target_distance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d43f6686",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import base\n",
    "import sys\n",
    "\n",
    "XMAX = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c4092e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_failed_targets(type):\n",
    "    grouped_users = base.get_dataset_and_group_by_user()\n",
    "    each_user_distances_top = {}\n",
    "    each_user_distances_som = {}\n",
    "\n",
    "    distance_indexes = []\n",
    "    for i in range(64):\n",
    "        distance_indexes.append('D' + str(i) + '_distance_to_target')\n",
    "\n",
    "    for username, group in grouped_users:\n",
    "        user_distances_top = []\n",
    "        user_distances_som = []\n",
    "        group = base.filter_out_mess(group)\n",
    "        group_by_target = group.groupby('target_id')\n",
    "\n",
    "        for target, target_group in group_by_target:\n",
    "            distances = target_group[target_group['type'] == 'feedback'][distance_indexes]\n",
    "            \n",
    "            result_ratios = []\n",
    "            for i in range(distances.shape[0]):\n",
    "                new_distances = distances.iloc[i].mean()\n",
    "                result_ratios.append(distances.iloc[i].mean(axis=0))\n",
    "            # check if user found the target\n",
    "            if target_group.iloc[0][\"display_type\"] == \"top\":\n",
    "                if target_group[target_group['guess_video'] == target_group['target_video']].empty:\n",
    "                    if type == 'asc':\n",
    "                        user_distances_top.append(result_ratios)\n",
    "                    else:\n",
    "                        user_distances_top.append(list(reversed(result_ratios)))\n",
    "\n",
    "            else:\n",
    "                if target_group[target_group['guess_video'] == target_group['target_video']].empty:\n",
    "                    if type == 'asc':\n",
    "                        user_distances_som.append(result_ratios)\n",
    "                    else:\n",
    "                        user_distances_som.append(list(reversed(result_ratios)))\n",
    "\n",
    "        # calculate median over columns (median for each iteration)\n",
    "        each_user_distances_top[username] = pd.DataFrame(user_distances_top).median(axis=0, skipna=True)\n",
    "        each_user_distances_som[username] = pd.DataFrame(user_distances_som).median(axis=0, skipna=True)\n",
    "\n",
    "    return each_user_distances_top, each_user_distances_som\n",
    "\n",
    "\n",
    "def get_success_targets(type):\n",
    "    grouped_users = base.get_dataset_and_group_by_user()\n",
    "    each_user_distances_top = {}\n",
    "    each_user_distances_som = {}\n",
    "\n",
    "    distance_indexes = []\n",
    "    for i in range(64):\n",
    "        distance_indexes.append('D' + str(i) + '_distance_to_target')\n",
    "\n",
    "    for username, group in grouped_users:\n",
    "        user_distances_top = []\n",
    "        user_distances_som = []\n",
    "        group = base.filter_out_mess(group)\n",
    "        group_by_target = group.groupby('target_id')\n",
    "\n",
    "        for target, target_group in group_by_target:\n",
    "            distances = target_group[target_group['type'] == 'feedback'][distance_indexes]\n",
    "\n",
    "            result_ratios = []\n",
    "            for i in range(distances.shape[0]):\n",
    "                new_distances = distances.iloc[i].mean()\n",
    "                result_ratios.append(distances.iloc[i].mean(axis=0))\n",
    "            # check if user found the target\n",
    "            if target_group.iloc[0][\"display_type\"] == \"top\":\n",
    "                if not target_group[target_group['guess_video'] == target_group['target_video']].empty:\n",
    "                    if type == 'asc':\n",
    "                        user_distances_top.append(result_ratios)\n",
    "                    else:\n",
    "                        user_distances_top.append(list(reversed(result_ratios)))\n",
    "\n",
    "            else:\n",
    "                if not target_group[target_group['guess_video'] == target_group['target_video']].empty:\n",
    "                    if type == 'asc':\n",
    "                        user_distances_som.append(result_ratios)\n",
    "                    else:\n",
    "                        user_distances_som.append(list(reversed(result_ratios)))\n",
    "\n",
    "        # calculate median over columns (median for each iteration)\n",
    "        each_user_distances_top[username] = pd.DataFrame(user_distances_top).median(axis=0, skipna=True)\n",
    "        each_user_distances_som[username] = pd.DataFrame(user_distances_som).median(axis=0, skipna=True)\n",
    "\n",
    "    return each_user_distances_top, each_user_distances_som\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2bdd8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target_rank_plot(type):\n",
    "    %matplotlib inline\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    fail_top, fail_som = get_failed_targets(type)\n",
    "    success_top, success_som = get_success_targets(type)\n",
    "\n",
    "    agregated_users_fail_top = pd.DataFrame(fail_top).median(axis=1, skipna=True)\n",
    "    agregated_users_fail_som = pd.DataFrame(fail_som).median(axis=1, skipna=True)\n",
    "\n",
    "    agregated_users_success_top = pd.DataFrame(success_top).median(axis=1, skipna=True)\n",
    "    agregated_users_success_som = pd.DataFrame(success_som).median(axis=1, skipna=True)\n",
    "\n",
    "    agregated_users_success_top.plot.line(label=\"TOP - success\")\n",
    "    agregated_users_fail_top.plot.line(label=\"TOP - failure\")\n",
    "    agregated_users_success_som.plot.line(label=\"SOM - success\")\n",
    "    agregated_users_fail_som.plot.line(label=\"SOM - failure\")\n",
    "\n",
    "\n",
    "    # print number of entries (failure)\n",
    "    iterator = 0\n",
    "    xmax = XMAX\n",
    "    if type=='asc':\n",
    "        plt.xlabel('Iteration')\n",
    "    else:\n",
    "        plt.xlabel('Reversed iterations (last is the last iteration, last-1 is the last but one)')\n",
    "    plt.ylabel('Mean target distance')\n",
    "\n",
    "    plt.xticks(list(range(xmax)))\n",
    "    plt.xlim(xmin=0, xmax=xmax)\n",
    "    plt.title(\"Mean target distance\")\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    if type=='desc':\n",
    "        plt.xticks(range(XMAX), [\"last\"] + [\"last-\" + str(i) for i in range(1,XMAX)])\n",
    "        plt.gca().invert_xaxis()\n",
    "    \n",
    "    plt.figure(figsize=(20,20))\n",
    "    fig.subplots_adjust(0,0,2.5,2.5) # make plots bigger in Jupyter\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3d3a1d9",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What was average target distance at the beggining of browsing session\n",
    "\n",
    "get_target_rank_plot('asc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17d4ef80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What was average target distance at the end of browsing session\n",
    "\n",
    "get_target_rank_plot('desc')"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}