{"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":"nvidiaTeslaT4","dataSources":[{"sourceId":102335,"databundleVersionId":12518947,"sourceType":"competition"}],"dockerImageVersionId":31041,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"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 # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# IMPORTANT: SOME KAGGLE DATA SOURCES ARE PRIVATE\n# RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES.\nimport kagglehub\nkagglehub.login()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:47:31.134547Z","iopub.status.idle":"2025-06-08T04:47:31.134903Z","shell.execute_reply.started":"2025-06-08T04:47:31.134716Z","shell.execute_reply":"2025-06-08T04:47:31.134732Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n# THEN FEEL FREE TO DELETE THIS CELL.\n# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n# NOTEBOOK.\n\ncmi_detect_behavior_with_sensor_data_path = kagglehub.competition_download('cmi-detect-behavior-with-sensor-data')\nricholson_cmi_2025_metric_copy_for_import_path = kagglehub.utility_script_install('richolson/cmi-2025-metric-copy-for-import')\n\nprint('Data source import complete.')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:47:31.135773Z","iopub.status.idle":"2025-06-08T04:47:31.136111Z","shell.execute_reply.started":"2025-06-08T04:47:31.135957Z","shell.execute_reply":"2025-06-08T04:47:31.135973Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Two‑Branch Human‑Activity‑Recognition Pipeline (IMU + Thermopile/TOF  + SE‑CNN + BiLSTM + Attention)\nimport os, json, joblib, numpy as np, pandas as pd\nfrom pathlib import Path\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\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\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:47:43.471548Z","iopub.execute_input":"2025-06-08T04:47:43.472227Z","iopub.status.idle":"2025-06-08T04:48:06.180517Z","shell.execute_reply.started":"2025-06-08T04:47:43.472203Z","shell.execute_reply":"2025-06-08T04:48:06.179976Z"}},"outputs":[],"execution_count":null},{"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\nPAD_PERCENTILE = 95\nLR_INIT = 1e-3\nWD = 3e-4\nMIXUP_ALPHA = 0.4\nEPOCHS = 160\nPATIENCE = 40\n\n\nprint(\"▶ imports ready · tensorflow\", tf.__version__)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:48:10.081911Z","iopub.execute_input":"2025-06-08T04:48:10.082867Z","iopub.status.idle":"2025-06-08T04:48:10.08866Z","shell.execute_reply.started":"2025-06-08T04:48:10.082836Z","shell.execute_reply":"2025-06-08T04:48:10.087841Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Tensor Manipulations\ndef time_sum(x):\n    return K.sum(x, axis=1)\n\ndef squeeze_last_axis(x):\n    return tf.squeeze(x, axis=-1)\n\ndef expand_last_axis(x):\n    return tf.expand_dims(x, axis=-1)\n\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\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\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","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:48:31.321369Z","iopub.execute_input":"2025-06-08T04:48:31.32158Z","iopub.status.idle":"2025-06-08T04:48:31.328867Z","shell.execute_reply.started":"2025-06-08T04:48:31.321548Z","shell.execute_reply":"2025-06-08T04:48:31.328155Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Normalizes and cleans the time series sequence. \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# MixUp the data argumentation in order to regularize the neural network. \n\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):\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):\n        np.random.shuffle(self.indices)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:48:39.19131Z","iopub.execute_input":"2025-06-08T04:48:39.192013Z","iopub.status.idle":"2025-06-08T04:48:39.198328Z","shell.execute_reply.started":"2025-06-08T04:48:39.191985Z","shell.execute_reply":"2025-06-08T04:48:39.197536Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n\ndef build_two_branch_model(pad_len, imu_dim, tof_dim, n_classes, wd=1e-4):\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    # IMU deep branch\n    x1 = residual_se_cnn_block(imu, 64, 3, drop=0.3, wd=wd)\n    x1 = residual_se_cnn_block(x1, 128, 5, drop=0.3, wd=wd)\n\n    # TOF/Thermal lighter branch\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(0.3)(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(0.3)(x2)\n\n    merged = Concatenate()([x1, x2])\n\n    x = Bidirectional(LSTM(128, return_sequences=True, kernel_regularizer=l2(wd)))(merged)\n    x = Dropout(0.4)(x)\n    x = attention_layer(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); 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)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:48:48.841961Z","iopub.execute_input":"2025-06-08T04:48:48.842399Z","iopub.status.idle":"2025-06-08T04:48:48.84959Z","shell.execute_reply.started":"2025-06-08T04:48:48.842376Z","shell.execute_reply":"2025-06-08T04:48:48.848964Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"if TRAIN:\n    print(\"▶ TRAIN MODE – loading dataset …\")\n    df = pd.read_csv(RAW_DIR / \"train.csv\")\n\n    # label encoding\n    le = LabelEncoder(); df['gesture_int'] = le.fit_transform(df['gesture'])\n    np.save(EXPORT_DIR / \"gesture_classes.npy\", le.classes_)\n\n    # feature list\n    meta_cols = {'gesture', 'gesture_int', 'sequence_type', 'behavior', 'orientation',\n                 'row_id', 'subject', 'phase', 'sequence_id', 'sequence_counter'}\n    feature_cols = [c for c in df.columns if c not in meta_cols]\n\n    imu_cols  = [c for c in feature_cols if not (c.startswith('thm_') or c.startswith('tof_'))]\n    tof_cols  = [c for c in feature_cols if c.startswith('thm_') or c.startswith('tof_')]\n    print(f\"  IMU {len(imu_cols)} | TOF/THM {len(tof_cols)} | total {len(feature_cols)} features\")\n\n    # global scaler\n    scaler = StandardScaler().fit(df[feature_cols].ffill().bfill().fillna(0).values)\n    joblib.dump(scaler, EXPORT_DIR / \"scaler.pkl\")\n\n    # build sequences\n    seq_gp = df.groupby('sequence_id')\n    X_list, y_list, lens = [], [], []\n    for seq_id, seq in seq_gp:\n        mat = preprocess_sequence(seq, feature_cols, scaler)\n        X_list.append(mat)\n        y_list.append(seq['gesture_int'].iloc[0])\n        lens.append(len(mat))\n    pad_len = int(np.percentile(lens, PAD_PERCENTILE))\n    np.save(EXPORT_DIR / \"sequence_maxlen.npy\", pad_len)\n    np.save(EXPORT_DIR / \"feature_cols.npy\", np.array(feature_cols))\n\n    X = pad_sequences(X_list, maxlen=pad_len, padding='post', truncating='post', dtype='float32')\n    y = to_categorical(y_list, num_classes=len(le.classes_))\n\n    # split\n    X_tr, X_val, y_tr, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y_list)\n\n    # class weights\n    cw_vals = compute_class_weight('balanced', classes=np.arange(len(le.classes_)), y=y_list)\n    class_weight = dict(enumerate(cw_vals))\n\n    # model\n    model = build_two_branch_model(pad_len, len(imu_cols), len(tof_cols), len(le.classes_), wd=WD)\n    steps = len(X_tr)//BATCH_SIZE\n    lr_sched = tf.keras.optimizers.schedules.CosineDecayRestarts(LR_INIT, first_decay_steps=5*steps)\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)\n    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\n    # quick metric\n    from cmi_2025_metric_copy_for_import import CompetitionMetric\n    preds = model.predict(X_val).argmax(1)\n    true  = y_val.argmax(1)\n    h_f1 = CompetitionMetric().calculate_hierarchical_f1(\n        pd.DataFrame({'gesture': le.classes_[true]}),\n        pd.DataFrame({'gesture': le.classes_[preds]}))\n    print(\"Hold‑out H‑F1 =\", round(h_f1, 4))\n\nelse:\n    print(\"▶ INFERENCE MODE – loading artefacts from\", PRETRAINED_DIR)\n    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    imu_cols = [c for c in feature_cols if not (c.startswith('thm_') or c.startswith('tof_'))]\n    tof_cols = [c for c in feature_cols if c.startswith('thm_') or c.startswith('tof_')]\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, pads loaded – ready for evaluation\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T04:49:00.001632Z","iopub.execute_input":"2025-06-08T04:49:00.001977Z","iopub.status.idle":"2025-06-08T04:56:49.351986Z","shell.execute_reply.started":"2025-06-08T04:49:00.001955Z","shell.execute_reply":"2025-06-08T04:56:49.350839Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# make sure gesture_classes exists in both modes\nif TRAIN:\n    gesture_classes = le.classes_\n\n\ndef predict(sequence: pl.DataFrame, demographics: pl.DataFrame) -> str:\n    global gesture_classes\n    if gesture_classes is None:\n        gesture_classes = np.load(PRETRAINED_DIR / \"gesture_classes.npy\", allow_pickle=True)\n\n    df_seq = sequence.to_pandas()\n    mat = preprocess_sequence(df_seq, feature_cols, scaler)\n    pad = pad_sequences([mat], maxlen=pad_len, padding='post', truncating='post', dtype='float32')\n    idx = int(model.predict(pad, verbose=0).argmax(1)[0])\n    return str(gesture_classes[idx])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-08T05:10:49.323629Z","iopub.execute_input":"2025-06-08T05:10:49.324348Z","iopub.status.idle":"2025-06-08T05:10:49.33262Z","shell.execute_reply.started":"2025-06-08T05:10:49.324324Z","shell.execute_reply":"2025-06-08T05:10:49.331957Z"}},"outputs":[],"execution_count":null},{"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,"execution":{"iopub.status.busy":"2025-06-08T05:11:08.1979Z","iopub.execute_input":"2025-06-08T05:11:08.198384Z","iopub.status.idle":"2025-06-08T05:11:09.280009Z","shell.execute_reply.started":"2025-06-08T05:11:08.198361Z","shell.execute_reply":"2025-06-08T05:11:09.279282Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}