{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor1 = pd.read_csv('D:/thesis/data/converted/raw/DAMAGE_1/DAMAGE_1_TEST1_01.csv',sep=',')\n",
    "sensor2 = pd.read_csv('D:/thesis/data/converted/raw/DAMAGE_1/DAMAGE_1_TEST1_02.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame()\n",
    "df1['s1'] = sensor1[sensor1.columns[-1]]\n",
    "df1['s2'] = sensor2[sensor2.columns[-1]]\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_two_sensors(damage_path, damage):\n",
    "    df = pd.DataFrame()\n",
    "    for file in os.listdir(damage_path):\n",
    "        pattern = re.compile(r'DAMAGE_\\d+_TEST\\d+_\\d{2}\\.csv')\n",
    "        try:\n",
    "            assert pattern.match(file), f\"File {file} does not match the required format, skipping...\"\n",
    "            # assert \"TEST01\" in file, f\"File {file} does not contain 'TEST01', skipping...\" #TODO: should be trained using the whole test file\n",
    "            print(f\"Processing file: {file}\")\n",
    "            # Append the full path of the file to sensor1 or sensor2 based on the filename\n",
    "            if file.endswith('_01.csv'):\n",
    "                df['sensor 1'] = pd.read_csv(os.path.join('D:/thesis/data/converted/raw', damage, file), sep=',', usecols=[1])\n",
    "            elif file.endswith('_02.csv'):\n",
    "                df['sensor 2'] = pd.read_csv(os.path.join('D:/thesis/data/converted/raw', damage, file), sep=',', usecols=[1])\n",
    "        except AssertionError as e:\n",
    "            print(e)\n",
    "            continue  # Skip to the next iteration\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "\n",
    "df = []\n",
    "for damage in os.listdir('D:/thesis/data/converted/raw'):\n",
    "    damage_path = os.path.join('D:/thesis/data/converted/raw', damage)\n",
    "    df.append(merge_two_sensors(damage_path, damage))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combined Plot for sensor 1 and sensor 2 from data1 file in which motor is operated at 800 rpm\n",
    "\n",
    "plt.plot(df1['s2'], label='sensor 2')\n",
    "plt.plot(df1['s1'], label='sensor 1', alpha=0.5)\n",
    "plt.xlabel(\"Number of samples\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.title(\"Raw vibration signal\")\n",
    "plt.ylim(-7.5, 5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_sensor1_test1 = []\n",
    "signal_sensor2_test1 = []\n",
    "\n",
    "for data in df:\n",
    "    if not data.empty and 'sensor 1' in data.columns and 'sensor 2' in data.columns:\n",
    "        signal_sensor1_test1.append(data['sensor 1'].values)\n",
    "        signal_sensor2_test1.append(data['sensor 2'].values)\n",
    "\n",
    "print(len(signal_sensor1_test1))\n",
    "print(len(signal_sensor2_test1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Short-Time Fourier Transform (STFT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.signal import stft, hann\n",
    "# from multiprocessing import Pool\n",
    "\n",
    "# Function to compute and append STFT data\n",
    "def process_stft(args):\n",
    "    # Define STFT parameters\n",
    "    window_size = 1024\n",
    "    hop_size = 512\n",
    "    window = hann(window_size)\n",
    "\n",
    "    Fs = 1024  # Sampling frequency in Hz\n",
    "    \n",
    "    damage_num, test_num, sensor_suffix = args\n",
    "    sensor_name = active_sensors[sensor_suffix]\n",
    "    sensor_num = sensor_suffix[-1]  # '1' or '2'\n",
    "    \n",
    "    # Construct the file path\n",
    "    file_name = f'DAMAGE_{damage_num}_TEST{test_num}_{sensor_suffix}.csv'\n",
    "    file_path = os.path.join(damage_base_path, f'DAMAGE_{damage_num}', file_name)\n",
    "    \n",
    "    # Check if the file exists\n",
    "    if not os.path.isfile(file_path):\n",
    "        print(f\"File {file_path} does not exist. Skipping...\")\n",
    "        return\n",
    "    \n",
    "    # Read the CSV\n",
    "    try:\n",
    "        df = pd.read_csv(file_path)\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading {file_path}: {e}. Skipping...\")\n",
    "        return\n",
    "    \n",
    "    # Ensure the CSV has exactly two columns\n",
    "    if df.shape[1] != 2:\n",
    "        print(f\"Unexpected number of columns in {file_path}. Skipping...\")\n",
    "        return\n",
    "    \n",
    "    # Extract sensor data\n",
    "    sensor_column = df.columns[1]\n",
    "    sensor_data = df[sensor_column].values\n",
    "    \n",
    "    # Compute STFT\n",
    "    frequencies, times, Zxx = stft(sensor_data, fs=Fs, window=window, nperseg=window_size, noverlap=window_size - hop_size)\n",
    "    magnitude = np.abs(Zxx)\n",
    "    df_stft = pd.DataFrame(magnitude, index=frequencies, columns=times).T\n",
    "    df_stft.columns = [f\"Freq_{i}\" for i in frequencies]\n",
    "    \n",
    "    # Define the output CSV file path\n",
    "    stft_file_name = f'stft_data{sensor_num}_{damage_num}.csv'\n",
    "    sensor_output_dir = os.path.join(damage_base_path, sensor_name.lower())\n",
    "    os.makedirs(sensor_output_dir, exist_ok=True)\n",
    "    stft_file_path = os.path.join(sensor_output_dir, stft_file_name)\n",
    "    # Append the flattened STFT to the CSV\n",
    "    try:\n",
    "        if not os.path.isfile(stft_file_path):\n",
    "            # Create a new CSV\n",
    "            df_stft.to_csv(stft_file_path, index=False, header=False)\n",
    "        else:\n",
    "            # Append to existing CSV\n",
    "            df_stft.to_csv(stft_file_path, mode='a', index=False, header=False)\n",
    "        print(f\"Appended STFT data to {stft_file_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error writing to {stft_file_path}: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the base path where DAMAGE_X folders are located\n",
    "damage_base_path = 'D:/thesis/data/converted/raw/'\n",
    "\n",
    "# Define active sensors\n",
    "active_sensors = {\n",
    "    '01': 'sensor1',  # Beginning map sensor\n",
    "    '02': 'sensor2'   # End map sensor\n",
    "}\n",
    "\n",
    "# Define damage cases and test runs\n",
    "damage_cases = range(1, 7)  # Adjust based on actual number of damage cases\n",
    "test_runs = range(1, 6)      # TEST01 to TEST05\n",
    "args_list = []\n",
    "\n",
    "# Prepare the list of arguments for parallel processing\n",
    "for damage_num in damage_cases:\n",
    "    for test_num in test_runs:\n",
    "        for sensor_suffix in active_sensors.keys():\n",
    "            args_list.append((damage_num, test_num, sensor_suffix))\n",
    "\n",
    "print(len(args_list))\n",
    "args_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process STFTs sequentially instead of in parallel\n",
    "if __name__ == \"__main__\":\n",
    "    print(f\"Starting sequential STFT processing...\")\n",
    "    for i, arg in enumerate(args_list, 1):\n",
    "        process_stft(arg)\n",
    "        print(f\"Processed {i}/{len(args_list)} files\")\n",
    "    print(\"STFT processing completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import stft, hann\n",
    "\n",
    "# Applying STFT\n",
    "vibration_data = signal_sensor1_test1[1]\n",
    "window_size = 1024\n",
    "hop_size = 512\n",
    "window = hann(window_size)  # Creating a Hanning window\n",
    "Fs = 1024\n",
    "\n",
    "frequencies, times, Zxx = stft(vibration_data, \n",
    "                               fs=Fs, \n",
    "                               window=window, \n",
    "                               nperseg=window_size, \n",
    "                               noverlap=window_size - hop_size)\n",
    "# Plotting the STFT Data\n",
    "plt.pcolormesh(times, frequencies, np.abs(Zxx), shading='gouraud')\n",
    "plt.title(f'STFT Magnitude for case {1} signal sensor 2')\n",
    "plt.ylabel(f'Frequency [Hz]')\n",
    "plt.xlabel(f'Time [sec]')\n",
    "plt.show()\n",
    "\n",
    "# get current y ticks in list\n",
    "print(len(frequencies))\n",
    "print(len(times))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading STFT Data from CSV Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.listdir('D:/thesis/data/working')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "ready_data1a = []\n",
    "for file in os.listdir('D:/thesis/data/converted/raw/sensor1'):\n",
    "    ready_data1a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file)))\n",
    "# colormesh give title x is frequency and y is time and rotate/transpose the data\n",
    "# Plotting the STFT Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ready_data1a)\n",
    "# plt.pcolormesh(ready_data1[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(6):\n",
    "    plt.pcolormesh(ready_data1a[i])\n",
    "    plt.title(f'STFT Magnitude for case {i} sensor 1')\n",
    "    plt.xlabel(f'Frequency [Hz]')\n",
    "    plt.ylabel(f'Time [sec]')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ready_data2a = []\n",
    "for file in os.listdir('D:/thesis/data/converted/raw/sensor2'):\n",
    "    ready_data2a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(ready_data1a))\n",
    "print(len(ready_data2a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1a = 0\n",
    "print(type(ready_data1a[0]))\n",
    "ready_data1a[0].iloc[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking length of the total array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1a = 0\n",
    "print(type(x1a))\n",
    "for i in range(len(ready_data1a)):\n",
    "    print(type(ready_data1a[i].shape[0]))\n",
    "    x1a = x1a + ready_data1a[i].shape[0]\n",
    "    print(type(x1a))\n",
    "\n",
    "print(x1a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2a = 0\n",
    "\n",
    "for i in range(len(ready_data2a)):\n",
    "    print(ready_data2a[i].shape)\n",
    "    x2a = x2a + ready_data2a[i].shape[0]\n",
    "\n",
    "print(x2a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flatten 6 array into one array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine all dataframes in ready_data1a into a single dataframe\n",
    "if ready_data1a:  # Check if the list is not empty\n",
    "    # Use pandas concat function instead of iterative concatenation\n",
    "    combined_data = pd.concat(ready_data1a, axis=0, ignore_index=True)\n",
    "    \n",
    "    print(f\"Type of combined data: {type(combined_data)}\")\n",
    "    print(f\"Shape of combined data: {combined_data.shape}\")\n",
    "    \n",
    "    # Display the combined dataframe\n",
    "    combined_data\n",
    "else:\n",
    "    print(\"No data available in ready_data1a list\")\n",
    "    combined_data = pd.DataFrame()\n",
    "\n",
    "# Store the result in x1a for compatibility with subsequent code\n",
    "x1a = combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine all dataframes in ready_data1a into a single dataframe\n",
    "if ready_data2a:  # Check if the list is not empty\n",
    "    # Use pandas concat function instead of iterative concatenation\n",
    "    combined_data = pd.concat(ready_data2a, axis=0, ignore_index=True)\n",
    "    \n",
    "    print(f\"Type of combined data: {type(combined_data)}\")\n",
    "    print(f\"Shape of combined data: {combined_data.shape}\")\n",
    "    \n",
    "    # Display the combined dataframe\n",
    "    combined_data\n",
    "else:\n",
    "    print(\"No data available in ready_data1a list\")\n",
    "    combined_data = pd.DataFrame()\n",
    "\n",
    "# Store the result in x1a for compatibility with subsequent code\n",
    "x2a = combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = 0\n",
    "y_2 = 1\n",
    "y_3 = 2\n",
    "y_4 = 3\n",
    "y_5 = 4\n",
    "y_6 = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = [y_1, y_2, y_3, y_4, y_5, y_6]\n",
    "y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(y_data)):\n",
    "    print(ready_data1a[i].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "for i in range(len(y_data)):\n",
    "    y_data[i] = [y_data[i]]*ready_data1a[i].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(y_data[0])\n",
    "y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y_data[0]\n",
    "\n",
    "for i in range(len(y_data) - 1):\n",
    "    #print(i)\n",
    "    y = np.concatenate((y, y_data[i+1]), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y.shape)\n",
    "print(np.unique(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.ml.model_selection import create_ready_data\n",
    "\n",
    "X1a, y = create_ready_data('D:/thesis/data/converted/raw/sensor1')\n",
    "X2a, y = create_ready_data('D:/thesis/data/converted/raw/sensor2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train1, x_test1, y_train, y_test = train_test_split(X1a, y, test_size=0.2, random_state=2)\n",
    "x_train2, x_test2, y_train, y_test = train_test_split(X2a, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the shapes of x_train and y_train\n",
    "print(\"Shape of x1_train:\", x_train1.shape)\n",
    "print(\"Shape of x2_train:\", x_train2.shape)\n",
    "print(\"Shape of y_train:\", y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies1 = []\n",
    "accuracies2 = []\n",
    "\n",
    "\n",
    "# 1. Random Forest\n",
    "rf_model1 = RandomForestClassifier()\n",
    "rf_model1.fit(x_train1, y_train)\n",
    "rf_pred1 = rf_model1.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, rf_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"Random Forest Accuracy for sensor 1:\", acc1)\n",
    "rf_model2 = RandomForestClassifier()\n",
    "rf_model2.fit(x_train2, y_train)\n",
    "rf_pred2 = rf_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, rf_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"Random Forest Accuracy for sensor 2:\", acc2)\n",
    "# print(rf_pred)\n",
    "# print(y_test)\n",
    "\n",
    "# 2. Bagged Trees\n",
    "bagged_model1 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
    "bagged_model1.fit(x_train1, y_train)\n",
    "bagged_pred1 = bagged_model1.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, bagged_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"Bagged Trees Accuracy for sensor 1:\", acc1)\n",
    "bagged_model2 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
    "bagged_model2.fit(x_train2, y_train)\n",
    "bagged_pred2 = bagged_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, bagged_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"Bagged Trees Accuracy for sensor 2:\", acc2)\n",
    "\n",
    "# 3. Decision Tree\n",
    "dt_model = DecisionTreeClassifier()\n",
    "dt_model.fit(x_train1, y_train)\n",
    "dt_pred1 = dt_model.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, dt_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"Decision Tree Accuracy for sensor 1:\", acc1)\n",
    "dt_model2 = DecisionTreeClassifier()\n",
    "dt_model2.fit(x_train2, y_train)\n",
    "dt_pred2 = dt_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, dt_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"Decision Tree Accuracy for sensor 2:\", acc2)\n",
    "\n",
    "# 4. KNeighbors\n",
    "knn_model = KNeighborsClassifier()\n",
    "knn_model.fit(x_train1, y_train)\n",
    "knn_pred1 = knn_model.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, knn_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"KNeighbors Accuracy for sensor 1:\", acc1)\n",
    "knn_model2 = KNeighborsClassifier()\n",
    "knn_model2.fit(x_train2, y_train)\n",
    "knn_pred2 = knn_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, knn_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"KNeighbors Accuracy for sensor 2:\", acc2)\n",
    "\n",
    "# 5. Linear Discriminant Analysis\n",
    "lda_model = LinearDiscriminantAnalysis()\n",
    "lda_model.fit(x_train1, y_train)\n",
    "lda_pred1 = lda_model.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, lda_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"Linear Discriminant Analysis Accuracy for sensor 1:\", acc1)\n",
    "lda_model2 = LinearDiscriminantAnalysis()\n",
    "lda_model2.fit(x_train2, y_train)\n",
    "lda_pred2 = lda_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, lda_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"Linear Discriminant Analysis Accuracy for sensor 2:\", acc2)\n",
    "\n",
    "# 6. Support Vector Machine\n",
    "svm_model = SVC()\n",
    "svm_model.fit(x_train1, y_train)\n",
    "svm_pred1 = svm_model.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, svm_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"Support Vector Machine Accuracy for sensor 1:\", acc1)\n",
    "svm_model2 = SVC()\n",
    "svm_model2.fit(x_train2, y_train)\n",
    "svm_pred2 = svm_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, svm_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"Support Vector Machine Accuracy for sensor 2:\", acc2)\n",
    "\n",
    "# 7. XGBoost\n",
    "xgboost_model = XGBClassifier()\n",
    "xgboost_model.fit(x_train1, y_train)\n",
    "xgboost_pred1 = xgboost_model.predict(x_test1)\n",
    "acc1 = accuracy_score(y_test, xgboost_pred1) * 100\n",
    "accuracies1.append(acc1)\n",
    "# format with color coded if acc1 > 90\n",
    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
    "print(\"XGBoost Accuracy:\", acc1)\n",
    "xgboost_model2 = XGBClassifier()\n",
    "xgboost_model2.fit(x_train2, y_train)\n",
    "xgboost_pred2 = xgboost_model2.predict(x_test2)\n",
    "acc2 = accuracy_score(y_test, xgboost_pred2) * 100\n",
    "accuracies2.append(acc2)\n",
    "# format with color coded if acc2 > 90\n",
    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
    "print(\"XGBoost Accuracy:\", acc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(accuracies1)\n",
    "print(accuracies2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "models = [rf_model, bagged_model, dt_model, knn_model, lda_model, svm_model, xgboost_model]\n",
    "model_names = [\"Random Forest\", \"Bagged Trees\", \"Decision Tree\", \"KNN\", \"LDA\", \"SVM\", \"XGBoost\"]\n",
    "\n",
    "bar_width = 0.35  # Width of each bar\n",
    "index = np.arange(len(model_names))  # Index for the bars\n",
    "\n",
    "# Plotting the bar graph\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Bar plot for Sensor 1\n",
    "plt.bar(index, accuracies1, width=bar_width, color='blue', label='Sensor 1')\n",
    "\n",
    "# Bar plot for Sensor 2\n",
    "plt.bar(index + bar_width, accuracies2, width=bar_width, color='orange', label='Sensor 2')\n",
    "\n",
    "# Add values on top of each bar\n",
    "for i, acc1, acc2 in zip(index, accuracies1, accuracies2):\n",
    "    plt.text(i, acc1 + .1, f'{acc1:.2f}%', ha='center', va='bottom', color='black')\n",
    "    plt.text(i + bar_width, acc2 + 1, f'{acc2:.2f}%', ha='center', va='bottom', color='black')\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Model Name →')\n",
    "plt.ylabel('Accuracy →')\n",
    "plt.title('Accuracy of classifiers for Sensors 1 and 2 with 513 features')\n",
    "plt.xticks(index + bar_width / 2, model_names)  # Set x-tick positions\n",
    "plt.legend()\n",
    "plt.ylim(0, 100)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.ml.model_selection import create_ready_data\n",
    "\n",
    "X1b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor1')\n",
    "X2b, y = create_ready_data('D:/thesis/data/converted/raw_B/sensor2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "# 4. Validate on Dataset B\n",
    "y_pred_svm = svm_model.predict(X1b)\n",
    "\n",
    "# 5. Evaluate\n",
    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
    "print(classification_report(y, y_pred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "# 4. Validate on Dataset B\n",
    "y_pred = rf_model2.predict(X2b)\n",
    "\n",
    "# 5. Evaluate\n",
    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred))\n",
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = svm_model2.predict(X2b.iloc[[5312],:])\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[5312]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "cm = confusion_matrix(y, y_pred_svm) # -> ndarray\n",
    "\n",
    "# get the class labels\n",
    "labels = svm_model.classes_\n",
    "\n",
    "# Plot\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "disp.plot(cmap=plt.cm.Blues)  # You can change colormap\n",
    "plt.title(\"SVM Sensor1 CM Train w/ Dataset A Val w/ Dataset B\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Self-test CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Predict sensor 1 on Dataset A\n",
    "y_train_pred = svm_model.predict(x_train1)\n",
    "\n",
    "# 2. Import confusion matrix tools\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 3. Create and plot confusion matrix\n",
    "cm_train = confusion_matrix(y_train, y_train_pred)\n",
    "labels = svm_model.classes_\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_train, display_labels=labels)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title(\"Confusion Matrix: Train & Test on Dataset A\")\n",
    "plt.show()\n"
   ]
  }
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