search_session_length.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9036c3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import base\n",
    "\n",
    "LENGTH = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f3a6299",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lengths_of_searches():\n",
    "    grouped_users = base.get_dataset_and_group_by_user()\n",
    "    each_user_lengths_success = {}\n",
    "    each_user_lengths_fail = {}\n",
    "    for username, group in grouped_users:\n",
    "        this_user_length_success = []\n",
    "        this_user_length_fail = []\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",
    "            # check if user found the target\n",
    "            if target_group[target_group['guess_video'] == target_group['target_video']].empty:\n",
    "                this_user_length_fail.append(target_group.shape[0])\n",
    "            else:\n",
    "                this_user_length_success.append(target_group.shape[0])\n",
    "        # add result to final dictionary\n",
    "        each_user_lengths_success[username] = this_user_length_success\n",
    "        each_user_lengths_fail[username] = this_user_length_fail\n",
    "    return each_user_lengths_success, each_user_lengths_fail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8710ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/usr/local/lib/python3.9/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean = um.true_divide(\n",
      "/usr/local/lib/python3.9/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
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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": [
    "%matplotlib inline\n",
    "\n",
    "users_success_search_lengths, users_failed_search_lengths = lengths_of_searches()\n",
    "x = []  # users\n",
    "y_success = []  # mean (successful searches)\n",
    "e_success = []  # standard deviation (successful searches)\n",
    "n_success = []  # number of successful searches\n",
    "y_fail = []  # mean (failed searches)\n",
    "e_fail = []  # standard deviation (failed searches)\n",
    "n_fail = []  # number of failed searches\n",
    "\n",
    "order = []\n",
    "for user, success_search_lengths in users_success_search_lengths.items():\n",
    "    succ_length = len(success_search_lengths)\n",
    "    overall_length = len(users_failed_search_lengths[user]) + succ_length\n",
    "    ratio = succ_length / overall_length\n",
    "    order.append((user, ratio))\n",
    "\n",
    "order.sort(key=lambda tup: tup[1])\n",
    "\n",
    "for (user, _) in order:\n",
    "    x.append(user)\n",
    "    tmp = len(users_success_search_lengths[user])\n",
    "    if not tmp:\n",
    "        tmp = 1\n",
    "    y_success.append(sum(users_success_search_lengths[user]) / tmp)\n",
    "    e_success.append(np.std(users_success_search_lengths[user]))\n",
    "    n_success.append(len(users_success_search_lengths[user]))\n",
    "\n",
    "for (user, _) in order:\n",
    "    y_fail.append(sum(users_failed_search_lengths[user]) / len(users_failed_search_lengths[user]))\n",
    "    e_fail.append(np.std(users_failed_search_lengths[user]))\n",
    "    n_fail.append(len(users_failed_search_lengths[user]))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "from matplotlib.transforms import ScaledTranslation\n",
    "trans1 = ax.transData + ScaledTranslation(-2 / 72, 0, fig.dpi_scale_trans)\n",
    "trans2 = ax.transData + ScaledTranslation(+2 / 72, 0, fig.dpi_scale_trans)\n",
    "er1 = ax.errorbar(x, y_success, yerr=e_success, linestyle='None', marker='^', transform=trans1, label=\"success\")\n",
    "er2 = ax.errorbar(x, y_fail, yerr=e_fail, linestyle='None', marker='^', transform=trans2, label=\"fail\")\n",
    "\n",
    "iterator = 0\n",
    "for i in n_fail:\n",
    "    plt.rc('font', size=12)\n",
    "    plt.text(x[iterator], y_fail[iterator] + 0.1, i, transform=trans2)\n",
    "    iterator += 1\n",
    "\n",
    "iterator = 0\n",
    "trans1 = ax.transData + ScaledTranslation(-11 / 72, 0, fig.dpi_scale_trans)\n",
    "for i in n_success:\n",
    "    plt.rc('font', size=12)\n",
    "    plt.text(x[iterator], y_success[iterator] + 0.1, i, transform=trans1)\n",
    "    iterator += 1\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "txt = 'The number indicates how many (un)successful atempts were made by user'\n",
    "plt.figtext(1.2, .08, txt, ha='center')\n",
    "plt.title(\"Mean value and deviance\")\n",
    "plt.figure(figsize=(20,20))\n",
    "fig.subplots_adjust(0,0,2.5,2.5) # make plots bigger in Jupyter\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "311803d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_search_session_lengths():\n",
    "    grouped_users = base.get_dataset_with_browsing_id_and_group_by_user()\n",
    "    length_count = [0] * LENGTH\n",
    "    for username, group in grouped_users:\n",
    "        group = base.filter_out_mess(group)\n",
    "        grouped_targets = group.groupby('target_id')\n",
    "        for target, target_group in grouped_targets:\n",
    "            last_browsing_id = target_group['browsing_id'].max()\n",
    "            last_browsing_session = target_group[target_group['browsing_id'] == last_browsing_id]\n",
    "            length = last_browsing_session.shape[0]\n",
    "            if length < LENGTH:\n",
    "                for i in range(length):\n",
    "                    length_count[i] += 1\n",
    "    return length_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6772a28e",
   "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": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "user_mean_len = get_search_session_lengths()\n",
    "\n",
    "plt.title(\"Length of search sessions\")\n",
    "plt.xlabel('Length')\n",
    "plt.ylabel('Volume of search sessions ')\n",
    "plt.plot(range(LENGTH), user_mean_len)\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()"
   ]
  }
 ],
 "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
}