{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workbook for Eye Tracking Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Basics\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import ipympl\n",
    "\n",
    "# Data processing\n",
    "import pandas as pd\n",
    "import awkward as ak\n",
    "\n",
    "# ML\n",
    "# from sklearn import     \n",
    "# patch_sklearn()\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Misc\n",
    "from pathlib import Path\n",
    "import pyarrow as pa\n",
    "import urllib.request\n",
    "from itertools import chain, combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import users and their score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imports the user score information\n",
    "user_data = pd.read_csv(r\"data/scores_WtG_PrePost.csv\", delimiter=\",\", usecols=[\"User\", \"Pre score\", \"Post score\", \"Difference\", \"Group cat\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defines my directory with the user data\n",
    "user_dir = 'data/with ET'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filters and drops non-relevant users\n",
    "def strip_non_relevant(user_data):\n",
    "    to_drop = []\n",
    "    for i, cat in enumerate(user_data[\"Group cat\"]):\n",
    "        if math.isnan(cat):\n",
    "            to_drop.append(i)\n",
    "    user_data = user_data.drop(to_drop)\n",
    "    user_data = user_data.reset_index()\n",
    "    \n",
    "    # Filters and drops users with no directory\n",
    "    not_existing_names = []\n",
    "    for i, user in enumerate(user_data[\"User\"]):\n",
    "        if not os.path.isdir(user_dir + '/' + user):\n",
    "            not_existing_names.append(i)\n",
    "    user_data = user_data.drop(not_existing_names)\n",
    "    user_data = user_data.reset_index()\n",
    "    \n",
    "    return user_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "user_data = strip_non_relevant(user_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert to awkward array\n",
    "array_user = ak.zip(dict(user_data))\n",
    "#array_user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Eye Tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creates dictionary with all the files for one user\n",
    "file_names = {}\n",
    "for user in user_data[\"User\"]:\n",
    "    #print(user)\n",
    "    available_files = []\n",
    "    available_files_temp = os.listdir(user_dir + '/' + user)\n",
    "    for file in available_files_temp:\n",
    "        if \"graph01-ET_planning\" in file:\n",
    "            available_files.append(file)\n",
    "    # print(available_files)\n",
    "    file_names[user] = available_files\n",
    "#file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "# Read each CSV file for one user, stored for each attempt\n",
    "df_attempt1 = []\n",
    "df_attempt2 = []\n",
    "attempt2_mask = []\n",
    "for user in user_data['User']:\n",
    "    files = file_names[user]\n",
    "    if len(files) == 2:\n",
    "        attempt2_mask.append(True)\n",
    "        # attempt2_mask.append(False)\n",
    "\n",
    "        df_attempt1.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "        df_attempt2.append(pd.read_csv(user_dir + '/' + user + '/' + files[1], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "    elif len(files) == 1:\n",
    "        attempt2_mask.append(False)\n",
    "        df_attempt1.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "        df_attempt2.append(pd.read_csv(user_dir + '/' + user + '/' + files[0], delimiter=\"\t\", usecols=[\"eyeDataTimestamp\", \"gazePointAOI_target_x\", \"gazePointAOI_target_y\"]))\n",
    "print(len(df_attempt1))\n",
    "print(len(df_attempt2))\n",
    "#df_attempt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add delta t list\n",
    "for attempt in [df_attempt1, df_attempt2]:\n",
    "    for i in range(len(attempt)):\n",
    "        temp_delta_t_list = []\n",
    "        for j in range(len(attempt[i][\"eyeDataTimestamp\"]) - 1):\n",
    "            temp_delta_t_list.append(attempt[i][\"eyeDataTimestamp\"][j+1] - attempt[i][\"eyeDataTimestamp\"][j])\n",
    "        temp_delta_t_list.append(np.mean(temp_delta_t_list))\n",
    "        attempt[i][\"deltaTimestamp\"] = temp_delta_t_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert df_attempts to ak.Array\n",
    "array_attempt1 = []\n",
    "array_attempt2 = []\n",
    "for df in df_attempt1:\n",
    "    array_attempt1.append(ak.Array(dict(df)))\n",
    "for df in df_attempt2:\n",
    "    array_attempt2.append(ak.Array(dict(df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Add Eye Tracking Data to user data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adds a list of arrays in a new column to an array\n",
    "def add_column(ak_array, arrays, col_name):\n",
    "    combined_entries = [\n",
    "        {**{k: ak_array[k][i] for k in ak_array.fields}, col_name: array} for i, (entry, array) in enumerate(zip(ak_array, arrays))\n",
    "    ]\n",
    "    return ak.Array(combined_entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creates arrays with labels for all 3 categories\n",
    "def create_labels(array_user):\n",
    "    labels_str = []\n",
    "    labels_int_expert = []\n",
    "    labels_int_good = []\n",
    "    labels_int_bad = []\n",
    "\n",
    "    for subject_name, pre_score, diff in zip(array_user[\"User\"], array_user[\"Pre score\"], array_user[\"Difference\"]):\n",
    "        if pre_score == 2 and diff == 0:\n",
    "            label_str = \"Expert\"\n",
    "            label_int_expert = 1\n",
    "            label_int_good = 0  \n",
    "            label_int_bad = 0\n",
    "        elif diff <= 0:\n",
    "            label_str = \"Bad\"\n",
    "            label_int_expert = 0\n",
    "            label_int_good = 0  \n",
    "            label_int_bad = 1\n",
    "        else:\n",
    "            label_str = \"Good\"\n",
    "            label_int_expert = 0\n",
    "            label_int_good = 1\n",
    "            label_int_bad = 0\n",
    "        labels_str.append(label_str)\n",
    "        labels_int_expert.append(label_int_expert)   \n",
    "        labels_int_good.append(label_int_good)    \n",
    "        labels_int_bad.append(label_int_bad)    \n",
    "\n",
    "    labels_str = ak.Array(labels_str)\n",
    "    labels_int_expert = ak.Array(labels_int_expert)\n",
    "    labels_int_good = ak.Array(labels_int_good)\n",
    "    labels_int_bad = ak.Array(labels_int_bad)\n",
    "    \n",
    "    return labels_str, labels_int_expert, labels_int_good, labels_int_bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "labels_str, labels_int_expert, labels_int_good, labels_int_bad = create_labels(array_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creates array with first and second attempts added\n",
    "array_data = add_column(array_user, array_attempt1, 'Attempt1')\n",
    "array_data = add_column(array_data, array_attempt2, 'Attempt2')\n",
    "array_data[\"Attempt 2 Mask\"] = ak.Array(attempt2_mask)\n",
    "\n",
    "array_data['Labels Str'] = labels_str\n",
    "array_data['Labels Expert'] = labels_int_expert\n",
    "array_data['Labels Good'] = labels_int_good\n",
    "array_data['Labels Bad'] = labels_int_bad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AOIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def minmax(data):\n",
    "    \"\"\"Get the min and max of an iterable in O(n) time and constant space.\"\"\"\n",
    "    minValue = data[0]\n",
    "    maxValue = data[0]\n",
    "    for d in data[1:]:\n",
    "        minValue = d if d < minValue else minValue\n",
    "        maxValue = d if d > maxValue else maxValue\n",
    "    return (minValue,maxValue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get Range of field of view\n",
    "def get_minmax(array_data):\n",
    "    min_max_x = []\n",
    "    min_max_y = []\n",
    "    for i, user in enumerate(array_data[\"User\"]):\n",
    "        min_x, max_x = minmax(array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"])\n",
    "        min_y, max_y = minmax(array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"])\n",
    "        min_max_x.extend([min_x, max_x])\n",
    "        min_max_y.extend([min_y, max_y])\n",
    "\n",
    "        if array_data[\"Attempt 2 Mask\"][i]:\n",
    "            min_x, max_x = minmax(array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"])\n",
    "            min_y, max_y = minmax(array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"])\n",
    "            min_max_x.extend([min_x, max_x])\n",
    "            min_max_y.extend([min_y, max_y])\n",
    "    min_x, max_x = minmax(min_max_x)\n",
    "    min_y, max_y = minmax(min_max_y)\n",
    "\n",
    "    return min_x, max_x, min_y, max_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "min_x, max_x, min_y, max_y = get_minmax(array_data)\n",
    "\n",
    "number_of_y_aios = 10\n",
    "number_of_x_aios = 15\n",
    "\n",
    "y_mesh = np.linspace(min_y, max_y, number_of_y_aios, endpoint=True)\n",
    "x_mesh = np.linspace(min_x, max_x, number_of_x_aios, endpoint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef433922a3a546eb8237af4830c6acf1",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib widget\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for i, user in enumerate(array_data[\"User\"]):\n",
    "    ax.plot(array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"], array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"], label=user + \" A1\")\n",
    "    if array_data[\"Attempt 2 Mask\"][i]:\n",
    "        ax.plot(array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"], array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"], label=user + \" A2\")\n",
    "ax.set_yticks(y_mesh)\n",
    "ax.set_xticks(x_mesh)\n",
    "ax.yaxis.grid(True, which='major')\n",
    "ax.xaxis.grid(True, which='major')\n",
    "ax.set_ylabel(\"y position of gaze\")\n",
    "ax.set_xlabel(\"x position of gaze\")\n",
    "\n",
    "\n",
    "# plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_aoi(x_coordinate, y_coordinate):\n",
    "    \"\"\"given the x and y, find the aoi; return two indexes corresponding to the x and y position of the aoi\"\"\"\n",
    "    x_index = np.argmin(np.abs(x_coordinate - x_mesh))\n",
    "    if x_coordinate - x_mesh[x_index] > 0:\n",
    "        x_index += 1\n",
    "    y_index = np.argmin(np.abs(y_coordinate - y_mesh))\n",
    "    if y_coordinate - y_mesh[y_index] > 0:\n",
    "        y_index += 1\n",
    "    return x_index, y_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Window Sliding Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "windows = []\n",
    "# windows_labels_int_expert = []\n",
    "# windows_labels_int_good = []\n",
    "# windows_labels_int_bad = []\n",
    "windows_labels_int = {\"Expert\": [], \"Good\": [], \"Bad\":[], \"Str\": []}\n",
    "windows_delta_t = []\n",
    "\n",
    "for i, user in enumerate(array_data[\"User\"]):\n",
    "    for j in range(int((len(array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"])-21)/14)):\n",
    "        try:\n",
    "            windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"][j*14:(j*14 + 21)], \"gazePointAOI_target_y\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"][j*14:(j*14 + 21)]})\n",
    "            # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "            # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "            # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "            windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "            windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "            windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "            windows_delta_t.append(array_data[\"Attempt1\"][i][\"deltaTimestamp\"][j*14:(j*14 + 21)])\n",
    "            \n",
    "            windows_labels_int[\"Str\"].append(array_data[\"Labels Str\"][i])\n",
    "\n",
    "        except IndexError:\n",
    "            windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_x\"][-21:], \"gazePointAOI_target_y\": array_data[\"Attempt1\"][i][\"gazePointAOI_target_y\"][-21:]})\n",
    "            # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "            # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "            # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "            windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "            windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "            windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "            windows_delta_t.append(array_data[\"Attempt1\"][i][\"deltaTimestamp\"][-21:])\n",
    "            \n",
    "            windows_labels_int[\"Str\"].append(array_data[\"Labels Str\"][i])\n",
    "\n",
    "        if array_data[\"Attempt 2 Mask\"][i]:\n",
    "            \n",
    "            try:\n",
    "                windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"][j*14:(j*14 + 21)], \"gazePointAOI_target_y\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"][j*14:(j*14 + 21)]})\n",
    "                # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "                # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "                # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "                windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "                windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "                windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "                windows_delta_t.append(array_data[\"Attempt2\"][i][\"deltaTimestamp\"][j*14:(j*14 + 21)])\n",
    "                \n",
    "                windows_labels_int[\"Str\"].append(array_data[\"Labels Str\"][i])\n",
    "\n",
    "            except IndexError:\n",
    "                windows.append({\"gazePointAOI_target_x\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_x\"][-21:], \"gazePointAOI_target_y\": array_data[\"Attempt2\"][i][\"gazePointAOI_target_y\"][-21:]})\n",
    "                # windows_labels_int_expert.append(array_data[\"Labels Expert\"][i])\n",
    "                # windows_labels_int_good.append(array_data[\"Labels Good\"][i])\n",
    "                # windows_labels_int_bad.append(array_data[\"Labels Bad\"][i])\n",
    "                windows_labels_int[\"Expert\"].append(array_data[\"Labels Expert\"][i])\n",
    "                windows_labels_int[\"Good\"].append(array_data[\"Labels Good\"][i])\n",
    "                windows_labels_int[\"Bad\"].append(array_data[\"Labels Bad\"][i])\n",
    "                windows_delta_t.append(array_data[\"Attempt2\"][i][\"deltaTimestamp\"][-21:])\n",
    "                \n",
    "                windows_labels_int[\"Str\"].append(array_data[\"Labels Str\"][i])\n",
    "                \n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "windows_dict = {\"GazePoints\": ak.Array(windows), \"Labels\": ak.Array(windows_labels_int), \"DeltaTimestamps\": ak.Array(windows_delta_t)}\n",
    "array_windows = ak.Array(windows_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# add metrics to array_windows\n",
    "\n",
    "windowsAOI = []\n",
    "\n",
    "#array with coordinates of AOIs\n",
    "AOIs = [0] * 150\n",
    "i = 0\n",
    "j = 0\n",
    "for x in range(0,len(AOIs)):\n",
    "\n",
    "    AOIs[x] = (int(j), int(i))\n",
    "    i = i + 1\n",
    "    if i == 10:\n",
    "        i = 0\n",
    "        j = j + 1\n",
    "\n",
    "for i, GazePoints in enumerate(array_windows[\"GazePoints\"]):\n",
    "    #TotalAOIs = np.append(TotalAOIs, AOIs)\n",
    "    GazePointsAOI = [0] * 150\n",
    "    GazePointsAOIinstances = [0] * 150    \n",
    "    DwellTime = [0] * 150\n",
    "    averageTime = [0] * 150 #average duration on aoi\n",
    "    averagePoints = [0] * 150 #average number of successive points on aoi\n",
    "    standardDevTime = [0] * 150\n",
    "    standardDevPoints = [0] * 150\n",
    "    for j, xGaze in enumerate(array_windows[\"GazePoints\"][i][\"gazePointAOI_target_x\"]):\n",
    "        aoi = get_aoi(array_windows[\"GazePoints\"][i][\"gazePointAOI_target_x\"][j], array_windows[\"GazePoints\"][i][\"gazePointAOI_target_y\"][j])\n",
    "        x = AOIs.index(aoi)\n",
    "        #calculating mean \n",
    "        if j == 0:\n",
    "            time = array_windows[\"DeltaTimestamps\"][i][j]\n",
    "            points = 1\n",
    "            k = x\n",
    "        elif j == len(array_windows[\"GazePoints\"][i][\"gazePointAOI_target_x\"])-1:\n",
    "            if aoi == last_aoi:\n",
    "                time = time + array_windows[\"DeltaTimestamps\"][i][j]\n",
    "                points = points + 1\n",
    "                averageTime[x] = (GazePointsAOIinstances[x]*averageTime[x]+time)/(GazePointsAOIinstances[x]+1)\n",
    "                averagePoints[x] = (GazePointsAOIinstances[x]*averagePoints[x]+points)/(GazePointsAOIinstances[x]+1)\n",
    "                GazePointsAOIinstances[x] = GazePointsAOIinstances[x] + 1\n",
    "            else:\n",
    "                \n",
    "                averageTime[k] = (GazePointsAOIinstances[k]*averageTime[k]+time)/(GazePointsAOIinstances[k]+1)\n",
    "                averagePoints[k] = (GazePointsAOIinstances[k]*averagePoints[k]+points)/(GazePointsAOIinstances[k]+1)\n",
    "                GazePointsAOIinstances[k] = GazePointsAOIinstances[k] + 1\n",
    "                time = array_windows[\"DeltaTimestamps\"][i][j]\n",
    "                points = 1\n",
    "                averageTime[x] = (GazePointsAOIinstances[x]*averageTime[x]+time)/(GazePointsAOIinstances[x]+1)\n",
    "                averagePoints[x] = (GazePointsAOIinstances[x]*averagePoints[x]+points)/(GazePointsAOIinstances[x]+1)\n",
    "                GazePointsAOIinstances[x] = GazePointsAOIinstances[x] + 1                \n",
    "            \n",
    "        else: #if index not first and not last: check, if aoi is same as last: if true add together \n",
    "            if aoi == last_aoi:\n",
    "                time = time + array_windows[\"DeltaTimestamps\"][i][j]\n",
    "                points = points + 1\n",
    "                k = x\n",
    "            else: #calculate an incremental mean, by multiplying the previous mean with number of instances and adding new value\n",
    "                \n",
    "                averageTime[k] = (GazePointsAOIinstances[k]*averageTime[k]+time)/(GazePointsAOIinstances[k]+1)\n",
    "                averagePoints[k] = (GazePointsAOIinstances[k]*averagePoints[k]+points)/(GazePointsAOIinstances[k]+1)\n",
    "                GazePointsAOIinstances[k] = GazePointsAOIinstances[k] + 1\n",
    "                time = array_windows[\"DeltaTimestamps\"][i][j]\n",
    "                points = 1\n",
    "                k = x\n",
    "            \n",
    "            \n",
    "       \n",
    "        DwellTime[x] = DwellTime[x] + array_windows[\"DeltaTimestamps\"][i][j]\n",
    "        GazePointsAOI[x] = GazePointsAOI[x] + 1\n",
    "        \n",
    "        last_aoi = aoi\n",
    "\n",
    "        \n",
    "#calculate standard deviations   \n",
    "    for s, xGaze in enumerate(array_windows[\"GazePoints\"][i][\"gazePointAOI_target_x\"]):\n",
    "        aoi = get_aoi(array_windows[\"GazePoints\"][i][\"gazePointAOI_target_x\"][s], array_windows[\"GazePoints\"][i][\"gazePointAOI_target_y\"][s])\n",
    "        x = AOIs.index(aoi)\n",
    "        if s == 0:\n",
    "            time = array_windows[\"DeltaTimestamps\"][i][s]\n",
    "            points = 1\n",
    "            k = x\n",
    "        elif s == len(array_windows[\"GazePoints\"][i][\"gazePointAOI_target_x\"])-1:\n",
    "            if aoi == latest_aoi:\n",
    "                time = time + array_windows[\"DeltaTimestamps\"][i][s]\n",
    "                points = points + 1\n",
    "                standardDevTime[x] = standardDevTime[x] + (averageTime[x] - time)**2 \n",
    "                standardDevPoints[x] = standardDevPoints[x] + (averagePoints[x] - points)**2 \n",
    "            else:\n",
    "                \n",
    "                standardDevTime[k] = standardDevTime[k] + (averageTime[k] - time)**2 \n",
    "                standardDevPoints[k] = standardDevPoints[k] + (averagePoints[k] - points)**2\n",
    "                time = array_windows[\"DeltaTimestamps\"][i][s]\n",
    "                points = 1\n",
    "                standardDevTime[x] = standardDevTime[x] + (averageTime[x] - time)**2 \n",
    "                standardDevPoints[x] = standardDevPoints[x] + (averagePoints[x] - points)**2\n",
    "              \n",
    "            \n",
    "        else: #if index not first and not last: check, if aoi is same as last: if true add together \n",
    "            if aoi == latest_aoi:\n",
    "                time = time + array_windows[\"DeltaTimestamps\"][i][s]\n",
    "                points = points + 1\n",
    "                k = x\n",
    "            else: #calculate an incremental mean, by multiplying the previous mean with number of instances and adding new value\n",
    "                \n",
    "                standardDevTime[k] = standardDevTime[k] + (averageTime[k] - time)**2 \n",
    "                standardDevPoints[k] = standardDevPoints[k] + (averagePoints[k] - points)**2\n",
    "                time = array_windows[\"DeltaTimestamps\"][i][s]\n",
    "                points = 1\n",
    "                k = x\n",
    "                    \n",
    "        latest_aoi = aoi\n",
    " \n",
    "        for l in range(0,len(standardDevTime)):\n",
    "            if GazePointsAOIinstances[l] != 0:\n",
    "                standardDevTime[l] = math.sqrt(standardDevTime[l])/GazePointsAOIinstances[l]\n",
    "                standardDevPoints[l] = math.sqrt(standardDevPoints[l])/GazePointsAOIinstances[l]\n",
    "        \n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "    AOI_dict = {\"AOI\": AOIs, \"TotalDwellTime\": DwellTime, \"GazePointsAOI\": GazePointsAOI,\n",
    "                \"Instances\": GazePointsAOIinstances, \"AvTime\": averageTime, \"AvPoints\": averagePoints,\n",
    "                \"StDtime\": standardDevTime, \"StDpoints\": standardDevPoints}\n",
    "    #AOIS = ak.Array(AOI_dict)\n",
    "    windowsAOI.append(AOI_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#windows_dict_feat = {\"GazePoints\": ak.Array(windows), \"Labels\": ak.Array(windows_labels_int), \"DeltaTimestamps\": ak.Array(windows_delta_t), \"features\": ak.Array(windowsAOI)}\n",
    "#array_windows_feat = ak.Array(windows_dict_feat)\n",
    "array_features = ak.from_iter(windowsAOI)\n",
    "array_windows_feat = add_column(array_windows, array_features, 'Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "array_windows_data = array_windows_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fields_wo_aoi = []\n",
    "for field in array_windows_data['Features'].fields:\n",
    "    if field != 'AOI':\n",
    "        fields_wo_aoi.append(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TotalDwellTime',\n",
       " 'GazePointsAOI',\n",
       " 'Instances',\n",
       " 'AvTime',\n",
       " 'AvPoints',\n",
       " 'StDtime',\n",
       " 'StDpoints']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fields_wo_aoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_data = array_windows_data['Features'][fields_wo_aoi]\n",
    "X_data = []\n",
    "for array in x_data:\n",
    "    temp = []\n",
    "    for field in fields_wo_aoi:\n",
    "        temp.extend(array[field].to_numpy())\n",
    "    X_data.append(temp)\n",
    "X_data = np.array(X_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaling = MinMaxScaler(feature_range=(-1,1)).fit(X_data)\n",
    "X_data = scaling.transform(X_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_bad = array_windows_data[\"Labels\"][\"Bad\"]\n",
    "y_good = array_windows_data[\"Labels\"][\"Good\"]\n",
    "y_expert = array_windows_data[\"Labels\"][\"Expert\"]\n",
    "y_dict = {\"Bad\": y_bad, \"Good\": y_good, \"Expert\": y_expert}\n",
    "\n",
    "y_good = (y_good - 1)* (-1)\n",
    "\n",
    "\n",
    "clf_bad = SVC(probability=True)\n",
    "clf_good  = SVC(probability=True)\n",
    "clf_expert = SVC(probability=True)\n",
    "svm_dict = {\"Bad\": clf_bad, \"Good\": clf_good, \"Expert\": clf_expert}\n",
    "\n",
    "'''\n",
    "clf_bad.fit(X_data, y_bad)\n",
    "clf_good .fit(X_data, y_good)\n",
    "clf_expert.fit(X_data, y_expert)\n",
    "'''\n",
    "#y_bad = y_good\n",
    "y_bad = y_expert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "save_dict = y_dict\n",
    "save_dict[\"Data\"] = X_data\n",
    "\n",
    "with open('data/data_and_labels_dict.pkl', 'wb') as f:\n",
    "    pickle.dump(save_dict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get the accuracy of a classifier\n",
    "def get_accuracy(clf, X, labels):\n",
    "    return np.sum([pred == label for pred, label in zip(clf.predict(X), labels)])/len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get the accuracy of a classifier with >p probability\n",
    "def get_accuracy_prob(clf, X, labels, p=0.5):\n",
    "    return np.sum([pred1 >= p for (pred0, pred1), label in zip(clf.predict_proba(X), labels)])/len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nacc_dict = {}\\nfor name, svmachine in svm_dict.items():\\n    acc_dict[name] = get_accuracy(svmachine, X_data,  y_dict[name])\\nacc_dict\\n'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "acc_dict = {}\n",
    "for name, svmachine in svm_dict.items():\n",
    "    acc_dict[name] = get_accuracy(svmachine, X_data,  y_dict[name])\n",
    "acc_dict\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nacc_dict_prob = {}\\nfor name, svmachine in svm_dict.items():\\n    acc_dict_prob[name] = get_accuracy_prob(svmachine, X_data,  y_dict[name], p=0.5)\\nacc_dict_prob\\n'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "acc_dict_prob = {}\n",
    "for name, svmachine in svm_dict.items():\n",
    "    acc_dict_prob[name] = get_accuracy_prob(svmachine, X_data,  y_dict[name], p=0.5)\n",
    "acc_dict_prob\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#np.sum([clf_good.predict_proba(X_data)[:, 1]>=0.5])/len(clf_good.predict_proba(X_data)[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\npredictions = []\\n\\nfor name, svmachine in svm_dict.items():\\n    predictions.append(svmachine.predict_proba(X_data))\\npredictions\\n'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "predictions = []\n",
    "\n",
    "for name, svmachine in svm_dict.items():\n",
    "    predictions.append(svmachine.predict_proba(X_data))\n",
    "predictions\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\npredictions_str = []\\npossible_outcome = [\"Bad\", \"Good\", \"Expert\"]\\nfor a, b, c in zip(predictions[0][:, 1], predictions[1][:, 1], predictions[2][:, 1]):\\n    list = [a, b, c]\\n    index = np.argmax(list)\\n    predictions_str.append(possible_outcome[index])\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "predictions_str = []\n",
    "possible_outcome = [\"Bad\", \"Good\", \"Expert\"]\n",
    "for a, b, c in zip(predictions[0][:, 1], predictions[1][:, 1], predictions[2][:, 1]):\n",
    "    list = [a, b, c]\n",
    "    index = np.argmax(list)\n",
    "    predictions_str.append(possible_outcome[index])\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nis_correct_list = [pred == label for pred, label in zip(predictions_str, array_windows_data[\"Labels\"][\"Str\"])]\\nacc_total = np.sum(is_correct_list)/len(is_correct_list)\\nacc_total\\n'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "is_correct_list = [pred == label for pred, label in zip(predictions_str, array_windows_data[\"Labels\"][\"Str\"])]\n",
    "acc_total = np.sum(is_correct_list)/len(is_correct_list)\n",
    "acc_total\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recall and Matthews Correlation Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculates the recall from given clf and data\n",
    "def get_recall(clf, X, labels):\n",
    "    return np.sum([pred == label and label == 1 for pred, label in zip(clf.predict(X), labels)])/np.sum(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculates the Matthews Correlation Coefficient from given clf and data\n",
    "def get_MCC(clf, X, labels):\n",
    "    tp = np.sum([pred == label and label == 1 for pred, label in zip(clf.predict(X), labels)])\n",
    "    tn = np.sum([pred == label and label == 0 for pred, label in zip(clf.predict(X), labels)])\n",
    "    fp = np.sum([pred != label and label == 0 for pred, label in zip(clf.predict(X), labels)])\n",
    "    fn = np.sum([pred != label and label == 1 for pred, label in zip(clf.predict(X), labels)])\n",
    "    return (tp*tn-fp*fn)/np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F1 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculates the F1 Score from given clf and data\n",
    "def get_F1Score(clf, X, labels):\n",
    "    tp = np.sum([pred == label and label == 1 for pred, label in zip(clf.predict(X), labels)])\n",
    "    fp = np.sum([pred != label and label == 0 for pred, label in zip(clf.predict(X), labels)])\n",
    "    fn = np.sum([pred != label and label == 1 for pred, label in zip(clf.predict(X), labels)])\n",
    "    #print([tp, fp, fn])\n",
    "    return 2*tp/(2*tp+fp+fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## ML Pipeline Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Gets all subsets of a list\n",
    "def get_subsets(fullset):\n",
    "    listrep = fullset\n",
    "    n = len(listrep)\n",
    "    return [[listrep[k] for k in range(n) if i & 1 << k] for i in range(2 ** n)][1:]\n",
    "\n",
    "\n",
    "string = [\"x\", \"y\", \"z\"]\n",
    "# print(get_subsets(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_dict = {'Features': get_subsets(fields_wo_aoi)}\n",
    "# print(len(feature_dict['Features']))\n",
    "#feature_dict['Features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "inner_StratKFold = StratifiedKFold(n_splits=5)\n",
    "X_bad_inner_train = inner_StratKFold.split(X_data, y_bad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for data in X_bad_inner_train:\n",
    "#     print(len(data[0]), len(data[1]))\n",
    "#     print(f\"Training:\\nLabels: {len(y_bad[data[0]])}\\nX: {X_data[data[0]].shape}\")\n",
    "#     print(f\"Test:\\nLabels: {len(y_bad[data[1]])}\\nX: {X_data[data[1]].shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "svm_model = SVC()\n",
    "#GS = GridSearchCV(svm_model, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nested Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the grid of hyperparameters to search\n",
    "param_grid = {\n",
    "    'C': [0.1, 1, 10, 100],        # Regularization parameter\n",
    "    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],  # Type of kernel function\n",
    "    'gamma': ['auto', 'scale']     # Kernel coefficient\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1521, 417) 1521\n"
     ]
    }
   ],
   "source": [
    "#remove all aois that are zero for all windows\n",
    "idx = np.argwhere(np.all(X_data[..., :] == -1, axis=0))\n",
    "reduced_X_data = np.delete(X_data, idx, axis=1)\n",
    "print(reduced_X_data.shape, len(y_bad))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Without StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n#shorten data set for rnCV\\n#labels have been sorted, to get classes one and zero take 30 from start and 30 from end\\nreduced_X_data1 = reduced_X_data[:30,:]\\nreduced_X_data2 = reduced_X_data[-30:,:]\\nreduced_X_data = np.concatenate((reduced_X_data1,reduced_X_data2),axis=0)\\ny_bad1 = y_bad[:30]\\ny_bad2 = y_bad[-30:]\\ny_bad = np.concatenate((y_bad1,y_bad2),axis=0)\\nprint(np.shape(reduced_X_data), np.shape(y_bad))\\n'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "#shorten data set for rnCV\n",
    "#labels have been sorted, to get classes one and zero take 30 from start and 30 from end\n",
    "reduced_X_data1 = reduced_X_data[:30,:]\n",
    "reduced_X_data2 = reduced_X_data[-30:,:]\n",
    "reduced_X_data = np.concatenate((reduced_X_data1,reduced_X_data2),axis=0)\n",
    "y_bad1 = y_bad[:30]\n",
    "y_bad2 = y_bad[-30:]\n",
    "y_bad = np.concatenate((y_bad1,y_bad2),axis=0)\n",
    "print(np.shape(reduced_X_data), np.shape(y_bad))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n        X_data_test = reduced_X_data[(i+1)*int((total/10)):,:]  \\n        X_data2 = reduced_X_data[:i*int((total/10)),:]\\n        #print(np.shape(X_data1), np.shape(X_data2))\\n        X_split = np.concatenate((X_data1,X_data2),axis=0)    \\n        y_bad1 = y_bad[(i+1)*int((total/10)):]  \\n        y_bad2 = y_bad[:i*int((total/10))]\\n        y_bad_split = np.concatenate((y_bad1,y_bad2),axis=0)\\n\\n\\n        X_test_data = reduced_X_data[i*int((total/10)):(i+1)*int((total/10)),:]\\n        y_bad_test_data = y_bad[i*int((total/10)):(i+1)*int((total/10))]\\n        #print(np.shape(X_split), np.shape(X_test_data))\\n\\n\\n        X_train, X_test, y_train_bad, y_test_bad = model_selection.train_test_split(X_split, y_bad_split, test_size=0.3, random_state=seed)\\n        #print(np.shape(X_train), np.shape(y_train_bad))\\n        \\n        '"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "        X_data_test = reduced_X_data[(i+1)*int((total/10)):,:]  \n",
    "        X_data2 = reduced_X_data[:i*int((total/10)),:]\n",
    "        #print(np.shape(X_data1), np.shape(X_data2))\n",
    "        X_split = np.concatenate((X_data1,X_data2),axis=0)    \n",
    "        y_bad1 = y_bad[(i+1)*int((total/10)):]  \n",
    "        y_bad2 = y_bad[:i*int((total/10))]\n",
    "        y_bad_split = np.concatenate((y_bad1,y_bad2),axis=0)\n",
    "\n",
    "\n",
    "        X_test_data = reduced_X_data[i*int((total/10)):(i+1)*int((total/10)),:]\n",
    "        y_bad_test_data = y_bad[i*int((total/10)):(i+1)*int((total/10))]\n",
    "        #print(np.shape(X_split), np.shape(X_test_data))\n",
    "\n",
    "\n",
    "        X_train, X_test, y_train_bad, y_test_bad = model_selection.train_test_split(X_split, y_bad_split, test_size=0.3, random_state=seed)\n",
    "        #print(np.shape(X_train), np.shape(y_train_bad))\n",
    "        \n",
    "        '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_x_data_made_of_specific_feats(desired_feats:list):\n",
    "    x_data = array_windows_data['Features'][desired_feats]\n",
    "    X_data = []\n",
    "    for array in x_data:\n",
    "        temp = []\n",
    "        for feature in desired_feats:\n",
    "            temp.extend(array[feature].to_numpy())\n",
    "        X_data.append(temp)\n",
    "    X_data = np.array(X_data)\n",
    "\n",
    "    scaling = MinMaxScaler(feature_range=(-1,1)).fit(X_data)\n",
    "    X_data = scaling.transform(X_data)\n",
    "    \n",
    "    idx = np.argwhere(np.all(X_data[..., :] == -1, axis=0))\n",
    "    X_data = np.delete(X_data, idx, axis=1)\n",
    "    return X_data\n",
    "\n",
    "# Function to get accuracy\n",
    "def get_accuracy(model, X_test, y_test):\n",
    "    return model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [03:55<00:00, 47.17s/it]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Define parameters\n",
    "#param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf'], 'gamma': ['scale', 'auto']}\n",
    "seeds = [1, 3, 7, 9, 42]\n",
    "output_list = [] \n",
    "# Function to get accuracy\n",
    "def get_accuracy(model, X_test, y_test):\n",
    "    return model.score(X_test, y_test)\n",
    "\n",
    "reduced_X_data = get_x_data_made_of_specific_feats(fields_wo_aoi)\n",
    "\n",
    "# Initialize results storage\n",
    "seedav = []\n",
    "\n",
    "# Outer loop: over different seeds\n",
    "output_list.append(f\"Performing repeated nested cross validation with a data set of length {len(reduced_X_data)}\")\n",
    "for seed in tqdm(seeds, position=0, leave=True):\n",
    "    # Initialize outer cross-validation\n",
    "    outer_cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "    accav = []\n",
    "\n",
    "    # Split data into training and test sets\n",
    "    counter_split = 0\n",
    "    for train_index, test_index in outer_cv.split(reduced_X_data, y_bad):\n",
    "        X_train, X_test = reduced_X_data[train_index], reduced_X_data[test_index]\n",
    "        y_train_bad, y_test_bad = y_bad[train_index], y_bad[test_index]\n",
    "\n",
    "        # Optionally reduce dataset size for training and testing\n",
    "        # Set cutoff to -1 to use the full dataset, or set to a positive number to limit the size\n",
    "        cutoff = -1\n",
    "        if cutoff > 0:\n",
    "            idx_list_train = np.random.permutation(len(y_train_bad))[:cutoff]\n",
    "            cut_X_train, cut_y_train_bad = X_train[idx_list_train], y_train_bad[idx_list_train]\n",
    "\n",
    "            idx_list_test = np.random.permutation(len(y_test_bad))[:cutoff]\n",
    "            cut_X_test, cut_y_test_bad = X_test[idx_list_test], y_test_bad[idx_list_test]\n",
    "        else:\n",
    "            cut_X_train, cut_y_train_bad = X_train, y_train_bad\n",
    "            cut_X_test, cut_y_test_bad = X_test, y_test_bad\n",
    "\n",
    "        # Define the SVM classifier and GridSearch\n",
    "        SVM = SVC()\n",
    "        inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\n",
    "        grid_search = GridSearchCV(estimator=SVM, param_grid=param_grid, cv=inner_cv, scoring='accuracy', n_jobs=-1, verbose=0)\n",
    "\n",
    "        # Perform grid search on reduced training set\n",
    "        grid_search.fit(cut_X_train, cut_y_train_bad)\n",
    "\n",
    "        # Best model from grid search\n",
    "        best_model = grid_search.best_estimator_\n",
    "        test_accuracy = best_model.score(cut_X_test, cut_y_test_bad)\n",
    "        output_list.append(f\"Seed {seed} - Split Variant {counter_split} - Test m-Accuracy: {test_accuracy}\")\n",
    "\n",
    "        # Train best model on full training set\n",
    "        best_params = grid_search.best_params_\n",
    "        best_clf = SVC(C=best_params[\"C\"], kernel=best_params[\"kernel\"], gamma=best_params[\"gamma\"])\n",
    "        best_clf.fit(X_train, y_train_bad)\n",
    "\n",
    "        # Evaluate on full test set\n",
    "        acc = get_accuracy(best_clf, X_test, y_test_bad)\n",
    "        output_list.append(f\"Seed {seed} - Split Variant {counter_split} - n-Accuracy: {acc}\")\n",
    "        accav.append(acc)\n",
    "        counter_split += 1\n",
    "\n",
    "    # Average accuracy for the current seed\n",
    "    meanacc = np.mean(accav)\n",
    "    seedav.append(meanacc)\n",
    "\n",
    "# Final average accuracy across all seeds\n",
    "rnCV = np.mean(seedav)\n",
    "output_list.append(f\"Repeated Nested Cross-Validation Accuracy: {rnCV}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"Output RNCV.txt\", output_list, fmt=\"%s\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score for field TotalDwellTime: 0.0\n",
      "F1 score for field GazePointsAOI: 0.0\n",
      "F1 score for field Instances: 0.10256410256410256\n",
      "F1 score for field AvTime: 0.0\n",
      "F1 score for field AvPoints: 0.0\n",
      "F1 score for field StDtime: 0.0\n",
      "F1 score for field StDpoints: 0.0\n",
      "Selected feature: Instances with index 2\n",
      "F1 score for field TotalDwellTime: 0.22727272727272727\n",
      "F1 score for field GazePointsAOI: 0.22727272727272727\n",
      "F1 score for field AvTime: 0.2727272727272727\n",
      "F1 score for field AvPoints: 0.2727272727272727\n",
      "F1 score for field StDtime: 0.10526315789473684\n",
      "F1 score for field StDpoints: 0.05405405405405406\n",
      "Selected feature: AvTime with index 2\n",
      "F1 score for field TotalDwellTime: 0.2608695652173913\n",
      "F1 score for field GazePointsAOI: 0.2608695652173913\n",
      "F1 score for field AvPoints: 0.2608695652173913\n",
      "F1 score for field StDtime: 0.27906976744186046\n",
      "F1 score for field StDpoints: 0.23809523809523808\n",
      "Selected feature: StDtime with index 3\n",
      "F1 score for field TotalDwellTime: 0.3111111111111111\n",
      "F1 score for field GazePointsAOI: 0.3111111111111111\n",
      "F1 score for field AvPoints: 0.2608695652173913\n",
      "F1 score for field StDpoints: 0.24390243902439024\n",
      "Selected feature: TotalDwellTime with index 0\n",
      "F1 score for field GazePointsAOI: 0.2978723404255319\n",
      "F1 score for field AvPoints: 0.2978723404255319\n",
      "F1 score for field StDpoints: 0.2727272727272727\n",
      "Selected feature: GazePointsAOI with index 0\n",
      "F1 score for field AvPoints: 0.30434782608695654\n",
      "F1 score for field StDpoints: 0.2727272727272727\n",
      "Selected feature: AvPoints with index 0\n",
      "F1 score for field StDpoints: 0.26666666666666666\n",
      "Selected feature: StDpoints with index 0\n"
     ]
    }
   ],
   "source": [
    "#create feature hirarchy by adding one feature and then\n",
    "#selecting the one which gives the highest F1 score\n",
    "\n",
    "remaining_feats = fields_wo_aoi\n",
    "picked_feats = []\n",
    "\n",
    "while len(remaining_feats) != 0: #while still elements in remaining_feats\n",
    "    F1s = []\n",
    "    for field in remaining_feats:\n",
    "            current_feats = np.append(picked_feats, field)\n",
    "            x_data = array_windows_data['Features'][current_feats]\n",
    "            X_data = []\n",
    "            for array in x_data:\n",
    "                temp = []\n",
    "                for feature in current_feats:\n",
    "                    temp.extend(array[feature].to_numpy())\n",
    "                X_data.append(temp)\n",
    "            X_data = np.array(X_data)\n",
    "            \n",
    "            scaling = MinMaxScaler(feature_range=(-1,1)).fit(X_data)\n",
    "            X_data = scaling.transform(X_data)\n",
    "            \n",
    "            idx = np.argwhere(np.all(X_data[..., :] == -1, axis=0))\n",
    "            X_data = np.delete(X_data, idx, axis=1)\n",
    "\n",
    "            X_train, X_test, y_train_bad, y_test_bad = train_test_split(X_data, y_bad, test_size=0.2, random_state=42)\n",
    "            \n",
    "\n",
    "            # Define the SVM classifier\n",
    "            best_clf = SVC(C = best_params[\"C\"], kernel = best_params[\"kernel\"], gamma = best_params[\"gamma\"])\n",
    "            best_clf.fit(X_train, y_train_bad)\n",
    "\n",
    "            F1 = get_F1Score(best_clf, X_test, y_test_bad)\n",
    "            print(f\"F1 score for field {field}: {F1}\")\n",
    "            F1s = np.append(F1s, F1)\n",
    "    idx = np.argmax(F1s) #get index of feature with highest F1 score\n",
    "    print(f\"Selected feature: {remaining_feats[idx]} with index {idx}\")\n",
    "    picked_feats = np.append(picked_feats, remaining_feats[idx])\n",
    "    remaining_feats = np.delete(remaining_feats,idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Instances', 'AvTime', 'StDtime', 'TotalDwellTime',\n",
       "       'GazePointsAOI', 'AvPoints', 'StDpoints'], dtype='<U32')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "picked_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "picked_feats_record = ['Instances', 'AvTime', 'StDtime', 'TotalDwellTime', 'GazePointsAOI', 'AvPoints', 'StDpoints']\n",
    "f1_score_feature_selection = [0.10256410256410256, 0.2727272727272727, 0.27906976744186046, 0.3111111111111111, 0.2978723404255319, 0.30434782608695654, 0.26666666666666666]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "347880ccda464005ba81d8a78877f754",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(range(len(picked_feats_record)), f1_score_feature_selection)\n",
    "plt.xticks(range(len(picked_feats_record)), picked_feats_record, rotation='vertical')\n",
    "plt.xlabel(\"Features to be added from left to right\")\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permutation Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Permutate labeled data\n",
    "def permutate(labels):\n",
    "    '''\n",
    "    labels: labels in a {0, 1} structure\n",
    "    return: labels shuffled\n",
    "    '''\n",
    "    shuffled_labels = np.copy(labels)\n",
    "    np.random.shuffle(shuffled_labels)\n",
    "    return shuffled_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Creating SVM based on Feature selection optimum\n",
    "def get_best_clf_from_specific_feats(feats_of_interest):\n",
    "    reduced_X_data = get_x_data_made_of_specific_feats(feats_of_interest)\n",
    "    print(f\"Searching for best clf in {param_grid} with {len(reduced_X_data)} datapoints with following features: {feats_of_interest}\")\n",
    "\n",
    "    seed = 7\n",
    "\n",
    "    outer_cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "    accav = []\n",
    "\n",
    "    # Split data into training and test sets\n",
    "    counter_split = 0\n",
    "    for train_index, test_index in outer_cv.split(reduced_X_data, y_bad):\n",
    "        X_train, X_test = reduced_X_data[train_index], reduced_X_data[test_index]\n",
    "        y_train_bad, y_test_bad = y_bad[train_index], y_bad[test_index]\n",
    "\n",
    "        # Optionally reduce dataset size for training and testing\n",
    "        # Set cutoff to -1 to use the full dataset, or set to a positive number to limit the size\n",
    "        cutoff = -1\n",
    "        if cutoff > 0:\n",
    "            idx_list_train = np.random.permutation(len(y_train_bad))[:cutoff]\n",
    "            cut_X_train, cut_y_train_bad = X_train[idx_list_train], y_train_bad[idx_list_train]\n",
    "\n",
    "            idx_list_test = np.random.permutation(len(y_test_bad))[:cutoff]\n",
    "            cut_X_test, cut_y_test_bad = X_test[idx_list_test], y_test_bad[idx_list_test]\n",
    "        else:\n",
    "            cut_X_train, cut_y_train_bad = X_train, y_train_bad\n",
    "            cut_X_test, cut_y_test_bad = X_test, y_test_bad\n",
    "\n",
    "        # Define the SVM classifier and GridSearch\n",
    "        SVM = SVC()\n",
    "        inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\n",
    "        grid_search = GridSearchCV(estimator=SVM, param_grid=param_grid, cv=inner_cv, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "\n",
    "        # Perform grid search on reduced training set\n",
    "        print(\"Performing Grid Search...\")\n",
    "        grid_search.fit(cut_X_train, cut_y_train_bad)\n",
    "\n",
    "        # Best model from grid search\n",
    "        best_model = grid_search.best_estimator_\n",
    "        test_accuracy = best_model.score(cut_X_test, cut_y_test_bad)\n",
    "        print(f\"Seed {seed} - Split Variant {counter_split} - Test m-Accuracy: {test_accuracy}\")\n",
    "\n",
    "        # Train best model on full training set\n",
    "        best_params = grid_search.best_params_\n",
    "        best_clf = SVC(C=best_params[\"C\"], kernel=best_params[\"kernel\"], gamma=best_params[\"gamma\"])\n",
    "        print(f\"Performing fit of best model with params: C - {best_params['C']}; Kernel - {best_params['kernel']}; Gamma - {best_params['gamma']} on full data set...\")\n",
    "        best_clf.fit(X_train, y_train_bad)\n",
    "\n",
    "        # Evaluate on full test set\n",
    "        acc = get_accuracy(best_clf, X_test, y_test_bad)\n",
    "        print(f\"Seed {seed} - Split Variant {counter_split} - n-Accuracy: {acc}\")\n",
    "        accav.append(acc)\n",
    "        counter_split += 1\n",
    "        return best_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculates the probability of null hypthesis (estimator not better than random prediction)\n",
    "def calc_prob_null_hypothesis(n_permutations, n_score):\n",
    "    '''\n",
    "    n_permutations: number of permutations\n",
    "    n_score: number of times with better prediction\n",
    "    return: probability of null hypothesis €[0, 1]\n",
    "    '''\n",
    "    return (1+n_score)/(1+n_permutations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for best clf in {'C': [0.1, 1, 10, 100], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'gamma': ['auto', 'scale']} with 1521 datapoints with following features: ['Instances', 'AvTime', 'StDtime', 'TotalDwellTime']\n",
      "Performing Grid Search...\n",
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "Seed 7 - Split Variant 0 - Test m-Accuracy: 0.9019607843137255\n",
      "Performing fit of best model with params: C - 0.1; Kernel - linear; Gamma - auto on full data set...\n",
      "Seed 7 - Split Variant 0 - n-Accuracy: 0.9019607843137255\n"
     ]
    }
   ],
   "source": [
    "feats_of_interest = picked_feats_record[:4]\n",
    "best_clf = get_best_clf_from_specific_feats(feats_of_interest)\n",
    "reduced_X_data = get_x_data_made_of_specific_feats(feats_of_interest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 999/999 [01:28<00:00, 11.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of null hypothesis: 0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Number of permutations\n",
    "n_permutations = 999\n",
    "# Score on original data\n",
    "data_score = get_F1Score(best_clf, reduced_X_data, y_bad)\n",
    "# Get scores for permutated labels\n",
    "scores = []\n",
    "y_bad_np = y_bad.to_numpy()\n",
    "for permutation in tqdm(range(n_permutations)):\n",
    "    #print(f\"Calculating permutation {permutation}/{n_permutations}\", end=\"\\r\")#, flush=True)\n",
    "    permutated_y_labels = permutate(y_bad_np)\n",
    "    scores.append(get_F1Score(best_clf, reduced_X_data, permutated_y_labels))\n",
    "# Calculate number of times where permutated labels performed better\n",
    "n_score = np.sum([score >= data_score for score in scores])\n",
    "# Calculate probability for null hypothesis\n",
    "p_hypothesis = calc_prob_null_hypothesis(n_permutations, n_score)\n",
    "print(f\"Probability of null hypothesis: {p_hypothesis}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}