IPS vs Non-IPS.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# _RecoGym_: IPS vs Non-IPS\n",
    "\n",
    "\n",
    "# Please Note the run time of this notebook is in the many many hours \n",
    "\n",
    "In this notebook you shall find comparision of performance of two _`Agents`_:\n",
    "* _Logistic Regression_ _**without**_ IPS;\n",
    "* _Logistic Regression_ _**with**_ IPS.\n",
    "\n",
    "In all cases, an training data will be used as a _Random_ _`Agent`_ has been applied in the _`Environment`_.\n",
    "\n",
    "**Note:** the evaluation of the data provided in that notebook takes some time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {},
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib notebook\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.rcParams['figure.figsize'] = [6, 3]\n",
    "\n",
    "from recogym import build_agent_init, env_1_args, gather_agent_stats, plot_agent_stats\n",
    "\n",
    "RandomSeed = 42\n",
    "\n",
    "TrainingDataSamples = (100, 500, 1000, 2000, 3000, 5000, 8000, 10000, 13000, 14000, 15000)\n",
    "TestingDataSamples = 15000\n",
    "StatEpochs = 5\n",
    "StatEpochsNewRandomSeed = True\n",
    "\n",
    "std_env_args = {\n",
    "    **env_1_args,\n",
    "    'random_seed': RandomSeed,\n",
    "}\n",
    "\n",
    "env = gym.make('reco-gym-v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## _Uniform_ Data Set\n",
    "\n",
    "In this experiment, we will use a uniform data set obtained after applying so called _Random_ _`Agent`_ i.e. the _`Agent`_ that randomply selects a _`Product`_.\n",
    "\n",
    "Data uniformity means that _Propensity Score_ is _**always**_ the same. In case of _10_ _`Products`_ $PS=\\frac{1}{10}=0.1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Import _`Agents`_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {},
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from recogym.agents import LogregPolyAgent, logreg_poly_args\n",
    "from recogym.agents import LogregMulticlassIpsAgent, logreg_multiclass_ips_args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "agent_inits = {\n",
    "    **build_agent_init(\n",
    "        'LogReg Poly IPS',\n",
    "        LogregPolyAgent,\n",
    "        {\n",
    "            **logreg_poly_args,\n",
    "            'with_ips': True,\n",
    "        }\n",
    "    ),\n",
    "    **build_agent_init(\n",
    "        'LogReg MC IPS',\n",
    "        LogregMulticlassIpsAgent,\n",
    "        {\n",
    "            **logreg_multiclass_ips_args,\n",
    "        }\n",
    "    ),\n",
    "    **build_agent_init(\n",
    "        'LogReg Poly Non-IPS',\n",
    "        LogregPolyAgent,\n",
    "        {\n",
    "            **logreg_poly_args,\n",
    "            'with_ips': False,\n",
    "        }\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Gathering statistics..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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      "Start: Num of Offline Users: 500\n",
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      "End: Agent Testing #2 (633.3289861679077s)\n",
      "End: Num of Offline Users: 15000 (633.9511857032776s)\n",
      "Agent: LogReg Poly Non-IPS\n",
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      "End: Agent Testing #0 (1718.3339676856995s)\n",
      "End: Agent Testing #4 (1756.925576210022s)\n",
      "End: Agent Testing #1 (1787.7738564014435s)\n",
      "End: Num of Offline Users: 100 (1787.9713559150696s)\n",
      "Start: Num of Offline Users: 500\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #3 (1783.9331970214844s)\n",
      "End: Agent Testing #0 (1802.2905004024506s)\n",
      "End: Agent Testing #2 (1814.9286029338837s)\n",
      "End: Agent Testing #4 (1821.6523385047913s)\n",
      "End: Agent Testing #1 (2322.2007653713226s)\n",
      "End: Num of Offline Users: 500 (2322.459484100342s)\n",
      "Start: Num of Offline Users: 1000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #0 (1811.555524110794s)\n",
      "End: Agent Testing #1 (1842.669049501419s)\n",
      "End: Agent Testing #2 (1848.5557565689087s)\n",
      "End: Agent Testing #3 (1856.7012121677399s)\n",
      "End: Agent Testing #4 (1976.8285269737244s)\n",
      "End: Num of Offline Users: 1000 (1977.0836148262024s)\n",
      "Start: Num of Offline Users: 2000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #2 (1911.7099747657776s)\n",
      "End: Agent Testing #0 (1927.8603072166443s)\n",
      "End: Agent Testing #1 (1951.2265989780426s)\n",
      "End: Agent Testing #4 (1982.445473432541s)\n",
      "End: Agent Testing #3 (2268.000550031662s)\n",
      "End: Num of Offline Users: 2000 (2268.3087372779846s)\n",
      "Start: Num of Offline Users: 3000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #3 (1941.4026436805725s)\n",
      "End: Agent Testing #2 (1953.3104138374329s)\n",
      "End: Agent Testing #0 (1975.599633693695s)\n",
      "End: Agent Testing #4 (1975.6919951438904s)\n",
      "End: Agent Testing #1 (2456.137384414673s)\n",
      "End: Num of Offline Users: 3000 (2456.3899512290955s)\n",
      "Start: Num of Offline Users: 5000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "End: Agent Testing #3 (2050.9159574508667s)\n",
      "End: Agent Testing #2 (2055.4260065555573s)\n",
      "End: Agent Testing #0 (2086.5075421333313s)\n",
      "End: Agent Testing #1 (2153.702278137207s)\n",
      "End: Agent Testing #4 (2342.307878255844s)\n",
      "End: Num of Offline Users: 5000 (2342.712946653366s)\n",
      "Start: Num of Offline Users: 8000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #0 (2100.415378808975s)\n",
      "End: Agent Testing #1 (2127.8301169872284s)\n",
      "End: Agent Testing #4 (2129.8117859363556s)\n",
      "End: Agent Testing #2 (2135.1566174030304s)\n",
      "End: Agent Testing #3 (2304.7597241401672s)\n",
      "End: Num of Offline Users: 8000 (2305.1330626010895s)\n",
      "Start: Num of Offline Users: 10000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #2 (2022.453545331955s)\n",
      "End: Agent Testing #0 (2034.382695198059s)\n",
      "End: Agent Testing #1 (2035.6800906658173s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End: Agent Testing #4 (2037.7801575660706s)\n",
      "End: Agent Testing #3 (2039.4104461669922s)\n",
      "End: Num of Offline Users: 10000 (2039.8572149276733s)\n",
      "Start: Num of Offline Users: 13000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #2 (2079.6795525550842s)\n",
      "End: Agent Testing #0 (2105.7659797668457s)\n",
      "End: Agent Testing #3 (2122.1428236961365s)\n",
      "End: Agent Testing #4 (2178.4573159217834s)\n",
      "End: Agent Testing #1 (2288.306578874588s)\n",
      "End: Num of Offline Users: 13000 (2288.860973596573s)\n",
      "Start: Num of Offline Users: 14000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #0 (2139.9414715766907s)\n",
      "End: Agent Testing #2 (2141.37504863739s)\n",
      "End: Agent Testing #1 (2146.1320464611053s)\n",
      "End: Agent Testing #3 (2157.927404642105s)\n",
      "End: Agent Testing #4 (2271.2948956489563s)\n",
      "End: Num of Offline Users: 14000 (2271.897241592407s)\n",
      "Start: Num of Offline Users: 15000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #2 (2133.0698137283325s)\n",
      "End: Agent Testing #1 (2171.352687358856s)\n",
      "End: Agent Testing #3 (2173.8745532035828s)\n",
      "End: Agent Testing #4 (2244.606478214264s)\n",
      "End: Agent Testing #0 (2414.229348421097s)\n",
      "End: Num of Offline Users: 15000 (2414.8596272468567s)\n"
     ]
    }
   ],
   "source": [
    "agent_stats01 = gather_agent_stats(\n",
    "    env,\n",
    "    std_env_args,\n",
    "    {\n",
    "        'num_products': 10,\n",
    "        'number_of_flips': 5,\n",
    "    },\n",
    "    agent_inits,\n",
    "    TrainingDataSamples,\n",
    "    TestingDataSamples,\n",
    "    StatEpochs,\n",
    "    StatEpochsNewRandomSeed\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Plotting results..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {},
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 481,
       "width": 964
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_agent_stats(agent_stats01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "<sup>_**Note:**_ here, _`Samples #`_ is the amount of user's data taken for training.</sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## _Non-Uniform_ Data Set\n",
    "\n",
    "On the contrary to the previous case, a _**non**-uniform_ data set will be used.\n",
    "The data are obtained via using _Organic User Event Counter_ _`Agent`_, i.e. the _`Agent`_ that selects the most popular _`Product`_ (most frequently viewed in _Organic_ _`Events`_) with the highest probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {},
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from recogym import Configuration\n",
    "from recogym.agents import OrganicUserEventCounterAgent, organic_user_count_args"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Gathering statistics..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agent: LogReg Poly IPS\n",
      "Start: Num of Offline Users: 100\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #1 (1801.902762413025s)\n",
      "End: Agent Testing #3 (1814.5190861225128s)\n",
      "End: Agent Testing #4 (1817.0288858413696s)\n",
      "End: Agent Testing #2 (1818.1759209632874s)\n",
      "End: Agent Testing #0 (2265.4033217430115s)\n",
      "End: Num of Offline Users: 100 (2265.6292266845703s)\n",
      "Start: Num of Offline Users: 500\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #1 (1916.9961535930634s)\n",
      "End: Agent Testing #4 (1958.3680062294006s)\n",
      "End: Agent Testing #3 (1966.4427201747894s)\n",
      "End: Agent Testing #2 (1968.2353956699371s)\n",
      "End: Agent Testing #0 (1998.5010669231415s)\n",
      "End: Num of Offline Users: 500 (1998.7680687904358s)\n",
      "Start: Num of Offline Users: 1000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #0 (2003.8638501167297s)\n",
      "End: Agent Testing #1 (2014.9926178455353s)\n",
      "End: Agent Testing #3 (2015.0028860569s)\n",
      "End: Agent Testing #4 (2019.8602895736694s)\n",
      "End: Agent Testing #2 (2026.1647446155548s)\n",
      "End: Num of Offline Users: 1000 (2026.3994779586792s)\n",
      "Start: Num of Offline Users: 2000\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #0 (2122.43549990654s)\n",
      "End: Agent Testing #1 (2171.3896763324738s)\n",
      "End: Agent Testing #4 (2177.4597177505493s)\n",
      "End: Agent Testing #2 (2182.190032482147s)\n",
      "End: Agent Testing #3 (2436.403225660324s)\n",
      "End: Num of Offline Users: 2000 (2436.6069202423096s)\n",
      "Start: Num of Offline Users: 3000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "End: Agent Testing #0 (2145.832134962082s)\n",
      "End: Agent Testing #1 (2168.055833339691s)\n",
      "End: Agent Testing #2 (2170.3686802387238s)\n",
      "End: Agent Testing #4 (2195.1510405540466s)\n",
      "End: Agent Testing #3 (2484.1356444358826s)\n",
      "End: Num of Offline Users: 3000 (2484.3812873363495s)\n",
      "Start: Num of Offline Users: 5000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #4 (2240.467137813568s)\n",
      "End: Agent Testing #1 (2241.8852384090424s)\n",
      "End: Agent Testing #2 (2245.6199259757996s)\n",
      "End: Agent Testing #3 (2250.738755464554s)\n",
      "End: Agent Testing #0 (2579.1326904296875s)\n",
      "End: Num of Offline Users: 5000 (2579.428035736084s)\n",
      "Start: Num of Offline Users: 8000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #0 (2257.1968874931335s)\n",
      "End: Agent Testing #3 (2316.6458892822266s)\n",
      "End: Agent Testing #4 (2352.328977584839s)\n",
      "End: Agent Testing #1 (2448.9497787952423s)\n",
      "End: Agent Testing #2 (2489.2477946281433s)\n",
      "End: Num of Offline Users: 8000 (2489.632205724716s)\n",
      "Start: Num of Offline Users: 10000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #0 (2269.908657312393s)\n",
      "End: Agent Testing #3 (2323.1443667411804s)\n",
      "End: Agent Testing #2 (2326.2210302352905s)\n",
      "End: Agent Testing #4 (2365.102258682251s)\n",
      "End: Agent Testing #1 (2767.153163433075s)\n",
      "End: Num of Offline Users: 10000 (2767.585988521576s)\n",
      "Start: Num of Offline Users: 13000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #1 (2386.183475971222s)\n",
      "End: Agent Testing #3 (2396.4253554344177s)\n",
      "End: Agent Testing #2 (2431.8701214790344s)\n",
      "End: Agent Testing #0 (2512.764686346054s)\n",
      "End: Agent Testing #4 (2614.3090257644653s)\n",
      "End: Num of Offline Users: 13000 (2614.9700145721436s)\n",
      "Start: Num of Offline Users: 14000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #1 (2421.433500289917s)\n",
      "End: Agent Testing #0 (2457.60346531868s)\n",
      "End: Agent Testing #2 (2497.2205777168274s)\n",
      "End: Agent Testing #3 (2664.1216485500336s)\n",
      "End: Agent Testing #4 (2757.2256152629852s)\n",
      "End: Num of Offline Users: 14000 (2757.966609477997s)\n",
      "Start: Num of Offline Users: 15000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #3 (2427.0623812675476s)\n",
      "End: Agent Testing #0 (2428.2771492004395s)\n",
      "End: Agent Testing #2 (2453.582023382187s)\n",
      "End: Agent Testing #4 (2509.6864585876465s)\n",
      "End: Agent Testing #1 (2691.2917273044586s)\n",
      "End: Num of Offline Users: 15000 (2691.8698065280914s)\n",
      "Agent: LogReg MC IPS\n",
      "Start: Num of Offline Users: 100\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #1 (247.90605568885803s)\n",
      "End: Agent Testing #4 (253.22250247001648s)\n",
      "End: Agent Testing #0 (258.5104730129242s)\n",
      "End: Agent Testing #2 (264.2562520503998s)\n",
      "End: Agent Testing #3 (264.5538811683655s)\n",
      "End: Num of Offline Users: 100 (264.7518985271454s)\n",
      "Start: Num of Offline Users: 500\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #4 (316.6542429924011s)\n",
      "End: Agent Testing #3 (333.69819808006287s)\n",
      "End: Agent Testing #2 (341.05859899520874s)\n",
      "End: Agent Testing #0 (350.92945075035095s)\n",
      "End: Agent Testing #1 (361.824072599411s)\n",
      "End: Num of Offline Users: 500 (362.08191180229187s)\n",
      "Start: Num of Offline Users: 1000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #3 (382.79923033714294s)\n",
      "End: Agent Testing #4 (383.9971888065338s)\n",
      "End: Agent Testing #1 (384.3878290653229s)\n",
      "End: Agent Testing #0 (394.07025027275085s)\n",
      "End: Agent Testing #2 (394.73004388809204s)\n",
      "End: Num of Offline Users: 1000 (394.988165140152s)\n",
      "Start: Num of Offline Users: 2000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "End: Agent Testing #0 (426.0658702850342s)\n",
      "End: Agent Testing #4 (437.96524453163147s)\n",
      "End: Agent Testing #2 (448.2162854671478s)\n",
      "End: Agent Testing #3 (453.2672574520111s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End: Agent Testing #1 (455.32191586494446s)\n",
      "End: Num of Offline Users: 2000 (455.6169159412384s)\n",
      "Start: Num of Offline Users: 3000\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #0 (479.318470954895s)\n",
      "End: Agent Testing #4 (547.0708658695221s)\n",
      "End: Agent Testing #1 (548.2455453872681s)\n",
      "End: Agent Testing #2 (556.9925065040588s)\n",
      "End: Agent Testing #3 (572.9061887264252s)\n",
      "End: Num of Offline Users: 3000 (573.2254211902618s)\n",
      "Start: Num of Offline Users: 5000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #0 (581.3446168899536s)\n",
      "End: Agent Testing #1 (622.4671771526337s)\n",
      "End: Agent Testing #3 (643.0971486568451s)\n",
      "End: Agent Testing #4 (643.4497561454773s)\n",
      "End: Agent Testing #2 (659.8883686065674s)\n",
      "End: Num of Offline Users: 5000 (660.277086019516s)\n",
      "Start: Num of Offline Users: 8000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #0 (735.1547772884369s)\n",
      "End: Agent Testing #1 (765.5772767066956s)\n",
      "End: Agent Testing #3 (774.1670322418213s)\n",
      "End: Agent Testing #4 (788.5499789714813s)\n",
      "End: Agent Testing #2 (833.5919368267059s)\n",
      "End: Num of Offline Users: 8000 (834.0460965633392s)\n",
      "Start: Num of Offline Users: 10000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #0\n",
      "End: Agent Testing #1 (792.3451781272888s)\n",
      "End: Agent Testing #2 (850.1645493507385s)\n",
      "End: Agent Testing #0 (856.0780284404755s)\n",
      "End: Agent Testing #3 (871.4297232627869s)\n",
      "End: Agent Testing #4 (872.7243168354034s)\n",
      "End: Num of Offline Users: 10000 (873.2835373878479s)\n",
      "Start: Num of Offline Users: 13000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #0 (934.0892775058746s)\n",
      "End: Agent Testing #3 (952.9732825756073s)\n",
      "End: Agent Testing #2 (962.958003282547s)\n",
      "End: Agent Testing #1 (974.5791466236115s)\n",
      "End: Agent Testing #4 (1070.7266910076141s)\n",
      "End: Num of Offline Users: 13000 (1071.3717274665833s)\n",
      "Start: Num of Offline Users: 14000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #0 (951.6120216846466s)\n",
      "End: Agent Testing #3 (1001.3209717273712s)\n",
      "End: Agent Testing #1 (1022.0608050823212s)\n",
      "End: Agent Testing #2 (1075.7938468456268s)\n",
      "End: Agent Testing #4 (1130.0532903671265s)\n",
      "End: Num of Offline Users: 14000 (1130.7899105548859s)\n",
      "Start: Num of Offline Users: 15000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #0 (963.1721622943878s)\n",
      "End: Agent Testing #3 (1053.3129427433014s)\n",
      "End: Agent Testing #1 (1091.4611954689026s)\n",
      "End: Agent Testing #2 (1100.604342699051s)\n",
      "End: Agent Testing #4 (1234.0925223827362s)\n",
      "End: Num of Offline Users: 15000 (1234.7924237251282s)\n",
      "Agent: LogReg Poly Non-IPS\n",
      "Start: Num of Offline Users: 100\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #0 (1718.4405674934387s)\n",
      "End: Agent Testing #1 (1740.956264257431s)\n",
      "End: Agent Testing #4 (1755.254168510437s)\n",
      "End: Agent Testing #2 (1766.124223947525s)\n",
      "End: Agent Testing #3 (1770.674031496048s)\n",
      "End: Num of Offline Users: 100 (1770.8883287906647s)\n",
      "Start: Num of Offline Users: 500\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #0 (1845.4644305706024s)\n",
      "End: Agent Testing #1 (1849.5136213302612s)\n",
      "End: Agent Testing #3 (1855.9025573730469s)\n",
      "End: Agent Testing #4 (1861.0531363487244s)\n",
      "End: Agent Testing #2 (1867.3437235355377s)\n",
      "End: Num of Offline Users: 500 (1867.5394825935364s)\n",
      "Start: Num of Offline Users: 1000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #0 (1884.3240811824799s)\n",
      "End: Agent Testing #3 (1897.4169385433197s)\n",
      "End: Agent Testing #2 (1910.197961807251s)\n",
      "End: Agent Testing #1 (2064.1232130527496s)\n",
      "End: Agent Testing #4 (2109.862909078598s)\n",
      "End: Num of Offline Users: 1000 (2110.0938200950623s)\n",
      "Start: Num of Offline Users: 2000\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #0 (2051.213133573532s)\n",
      "End: Agent Testing #1 (2064.0191538333893s)\n",
      "End: Agent Testing #2 (2076.5581605434418s)\n",
      "End: Agent Testing #4 (2088.4787364006042s)\n",
      "End: Agent Testing #3 (2558.281997203827s)\n",
      "End: Num of Offline Users: 2000 (2558.5196661949158s)\n",
      "Start: Num of Offline Users: 3000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "End: Agent Testing #2 (2158.8878943920135s)\n",
      "End: Agent Testing #4 (2205.0874485969543s)\n",
      "End: Agent Testing #3 (2213.6842091083527s)\n",
      "End: Agent Testing #0 (2267.894539833069s)\n",
      "End: Agent Testing #1 (2545.9665620326996s)\n",
      "End: Num of Offline Users: 3000 (2546.2379088401794s)\n",
      "Start: Num of Offline Users: 5000\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #0\n",
      "End: Agent Testing #1 (2287.952070713043s)\n",
      "End: Agent Testing #3 (2304.9895856380463s)\n",
      "End: Agent Testing #0 (2322.1395564079285s)\n",
      "End: Agent Testing #2 (2331.8127315044403s)\n",
      "End: Agent Testing #4 (2664.3112032413483s)\n",
      "End: Num of Offline Users: 5000 (2664.6315863132477s)\n",
      "Start: Num of Offline Users: 8000\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #2\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "End: Agent Testing #0 (2326.7491137981415s)\n",
      "End: Agent Testing #3 (2378.343650341034s)\n",
      "End: Agent Testing #1 (2387.2972190380096s)\n",
      "End: Agent Testing #4 (2444.706783771515s)\n",
      "End: Agent Testing #2 (2770.0825369358063s)\n",
      "End: Num of Offline Users: 8000 (2770.488278388977s)\n",
      "Start: Num of Offline Users: 10000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #1 (2470.4743320941925s)\n",
      "End: Agent Testing #2 (2488.8489484786987s)\n",
      "End: Agent Testing #3 (2515.4414134025574s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End: Agent Testing #0 (2521.753465652466s)\n",
      "End: Agent Testing #4 (2640.742980480194s)\n",
      "End: Num of Offline Users: 10000 (2641.307643175125s)\n",
      "Start: Num of Offline Users: 13000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #0\n",
      "End: Agent Testing #0 (2432.998384475708s)\n",
      "End: Agent Testing #1 (2488.246004343033s)\n",
      "End: Agent Testing #2 (2563.986263990402s)\n",
      "End: Agent Testing #4 (2571.883232831955s)\n",
      "End: Agent Testing #3 (2939.101254463196s)\n",
      "End: Num of Offline Users: 13000 (2939.6213014125824s)\n",
      "Start: Num of Offline Users: 14000\n",
      "Start: Agent Training #4\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #0\n",
      "Start: Agent Testing #3\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #0\n",
      "End: Agent Testing #3 (2558.488128185272s)\n",
      "End: Agent Testing #0 (2586.245452642441s)\n",
      "End: Agent Testing #1 (2590.031410217285s)\n",
      "End: Agent Testing #4 (2650.7615287303925s)\n",
      "End: Agent Testing #2 (3128.510712623596s)\n",
      "End: Num of Offline Users: 14000 (3129.1313688755035s)\n",
      "Start: Num of Offline Users: 15000\n",
      "Start: Agent Training #1\n",
      "Start: Agent Training #0\n",
      "Start: Agent Training #3\n",
      "Start: Agent Training #2\n",
      "Start: Agent Training #4\n",
      "Start: Agent Testing #0\n",
      "Start: Agent Testing #4\n",
      "Start: Agent Testing #2\n",
      "Start: Agent Testing #1\n",
      "Start: Agent Testing #3\n",
      "End: Agent Testing #3 (2507.207220554352s)\n",
      "End: Agent Testing #0 (2565.1233851909637s)\n",
      "End: Agent Testing #1 (2606.974851131439s)\n",
      "End: Agent Testing #4 (2647.8876259326935s)\n",
      "End: Agent Testing #2 (2693.805719614029s)\n",
      "End: Num of Offline Users: 15000 (2694.3780987262726s)\n"
     ]
    }
   ],
   "source": [
    "agent_stats02 = gather_agent_stats(\n",
    "    env,\n",
    "    std_env_args,\n",
    "    {\n",
    "        'num_products': 10,\n",
    "        'number_of_flips': 5,\n",
    "        'agent': OrganicUserEventCounterAgent(Configuration({\n",
    "            **organic_user_count_args,\n",
    "            **std_env_args,\n",
    "            'select_randomly': True,\n",
    "        })),\n",
    "    },\n",
    "    agent_inits,\n",
    "    TrainingDataSamples,\n",
    "    TestingDataSamples,\n",
    "    StatEpochs,\n",
    "    StatEpochsNewRandomSeed\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Plotting results..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 481,
       "width": 964
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_agent_stats(agent_stats02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "<sup>_**Note:**_ here, _`Samples #`_ is the amount of user's data taken for training.</sup>"
   ]
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    "# Resolution\n",
    "\n",
    "* _**Uniform**_ Data Set\n",
    "    * All kinds of _Logistic Regressions_ show almost the same performance.\n",
    "* _**Non-Uniform**_ Data Set\n",
    "    * _Logistic Regression with Multi-Classification_ (_LogReg MC IPS_) reveals a pretty good performance results under _Non-Uniform_ training data set.\n",
    "    * _Logistic Regression_ _**without**_ IPS (_LogReg Poly Non-IPS_) shows a slightly better results, rather than _Logistic Regression **with** IPS_ (_LogReg Poly IPS_).\n",
    "    \n",
    "\n",
    "Also, in that study, it is quite evident that for _Logistic Regression_ there is a strong correlation: the more significant volume of training data is, the better _Click-Through Rare_ is."
   ]
  }
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