LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8dfd1da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from logging import getLogger\n",
    "import math\n",
    "import traceback\n",
    "from typing import List, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.linalg import norm\n",
    "from scipy import stats\n",
    "from scipy.fftpack import fft\n",
    "from scipy.signal import butter, filtfilt\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "from statsmodels.regression.linear_model import burg\n",
    "\n",
    "logger = getLogger(__name__)\n",
    "\n",
    "\n",
    "class Preprocess:\n",
    "    def __init__(self, fs: int = 50) -> None:\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            fs (int, default=50): Sampling frequency of sensor signals\n",
    "        \"\"\"\n",
    "        self.fs = fs\n",
    "\n",
    "    def apply_filter(\n",
    "        self, signal: pd.DataFrame, filter: str = \"median\", window: int = 5\n",
    "    ) -> pd.DataFrame:\n",
    "        \"\"\"A denosing filter is applied to remove noise in signals.\n",
    "        Args:\n",
    "            signal (pd.DataFrame): Raw signal\n",
    "            filter (str, default='median'): Filter name is chosen from 'mean', 'median', or 'butterworth'\n",
    "            window (int, default=5): Length of filter\n",
    "        Returns:\n",
    "            signal (pd.DataFrame): Filtered signal\n",
    "        See Also:\n",
    "            'butterworth' applies a 3rd order low-pass Butterworth filter with a corner frequency of 20 Hz.\n",
    "        \"\"\"\n",
    "        if filter == \"mean\":\n",
    "            signal = signal.rolling(window=window, center=True, min_periods=1).mean()\n",
    "        elif filter == \"median\":\n",
    "            signal = signal.rolling(window=window, center=True, min_periods=1).median()\n",
    "        elif filter == \"butterworth\":\n",
    "            fc = 20  # cutoff frequency\n",
    "            w = fc / (self.fs / 2)  # Normalize the frequency\n",
    "            b, a = butter(3, w, \"low\")  # 3rd order low-pass Butterworth filter\n",
    "            signal = pd.DataFrame(filtfilt(b, a, signal, axis=0), columns=signal.columns)\n",
    "        else:\n",
    "            try:\n",
    "                raise ValueError(\"Not defined filter. See Args.\")\n",
    "            except ValueError:\n",
    "                logger.error(traceback.format_exc())\n",
    "\n",
    "        return signal\n",
    "\n",
    "    def normalize(self, signal: pd.DataFrame) -> pd.DataFrame:\n",
    "        \"\"\"Apply normalization\n",
    "        Args:\n",
    "            signal (pd.DataFrame): Raw signal\n",
    "        Returns:\n",
    "            signal (pd.DataFrame): Normalized signal\n",
    "        \"\"\"\n",
    "        df_mean = signal.mean()\n",
    "        df_std = signal.std()\n",
    "        signal = (signal - df_mean) / df_std\n",
    "        return signal\n",
    "\n",
    "    def segment_signal(\n",
    "        self,\n",
    "        signal: pd.DataFrame,\n",
    "        window_size: int = 128,\n",
    "        overlap_rate: int = 0.5,\n",
    "        res_type: str = \"dataframe\",\n",
    "    ) -> List[pd.DataFrame]:\n",
    "        \"\"\"Sample sensor signals in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window).\n",
    "        Args:\n",
    "            signal (pandas.DataFrame): Raw signal\n",
    "            window_size (int, default=128): Window size of sliding window to segment raw signals.\n",
    "            overlap_rate (float, default=0.5): Overlap rate of sliding window to segment raw signals.\n",
    "            res_type (str, default='dataframe'): Type of return value; 'array' or 'dataframe'\n",
    "        Returns:\n",
    "            signal_seg (list of pandas.DataFrame): List of segmented sigmal.\n",
    "        \"\"\"\n",
    "        signal_seg = []\n",
    "\n",
    "        for start_idx in range(0, len(signal) - window_size, int(window_size * overlap_rate)):\n",
    "            seg = signal.iloc[start_idx : start_idx + window_size].reset_index(drop=True)\n",
    "            if res_type == \"array\":\n",
    "                seg = seg.values\n",
    "            signal_seg.append(seg)\n",
    "\n",
    "        if res_type == \"array\":\n",
    "            signal_seg = np.array(signal_seg)\n",
    "\n",
    "        return signal_seg\n",
    "\n",
    "    def separate_gravity(self, acc: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:\n",
    "        \"\"\"\n",
    "        Separate acceleration signal into body and gravity acceleration signal.\n",
    "        Another low pass Butterworth filter with a corner frequency of 0.3 Hz is applied.\n",
    "        Args:\n",
    "            acc (pd.DataFrame): Segmented acceleration signal\n",
    "        Returns:\n",
    "            acc_body (pd.DataFrame): Body acceleration signal\n",
    "            acc_grav (pd.DataFrame): Gravity acceleration signal\n",
    "        \"\"\"\n",
    "        fc = 0.3  # cutoff frequency\n",
    "        w = fc / (self.fs / 2)  # Normalize the frequency\n",
    "        b, a = butter(3, w, \"low\")  # 3rd order low pass Butterworth filter\n",
    "        acc_grav = pd.DataFrame(\n",
    "            filtfilt(b, a, acc, axis=0), columns=acc.columns\n",
    "        )  # Apply Butterworth filter\n",
    "\n",
    "        # Substract gravity acceleration from acceleration sigal.\n",
    "        acc_body = acc - acc_grav\n",
    "        return acc_body, acc_grav\n",
    "\n",
    "    def obtain_jerk_signal(self, signal: pd.DataFrame) -> pd.DataFrame:\n",
    "        \"\"\"Derive signal to obtain Jerk signals\n",
    "        Args:\n",
    "            signal (pd.DataFrame)\n",
    "        Returns:\n",
    "            jerk_signal (pd.DataFrame):\n",
    "        \"\"\"\n",
    "        jerk_signal = signal.diff(periods=1)  # Calculate difference\n",
    "        jerk_signal.iloc[0] = jerk_signal.iloc[1]  # Fillna\n",
    "        jerk_signal = jerk_signal / (1 / self.fs)  # Derive in time (1 / sampling frequency)\n",
    "        return jerk_signal\n",
    "\n",
    "    def obtain_magnitude(self, signal):\n",
    "        \"\"\"Calculate the magnitude of these three-dimensional signals using the Euclidean norm\n",
    "        Args:\n",
    "            signal (pandas.DataFrame): Three-dimensional signals\n",
    "        Returns:\n",
    "            res (pandas.DataFrame): Magnitude of three-dimensional signals\n",
    "        \"\"\"\n",
    "        return pd.DataFrame(norm(signal, ord=2, axis=1))\n",
    "\n",
    "    def obtain_spectrum(self, signal):\n",
    "        \"\"\"Obtain spectrum using Fast Fourier Transform (FFT).\n",
    "        Args:\n",
    "            signal (pandas.DataFrame): Time domain signals\n",
    "        Returns:\n",
    "            amp (pandas.DataFrame): Amplitude spectrum\n",
    "            phase (pandas.DataFrame): Phase spectrum\n",
    "        \"\"\"\n",
    "        N = len(signal)\n",
    "        columns = signal.columns\n",
    "\n",
    "        for col in columns:\n",
    "            signal[col] = signal[col] * np.hamming(N)  # hamming window\n",
    "\n",
    "        F = fft(signal, axis=0)  # Apply FFT\n",
    "        F = F[: N // 2, :]  # Remove the overlapping part\n",
    "\n",
    "        amp = np.abs(F)  # Obtain the amplitude spectrum\n",
    "        amp = amp / N * 2\n",
    "        amp[0] = amp[0] / 2\n",
    "        amp = pd.DataFrame(amp, columns=columns)  # Convert array to DataFrame\n",
    "        phase = np.angle(F)\n",
    "        phase = pd.DataFrame(phase, columns=columns)  # Convert array to DataFrame\n",
    "\n",
    "        return amp, phase\n",
    "\n",
    "    def obtain_ecdf_percentile(self, signal, n_bins=10):\n",
    "        \"\"\"Obtain ECDF (empirical cumulative distribution function) percentile values.\n",
    "        Args:\n",
    "            signal (DataFrame): Time domain signals\n",
    "            n_bins (int, default: 10): How many percentiles to use as a feature\n",
    "        Returns:\n",
    "            features (array): ECDF percentile values.\n",
    "        \"\"\"\n",
    "        idx = np.linspace(0, signal.shape[0] - 1, n_bins)  # Take n_bins linspace percentile.\n",
    "        idx = [int(Decimal(str(ix)).quantize(Decimal(\"0\"), rounding=ROUND_HALF_UP)) for ix in idx]\n",
    "        features = np.array([])\n",
    "        for col in signal.columns:\n",
    "            ecdf = ECDF(signal[col].values)  # fit\n",
    "            x = ecdf.x[1:]  # Remove -inf\n",
    "            feat = x[idx]\n",
    "            features = np.hstack([features, feat])\n",
    "\n",
    "        return features\n",
    "\n",
    "    def obtain_mean(self, signal) -> np.ndarray:\n",
    "        return signal.mean().values\n",
    "\n",
    "    def obtain_std(self, signal) -> np.ndarray:\n",
    "        return signal.std().values\n",
    "\n",
    "    def obtain_mad(self, signal) -> np.ndarray:\n",
    "        return stats.median_abs_deviation(signal, scale=1/1.4826)\n",
    "        #return stats.median_absolute_deviation(signal, axis=0)\n",
    "\n",
    "    def obtain_max(self, signal) -> np.ndarray:\n",
    "        return signal.max().values\n",
    "\n",
    "    def obtain_min(self, signal) -> np.ndarray:\n",
    "        return signal.min().values\n",
    "\n",
    "    def obtain_sma(self, signal, window_size=128) -> np.ndarray:\n",
    "        window_second = window_size / self.fs\n",
    "        return sum(signal.sum().values - self.obtain_min(signal) * len(signal)) / window_second\n",
    "\n",
    "    def obtain_energy(self, signal) -> np.ndarray:\n",
    "        return norm(signal, ord=2, axis=0) ** 2 / len(signal)\n",
    "\n",
    "    def obtain_iqr(self, signal) -> np.ndarray:\n",
    "        return signal.quantile(0.75).values - signal.quantile(0.25).values\n",
    "\n",
    "    def obtain_entropy(self, signal) -> np.ndarray:\n",
    "        signal = signal - signal.min()\n",
    "        return stats.entropy(signal)\n",
    "\n",
    "    def obtain_arCoeff(self, signal) -> np.ndarray:\n",
    "        arCoeff = np.array([])\n",
    "        for col in signal.columns:\n",
    "            val, _ = burg(signal[col], order=4)\n",
    "            arCoeff = np.hstack((arCoeff, val))\n",
    "        return arCoeff\n",
    "\n",
    "    def obtain_correlation(self, signal) -> np.ndarray:\n",
    "        if signal.shape[1] == 1:  # Signal dimension is 1\n",
    "            correlation = np.array([])\n",
    "        else:  # Signal dimension is 3\n",
    "            xy = np.corrcoef(signal[\"x\"], signal[\"y\"])[0][1]\n",
    "            yz = np.corrcoef(signal[\"y\"], signal[\"z\"])[0][1]\n",
    "            zx = np.corrcoef(signal[\"z\"], signal[\"x\"])[0][1]\n",
    "            correlation = np.hstack((xy, yz, zx))\n",
    "        return correlation\n",
    "\n",
    "    def obtain_maxInds(self, signal) -> np.ndarray:\n",
    "        return signal.idxmax().values\n",
    "\n",
    "    def obtain_meanFreq(self, signal) -> np.ndarray:\n",
    "        meanFreq = np.array([])\n",
    "        for col in signal.columns:\n",
    "            val = np.mean(signal[col] * np.arange(len(signal)))\n",
    "            meanFreq = np.hstack((meanFreq, val))\n",
    "        return meanFreq\n",
    "\n",
    "    def obtain_skewness(self, signal) -> np.ndarray:\n",
    "        return signal.skew().values\n",
    "\n",
    "    def obtain_kurtosis(self, signal) -> np.ndarray:\n",
    "        return signal.kurt().values\n",
    "\n",
    "    def obtain_bandsEnergy(self, signal) -> np.ndarray:\n",
    "        bandsEnergy = np.array([])\n",
    "        bins = [0, 4, 8, 12, 16, 20, 24, 29, 34, 39, 44, 49, 54, 59, 64]\n",
    "        for i in range(len(bins) - 1):\n",
    "            df = signal.iloc[bins[i] : bins[i + 1]]\n",
    "            arr = self.obtain_energy(df)\n",
    "            bandsEnergy = np.hstack((bandsEnergy, arr))\n",
    "        return bandsEnergy\n",
    "\n",
    "    def obtain_angle(self, v1, v2) -> np.ndarray:\n",
    "        length = lambda v: math.sqrt(np.dot(v, v))\n",
    "        return math.acos(np.dot(v1, v2) / (length(v1) * length(v2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e76df288",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def create_features(acc_raw: pd.DataFrame, gyro_raw: pd.DataFrame) -> np.ndarray:\n",
    "    \"\"\"Create features from raw acceleration and gyroscope sensor data\n",
    "    Args:\n",
    "        acc_raw (pd.DataFrame): Raw 3-axial accelerometer signals with columns denoting axes.\n",
    "        gyro_raw (pd.DataFrame): Raw 3-axial gyroscope signals with columns denoting axes.\n",
    "    Returns:\n",
    "        features (np.ndarray): Created features corresponding args with columns denoting feature names.\n",
    "    \"\"\"\n",
    "    of = Preprocess(fs=50)  # Create an instance.\n",
    "\n",
    "    # Remove noises by median filter & Butterworth filter\n",
    "    acc_raw = of.apply_filter(signal=acc_raw, filter=\"median\", window=5)\n",
    "    acc_raw = of.apply_filter(signal=acc_raw, filter=\"butterworth\")\n",
    "    gyro_raw = of.apply_filter(signal=gyro_raw, filter=\"median\", window=5)\n",
    "    gyro_raw = of.apply_filter(signal=gyro_raw, filter=\"butterworth\")\n",
    "\n",
    "    # Sample signals in fixed-width sliding windows\n",
    "    tAccXYZ = of.segment_signal(acc_raw, window_size=128, overlap_rate=0.5, res_type=\"dataframe\")\n",
    "    tBodyGyroXYZ = of.segment_signal(\n",
    "        gyro_raw, window_size=128, overlap_rate=0.5, res_type=\"dataframe\"\n",
    "    )\n",
    "\n",
    "    # Separate acceleration signal into body and gravity acceleration signal\n",
    "    tBodyAccXYZ, tGravityAccXYZ = [], []\n",
    "    for acc in tAccXYZ:\n",
    "        body_acc, grav_acc = of.separate_gravity(acc.copy())\n",
    "        tBodyAccXYZ.append(body_acc)\n",
    "        tGravityAccXYZ.append(grav_acc)\n",
    "\n",
    "    # Obtain Jerk signals of body linear acceleration and angular velocity\n",
    "    tBodyAccJerkXYZ, tBodyGyroJerkXYZ = [], []\n",
    "    for body_acc, gyro in zip(tBodyAccXYZ, tBodyGyroXYZ):\n",
    "        body_acc_jerk = of.obtain_jerk_signal(body_acc.copy())\n",
    "        gyro_jerk = of.obtain_jerk_signal(gyro.copy())\n",
    "\n",
    "        tBodyAccJerkXYZ.append(body_acc_jerk)\n",
    "        tBodyGyroJerkXYZ.append(gyro_jerk)\n",
    "\n",
    "    # Calculate the magnitude of three-dimensional signals using the Euclidean norm\n",
    "    tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag = (\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "        [],\n",
    "    )\n",
    "    for body_acc, grav_acc, body_acc_jerk, gyro, gyro_jerk in zip(\n",
    "        tBodyAccXYZ, tGravityAccXYZ, tBodyAccJerkXYZ, tBodyGyroXYZ, tBodyGyroJerkXYZ\n",
    "    ):\n",
    "        body_acc_mag = of.obtain_magnitude(body_acc.copy())\n",
    "        grav_acc_mag = of.obtain_magnitude(grav_acc.copy())\n",
    "        body_acc_jerk_mag = of.obtain_magnitude(body_acc_jerk.copy())\n",
    "        gyro_mag = of.obtain_magnitude(gyro.copy())\n",
    "        gyro_jerk_mag = of.obtain_magnitude(gyro_jerk.copy())\n",
    "\n",
    "        tBodyAccMag.append(body_acc_mag)\n",
    "        tGravityAccMag.append(grav_acc_mag)\n",
    "        tBodyAccJerkMag.append(body_acc_jerk_mag)\n",
    "        tBodyGyroMag.append(gyro_mag)\n",
    "        tBodyGyroJerkMag.append(gyro_jerk_mag)\n",
    "\n",
    "    # Obtain amplitude spectrum using Fast Fourier Transform (FFT).\n",
    "    (\n",
    "        fBodyAccXYZAmp,\n",
    "        fBodyAccJerkXYZAmp,\n",
    "        fBodyGyroXYZAmp,\n",
    "        fBodyAccMagAmp,\n",
    "        fBodyAccJerkMagAmp,\n",
    "        fBodyGyroMagAmp,\n",
    "        fBodyGyroJerkMagAmp,\n",
    "    ) = ([], [], [], [], [], [], [])\n",
    "    (\n",
    "        fBodyAccXYZPhs,\n",
    "        fBodyAccJerkXYZPhs,\n",
    "        fBodyGyroXYZPhs,\n",
    "        fBodyAccMagPhs,\n",
    "        fBodyAccJerkMagPhs,\n",
    "        fBodyGyroMagPhs,\n",
    "        fBodyGyroJerkMagPhs,\n",
    "    ) = ([], [], [], [], [], [], [])\n",
    "    for (\n",
    "        body_acc,\n",
    "        body_acc_jerk,\n",
    "        gyro,\n",
    "        body_acc_mag,\n",
    "        body_acc_jerk_mag,\n",
    "        gyro_mag,\n",
    "        gyro_jerk_mag,\n",
    "    ) in zip(\n",
    "        tBodyAccXYZ,\n",
    "        tBodyAccJerkXYZ,\n",
    "        tBodyGyroXYZ,\n",
    "        tBodyAccMag,\n",
    "        tBodyAccJerkMag,\n",
    "        tBodyGyroMag,\n",
    "        tBodyGyroJerkMag,\n",
    "    ):\n",
    "        body_acc_amp, body_acc_phase = of.obtain_spectrum(body_acc.copy())\n",
    "        body_acc_jerk_amp, body_acc_jerk_phase = of.obtain_spectrum(body_acc_jerk.copy())\n",
    "        gyro_amp, gyro_phase = of.obtain_spectrum(gyro.copy())\n",
    "        body_acc_mag_amp, body_acc_mag_phase = of.obtain_spectrum(body_acc_mag.copy())\n",
    "        body_acc_jerk_mag_amp, body_acc_jerk_mag_phase = of.obtain_spectrum(\n",
    "            body_acc_jerk_mag.copy()\n",
    "        )\n",
    "        gyro_mag_amp, gyro_mag_phase = of.obtain_spectrum(gyro_mag.copy())\n",
    "        gyro_jerk_mag_amp, gyro_jerk_mag_phase = of.obtain_spectrum(gyro_jerk_mag.copy())\n",
    "\n",
    "        fBodyAccXYZAmp.append(body_acc_amp)\n",
    "        fBodyAccJerkXYZAmp.append(body_acc_jerk_amp)\n",
    "        fBodyGyroXYZAmp.append(gyro_amp)\n",
    "        fBodyAccMagAmp.append(body_acc_mag_amp)\n",
    "        fBodyAccJerkMagAmp.append(body_acc_jerk_mag_amp)\n",
    "        fBodyGyroMagAmp.append(gyro_mag_amp)\n",
    "        fBodyGyroJerkMagAmp.append(gyro_jerk_mag_amp)\n",
    "\n",
    "        fBodyAccXYZPhs.append(body_acc_phase)\n",
    "        fBodyAccJerkXYZPhs.append(body_acc_jerk_phase)\n",
    "        fBodyGyroXYZPhs.append(gyro_phase)\n",
    "        fBodyAccMagPhs.append(body_acc_mag_phase)\n",
    "        fBodyAccJerkMagPhs.append(body_acc_jerk_mag_phase)\n",
    "        fBodyGyroMagPhs.append(gyro_mag_phase)\n",
    "        fBodyGyroJerkMagPhs.append(gyro_jerk_mag_phase)\n",
    "\n",
    "    #  Following signals are obtained by implementing above functions.\n",
    "    time_signals = [\n",
    "        tBodyAccXYZ,\n",
    "        tGravityAccXYZ,\n",
    "        tBodyAccJerkXYZ,\n",
    "        tBodyGyroXYZ,\n",
    "        tBodyGyroJerkXYZ,\n",
    "        tBodyAccMag,\n",
    "        tGravityAccMag,\n",
    "        tBodyAccJerkMag,\n",
    "        tBodyGyroMag,\n",
    "        tBodyGyroJerkMag,\n",
    "    ]\n",
    "    freq_signals = [\n",
    "        fBodyAccXYZAmp,\n",
    "        fBodyAccJerkXYZAmp,\n",
    "        fBodyGyroXYZAmp,\n",
    "        fBodyAccMagAmp,\n",
    "        fBodyAccJerkMagAmp,\n",
    "        fBodyGyroMagAmp,\n",
    "        fBodyGyroJerkMagAmp,\n",
    "        fBodyAccXYZPhs,\n",
    "        fBodyAccJerkXYZPhs,\n",
    "        fBodyGyroXYZPhs,\n",
    "        fBodyAccMagPhs,\n",
    "        fBodyAccJerkMagPhs,\n",
    "        fBodyGyroMagPhs,\n",
    "        fBodyGyroJerkMagPhs,\n",
    "    ]\n",
    "\n",
    "    all_signals = time_signals + freq_signals\n",
    "\n",
    "    # Calculate feature vectors by using signals\n",
    "    features = []\n",
    "\n",
    "    for i in range(len(tBodyAccXYZ)):\n",
    "        feature_vector = np.array([])\n",
    "\n",
    "        # mean, std, mad, max, min, sma, energy, iqr, entropy\n",
    "        for t_signal in all_signals:\n",
    "            sig = t_signal[i].copy()\n",
    "            mean = of.obtain_mean(sig)\n",
    "            std = of.obtain_std(sig)\n",
    "            mad = of.obtain_mad(sig)\n",
    "            max_val = of.obtain_max(sig)\n",
    "            min_val = of.obtain_min(sig)\n",
    "            sma = of.obtain_sma(sig)\n",
    "            energy = of.obtain_energy(sig)\n",
    "            iqr = of.obtain_iqr(sig)\n",
    "            entropy = of.obtain_entropy(sig)\n",
    "            feature_vector = np.hstack(\n",
    "                (feature_vector, mean, std, mad, max_val, min_val, sma, energy, iqr, entropy)\n",
    "            )\n",
    "\n",
    "        # arCoeff\n",
    "        for t_signal in time_signals:\n",
    "            sig = t_signal[i].copy()\n",
    "            arCoeff = of.obtain_arCoeff(sig)\n",
    "            feature_vector = np.hstack((feature_vector, arCoeff))\n",
    "\n",
    "        # correlation\n",
    "        for t_signal in [\n",
    "            tBodyAccXYZ,\n",
    "            tGravityAccXYZ,\n",
    "            tBodyAccJerkXYZ,\n",
    "            tBodyGyroXYZ,\n",
    "            tBodyGyroJerkXYZ,\n",
    "        ]:\n",
    "            sig = t_signal[i].copy()\n",
    "            correlation = of.obtain_correlation(sig)\n",
    "            feature_vector = np.hstack((feature_vector, correlation))\n",
    "\n",
    "        # maxInds, meanFreq, skewness, kurtosis\n",
    "        for t_signal in freq_signals:\n",
    "            sig = t_signal[i].copy()\n",
    "            maxInds = of.obtain_maxInds(sig)\n",
    "            meanFreq = of.obtain_meanFreq(sig)\n",
    "            skewness = of.obtain_skewness(sig)\n",
    "            kurtosis = of.obtain_kurtosis(sig)\n",
    "            feature_vector = np.hstack((feature_vector, maxInds, meanFreq, skewness, kurtosis))\n",
    "\n",
    "        # bandsEnergy\n",
    "        for t_signal in [tBodyAccXYZ, tBodyAccJerkXYZ, tBodyGyroXYZ]:\n",
    "            sig = t_signal[i].copy()\n",
    "            bandsEnergy = of.obtain_bandsEnergy(sig)\n",
    "            feature_vector = np.hstack((feature_vector, bandsEnergy))\n",
    "\n",
    "        # angle\n",
    "        gravityMean = tGravityAccXYZ[i].mean()\n",
    "        tBodyAccMean = tBodyAccXYZ[i].mean()\n",
    "        tBodyAccJerkMean = tBodyAccJerkXYZ[i].mean()\n",
    "        tBodyGyroMean = tBodyGyroXYZ[i].mean()\n",
    "        tBodyGyroJerkMean = tBodyGyroJerkXYZ[i].mean()\n",
    "        tXAxisAcc = tAccXYZ[i][\"x\"]\n",
    "        tXAxisGravity = tGravityAccXYZ[i][\"x\"]\n",
    "        tYAxisAcc = tAccXYZ[i][\"y\"]\n",
    "        tYAxisGravity = tGravityAccXYZ[i][\"y\"]\n",
    "        tZAxisAcc = tAccXYZ[i][\"z\"]\n",
    "        tZAxisGravity = tGravityAccXYZ[i][\"z\"]\n",
    "\n",
    "        tBodyAccWRTGravity = of.obtain_angle(tBodyAccMean, gravityMean)\n",
    "        tBodyAccJerkWRTGravity = of.obtain_angle(tBodyAccJerkMean, gravityMean)\n",
    "        tBodyGyroWRTGravity = of.obtain_angle(tBodyGyroMean, gravityMean)\n",
    "        tBodyGyroJerkWRTGravity = of.obtain_angle(tBodyGyroJerkMean, gravityMean)\n",
    "        tXAxisAccWRTGravity = of.obtain_angle(tXAxisAcc, tXAxisGravity)\n",
    "        tYAxisAccWRTGravity = of.obtain_angle(tYAxisAcc, tYAxisGravity)\n",
    "        tZAxisAccWRTGravity = of.obtain_angle(tZAxisAcc, tZAxisGravity)\n",
    "\n",
    "        feature_vector = np.hstack(\n",
    "            (\n",
    "                feature_vector,\n",
    "                tBodyAccWRTGravity,\n",
    "                tBodyAccJerkWRTGravity,\n",
    "                tBodyGyroWRTGravity,\n",
    "                tBodyGyroJerkWRTGravity,\n",
    "                tXAxisAccWRTGravity,\n",
    "                tYAxisAccWRTGravity,\n",
    "                tZAxisAccWRTGravity,\n",
    "            )\n",
    "        )\n",
    "\n",
    "        # ECDF\n",
    "        for t_signal in [tBodyAccXYZ, tBodyGyroXYZ]:\n",
    "            sig = t_signal[i].copy()\n",
    "            ecdf = of.obtain_ecdf_percentile(sig)\n",
    "            feature_vector = np.hstack((feature_vector, ecdf))\n",
    "\n",
    "        features.append(feature_vector)\n",
    "\n",
    "    return np.array(features)\n",
    "\n",
    "\n",
    "def get_feature_names() -> List[str]:\n",
    "    \"\"\"Get feature names\n",
    "    Returns:\n",
    "        feature_names (List[str]): Title of features\n",
    "    \"\"\"\n",
    "    time_signal_names = [\n",
    "        \"tBodyAccXYZ\",\n",
    "        \"tGravityAccXYZ\",\n",
    "        \"tBodyAccJerkXYZ\",\n",
    "        \"tBodyGyroXYZ\",\n",
    "        \"tBodyGyroJerkXYZ\",\n",
    "        \"tBodyAccMag\",\n",
    "        \"tGravityAccMag\",\n",
    "        \"tBodyAccJerkMag\",\n",
    "        \"tBodyGyroMag\",\n",
    "        \"tBodyGyroJerkMag\",\n",
    "    ]\n",
    "    freq_signal_names = [\n",
    "        \"fBodyAccXYZAmp\",\n",
    "        \"fBodyAccJerkXYZAmp\",\n",
    "        \"fBodyGyroXYZAmp\",\n",
    "        \"fBodyAccMagAmp\",\n",
    "        \"fBodyAccJerkMagAmp\",\n",
    "        \"fBodyGyroMagAmp\",\n",
    "        \"fBodyGyroJerkMagAmp\",\n",
    "        \"fBodyAccXYZPhs\",\n",
    "        \"fBodyAccJerkXYZPhs\",\n",
    "        \"fBodyGyroXYZPhs\",\n",
    "        \"fBodyAccMagPhs\",\n",
    "        \"fBodyAccJerkMagPhs\",\n",
    "        \"fBodyGyroMagPhs\",\n",
    "        \"fBodyGyroJerkMagPhs\",\n",
    "    ]\n",
    "    all_signal_names = time_signal_names + freq_signal_names\n",
    "    feature_names = []\n",
    "\n",
    "    for name in all_signal_names:\n",
    "        for s in [\"Mean\", \"Std\", \"Mad\", \"Max\", \"Min\", \"Sma\", \"Energy\", \"Iqr\", \"Entropy\"]:\n",
    "            if s == \"Sma\":\n",
    "                feature_names.append(f\"{name}{s}\")\n",
    "                continue\n",
    "            if \"XYZ\" in name:\n",
    "                n = name.replace(\"XYZ\", \"\")\n",
    "                feature_names += [f\"{n}{s}-{ax}\" for ax in [\"X\", \"Y\", \"Z\"]]\n",
    "            else:\n",
    "                feature_names.append(f\"{name}{s}\")\n",
    "\n",
    "    for name in time_signal_names:\n",
    "        if \"XYZ\" in name:\n",
    "            n = name.replace(\"XYZ\", \"\")\n",
    "            feature_names += [f\"{n}ArCoeff-{ax}{i}\" for ax in [\"X\", \"Y\", \"Z\"] for i in range(4)]\n",
    "        else:\n",
    "            feature_names += [f\"{name}ArCoeff{i}\" for i in range(4)]\n",
    "\n",
    "    for name in [\n",
    "        \"tBodyAccXYZ\",\n",
    "        \"tGravityAccXYZ\",\n",
    "        \"tBodyAccJerkXYZ\",\n",
    "        \"tBodyGyroXYZ\",\n",
    "        \"tBodyGyroJerkXYZ\",\n",
    "    ]:\n",
    "        n = name.replace(\"XYZ\", \"\")\n",
    "        feature_names += [f\"{n}Correlation-{ax}\" for ax in [\"X\", \"Y\", \"Z\"]]\n",
    "\n",
    "    for name in freq_signal_names:\n",
    "        for s in [\"MaxInds\", \"MeanFreq\", \"Skewness\", \"Kurtosis\"]:\n",
    "            if \"XYZ\" in name:\n",
    "                n = name.replace(\"XYZ\", \"\")\n",
    "                feature_names += [f\"{n}{s}-{ax}\" for ax in [\"X\", \"Y\", \"Z\"]]\n",
    "            else:\n",
    "                feature_names.append(f\"{name}{s}\")\n",
    "\n",
    "    for name in [\"tBodyAccXYZ\", \"tBodyAccJerkXYZ\", \"tBodyGyroXYZ\"]:\n",
    "        n = name.replace(\"XYZ\", \"\")\n",
    "        feature_names += [f\"{n}BandsEnergy-{ax}{i}\" for i in range(14) for ax in [\"X\", \"Y\", \"Z\"]]\n",
    "\n",
    "    feature_names += [\n",
    "        \"tBodyAccWRTGravity\",\n",
    "        \"tBodyAccJerkWRTGravity\",\n",
    "        \"tBodyGyroWRTGravity\",\n",
    "        \"tBodyGyroJerkWRTGravity\",\n",
    "        \"tXAxisAccWRTGravity\",\n",
    "        \"tYAxisAccWRTGravity\",\n",
    "        \"tZAxisAccWRTGravity\",\n",
    "    ]\n",
    "\n",
    "    feature_names += [\n",
    "        f\"tBody{sensor}ECDF-{axis}{i}\"\n",
    "        for sensor in [\"Acc\", \"Gyro\"]\n",
    "        for axis in [\"X\", \"Y\", \"Z\"]\n",
    "        for i in range(10)\n",
    "    ]\n",
    "    return feature_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29f97314",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from typing import Optional, Tuple\n",
    "import glob\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "CUR_DIR = os.getcwd()\n",
    "DATA_DIR = CUR_DIR\n",
    "TRAIN_SUBJECTS = [1, 2, 3, 4, 5, 7]\n",
    "TEST_SUBJECTS = [8, 6]\n",
    "\n",
    "\n",
    "def preprocess_signal(signal: pd.DataFrame) -> pd.DataFrame:\n",
    "    _signal = signal.copy()\n",
    "    of = Preprocess()\n",
    "    _signal = of.apply_filter(_signal, filter=\"median\")\n",
    "    _signal = of.apply_filter(_signal, filter=\"butterworth\")\n",
    "    _signal = of.segment_signal(_signal)\n",
    "    return _signal\n",
    "\n",
    "\n",
    "def scale(\n",
    "    signal: pd.DataFrame, scaler=\"normalize\", minmax_range: Optional[Tuple[int, int]] = (0, 1)\n",
    ") -> pd.DataFrame:\n",
    "    if scaler == \"normalize\":\n",
    "        signal = StandardScaler().fit_transform(signal)\n",
    "        return pd.DataFrame(signal, columns=[\"x\", \"y\", \"z\"])\n",
    "    elif scaler == \"minmax\":\n",
    "        signal = MinMaxScaler(feature_range=minmax_range).fit_transform(signal)\n",
    "        return pd.DataFrame(signal, columns=[\"x\", \"y\", \"z\"])\n",
    "\n",
    "\n",
    "def preprocess_raw_data(scaler):\n",
    "    acc_files = sorted(glob.glob(os.path.join(DATA_DIR, \"acc*.txt\")))\n",
    "    gyro_files = sorted(glob.glob(os.path.join(DATA_DIR, \"gyro*.txt\")))\n",
    "    label_info = pd.read_table(\n",
    "        os.path.join(DATA_DIR, \"labels.txt\"),\n",
    "        sep=\" \",\n",
    "        header=None,\n",
    "        names=[\"ExpID\", \"UserID\", \"ActID\", \"ActStart\", \"ActEnd\"],\n",
    "    )\n",
    "\n",
    "    X_train = np.array([])\n",
    "    X_test = np.array([])\n",
    "\n",
    "    for acc_file, gyro_file in zip(acc_files, gyro_files):\n",
    "        exp_id = int(acc_file.split(\"exp\")[1][:2])\n",
    "        user_id = int(acc_file.split(\"user\")[1][:2])\n",
    "\n",
    "        temp_label_info = label_info[\n",
    "            (label_info.ExpID == exp_id)\n",
    "            & (label_info.UserID == user_id)\n",
    "            & (label_info.ActID.isin([1, 2, 3, 4, 5]))\n",
    "        ]\n",
    "\n",
    "        acc_raw = pd.read_table(acc_file, sep=\" \", header=None, names=[\"x\", \"y\", \"z\"])\n",
    "        gyro_raw = pd.read_table(gyro_file, sep=\" \", header=None, names=[\"x\", \"y\", \"z\"])\n",
    "\n",
    "        acc_raw = scale(acc_raw, scaler=scaler)\n",
    "        gyro_raw = scale(gyro_raw, scaler=scaler)\n",
    "\n",
    "        for _, _, act_id, act_start, act_end in temp_label_info.values:\n",
    "            temp_acc_raw = acc_raw.iloc[act_start : act_end + 1]\n",
    "            temp_gyro_raw = gyro_raw.iloc[act_start : act_end + 1]\n",
    "            tAccXYZ = preprocess_signal(temp_acc_raw)\n",
    "            tBodyGyroXYZ = preprocess_signal(temp_gyro_raw)\n",
    "            features = np.zeros((len(tAccXYZ), 128, 6))\n",
    "\n",
    "            for i in range(len(tAccXYZ)):\n",
    "                feature = pd.DataFrame(\n",
    "                    np.concatenate((tAccXYZ[i], tBodyGyroXYZ[i]), 1),\n",
    "                    columns=[\"AccX\", \"AccY\", \"AccZ\", \"GyroX\", \"GyroY\", \"GyroZ\"],\n",
    "                )\n",
    "                features[i] = feature\n",
    "\n",
    "            if user_id in TRAIN_SUBJECTS:\n",
    "                if len(X_train) == 0:\n",
    "                    X_train = features\n",
    "                else:\n",
    "                    X_train = np.vstack((X_train, features))\n",
    "            else:\n",
    "                if len(X_test) == 0:\n",
    "                    X_test = features\n",
    "                else:\n",
    "                    X_test = np.vstack((X_test, features))\n",
    "\n",
    "    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 6, 1)\n",
    "    X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 6, 1)\n",
    "\n",
    "    return X_train, X_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6dbd84cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "import os\n",
    "from typing import Dict, Tuple\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "CUR_DIR = os.getcwd()\n",
    "DATA_DIR = CUR_DIR\n",
    "\n",
    "\n",
    "def load_features() -> Tuple[\n",
    "    pd.DataFrame,\n",
    "    pd.DataFrame,\n",
    "    pd.DataFrame,\n",
    "    pd.DataFrame,\n",
    "    Dict[int, str],\n",
    "    Dict[str, int],\n",
    "]:\n",
    "    \"\"\"Load created features.\n",
    "    The following six classes are included in this experiment.\n",
    "        - WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING\n",
    "    The following transition classes are excluded.\n",
    "        - LAYING, STAND_TO_SIT, SIT_TO_STAND, SIT_TO_LIE, LIE_TO_SIT, STAND_TO_LIE, and LIE_TO_STAND\n",
    "    Returns:\n",
    "        X_train (pd.DataFrame): Explanatory variable in train data\n",
    "        X_test (pd.DataFrame): Explanatory variable in test data\n",
    "        y_train (pd.DataFrame): Teacher data in train data\n",
    "        y_test (pd.DataFrame): Teacher data in test data\n",
    "        label2act (Dict[int, str]): Dict of label_id to title_of_class\n",
    "        act2label (Dict[str, int]): Dict of title_of_class to label_id\n",
    "    \"\"\"\n",
    "    X_train = pd.read_pickle(os.path.join(DATA_DIR, \"my_dataset/X_train.pickle\"))\n",
    "    y_train = pd.DataFrame(np.load(os.path.join(DATA_DIR, \"my_dataset/y_train.npy\")))\n",
    "    subject_id_train = pd.read_table(\n",
    "        os.path.join(DATA_DIR, \"Train/subject_id_train.txt\"), sep=\" \", header=None\n",
    "    )\n",
    "\n",
    "    X_test = pd.read_pickle(os.path.join(DATA_DIR, \"my_dataset/X_test.pickle\"))\n",
    "    y_test = pd.DataFrame(np.load(os.path.join(DATA_DIR, \"my_dataset/y_test.npy\")))\n",
    "    subject_id_test = pd.read_table(\n",
    "        os.path.join(DATA_DIR, \"Test/subject_id_test.txt\"), sep=\" \", header=None\n",
    "    )\n",
    "\n",
    "    activity_labels = pd.read_table(\n",
    "        os.path.join(DATA_DIR, \"activity_labels.txt\"), header=None\n",
    "    ).values.flatten()\n",
    "    activity_labels = np.array([label.rstrip().split() for label in activity_labels])\n",
    "    label2act, act2label = {}, {}\n",
    "    for label, activity in activity_labels:\n",
    "        label2act[int(label)] = activity\n",
    "        act2label[activity] = int(label)\n",
    "\n",
    "    class_names_inc = [\n",
    "        \"WALKING\",\n",
    "        \"WALKING_UPSTAIRS\",\n",
    "        \"WALKING_DOWNSTAIRS\",\n",
    "        \"SITTING\",\n",
    "        \"STANDING\",\n",
    "    ]\n",
    "    class_ids_inc = [act2label[c] for c in class_names_inc]\n",
    "\n",
    "    idx_train = y_train[y_train[0].isin(class_ids_inc)].index\n",
    "    X_train = X_train.iloc[idx_train].reset_index(drop=True)\n",
    "    y_train = y_train.iloc[idx_train].reset_index(drop=True)\n",
    "    # subject_id_train = subject_id_train.iloc[idx_train].reset_index(drop=True)\n",
    "\n",
    "    idx_test = y_test[y_test[0].isin(class_ids_inc)].index\n",
    "    X_test = X_test.iloc[idx_test].reset_index(drop=True)\n",
    "    y_test = y_test.iloc[idx_test].reset_index(drop=True)\n",
    "    # subject_id_test = subject_id_test.iloc[idx_test].reset_index(drop=True)\n",
    "\n",
    "    # Replace 5 to 0\n",
    "    rep_activity = label2act[5]\n",
    "    label2act[0] = rep_activity\n",
    "    label2act.pop(5)\n",
    "    act2label[rep_activity] = 0\n",
    "\n",
    "    y_train = y_train.replace(5, 0)\n",
    "    y_test = y_test.replace(5, 0)\n",
    "\n",
    "    return X_train, X_test, y_train, y_test, label2act, act2label\n",
    "\n",
    "\n",
    "def load_raw_data(\n",
    "        scaler: str = \"normalize\",\n",
    ") -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, Dict[int, str], Dict[str, int]]:\n",
    "    \"\"\"Load raw dataset.\n",
    "    The following six classes are included in this experiment.\n",
    "        - WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING\n",
    "    The following transition classes are excluded.\n",
    "        - STAND_TO_SIT, SIT_TO_STAND, SIT_TO_LIE, LIE_TO_SIT, STAND_TO_LIE, and LIE_TO_STAND\n",
    "    Args:\n",
    "        scaler (str): scaler for raw signals, chosen from normalize or minmax\n",
    "    Returns:\n",
    "        X_train (pd.DataFrame):\n",
    "        X_test (pd.DataFrame):\n",
    "        y_train (pd.DataFrame):\n",
    "        y_test (pd.DataFrame):\n",
    "        label2act (Dict[int, str]): Dict of label_id to title_of_class\n",
    "        act2label (Dict[str, int]): Dict of title_of_class to label_id\n",
    "    \"\"\"\n",
    "    X_train, X_test = preprocess_raw_data(scaler=scaler)\n",
    "    # X_train = np.load(os.path.join(DATA_DIR, \"my_dataset/Raw_X_train.npy\"))\n",
    "    # X_test = np.load(os.path.join(DATA_DIR, \"my_dataset/Raw_X_test.npy\"))\n",
    "    y_train = pd.read_table(os.path.join(DATA_DIR, \"Train/y_train.txt\"), sep=\" \", header=None)\n",
    "    y_test = pd.read_table(os.path.join(DATA_DIR, \"Test/y_test.txt\"), sep=\" \", header=None)\n",
    "\n",
    "    activity_labels = pd.read_table(\n",
    "        os.path.join(DATA_DIR, \"activity_labels.txt\"), header=None\n",
    "    ).values.flatten()\n",
    "    activity_labels = np.array([label.rstrip().split() for label in activity_labels])\n",
    "    label2act, act2label = {}, {}\n",
    "    for label, activity in activity_labels:\n",
    "        label2act[int(label)] = activity\n",
    "        act2label[activity] = int(label)\n",
    "\n",
    "    # Replace 6 to 0\n",
    "    rep_activity = label2act[5]\n",
    "    label2act[0] = rep_activity\n",
    "    label2act.pop(5)\n",
    "    act2label[rep_activity] = 0\n",
    "\n",
    "    y_train = y_train.replace(5, 0)\n",
    "    y_test = y_test.replace(5, 0)\n",
    "\n",
    "    return X_train, X_test, y_train.values, y_test.values, label2act, act2label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return 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r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\nn===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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   "source": [
    "# Collection of utility functions\n",
    "\n",
    "from decimal import Decimal, ROUND_HALF_UP\n",
    "from collections import Counter\n",
    "from logging import getLogger\n",
    "from typing import Any, Dict, List, Optional, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import shap\n",
    "\n",
    "shap.initjs()\n",
    "logger = getLogger(__name__)\n",
    "\n",
    "\n",
    "def color_generator(i: int) -> str:\n",
    "    l = [\"#FFAF6D\", \"#DC4195\", \"#F1E898\", \"#6DCBB9\", \"#3E89C4\", \"#6F68CF\"]\n",
    "    return l[i]\n",
    "\n",
    "\n",
    "def round_float(f: float, r: float = 0.000001) -> float:\n",
    "    return float(Decimal(str(f)).quantize(Decimal(str(r)), rounding=ROUND_HALF_UP))\n",
    "\n",
    "\n",
    "def round_list(l: List[float], r: float = 0.000001) -> List[float]:\n",
    "    return [round_float(f, r) for f in l]\n",
    "\n",
    "\n",
    "def round_dict(d: Dict[Any, Any], r: float = 0.000001) -> Dict[Any, Any]:\n",
    "    return {key: round(d[key], r) for key in d.keys()}\n",
    "\n",
    "\n",
    "def round(arg: Any, r: float = 0.000001) -> Any:\n",
    "    if type(arg) == float or type(arg) == np.float64 or type(arg) == np.float32:\n",
    "        return round_float(arg, r)\n",
    "    elif type(arg) == list or type(arg) == np.ndarray:\n",
    "        return round_list(arg, r)\n",
    "    elif type(arg) == dict:\n",
    "        return round_dict(arg, r)\n",
    "    else:\n",
    "        logger.error(f\"Arg type {type(arg)} is not supported\")\n",
    "        return arg\n",
    "\n",
    "\n",
    "def check_class_balance(\n",
    "        y_train: np.ndarray, y_test: np.ndarray, label2act: Dict[int, str], n_class: int = 5\n",
    ") -> None:\n",
    "    c_train = Counter(y_train)\n",
    "    c_test = Counter(y_test)\n",
    "\n",
    "    for c, mode in zip([c_train, c_test], [\"train\", \"test\"]):\n",
    "        logger.debug(f\"{mode} labels\")\n",
    "        len_y = sum(c.values())\n",
    "        for label_id in range(n_class):\n",
    "            logger.debug(\n",
    "                f\"{label2act[label_id]} ({label_id}): {c[label_id]} samples ({c[label_id] / len_y * 100:.04} %)\"\n",
    "            )\n",
    "\n",
    "\n",
    "def plot_feature_importance(\n",
    "        models: List[Any],\n",
    "        num_features: int,\n",
    "        cols: List[str],\n",
    "        importance_type: str = \"gain\",\n",
    "        path: str = \"importance.png\",\n",
    "        figsize: Tuple[int, int] = (16, 10),\n",
    "        max_display: int = -1,\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        importance_type: chosen from \"gain\" or \"split\"\n",
    "    \"\"\"\n",
    "    importances = np.zeros((len(models), num_features))\n",
    "    for i, model in enumerate(models):\n",
    "        importances[i] = model.feature_importance(importance_type=importance_type)\n",
    "\n",
    "    importance = np.mean(importances, axis=0)\n",
    "    importance_df = pd.DataFrame({\"Feature\": cols, \"Value\": importance})\n",
    "    importance_df = importance_df.sort_values(by=\"Value\", ascending=False)[:max_display]\n",
    "\n",
    "    plt.figure(figsize=figsize)\n",
    "    sns.barplot(x=\"Value\", y=\"Feature\", data=importance_df)\n",
    "    plt.title(\"Feature Importance (avg over folds)\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(path)\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "def plot_shap_summary(\n",
    "        models: List[Any],\n",
    "        X_train: pd.DataFrame,\n",
    "        class_names: List[str],\n",
    "        path: str = \"shap_summary_plot.png\",\n",
    "        max_display: Optional[int] = None,\n",
    ") -> None:\n",
    "    shap_values_list = []\n",
    "    for model in models:\n",
    "        explainer = shap.TreeExplainer(\n",
    "            model,\n",
    "            num_iteration=model.best_iteration,\n",
    "            feature_perturbation=\"tree_path_dependent\",\n",
    "        )\n",
    "        shap_value_oof = explainer.shap_values(X_train)\n",
    "        shap_values_list.append(shap_value_oof)\n",
    "\n",
    "    shap_values = [np.zeros(shap_values_list[0][0].shape) for _ in range(len(class_names))]\n",
    "    for shap_value_oof in shap_values_list:\n",
    "        for i in range(len(class_names)):\n",
    "            shap_values[i] += shap_value_oof[i]\n",
    "\n",
    "    for i in range(len(class_names)):\n",
    "        shap_values[i] /= len(models)\n",
    "\n",
    "    shap.summary_plot(\n",
    "        shap_values,\n",
    "        X_train,\n",
    "        max_display=max_display,\n",
    "        class_names=class_names,\n",
    "        color=color_generator,\n",
    "        show=False,\n",
    "    )\n",
    "    plt.savefig(path, bbox_inches=\"tight\")\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(\n",
    "        cms: Dict[str, np.ndarray],\n",
    "        labels: Optional[List[str]] = None,\n",
    "        path: str = \"confusion_matrix.png\",\n",
    ") -> None:\n",
    "    \"\"\"Plot confusion matrix\"\"\"\n",
    "    cms = [np.mean(cms[mode], axis=0) for mode in [\"train\", \"valid\", \"test\"]]\n",
    "\n",
    "    fig, ax = plt.subplots(ncols=3, figsize=(20, 7))\n",
    "    for i, (cm, mode) in enumerate(zip(cms, [\"train\", \"valid\", \"test\"])):\n",
    "        sns.heatmap(\n",
    "            cm,\n",
    "            annot=True,\n",
    "            cmap=\"Blues\",\n",
    "            square=True,\n",
    "            vmin=0,\n",
    "            vmax=1.0,\n",
    "            xticklabels=labels,\n",
    "            yticklabels=labels,\n",
    "            ax=ax[i],\n",
    "        )\n",
    "        ax[i].set_xlabel(\"Predicted label\")\n",
    "        ax[i].set_ylabel(\"True label\")\n",
    "        ax[i].set_title(f\"Normalized confusion matrix - {mode}\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(path)\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "def plot_model(model: Any, path: str) -> None:\n",
    "    if not os.path.isfile(path):\n",
    "        keras.utils.plot_model(model, to_file=path, show_shapes=True)\n",
    "\n",
    "\n",
    "def plot_learning_history(fit: Any, metric: str = \"accuracy\", path: str = \"history.png\") -> None:\n",
    "    \"\"\"Plot learning curve\n",
    "    Args:\n",
    "        fit (Any): History object\n",
    "        path (str, default=\"history.png\")\n",
    "    \"\"\"\n",
    "    fig, (axL, axR) = plt.subplots(ncols=2, figsize=(10, 4))\n",
    "    axL.plot(fit.history[\"loss\"], label=\"train\")\n",
    "    axL.plot(fit.history[\"val_loss\"], label=\"validation\")\n",
    "    axL.set_title(\"Loss\")\n",
    "    axL.set_xlabel(\"epoch\")\n",
    "    axL.set_ylabel(\"loss\")\n",
    "    axL.legend(loc=\"upper right\")\n",
    "\n",
    "    axR.plot(fit.history[metric], label=\"train\")\n",
    "    axR.plot(fit.history[f\"val_{metric}\"], label=\"validation\")\n",
    "    axR.set_title(metric.capitalize())\n",
    "    axR.set_xlabel(\"epoch\")\n",
    "    axR.set_ylabel(metric)\n",
    "    axR.legend(loc=\"upper right\")\n",
    "\n",
    "    fig.savefig(path)\n",
    "    plt.close()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "from logging import getLogger\n",
    "from typing import Any, Dict, List, Optional\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tensorflow.keras.callbacks import Callback, ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "logger = getLogger(__name__)\n",
    "\n",
    "class F1Callback(Callback):\n",
    "    \"\"\"Plot f1 value of every epoch\"\"\"\n",
    "\n",
    "    def __init__(\n",
    "            self,\n",
    "            model: Model,\n",
    "            path_f1_history: str,\n",
    "            X_tr: np.ndarray,\n",
    "            y_tr: np.ndarray,\n",
    "            X_val: np.ndarray,\n",
    "            y_val: np.ndarray,\n",
    "    ) -> None:\n",
    "        self.model = model\n",
    "        self.path_f1_history = path_f1_history\n",
    "        self.X_tr = X_tr\n",
    "        self.y_tr = y_tr\n",
    "        self.X_val = X_val\n",
    "        self.y_val = y_val\n",
    "        self.tr_fscores: List[float] = []  # train f1 of every epoch\n",
    "        self.val_fscores: List[float] = []  # valid f1 of every epoch\n",
    "\n",
    "    def on_epoch_end(self, epoch: int, logs: Optional[Dict[str, float]] = None) -> None:\n",
    "        tr_pred = self.model.predict(self.X_tr)\n",
    "        tr_macro_f1 = f1_score(self.y_tr.argmax(axis=1), tr_pred.argmax(axis=1), average=\"macro\")\n",
    "        self.tr_fscores.append(tr_macro_f1)\n",
    "        val_pred = self.model.predict(self.X_val)\n",
    "        val_macro_f1 = f1_score(self.y_val.argmax(axis=1), val_pred.argmax(axis=1), average=\"macro\")\n",
    "        self.val_fscores.append(val_macro_f1)\n",
    "\n",
    "        fig = plt.figure(figsize=(5, 4))\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(self.tr_fscores, label=\"f1_score for training\")\n",
    "        ax.plot(self.val_fscores, label=\"f1_score for validation\")\n",
    "        ax.set_title(\"model f1_score\")\n",
    "        ax.set_xlabel(\"epoch\")\n",
    "        ax.set_ylabel(\"f1_score\")\n",
    "        ax.legend(loc=\"upper right\")\n",
    "        fig.savefig(self.path_f1_history)\n",
    "        plt.close()\n",
    "\n",
    "\n",
    "class PeriodicLogger(Callback):\n",
    "    \"\"\"Logging history every n epochs\"\"\"\n",
    "\n",
    "    def __init__(\n",
    "            self, metric: str = \"accuracy\", verbose: int = 1, epochs: Optional[int] = None\n",
    "    ) -> None:\n",
    "        self.metric = metric\n",
    "        self.verbose = verbose\n",
    "        self.epochs = epochs\n",
    "\n",
    "    def on_epoch_end(self, epoch: int, logs: Optional[Dict[str, float]] = None) -> None:\n",
    "        epoch += 1\n",
    "        if epoch % self.verbose == 0:\n",
    "            msg = \" - \".join(\n",
    "                [\n",
    "                    f\"Epoch {epoch}/{self.epochs}\",\n",
    "                    f\"loss: {round(logs['loss'], 0.0001)}\",\n",
    "                    f\"{self.metric}: {round(logs[self.metric], 0.0001)}\",\n",
    "                    f\"val_loss: {round(logs['val_loss'], 0.0001)}\",\n",
    "                    f\"val_{self.metric}: {round(logs[f'val_{self.metric}'], 0.0001)}\",\n",
    "                ]\n",
    "            )\n",
    "            logger.debug(msg)\n",
    "\n",
    "\n",
    "def create_callback(\n",
    "        model: Model, path_chpt: str, patience: int = 30, metric: str = \"accuracy\", verbose: int = 10, epochs: Optional[int] = None\n",
    ") -> List[Any]:\n",
    "    \"\"\"callback settinngs\n",
    "    Args:\n",
    "        model (Model)\n",
    "        path_chpt (str): path to save checkpoint\n",
    "    Returns:\n",
    "        callbacks (List[Any]): List of Callback\n",
    "    \"\"\"\n",
    "    callbacks = []\n",
    "    callbacks.append(\n",
    "        EarlyStopping(monitor=\"val_loss\", min_delta=0, patience=patience, verbose=1, mode=\"min\")\n",
    "    )\n",
    "    callbacks.append(ModelCheckpoint(filepath=path_chpt, save_best_only=True))\n",
    "    callbacks.append(PeriodicLogger(metric=metric, verbose=verbose, epochs=epochs))\n",
    "    return callbacks\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "# Trainging LSTM (DeepConvLSTM)\n",
    "from typing import Any, Dict, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout, Conv2D, LSTM, Reshape\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "tf.random.set_seed(0)\n",
    "\n",
    "\n",
    "def train_and_predict(\n",
    "        LOG_DIR: str,\n",
    "        fold_id: int,\n",
    "        X_train: np.ndarray,\n",
    "        X_valid: np.ndarray,\n",
    "        X_test: np.ndarray,\n",
    "        y_train: np.ndarray,\n",
    "        y_valid: np.ndarray,\n",
    "        dcl_params: Dict[str, Any],\n",
    ") -> Tuple[np.ndarray, np.ndarray, np.ndarray, Model]:\n",
    "    \"\"\"Train DeepConvLSTM\n",
    "    Args:\n",
    "        X_train, X_valid, X_test: input signals of shape (num_samples, window_size, num_channels, 1)\n",
    "        y_train, y_valid, y_test: onehot-encoded labels\n",
    "    Returns:\n",
    "        pred_train: train prediction\n",
    "        pred_valid: train prediction\n",
    "        pred_test: train prediction\n",
    "        model: trained best model\n",
    "    \"\"\"\n",
    "    model = build_model(\n",
    "        input_shape=X_train.shape[1:], output_dim=y_train.shape[1], lr=dcl_params[\"lr\"]\n",
    "    )\n",
    "    plot_model(model, path=f\"{LOG_DIR}/model.png\")\n",
    "\n",
    "    callbacks = create_callback(\n",
    "        model=model,\n",
    "        path_chpt=f\"{LOG_DIR}/trained_model_fold{fold_id}.h5\",\n",
    "        verbose=10,\n",
    "        epochs=dcl_params[\"epochs\"],\n",
    "    )\n",
    "\n",
    "    fit = model.fit(\n",
    "        X_train,\n",
    "        y_train,\n",
    "        batch_size=dcl_params[\"batch_size\"],\n",
    "        epochs=dcl_params[\"epochs\"],\n",
    "        verbose=dcl_params[\"verbose\"],\n",
    "        validation_data=(X_valid, y_valid),\n",
    "        callbacks=callbacks,\n",
    "    )\n",
    "\n",
    "    plot_learning_history(fit=fit, path=f\"{LOG_DIR}/history_fold{fold_id}.png\")\n",
    "    model = keras.models.load_model(f\"{LOG_DIR}/trained_model_fold{fold_id}.h5\")\n",
    "\n",
    "    pred_train = model.predict(X_train)\n",
    "    pred_valid = model.predict(X_valid)\n",
    "    pred_test = model.predict(X_test)\n",
    "\n",
    "    K.clear_session()\n",
    "    return pred_train, pred_valid, pred_test, model\n",
    "\n",
    "\n",
    "def build_model(\n",
    "        input_shape: Tuple[int, int, int] = (128, 5, 1), output_dim: int = 5, lr: float = 0.02\n",
    ") -> Model:\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(64, kernel_size=(5, 1), input_shape=input_shape))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(Conv2D(64, kernel_size=(5, 1)))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(Conv2D(64, kernel_size=(5, 1)))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(Conv2D(64, kernel_size=(5, 1)))\n",
    "    model.add(Activation(\"relu\"))\n",
    "    model.add(Reshape((112, 6 * 64)))\n",
    "    model.add(LSTM(128, activation=\"tanh\", return_sequences=True))\n",
    "    model.add(Dropout(0.5, seed=0))\n",
    "    model.add(LSTM(128, activation=\"tanh\"))\n",
    "    model.add(Dropout(0.5, seed=1))\n",
    "    model.add(Dense(output_dim))\n",
    "    model.add(Activation(\"softmax\"))\n",
    "    model.compile(\n",
    "        loss=\"categorical_crossentropy\", optimizer=optimizers.Adam(lr=lr), metrics=[\"accuracy\"]\n",
    "    )\n",
    "    return model\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "/Users/andreas/Documents/git/dsProject/s8/logs/deep-conv-lstm-20211211-180229/deep-conv-lstm-20211211-180229.log\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_train.shape=(2459, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "y_train.shape=(2459, 1) y_test.shape=(830, 1)\n",
      "train labels\n",
      "train labels\n",
      "train labels\n",
      "train labels\n",
      "train labels\n",
      "train labels\n",
      "train labels\n",
      "train labels\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "STANDING (0): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING (1): 498 samples (20.25 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_UPSTAIRS (2): 458 samples (18.63 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "WALKING_DOWNSTAIRS (3): 496 samples (20.17 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "SITTING (4): 509 samples (20.7 %)\n",
      "test labels\n",
      "test labels\n",
      "test labels\n",
      "test labels\n",
      "test labels\n",
      "test labels\n",
      "test labels\n",
      "test labels\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "STANDING (0): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING (1): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_UPSTAIRS (2): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "WALKING_DOWNSTAIRS (3): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "SITTING (4): 166 samples (20.0 %)\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "dcl_params={'batch_size': 128, 'epochs': 10000, 'lr': 0.001, 'verbose': 0}\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `lr` argument is deprecated, use `learning_rate` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0861 - accuracy: 0.968 - val_loss: 0.1287 - val_accuracy: 0.9533\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 20/10000 - loss: 0.0573 - accuracy: 0.9792 - val_loss: 0.1278 - val_accuracy: 0.9634\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 30/10000 - loss: 0.0327 - accuracy: 0.9888 - val_loss: 0.0937 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.1433 - val_accuracy: 0.9695\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0149 - accuracy: 0.9954 - val_loss: 0.1111 - val_accuracy: 0.9776\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0059 - accuracy: 0.998 - val_loss: 0.1037 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 70/10000 - loss: 0.0058 - accuracy: 0.999 - val_loss: 0.0898 - val_accuracy: 0.9837\n",
      "Epoch 00075: early stopping\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `lr` argument is deprecated, use `learning_rate` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 10/10000 - loss: 0.0832 - accuracy: 0.9741 - val_loss: 0.1292 - val_accuracy: 0.9533\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 20/10000 - loss: 0.0891 - accuracy: 0.9771 - val_loss: 0.103 - val_accuracy: 0.9654\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 30/10000 - loss: 0.0306 - accuracy: 0.9898 - val_loss: 0.1282 - val_accuracy: 0.9614\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 40/10000 - loss: 0.0753 - accuracy: 0.9832 - val_loss: 0.1245 - val_accuracy: 0.9675\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 50/10000 - loss: 0.0242 - accuracy: 0.9929 - val_loss: 0.0913 - val_accuracy: 0.9817\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 60/10000 - loss: 0.0157 - accuracy: 0.9969 - val_loss: 0.1061 - val_accuracy: 0.9756\n",
      "Epoch 00064: early stopping\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `lr` argument is deprecated, use `learning_rate` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0635 - accuracy: 0.9781 - val_loss: 0.0677 - val_accuracy: 0.9756\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 20/10000 - loss: 0.089 - accuracy: 0.972 - val_loss: 0.0982 - val_accuracy: 0.9715\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 30/10000 - loss: 0.0336 - accuracy: 0.9898 - val_loss: 0.0974 - val_accuracy: 0.9695\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 40/10000 - loss: 0.0305 - accuracy: 0.9903 - val_loss: 0.0921 - val_accuracy: 0.9797\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 50/10000 - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.081 - val_accuracy: 0.9756\n",
      "Epoch 00051: early stopping\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1967, 128, 6, 1) X_val.shape=(492, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1967, 5) y_val.shape=(492, 5) y_test.shape=(830, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `lr` argument is deprecated, use `learning_rate` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 10/10000 - loss: 0.0924 - accuracy: 0.967 - val_loss: 0.1295 - val_accuracy: 0.9573\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 20/10000 - loss: 0.0952 - accuracy: 0.9771 - val_loss: 0.0926 - val_accuracy: 0.9736\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 30/10000 - loss: 0.0351 - accuracy: 0.9873 - val_loss: 0.0598 - val_accuracy: 0.9837\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 40/10000 - loss: 0.0289 - accuracy: 0.9924 - val_loss: 0.0994 - val_accuracy: 0.9736\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 50/10000 - loss: 0.0089 - accuracy: 0.9985 - val_loss: 0.0697 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 60/10000 - loss: 0.005 - accuracy: 0.999 - val_loss: 0.091 - val_accuracy: 0.9858\n",
      "Epoch 00065: early stopping\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "X_tr.shape=(1968, 128, 6, 1) X_val.shape=(491, 128, 6, 1) X_test.shape=(830, 128, 6, 1)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n",
      "y_tr.shape=(1968, 5) y_val.shape=(491, 5) y_test.shape=(830, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `lr` argument is deprecated, use `learning_rate` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 10/10000 - loss: 0.0642 - accuracy: 0.9812 - val_loss: 0.0643 - val_accuracy: 0.9756\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 20/10000 - loss: 0.049 - accuracy: 0.9812 - val_loss: 0.0872 - val_accuracy: 0.9674\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 30/10000 - loss: 0.0314 - accuracy: 0.9898 - val_loss: 0.0696 - val_accuracy: 0.9796\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 40/10000 - loss: 0.0501 - accuracy: 0.9776 - val_loss: 0.0653 - val_accuracy: 0.9776\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 50/10000 - loss: 0.0253 - accuracy: 0.9924 - val_loss: 0.1053 - val_accuracy: 0.9715\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 60/10000 - loss: 0.0097 - accuracy: 0.997 - val_loss: 0.0907 - val_accuracy: 0.9735\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 70/10000 - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0951 - val_accuracy: 0.9817\n",
      "Epoch 00072: early stopping\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---Cross Validation Scores---\n",
      "---train---\n",
      "---train---\n",
      "---train---\n",
      "---train---\n",
      "---train---\n",
      "---train---\n",
      "---train---\n",
      "---train---\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "logloss=0.021739\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "accuracy=0.993595\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "precision=0.993511\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "recall=0.993421\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "f1=0.993452\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "per-class f1={'STANDING': 0.995494, 'WALKING': 0.995971, 'WALKING_UPSTAIRS': 0.986601, 'WALKING_DOWNSTAIRS': 0.989195, 'SITTING': 1.0}\n",
      "---valid---\n",
      "---valid---\n",
      "---valid---\n",
      "---valid---\n",
      "---valid---\n",
      "---valid---\n",
      "---valid---\n",
      "---valid---\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "logloss=0.05576\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "accuracy=0.98089\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "precision=0.980553\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "recall=0.980673\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "f1=0.980513\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "per-class f1={'STANDING': 0.99002, 'WALKING': 0.986873, 'WALKING_UPSTAIRS': 0.961229, 'WALKING_DOWNSTAIRS': 0.967429, 'SITTING': 0.997015}\n",
      "---test---\n",
      "---test---\n",
      "---test---\n",
      "---test---\n",
      "---test---\n",
      "---test---\n",
      "---test---\n",
      "---test---\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "logloss=0.629058\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "accuracy=0.888193\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "precision=0.898182\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "recall=0.888193\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "f1=0.888147\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "per-class f1={'STANDING': 0.888518, 'WALKING': 0.837366, 'WALKING_UPSTAIRS': 0.842045, 'WALKING_DOWNSTAIRS': 0.876429, 'SITTING': 0.996378}\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "---Final Test Scores Averaged over Folds---\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "accuracy=0.9240963855421687\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "precision=0.9288940318395399\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "recall=0.9240963855421688\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "f1=0.9246721844987433\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n",
      "per-class f1=[0.90853659 0.88888889 0.92651757 0.90243902 0.99697885]\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import json\n",
    "from logging import basicConfig, getLogger, StreamHandler, DEBUG, WARNING\n",
    "import os\n",
    "import sys\n",
    "from typing import Any, Dict, List\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    confusion_matrix,\n",
    "    f1_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    ")\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from tensorflow import keras\n",
    "\n",
    "CUR_DIR = os.getcwd()\n",
    "\n",
    "# Logging settings\n",
    "EXEC_TIME = \"deep-conv-lstm-\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "LOG_DIR = os.path.join(CUR_DIR, f\"logs/{EXEC_TIME}\")\n",
    "os.makedirs(LOG_DIR, exist_ok=True)  # Create log directory\n",
    "\n",
    "formatter = \"%(levelname)s: %(asctime)s: %(filename)s: %(funcName)s: %(message)s\"\n",
    "basicConfig(filename=f\"{LOG_DIR}/{EXEC_TIME}.log\", level=DEBUG, format=formatter)\n",
    "mpl_logger = getLogger(\"matplotlib\")  # Suppress matplotlib logging\n",
    "mpl_logger.setLevel(WARNING)\n",
    "# Handle logging to both logging and stdout.\n",
    "getLogger().addHandler(StreamHandler(sys.stdout))\n",
    "\n",
    "logger = getLogger(__name__)\n",
    "logger.setLevel(DEBUG)\n",
    "logger.debug(f\"{LOG_DIR}/{EXEC_TIME}.log\")\n",
    "\n",
    "X_train, X_test, y_train, y_test, label2act, act2label = load_raw_data()\n",
    "logger.debug(f\"{X_train.shape=} {X_test.shape=}\")\n",
    "logger.debug(f\"{y_train.shape=} {y_test.shape=}\")\n",
    "\n",
    "check_class_balance(y_train.flatten(), y_test.flatten(), label2act=label2act)\n",
    "\n",
    "# Split data by preserving the percentage of samples for each class.\n",
    "n_splits = 5\n",
    "cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=71)\n",
    "valid_preds = np.zeros((X_train.shape[0], 5))\n",
    "test_preds = np.zeros((n_splits, X_test.shape[0], 5))\n",
    "models = []\n",
    "scores: Dict[str, Dict[str, List[Any]]] = {\n",
    "    \"logloss\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "    \"accuracy\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "    \"precision\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "    \"recall\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "    \"f1\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "    \"cm\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "    \"per_class_f1\": {\"train\": [], \"valid\": [], \"test\": []},\n",
    "}\n",
    "# Load hyper-parameters\n",
    "with open(os.path.join(CUR_DIR, \"configs/default.json\"), \"r\") as f:\n",
    "    dcl_params = json.load(f)[\"deep_conv_lstm_params\"]\n",
    "    logger.debug(f\"{dcl_params=}\")\n",
    "\n",
    "y_test = keras.utils.to_categorical(y_test, 5)\n",
    "\n",
    "for fold_id, (train_index, valid_index) in enumerate(cv.split(X_train, y_train)):\n",
    "    X_tr = X_train[train_index, :]\n",
    "    X_val = X_train[valid_index, :]\n",
    "    y_tr = y_train[train_index]\n",
    "    y_val = y_train[valid_index]\n",
    "\n",
    "    y_tr = keras.utils.to_categorical(y_tr, 5)\n",
    "    y_val = keras.utils.to_categorical(y_val, 5)\n",
    "\n",
    "    logger.debug(f\"{X_tr.shape=} {X_val.shape=} {X_test.shape=}\")\n",
    "    logger.debug(f\"{y_tr.shape=} {y_val.shape=} {y_test.shape=}\")\n",
    "\n",
    "    pred_tr, pred_val, pred_test, model = train_and_predict(\n",
    "        LOG_DIR, fold_id, X_tr, X_val, X_test, y_tr, y_val, dcl_params\n",
    "    )\n",
    "    models.append(model)\n",
    "\n",
    "    valid_preds[valid_index] = pred_val\n",
    "    test_preds[fold_id] = pred_test\n",
    "\n",
    "    for pred, X, y, mode in zip(\n",
    "            [pred_tr, pred_val, pred_test], [X_tr, X_val, X_test], [y_tr, y_val, y_test], [\"train\", \"valid\", \"test\"]\n",
    "    ):\n",
    "        loss, acc = model.evaluate(X, y, verbose=0)\n",
    "        pred = pred.argmax(axis=1)\n",
    "        y = y.argmax(axis=1)\n",
    "        scores[\"logloss\"][mode].append(loss)\n",
    "        scores[\"accuracy\"][mode].append(acc)\n",
    "        scores[\"precision\"][mode].append(precision_score(y, pred, average=\"macro\"))\n",
    "        scores[\"recall\"][mode].append(recall_score(y, pred, average=\"macro\"))\n",
    "        scores[\"f1\"][mode].append(f1_score(y, pred, average=\"macro\"))\n",
    "        scores[\"cm\"][mode].append(confusion_matrix(y, pred, normalize=\"true\"))\n",
    "        scores[\"per_class_f1\"][mode].append(f1_score(y, pred, average=None))\n",
    "\n",
    "# Output Cross Validation Scores\n",
    "logger.debug(\"---Cross Validation Scores---\")\n",
    "for mode in [\"train\", \"valid\", \"test\"]:\n",
    "    logger.debug(f\"---{mode}---\")\n",
    "    for metric in [\"logloss\", \"accuracy\", \"precision\", \"recall\", \"f1\"]:\n",
    "        logger.debug(f\"{metric}={round(np.mean(scores[metric][mode]))}\")\n",
    "\n",
    "    class_f1_mat = scores[\"per_class_f1\"][mode]\n",
    "    class_f1_result = {}\n",
    "    for class_id in range(5):\n",
    "        mean_class_f1 = np.mean([class_f1_mat[i][class_id] for i in range(n_splits)])\n",
    "        class_f1_result[label2act[class_id]] = mean_class_f1\n",
    "    logger.debug(f\"per-class f1={round(class_f1_result)}\")\n",
    "\n",
    "# Output Final Scores Averaged over Folds\n",
    "logger.debug(\"---Final Test Scores Averaged over Folds---\")\n",
    "test_pred = np.mean(test_preds, axis=0).argmax(axis=1)  # average over folds\n",
    "y_test = y_test.argmax(axis=1)\n",
    "logger.debug(f\"accuracy={accuracy_score(y_test, test_pred)}\")\n",
    "logger.debug(f\"precision={precision_score(y_test, test_pred, average='macro')}\")\n",
    "logger.debug(f\"recall={recall_score(y_test, test_pred, average='macro')}\")\n",
    "logger.debug(f\"f1={f1_score(y_test, test_pred, average='macro')}\")\n",
    "logger.debug(f\"per-class f1={f1_score(y_test, test_pred, average=None)}\")\n",
    "\n",
    "# Plot comfusion matrix\n",
    "plot_confusion_matrix(\n",
    "    cms=scores[\"cm\"],\n",
    "    labels=[\n",
    "        \"STANDING\",\n",
    "        \"WALKING\",\n",
    "        \"WALKING_UPSTAIRS\",\n",
    "        \"WALKING_DOWNSTAIRS\",\n",
    "        \"SITTING\",\n",
    "    ],\n",
    "    path=f\"{LOG_DIR}/comfusion_matrix.png\",\n",
    ")\n",
    "\n",
    "np.save(f\"{LOG_DIR}/valid_oof.npy\", valid_preds)\n",
    "np.save(f\"{LOG_DIR}/test_oof.npy\", np.mean(test_preds, axis=0))  # Averaging\n"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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