensorflow as tf\nimport polars as pl\nfrom sklearn.model_selection import StratifiedGroupKFold\n\n#6/18\nfrom tensorflow.keras.layers import MultiHeadAttention","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:24:41.205661Z","iopub.execute_input":"2025-06-18T11:24:41.205833Z","iopub.status.idle":"2025-06-18T11:24:58.029763Z","shell.execute_reply.started":"2025-06-18T11:24:41.205817Z","shell.execute_reply":"2025-06-18T11:24:58.028934Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"✅ 一、Scikit-learn 库（用于数据处理和模型辅助）\n\nfrom sklearn.model_selection import train_test_split\n功能：把数据集随机划分为训练集和测试集。\n\n\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nStandardScaler：标准化特征（均值为 0，方差为 1），用于数值型数据预处理。\n\nLabelEncoder：将标签从文本转为数字，比如 \"cat\", \"dog\" → 0, 1。\nfrom sklearn.utils.class_weight import compute_class_weight\n功能：当你的数据是类别不平衡时，这个函数可以自动计算每个类别的“权重”，在训练中用来减少偏差。\nfrom sklearn.model_selection import StratifiedGroupKFold\n功能：分层分组交叉验证（Stratified K-Fold），常用于保持每一折中的类别比例一致，同时确保同一组（如同一个人）的数据不会同时出现在训练集和验证集中。\n\n✅ 二、TensorFlow / Keras（深度学习框架）\nfrom tensorflow.keras.utils import Sequence, to_categorical, pad_sequences\nSequence：自定义数据生成器（在训练时逐批加载数据，更节省内存）。\nto_categorical：将整数型标签转为独热编码（如：2 → [0,0,1]）。\npad_sequences：对序列进行补齐（如 LSTM 输入统一长度）。\nfrom tensorflow.keras.models import Model, load_model\nModel：创建深度学习模型的基础类。\nload_model：加载已经保存好的模型。\nfrom tensorflow.keras.layers import (\n    Input, Conv1D, BatchNormalization, Activation, add, MaxPooling1D, Dropout,\n    Bidirectional, LSTM, GlobalAveragePooling1D, Dense, Multiply, Reshape,\n    Lambda, Concatenate, GRU, GaussianNoise\n)\n这些都是 Keras 的神经网络层：\n\n| 层名                     | 中文含义     | 作用                     |\n| ---------------------- | -------- | ---------------------- |\n| Input                  | 输入层      | 输入模型的数据                |\n| Conv1D                 | 一维卷积层    | 常用于处理序列数据（如时间序列、ToF信号） |\n| BatchNormalization     | 批标准化层    | 加快训练，减少过拟合             |\n| Activation             | 激活函数层    | 常用如 ReLU、Softmax 等     |\n| add                    | 残差连接     | 类似 ResNet 的加法连接        |\n| MaxPooling1D           | 最大池化层    | 降低特征维度                 |\n| Dropout                | 随机丢弃神经元  | 防止过拟合                  |\n| Bidirectional          | 双向RNN包装器 | 使 LSTM/GRU 能前后看上下文     |\n| LSTM                   | 长短期记忆层   | 一种常用的RNN，擅长处理序列        |\n| GRU                    | 门控循环单元   | 比 LSTM 更轻量的 RNN        |\n| Dense                  | 全连接层     | 常用于输出层                 |\n| GlobalAveragePooling1D | 全局平均池化层  | 将时间维度的序列变成单一向量         |\n| Multiply               | 点乘层      | 常用于注意力机制或通道融合          |\n| Reshape                | 形状调整     | 改变 tensor 的形状          |\n| Lambda                 | 自定义操作    | 可以插入任意函数               |\n| Concatenate            | 拼接层      | 将多个向量拼接在一起             |\n| GaussianNoise          | 加性高斯噪声层  | 增强模型鲁棒性，类似数据增强         |\n\n\nfrom tensorflow.keras.regularizers import l2\nL2 正则化：防止模型过拟合，让权重尽量小。\n\nfrom tensorflow.keras.optimizers import Adam\nAdam 优化器：常用的神经网络训练优化算法，自动调节学习率。\n\nfrom tensorflow.keras.callbacks import EarlyStopping\n早停机制：当验证集性能停止提升时，自动停止训练，防止过拟合。\n\nfrom tensorflow.keras import backend as K\n底层操作接口：访问 TensorFlow 后端，比如自定义损失函数或指标。\n\n✅ 三、Polars（高性能数据处理）\n\nimport polars as pl\nPolars 是比 Pandas 更快的数据处理库，适合大规模结构化数据。\n\n用法类似 Pandas，比如读 CSV、筛选、分组等。\n\n![image.png](attachment:3d574c64-a318-4944-b2f1-181a72157a6a.png)","metadata":{},"attachments":{"3d574c64-a318-4944-b2f1-181a72157a6a.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### 》》》**Fix Seed**","metadata":{}},{"cell_type":"code","source":"import random\ndef seed_everything(seed):\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    random.seed(seed)\n    np.random.seed(seed)\n    tf.random.set_seed(seed)\n    tf.experimental.numpy.random.seed(seed)\n    os.environ['TF_CUDNN_DETERMINISTIC'] = '1'\n    os.environ['TF_DETERMINISTIC_OPS'] = '1'\nseed_everything(seed=42)\n#fix seed：为了可复现的seed","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:25:08.919837Z","iopub.execute_input":"2025-06-18T11:25:08.920573Z","iopub.status.idle":"2025-06-18T11:25:08.925461Z","shell.execute_reply.started":"2025-06-18T11:25:08.920548Z","shell.execute_reply":"2025-06-18T11:25:08.924523Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 》》》**Configuration**","metadata":{}},{"cell_type":"code","source":"# (Competition metric will only be imported when TRAINing)\nTRAIN = True                     # ← set to True when you want to train\nRAW_DIR = Path(\"/kaggle/input/cmi-detect-behavior-with-sensor-data\")\nPRETRAINED_DIR = Path(\"/kaggle/input/pretrained-model\")  # used when TRAIN=False\nEXPORT_DIR = Path(\"./\")                                    # artefacts will be saved here\nBATCH_SIZE = 64                                        #批次大小，每次喂给神经网络多少条样本。\nPAD_PERCENTILE = 95    #用于序列补齐（pad）的长度标准。意思是“对所有序列长度取第 95 个百分位数”，作为标准长度。\nLR_INIT = 5e-4        #初始学习率，即网络一开始更新参数的步长。\nWD = 3e-3             #权重衰减（weight decay，L2 正则化的系数），用于防止过拟合。\nMIXUP_ALPHA = 0.4       #用于 mixup 数据增强的参数（混合样本与标签的程度）。\nEPOCHS = 200           #最多训练 160 轮。\nPATIENCE = 60        #如果连续 40 轮验证集没有提升，早停训练。\n\n\nprint(\"▶ imports ready · tensorflow\", tf.__version__)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:25:10.580587Z","iopub.execute_input":"2025-06-18T11:25:10.581199Z","iopub.status.idle":"2025-06-18T11:25:10.587758Z","shell.execute_reply.started":"2025-06-18T11:25:10.581168Z","shell.execute_reply":"2025-06-18T11:25:10.586973Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"绘图函数： ","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef save_training_plots(history, save_path_prefix=\"training_plot\"):\n    plt.figure(figsize=(12, 4))\n\n    # loss\n    plt.subplot(1, 2, 1)\n    plt.plot(history.history['loss'], label='train_loss')\n    plt.plot(history.history.get('val_loss', []), label='val_loss')\n    plt.title('Loss over epochs')\n    plt.xlabel('Epoch')\n    plt.ylabel('Loss')\n    plt.legend()\n\n    # accuracy\n    plt.subplot(1, 2, 2)\n    plt.plot(history.history.get('accuracy', history.history.get('acc', [])), label='train_acc')\n    plt.plot(history.history.get('val_accuracy', history.history.get('val_acc', [])), label='val_acc')\n    plt.title('Accuracy over epochs')\n    plt.xlabel('Epoch')\n    plt.ylabel('Accuracy')\n    plt.legend()\n\n    plt.tight_layout()\n    plt.savefig(f\"{save_path_prefix}.png\")\n    plt.close()\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:25:14.301336Z","iopub.execute_input":"2025-06-18T11:25:14.302124Z","iopub.status.idle":"2025-06-18T11:25:14.310737Z","shell.execute_reply.started":"2025-06-18T11:25:14.302092Z","shell.execute_reply":"2025-06-18T11:25:14.310002Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 》》》**Utility Functions**","metadata":{}},{"cell_type":"code","source":"#Tensor Manipulations\ndef time_sum(x):\n    return K.sum(x, axis=1)\n#沿时间维度（axis=1）对张量求和。常用于注意力（Attention）输出加权后的汇总操作。\ndef squeeze_last_axis(x):\n    return tf.squeeze(x, axis=-1)\n#去掉张量的最后一个维度（比如 shape 从 (batch, time, 1) → (batch, time)）。\ndef expand_last_axis(x):\n    return tf.expand_dims(x, axis=-1)\n#在最后一个维度上扩展维度（反操作，(batch, time) → (batch, time, 1)）。\ndef se_block(x, reduction=8):\n    ch = x.shape[-1]\n    se = GlobalAveragePooling1D()(x)\n    se = Dense(ch // reduction, activation='relu')(se)\n    se = Dense(ch, activation='sigmoid')(se)\n    se = Reshape((1, ch))(se)\n    return Multiply()([x, se])\n#这是一个 SE注意力机制模块，用于增强特征的通道表示能力。\n\n# Residual CNN Block with SE\ndef residual_se_cnn_block(x, filters, kernel_size, pool_size=2, drop=0.3, wd=1e-4):\n    shortcut = x\n    for _ in range(2):\n        x = Conv1D(filters, kernel_size, padding='same', use_bias=False,\n                   kernel_regularizer=l2(wd))(x)\n        x = BatchNormalization()(x)\n        x = Activation('relu')(x)\n    x = se_block(x)\n    if shortcut.shape[-1] != filters:\n        shortcut = Conv1D(filters, 1, padding='same', use_bias=False,\n                          kernel_regularizer=l2(wd))(shortcut)\n        shortcut = BatchNormalization()(shortcut)\n    x = add([x, shortcut])\n    x = Activation('relu')(x)\n    x = MaxPooling1D(pool_size)(x)\n    x = Dropout(drop)(x)\n    return x\n\n# 这是一个卷积残差块（CNN + ResNet + SE），常用于时间序列特征提取。结构流程：\n# 两层 Conv1D + BN + ReLU\n# SE模块（对通道进行注意力加权）\n# 残差连接：\n# 如果输入通道数不同，则通过 1x1 卷积调整 shortcut 的通道数。\n# Add + ReLU：残差加和并激活。\n# MaxPooling + Dropout：降采样并防止过拟合。\n# 用途：提取局部时序特征，同时保持深层网络的可训练性（残差结构）。\n\ndef attention_layer(inputs):\n    score = Dense(1, activation='tanh')(inputs)\n    score = Lambda(squeeze_last_axis)(score)\n    weights = Activation('softmax')(score)\n    weights = Lambda(expand_last_axis)(weights)\n    context = Multiply()([inputs, weights])\n    context = Lambda(time_sum)(context)\n    return context\n# 实现了一个 简单的时间注意力机制，流程如下：\n# Dense(1, activation='tanh')：为每个时间步计算一个得分。\n\n# softmax：对所有时间步的得分进行归一化，得到注意力权重。\n\n# Multiply([inputs, weights])：将输入序列按权重加权。\n\n# sum over time：加权后的时间步聚合为一个向量。\n\n# 🧠 效果：让模型自动学习关注序列中的关键时刻（而不是平均所有时间步）。\nclass TransformerBlock(tf.keras.layers.Layer):\n    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n        super().__init__()\n        self.att = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n        self.ffn = tf.keras.Sequential([\n            tf.keras.layers.Dense(ff_dim, activation='relu'),\n            tf.keras.layers.Dense(embed_dim),\n        ])\n        self.layernorm1 = tf.keras.layers.LayerNormalization()\n        self.layernorm2 = tf.keras.layers.LayerNormalization()\n        self.dropout1 = tf.keras.layers.Dropout(rate)\n        self.dropout2 = tf.keras.layers.Dropout(rate)\n\n    def call(self, inputs, training=False):\n        attn_output = self.att(inputs, inputs)\n        out1 = self.layernorm1(inputs + self.dropout1(attn_output, training=training))\n        ffn_output = self.ffn(out1)\n        return self.layernorm2(out1 + self.dropout2(ffn_output, training=training))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:25:20.054323Z","iopub.execute_input":"2025-06-18T11:25:20.054603Z","iopub.status.idle":"2025-06-18T11:25:20.067809Z","shell.execute_reply.started":"2025-06-18T11:25:20.054585Z","shell.execute_reply":"2025-06-18T11:25:20.067128Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 》》》**Data Helpers**","metadata":{}},{"cell_type":"code","source":"# Normalizes and cleans the time series sequence. \n\n#preprocess_sequence：对每条时间序列的多维特征列做标准化处理，确保模型输入数据在统一尺度，有助于收敛。\n\ndef preprocess_sequence(df_seq: pd.DataFrame, feature_cols: list[str], scaler: StandardScaler):\n    mat = df_seq[feature_cols].ffill().bfill().fillna(0).values\n    return scaler.transform(mat).astype('float32')\n    \n#ffill()：向前填补缺失值（NaN）；bfill()：向后填补缺失值；fillna(0)：如果还存在空值，用 0 替代。\n\n# MixUp the data argumentation in order to regularize the neural network. \n#MixUp 是一种正则化方法，它将两个样本的数据和标签线性混合，增加样本多样性，提高模型泛化能力。\nclass MixupGenerator(Sequence):\n    def __init__(self, X, y, batch_size, alpha=0.2):\n        self.X, self.y = X, y\n        self.batch = batch_size\n        self.alpha = alpha\n        self.indices = np.arange(len(X))\n    def __len__(self):\n        return int(np.ceil(len(self.X) / self.batch))\n    def __getitem__(self, i):  #从 Beta 分布中采样一个 𝜆控制样本混合的权重。\n        idx = self.indices[i*self.batch:(i+1)*self.batch]\n        Xb, yb = self.X[idx], self.y[idx]\n        lam = np.random.beta(self.alpha, self.alpha)\n        perm = np.random.permutation(len(Xb))\n        X_mix = lam * Xb + (1-lam) * Xb[perm]\n        y_mix = lam * yb + (1-lam) * yb[perm]\n        return X_mix, y_mix\n    def on_epoch_end(self): #每轮结束后打乱索引，增加训练随机性。\n        np.random.shuffle(self.indices)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-18T11:25:49.326990Z","iopub.execute_input":"2025-06-18T11:25:49.327292Z","iopub.status.idle":"2025-06-18T11:25:49.334014Z","shell.execute_reply.started":"2025-06-18T11:25:49.327271Z","shell.execute_reply":"2025-06-18T11:25:49.333244Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:0a6f38ef-faa9-4a94-8568-82d381e5ce24.png)","metadata":{},"attachments":{"0a6f38ef-faa9-4a94-8568-82d381e5ce24.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### 》》》**Model Definition - Two Branch Architecture**","metadata":{}},{"cell_type":"markdown","source":"Two Branch Architecture\n双分支结构 是一种 多通道处理策略，主要思想是：\n\n将输入序列从多个角度或不同特征域提取信息，然后合并表示进行决策（分类）。\n\n\n处理不同模态的输入（如 IMU+TOF）；\n\n提取时序特征 + 局部卷积特征；\n\n建立空间路径 + 时间路径；\n\n提高模型的泛化能力和鲁棒性。","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.layers import LayerNormalization, Add, Dropout, Dense, GlobalAveragePooling1D, MultiHeadAttention\n\ndef transformer_block(inputs, num_heads=4, key_dim=64, ff_dim=128, dropout_rate=0.4):\n    attn_output = MultiHeadAttention(num_heads=num_heads, key_dim=key_dim)(inputs, inputs)\n    attn_output = Dropout(dropout_rate)(attn_output)\n    out1 = LayerNormalization(epsilon=1e-6)(Add()([inputs, attn_output]))\n\n    ffn_output = Dense(ff_dim, activation='relu')(out1)\n    ffn_output = Dense(key_dim)(ffn_output)\n    ffn_output = Dropout(dropout_rate)(ffn_output)\n    out2 = LayerNormalization(epsilon=1e-6)(Add()([out1, ffn_output]))\n    return out2\n\ndef build_two_branch_model(pad_len, imu_dim, tof_dim, n_classes, wd=1e-3):\n    inp = Input(shape=(pad_len, imu_dim+tof_dim))\n    imu = Lambda(lambda t: t[:, :, :imu_dim])(inp)\n    tof = Lambda(lambda t: t[:, :, imu_dim:])(inp)\n\n    x1 = residual_se_cnn_block(imu, 64, 3, drop=0.1, wd=wd)\n    x1 = residual_se_cnn_block(x1, 128, 5, drop=0.1, wd=wd)\n\n    x2 = Conv1D(64, 3, padding='same', use_bias=False, kernel_regularizer=l2(wd))(tof)\n    x2 = BatchNormalization()(x2); x2 = Activation('relu')(x2)\n    x2 = MaxPooling1D(2)(x2); x2 = Dropout(0.2)(x2)\n    x2 = Conv1D(128, 3, padding='same', use_bias=False, kernel_regularizer=l2(wd))(x2)\n    x2 = BatchNormalization()(x2); x2 = Activation('relu')(x2)\n    x2 = MaxPooling1D(2)(x2); x2 = Dropout(0.2)(x2)\n\n    merged = Concatenate()([x1, x2])\n\n    xa = Bidirectional(LSTM(128, return_sequences=True, kernel_regularizer=l2(wd)))(merged)\n    xb = Bidirectional(GRU(128, return_sequences=True, kernel_regularizer=l2(wd)))(merged)\n    xc = GaussianNoise(0.09)(merged)\n    xc = Dense(16, activation='elu')(xc)\n\n    x = Concatenate()([xa, xb, xc])\n    x = Dropout(0.4)(x)\n\n    x = Dense(64)(x)  # 映射通道数到64，和多头注意力key_dim对应\n    x = transformer_block(x, num_heads=4, key_dim=64, ff_dim=128, dropout_rate=0.4)\n    x = GlobalAveragePooling1D()(x)\n\n    for units, drop in [(256, 0.5), (128, 0.3)]:\n        x = Dense(units, use_bias=False, kernel_regularizer=l2(wd))(x)\n        x = BatchNormalization()(x)\n        x = Activation('relu')(x)\n        x = Dropout(drop)(x)\n\n    out = Dense(n_classes, activation='softmax', kernel_regularizer=l2(wd))(x)\n    return Model(inp, out)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 》》》**Training / Inference Pipeline**","metadata":{}},{"cell_type":"code","source":"if TRAIN:\n    print(\"▶ TRAIN MODE – loading dataset …\")\n    df = pd.read_csv(RAW_DIR / \"train.csv\")\n\n    train_dem_df = pd.read_csv(RAW_DIR / \"train_demographics.csv\")\n    df_for_groups = pd.merge(df.copy(), train_dem_df, on='subject', how='left')\n\n    le = LabelEncoder()\n    df['gesture_int'] = le.fit_transform(df['gesture'])\n    np.save(EXPORT_DIR / \"gesture_classes.npy\", le.classes_)\n    gesture_classes = le.classes_\n\n    print(\"  Calculating engineered IMU features (magnitude, angle)...\")\n    df['acc_mag'] = np.sqrt(df['acc_x']**2 + df['acc_y']**2 + df['acc_z']**2)\n    df['rot_angle'] = 2 * np.arccos(df['rot_w'].clip(-1, 1))\n    \n    print(\"  Calculating engineered IMU derivatives (jerk, angular velocity)...\")\n    df['acc_mag_jerk'] = df.groupby('sequence_id')['acc_mag'].diff().fillna(0)\n    df['rot_angle_vel'] = df.groupby('sequence_id')['rot_angle'].diff().fillna(0)\n\n    meta_cols = {'gesture', 'gesture_int', 'sequence_type', 'behavior', 'orientation',\n                 'row_id', 'subject', 'phase', 'sequence_id', 'sequence_counter'}\n\n    imu_cols = [c for c in df.columns if c.startswith('acc_') and c not in ['acc_mag', 'acc_mag_jerk']]\n    imu_cols.extend([c for c in df.columns if c.startswith('rot_') and c not in ['rot_angle', 'rot_angle_vel']])\n    imu_cols.extend(['acc_mag', 'rot_angle', 'acc_mag_jerk', 'rot_angle_vel'])\n\n    thm_cols_original = [c for c in df.columns if c.startswith('thm_')]\n    \n    tof_aggregated_cols_template = []\n    for i in range(1, 6):\n        tof_aggregated_cols_template.extend([f'tof_{i}_mean', f'tof_{i}_std', f'tof_{i}_min', f'tof_{i}_max'])\n\n    final_feature_cols = imu_cols + thm_cols_original + tof_aggregated_cols_template\n    imu_dim_final = len(imu_cols)\n    tof_thm_aggregated_dim_final = len(thm_cols_original) + len(tof_aggregated_cols_template)\n    \n    print(f\"  IMU (incl. engineered & derivatives) {imu_dim_final} | THM + Aggregated TOF {tof_thm_aggregated_dim_final} | total {len(final_feature_cols)} features\")\n    np.save(EXPORT_DIR / \"feature_cols.npy\", np.array(final_feature_cols))\n\n    print(\"  Building sequences with aggregated TOF and preparing data for scaler...\")\n    seq_gp = df.groupby('sequence_id') \n    \n    all_steps_for_scaler_list = []\n    X_list_unscaled, y_list_int_for_stratify, lens = [], [], [] \n\n    for seq_id, seq_df_orig in seq_gp:\n        seq_df = seq_df_orig.copy()\n\n        for i in range(1, 6):\n            pixel_cols_tof = [f\"tof_{i}_v{p}\" for p in range(64)]\n            tof_sensor_data = seq_df[pixel_cols_tof].replace(-1, np.nan)\n            seq_df[f'tof_{i}_mean'] = tof_sensor_data.mean(axis=1)\n            seq_df[f'tof_{i}_std']  = tof_sensor_data.std(axis=1)\n            seq_df[f'tof_{i}_min']  = tof_sensor_data.min(axis=1)\n            seq_df[f'tof_{i}_max']  = tof_sensor_data.max(axis=1)\n        \n        mat_unscaled = seq_df[final_feature_cols].ffill().bfill().fillna(0).values.astype('float32')\n        \n        all_steps_for_scaler_list.append(mat_unscaled)\n        X_list_unscaled.append(mat_unscaled)\n        y_list_int_for_stratify.append(seq_df['gesture_int'].iloc[0])\n        lens.append(len(mat_unscaled))\n\n    print(\"  Fitting StandardScaler...\")\n    all_steps_concatenated = np.concatenate(all_steps_for_scaler_list, axis=0)\n    scaler = StandardScaler().fit(all_steps_concatenated)\n    joblib.dump(scaler, EXPORT_DIR / \"scaler.pkl\")\n    del all_steps_for_scaler_list, all_steps_concatenated\n\n    print(\"  Scaling and padding sequences...\")\n    X_scaled_list = [scaler.transform(x_seq) for x_seq in X_list_unscaled]\n    del X_list_unscaled\n\n    pad_len = int(np.percentile(lens, PAD_PERCENTILE))\n    np.save(EXPORT_DIR / \"sequence_maxlen.npy\", pad_len)\n    \n    X = pad_sequences(X_scaled_list, maxlen=pad_len, padding='post', truncating='post', dtype='float32')\n    del X_scaled_list\n    \n    y_int_for_stratify = np.array(y_list_int_for_stratify)\n    y = to_categorical(y_int_for_stratify, num_classes=len(le.classes_))\n\n    print(\"  Splitting data and preparing for training...\")\n    X_tr, X_val, y_tr, y_val = train_test_split(X, y, test_size=0.1, random_state=82, stratify=y_int_for_stratify)\n\n    cw_vals = compute_class_weight('balanced', classes=np.arange(len(le.classes_)), y=y_int_for_stratify)\n    class_weight = dict(enumerate(cw_vals))\n\n    model = build_two_branch_model(pad_len, imu_dim_final, tof_thm_aggregated_dim_final, len(le.classes_), wd=WD)\n    \n    steps = len(X_tr) // BATCH_SIZE\n    lr_sched = tf.keras.optimizers.schedules.CosineDecayRestarts(5e-4, first_decay_steps=15 * steps) \n    \n    model.compile(optimizer=Adam(lr_sched),\n                  loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),\n                  metrics=['accuracy'])\n\n    train_gen = MixupGenerator(X_tr, y_tr, batch_size=BATCH_SIZE, alpha=MIXUP_ALPHA)\n    cb = EarlyStopping(patience=PATIENCE, restore_best_weights=True, verbose=1, monitor='val_accuracy', mode='max')\n    \n    print(\"  Starting model training...\")\n    history=model.fit(train_gen, epochs=EPOCHS, validation_data=(X_val, y_val),\n              class_weight=class_weight, callbacks=[cb], verbose=1)\n\n    model.save(EXPORT_DIR / \"gesture_two_branch_mixup.h5\")\n    print(\"✔ Training done – artefacts saved in\", EXPORT_DIR)\n    #baocunhuitu wenj\n    save_training_plots(history, \"training_process\")\n\n    from cmi_2025_metric_copy_for_import import CompetitionMetric\n    preds_val = model.predict(X_val).argmax(1)\n    true_val_int  = y_val.argmax(1)\n    \n    h_f1 = CompetitionMetric().calculate_hierarchical_f1(\n        pd.DataFrame({'gesture': le.classes_[true_val_int]}),\n        pd.DataFrame({'gesture': le.classes_[preds_val]}))\n    print(\"Hold‑out H‑F1 =\", round(h_f1, 4))\n\nelse:\n    print(\"▶ INFERENCE MODE – loading artefacts from\", PRETRAINED_DIR)\n    final_feature_cols = np.load(PRETRAINED_DIR / \"feature_cols.npy\", allow_pickle=True).tolist()\n    pad_len        = int(np.load(PRETRAINED_DIR / \"sequence_maxlen.npy\"))\n    scaler         = joblib.load(PRETRAINED_DIR / \"scaler.pkl\")\n    gesture_classes = np.load(PRETRAINED_DIR / \"gesture_classes.npy\", allow_pickle=True)\n\n    temp_imu_cols = [c for c in final_feature_cols if c.startswith('acc_') or c.startswith('rot_')]\n    imu_dim_final = len(temp_imu_cols)\n    tof_thm_aggregated_dim_final = len(final_feature_cols) - imu_dim_final\n\n    custom_objs = {\n        'time_sum': time_sum,\n        'squeeze_last_axis': squeeze_last_axis,\n        'expand_last_axis': expand_last_axis,\n        'se_block': se_block,\n        'residual_se_cnn_block': residual_se_cnn_block,\n        'attention_layer': attention_layer,\n    }\n    model = load_model(PRETRAINED_DIR / \"gesture_two_branch_mixup.h5\",\n                       compile=False, custom_objects=custom_objs)\n    print(\"  Model, scaler, feature_cols, pad_len loaded – ready for evaluation\")","metadata":{"trusted":true,"scrolled":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 》》》**Predict**","metadata":{}},{"cell_type":"code","source":"def predict(sequence: pl.DataFrame, demographics: pl.DataFrame) -> str:\n    df_seq = sequence.to_pandas()\n\n    df_seq['acc_mag'] = np.sqrt(df_seq['acc_x']**2 + df_seq['acc_y']**2 + df_seq['acc_z']**2)\n    df_seq['rot_angle'] = 2 * np.arccos(df_seq['rot_w'].clip(-1, 1))\n    df_seq['acc_mag_jerk'] = df_seq['acc_mag'].diff().fillna(0)\n    df_seq['rot_angle_vel'] = df_seq['rot_angle'].diff().fillna(0)\n    \n    for i in range(1, 6): \n        pixel_cols_tof = [f\"tof_{i}_v{p}\" for p in range(64)]\n        tof_sensor_data = df_seq[pixel_cols_tof].replace(-1, np.nan)\n        \n        df_seq[f'tof_{i}_mean'] = tof_sensor_data.mean(axis=1)\n        df_seq[f'tof_{i}_std']  = tof_sensor_data.std(axis=1)\n        df_seq[f'tof_{i}_min']  = tof_sensor_data.min(axis=1)\n        df_seq[f'tof_{i}_max']  = tof_sensor_data.max(axis=1)\n        \n    df_seq_reordered = pd.DataFrame(columns=final_feature_cols)\n    for col in final_feature_cols:\n        if col in df_seq.columns:\n            df_seq_reordered[col] = df_seq[col]\n\n    mat_unscaled = df_seq_reordered.ffill().bfill().fillna(0).values.astype('float32')\n    \n    mat_scaled = scaler.transform(mat_unscaled)\n    \n    pad_input = pad_sequences([mat_scaled], maxlen=pad_len, padding='post', truncating='post', dtype='float32')\n    \n    idx = int(model.predict(pad_input, verbose=0).argmax(1)[0])\n    return str(gesture_classes[idx])","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 》》》**Submit Inference server**","metadata":{}},{"cell_type":"code","source":"import kaggle_evaluation.cmi_inference_server\ninference_server = kaggle_evaluation.cmi_inference_server.CMIInferenceServer(predict)\n\nif os.getenv('KAGGLE_IS_COMPETITION_RERUN'):\n    inference_server.serve()\nelse:\n    inference_server.run_local_gateway(\n        data_paths=(\n            '/kaggle/input/cmi-detect-behavior-with-sensor-data/test.csv',\n            '/kaggle/input/cmi-detect-behavior-with-sensor-data/test_demographics.csv',\n        )\n    )","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}