Likelihood Agents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning from Logged Bandit Feedbacks\n",
    "\n",
    "One of the most common ways that recommender systems are developed in practice involves building **models** trained on the historical behaviour of the running policy.  This is to be distinguished from bandit approaches such as _Upper Confidence Bound_ or _Thompson Sampling_ or _Full Reinforcement Learning_ as in these systems there is no clear separation between a learning stage and an acting stage.  In the approaches considered here, we first learn a model and then deploy a static model that does not change further.\n",
    "\n",
    "Here we describe a simple supervised approach where we model the probability of the click conditional upon features that are created from a combination of the user's attributes and the recommendation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym, reco_gym\n",
    "from copy import deepcopy\n",
    "from reco_gym import env_1_args\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib notebook\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.rcParams['figure.figsize'] = [6, 3]\n",
    "\n",
    "ABTestNumberOfUsers = 10000\n",
    "RandomSeed = 42\n",
    "NumberOfProducts = 10\n",
    "\n",
    "NumberOfSamples = 20\n",
    "\n",
    "env_1_args['random_seed'] = RandomSeed\n",
    "env_1_args['num_products'] = NumberOfProducts\n",
    "env_1_args['K'] = 5\n",
    "env_1_args['number_of_flips'] = 1\n",
    "\n",
    "env = gym.make('reco-gym-v1')\n",
    "env.init_gym(env_1_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = deepcopy(env).generate_logs(ABTestNumberOfUsers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the content of the logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>ps</th>\n",
       "      <th>ps-a</th>\n",
       "      <th>t</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bandit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bandit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bandit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bandit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bandit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bandit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      a    c   ps                                               ps-a   t  u  \\\n",
       "0   NaN  NaN  NaN                                               None   0  0   \n",
       "1   3.0  0.0  0.1  [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...   1  0   \n",
       "2   4.0  0.0  0.1  [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...   2  0   \n",
       "3   5.0  0.0  0.1  [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...   3  0   \n",
       "4   NaN  NaN  NaN                                               None   0  1   \n",
       "5   2.0  0.0  0.1  [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...   1  1   \n",
       "6   8.0  0.0  0.1  [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...   2  1   \n",
       "7   4.0  0.0  0.1  [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, ...   3  1   \n",
       "8   NaN  NaN  NaN                                               None   4  1   \n",
       "9   NaN  NaN  NaN                                               None   5  1   \n",
       "10  NaN  NaN  NaN                                               None   6  1   \n",
       "11  NaN  NaN  NaN                                               None   7  1   \n",
       "12  NaN  NaN  NaN                                               None   8  1   \n",
       "13  NaN  NaN  NaN                                               None   9  1   \n",
       "14  NaN  NaN  NaN                                               None  10  1   \n",
       "15  NaN  NaN  NaN                                               None  11  1   \n",
       "16  NaN  NaN  NaN                                               None  12  1   \n",
       "17  NaN  NaN  NaN                                               None  13  1   \n",
       "18  NaN  NaN  NaN                                               None  14  1   \n",
       "19  NaN  NaN  NaN                                               None  15  1   \n",
       "\n",
       "      v        z  \n",
       "0   0.0  organic  \n",
       "1   NaN   bandit  \n",
       "2   NaN   bandit  \n",
       "3   NaN   bandit  \n",
       "4   1.0  organic  \n",
       "5   NaN   bandit  \n",
       "6   NaN   bandit  \n",
       "7   NaN   bandit  \n",
       "8   4.0  organic  \n",
       "9   4.0  organic  \n",
       "10  4.0  organic  \n",
       "11  4.0  organic  \n",
       "12  4.0  organic  \n",
       "13  6.0  organic  \n",
       "14  4.0  organic  \n",
       "15  4.0  organic  \n",
       "16  4.0  organic  \n",
       "17  6.0  organic  \n",
       "18  4.0  organic  \n",
       "19  1.0  organic  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:NumberOfSamples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Shape: (1005241, 8)\n"
     ]
    }
   ],
   "source": [
    "print(\"Data Shape:\", data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the data the following columns are present:\n",
    "* **`t`**—_`Time`_. Currently, _`Time`_ reflects the order of _`Events`_ but it does **not** represent the notion of the time in a physical sense.\n",
    "* **`u`**—_`User`_.\n",
    "* **`z`**—_`Event Type`_. There are two types of _`Events`_: _Organic_ and _Bandit_.\n",
    "* **`v`**—_`View`_. The column shows which _`Product`_ was shown to a _`User`_ in an _Organic_ _`Event`_.\n",
    "* **`a`**—_`Action`_. Currently, _`Action`_ is a _`Product`_ that was provided to a _`User`_ during a _Bandit_ _`Event`_.\n",
    "* **`c`**—_`Click`_. This is a _Reward_ for an _`Action`_ provided by the _`Agent`_.\n",
    "* **`ps`**—Probability of selecting a particular _`Action`_.\n",
    "\n",
    "**Note #1:** _`Time`_, _`User`_, _`Views`_, and _`Actions`_ **all** them start with _`0`_.\n",
    "\n",
    "**Note #2:** For any _`User`_, _Organic_ _`Event`_ _**always**_ precedes a _Bandit_ _`Event`_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "Let's analyse these histograms:\n",
    "* _Actions per Product_\n",
    "* _Clicks per Product_\n",
    "* _Views per Product_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126445908>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "ax.set_title('Histogram of Actions per Product')\n",
    "\n",
    "ax.hist(data[data['z'] == 'bandit']['a'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is evident that all _`Actions`_ are _*evenly*_ distributed.\n",
    "That is an expected behaviour because *all* _`Actions`_ are randomly applied for all _Bandit_ _`Events`_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clicks:  [924.0, 732.0, 1038.0, 626.0, 1187.0, 1129.0, 609.0, 709.0, 840.0, 1136.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123ede588>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clicks = []\n",
    "bandits = data[data['z'] == 'bandit']\n",
    "for product_id in range(NumberOfProducts):\n",
    "    actions = bandits[bandits['a'] == product_id]\n",
    "    clicks.append(np.sum(actions[actions['c'] == 1]['c']))\n",
    "    \n",
    "print(\"Clicks: \", clicks)\n",
    "\n",
    "_, ax = plt.subplots()\n",
    "ax.set_title('Histogram of Clicks per Product')\n",
    "\n",
    "ax.bar(range(NumberOfProducts), clicks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Views:  [19273, 5141, 858, 15776, 63187, 31339, 2996, 12900, 6980, 58392]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12af2aa58>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "organics = data[data['z'] == 'organic']\n",
    "\n",
    "views = []\n",
    "for product_id in range(NumberOfProducts):\n",
    "    views.append(organics[organics['v'] == product_id].shape[0])\n",
    "\n",
    "print(\"Views: \", views)\n",
    "\n",
    "_, ax = plt.subplots()\n",
    "ax.set_title('Histogram of Views per Product')\n",
    "\n",
    "ax.bar(range(NumberOfProducts), views)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our analysis is based on _Click-Through Rates_ (_CTR_), let's build dependencies of CTR from the number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fa09358>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 264,
       "width": 939
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axs = plt.subplots(1, 3, figsize=(16, 4))\n",
    "\n",
    "for plot_ix, amount_of_users in [(0, 100), (1, 1000), (2, 10000)]:\n",
    "    ctr_data = deepcopy(env).generate_logs(amount_of_users)\n",
    "    ctrs = [[] for _ in range(NumberOfProducts)]\n",
    "    bandits = ctr_data[ctr_data['z'] == 'bandit']\n",
    "    for user_id in range(amount_of_users):\n",
    "        user_data = bandits[bandits['u'] == user_id]\n",
    "        for product_id in range(NumberOfProducts):\n",
    "            bandits_for_product = user_data[user_data['a'] == product_id]\n",
    "            if bandits_for_product.shape[0] == 0:\n",
    "                ctr = 0\n",
    "            else:\n",
    "                amount_of_clicks = bandits_for_product[bandits_for_product['c'] == 1].shape[0]\n",
    "                ctr = float(amount_of_clicks) / bandits_for_product.shape[0]\n",
    "            ctrs[product_id].append(ctr)\n",
    "    stats = []\n",
    "    for product_id in range(NumberOfProducts):\n",
    "        stats.append(\n",
    "            (\n",
    "                np.var(ctrs[product_id]), \n",
    "                np.mean(ctrs[product_id]), \n",
    "                np.max(ctrs[product_id]), \n",
    "                np.min(ctrs[product_id])\n",
    "            )\n",
    "        )\n",
    "    axs[plot_ix].set_title(f'$Click-Through\\ Rates$ for {amount_of_users} Users')\n",
    "    axs[plot_ix].boxplot(stats)\n",
    "    axs[plot_ix].set_yscale('log')\n",
    "\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's build the histogram of the distribution of _`Actions`_ per users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120238b00>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 495
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [8, 4]\n",
    "_, ax = plt.subplots()\n",
    "ax.set_title('Histogram of Actions Distribution per User')\n",
    "\n",
    "actions_per_user = []\n",
    "for user_id in range(int(data.u.max()) + 1):\n",
    "    user_data = bandits[bandits['u'] == user_id]\n",
    "    user_actions = user_data[user_data['z'] == 'bandit']\n",
    "    actions_per_user.append(len(user_actions))\n",
    "\n",
    "ax.hist(actions_per_user)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turn Data into Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to build a _Logistic Regression_ model.\n",
    "\n",
    "The model will predict _the probability of the click_ for the following data:\n",
    "* _`Views`_ is a total amount of views of a particular _`Product`_ shown during _Organic_ _`Events`_ **before** a _Bandit_ _`Event`_.\n",
    "* _`Action`_ is a proposed _`Product`_ at a _Bandit_ _`Event`_.\n",
    "\n",
    "For example, assume that we have _`10`_ products. In _Organic_ _`Events`_, these products  were shown to a user as follows:\n",
    "<table>\n",
    "    <tr>\n",
    "        <th>Product ID</th>\n",
    "        <th>Views</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>0</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>1</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>2</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>3</td>\n",
    "        <td>7</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>4</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>5</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>6</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>7</td>\n",
    "        <td>8</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>8</td>\n",
    "        <td>11</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>9</td>\n",
    "        <td>0</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "When we want to know the probability of the click for _`Product`_ = _`8`_ with available amounts of _`Views`_, the input data for the model will be:\n",
    "\n",
    "_`0 0 0 7 0 0 0 0 8 11 0`_ _**`8`**_\n",
    "\n",
    "The first 10 numbers are _`Views`_ of _`Products`_ (see above), the latest one is the _`Action`_.\n",
    "\n",
    "The output will be two numbers:\n",
    "* $0^{th}$ index: $1 - \\mathbb{P}_c(P=p|V)$.\n",
    "* $1^{st}$ index: $\\mathbb{P}_c(P=p|V)$.\n",
    "\n",
    "Here, $\\mathbb{P}_c(P=p|V)$ is the probability of the click for a _`Product`_ $p$, provided that we have _`Views`_ $V$.\n",
    "\n",
    "\n",
    "In all following models, an _`Action`_ will not be used as a number, but it will be decoded as a _vector_.\n",
    "In our current example, the _`Action`_ is _`8`_. Thus, it is encoded as:\n",
    "\n",
    "_`0 0 0 0 0 0 0 0`_ _**`1`**_ _`0`_\n",
    "\n",
    "Here,\n",
    "* Vector of _`Actions`_ has a size that is equal to the _*number of `Products`*_ i.e. _`10`_.\n",
    "* _`Action`_ _`8`_ is marked as _`1`_ (_`Action`_ starts with _`0`_)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def build_train_data(\n",
    "        data,\n",
    "        mark_action_in_views = False,\n",
    "        with_normalization = False,\n",
    "        weight_history_function = None\n",
    "):\n",
    "    \"\"\"\n",
    "    Build Train Data\n",
    "\n",
    "        Parameters:\n",
    "            data: offline experiment logs\n",
    "                the data contains both Organic and Bandit Events\n",
    "            mark_action_in_views(bool): adds an extra column into a feature set;\n",
    "                the column has 1 if in an Action that corresponds to a certain Product\n",
    "                 has at least one preceding View in Organic Events for the current User\n",
    "            with_normalization(bool): all Views are normalized to the value in range [0; 1];\n",
    "            weight_history_function(function): weight functions that assigns an appropriate weight\n",
    "                for View in Organic Event for a certain Product\n",
    "\n",
    "        Returns:\n",
    "            :(features, outs)\n",
    "    \"\"\"\n",
    "    num_products = int(data.v.max() + 1)\n",
    "    number_of_users = int(data.u.max()) + 1\n",
    "\n",
    "    features = []\n",
    "    outs = []\n",
    "\n",
    "    for user_id in range(number_of_users):\n",
    "        views = np.zeros((0, num_products))\n",
    "        for _, user_datum in data[data['u'] == user_id].iterrows():\n",
    "            if user_datum['z'] == 'organic':\n",
    "                assert (math.isnan(user_datum['a']))\n",
    "                assert (math.isnan(user_datum['c']))\n",
    "                assert (not math.isnan(user_datum['v']))\n",
    "\n",
    "                view = int(user_datum['v'])\n",
    "\n",
    "                tmp_view = np.zeros(num_products)\n",
    "\n",
    "                tmp_view[view] = 1\n",
    "\n",
    "                # Append the latest view at the beginning of all views.\n",
    "                views = np.append(tmp_view[np.newaxis, :], views, axis = 0)\n",
    "            else:\n",
    "                assert (user_datum['z'] == 'bandit')\n",
    "                assert (not math.isnan(user_datum['a']))\n",
    "                assert (not math.isnan(user_datum['c']))\n",
    "                assert (math.isnan(user_datum['v']))\n",
    "\n",
    "                action = int(user_datum['a'])\n",
    "                action_flags = np.zeros(num_products, dtype = np.int8)\n",
    "                action_flags[int(action)] = 1\n",
    "\n",
    "                click = int(user_datum['c'])\n",
    "\n",
    "                if weight_history_function is None:\n",
    "                    train_views = views\n",
    "                else:\n",
    "                    history = np.array(range(views.shape[0])).reshape(views.shape[0], 1)\n",
    "                    weights = weight_history_function(history)\n",
    "                    train_views = views * weights\n",
    "\n",
    "                feature = np.sum(train_views, axis = 0)\n",
    "\n",
    "                if with_normalization:\n",
    "                    if np.sum(feature) == 0:\n",
    "                        feature = feature / np.sum(feature)\n",
    "\n",
    "                feature = np.append(feature, action_flags)\n",
    "\n",
    "                if mark_action_in_views:\n",
    "                    feature = np.append(feature, 0 if feature[action] == 0 else 1)\n",
    "\n",
    "                features.append(feature)\n",
    "                outs.append(click)\n",
    "\n",
    "    return np.array(features), np.array(outs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features01, train_outs01 = build_train_data(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample of the training data you shall find below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Features:\n",
      " [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n",
      "Click Probabilities (Outputs):\n",
      " [0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Features:\\n\", train_features01[:5])\n",
    "print(\"Click Probabilities (Outputs):\\n\", train_outs01[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the train data.\n",
    "\n",
    "* At time _`0`_, for a _`User`_ = _`0`_ there is an _Organic_ _`Event`_. The _`Event`_ contains a _`View`_ of _`Product`_ = _`4`_.\n",
    "* At time _`1`_, for the same _`User`_ a new _Bandit_ _`Event`_ goes. Now, when the _Bandit_ _`Event`_ happens, we already have the following _`Views`_: _`0 0 0 0`_ _**`1`**_ _`0 0 0 0 0`_. Next, we apply an _`Action`_ _`6`_ and receive for that _`Action`_ reward (click) _`0`_. Thus, the input for the training of our _Logistic Regression_ model will be:\n",
    "    * **Input:**\n",
    "        * _`Views`:_ _`0 0 0 0`_ _**`1`**_ _`0 0 0 0 0`_\n",
    "        * _`Action`:_ _`0 0 0 0 0 0`_ _**`1`**_ _`0 0 0`_\n",
    "    * **Output:** _`0`_\n",
    "\n",
    "* Next, at time _`2`_, a next _Bandit_ _`Event`_ goes. Since we have only one _Organic_ _`Event`_, _`Views`_ have not changed and they remain the same as for the _Bandit_ _`Event`_ at time _`1`_. However, this time we try an _`Action`_ = _`3`_. Thus, the training data looks as follows:\n",
    "    * **Input:**\n",
    "        * _`Views`:_ _`0 0 0 0`_ _**`1`**_ _`0 0 0 0 0`_\n",
    "        * _`Action`:_ _`0 0 0`_ _**`1`**_ _`0 0 0 0 0 0`_\n",
    "    * **Output:** _`0`_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg_base = LogisticRegression(\n",
    "    solver = 'lbfgs',\n",
    "    max_iter = 2000,\n",
    "    random_state = RandomSeed\n",
    ")\n",
    "\n",
    "lr_base = logreg_base.fit(train_features01, train_outs01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how the _Logistic Regression_ model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Features:\n",
      " [[ 0.  0. 10.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.\n",
      "   0.  0.]]\n",
      "Click Probabilities (Outputs):  [[0.98738126 0.01261874]]\n"
     ]
    }
   ],
   "source": [
    "# Check the probability of getting a click for Product ID '2' with 10 observations for that Product.\n",
    "test_features01 = np.zeros((1, 2 * NumberOfProducts))\n",
    "\n",
    "test_features01[:, 2] = 10\n",
    "test_features01[:, NumberOfProducts + 2] = 1\n",
    "test_out01 = lr_base.predict_proba(test_features01)\n",
    "\n",
    "print(\"Test Features:\\n\", test_features01)\n",
    "print(\"Click Probabilities (Outputs): \", test_out01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below you shall find a plot of _the probability of the click_ for different _`Products`_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe6b208>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 939
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "for product_id in range(NumberOfProducts):\n",
    "    counts = []\n",
    "    prob1s = []\n",
    "    test_data = np.zeros((1, 2 * NumberOfProducts))\n",
    "    test_data[:, NumberOfProducts + product_id] = 1\n",
    "    for count in range(1, 200):\n",
    "        test_data[:, product_id] = count\n",
    "        test_out = lr_base.predict_proba(test_data)\n",
    "        counts.append(count)\n",
    "        prob1s.append(test_out[0][1])\n",
    "\n",
    "    axs[0].loglog(counts, prob1s, label = f\"{product_id}\")\n",
    "    axs[1].plot(counts, prob1s, label = f\"{product_id}\")\n",
    "\n",
    "labels = [f\"{ix}\" for ix in range(NumberOfProducts)]\n",
    "axs[0].set_title(f'LogLog Scale')\n",
    "axs[0].legend(labels)\n",
    "axs[1].set_title(f'Normal Scale')\n",
    "axs[1].legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00682682, -0.00513229, -0.00210275,  0.01438348,  0.00197324,\n",
       "        -0.0027341 ,  0.00108254, -0.00377397,  0.00614944,  0.00133733,\n",
       "        -0.34703091, -0.5872998 , -0.2295926 , -0.73973899, -0.09535172,\n",
       "        -0.14542796, -0.76193027, -0.60896858, -0.44172883, -0.13473659]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_base.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the _probability of the click_ grows quite slowly for most of _`Products`_ as grows the amount of _`Views`_ or these _`Products`_ in _Organic_ _`Events`_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency of _`Views`_\n",
    "\n",
    "In a new model, _`Views`_ are normalised: instead of using an exact number of _`Views`_, a frequency of _`Views`_ is used:\n",
    "\n",
    "$$ \\tilde{v}_i = \\frac{v_i}{\\sum_{p \\in P}v_p} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128d125f8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 956
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_features02, test_outs02 = build_train_data(data, False, True)\n",
    "\n",
    "logreg_freq = LogisticRegression(\n",
    "    solver = 'lbfgs',\n",
    "    max_iter = 2000,\n",
    "    random_state = RandomSeed\n",
    ")\n",
    "\n",
    "lr_freq = logreg_freq.fit(test_features02, test_outs02)\n",
    "\n",
    "figs, axs = plt.subplots(1, 2, figsize = (16, 8))\n",
    "\n",
    "for product_id in range(NumberOfProducts):\n",
    "    counts = []\n",
    "    prob1s = []\n",
    "    test_data = np.zeros((1, 2 * NumberOfProducts))\n",
    "    test_data[:, NumberOfProducts + product_id] = 1\n",
    "    test_data[:, :NumberOfProducts] = 1  # Set 1 for ALL products.\n",
    "    for count in range(1, 100):\n",
    "        test_data[:, product_id] = count\n",
    "        norm_test_data = test_data.copy()\n",
    "        norm_test_data[:, :NumberOfProducts] = test_data[:, :NumberOfProducts] / np.sum(test_data[:, :NumberOfProducts])\n",
    "        test_out = lr_freq.predict_proba(norm_test_data)\n",
    "        counts.append(count)\n",
    "        prob1s.append(test_out[0][1])\n",
    "\n",
    "    axs[0].loglog(counts, prob1s)\n",
    "    axs[1].plot(counts, prob1s)\n",
    "\n",
    "axs[0].set_title(f'LogLog Scale')\n",
    "axs[0].legend(labels)\n",
    "axs[1].set_title(f'Normal Scale')\n",
    "axs[1].legend(labels)\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00682682, -0.00513229, -0.00210275,  0.01438348,  0.00197324,\n",
       "        -0.0027341 ,  0.00108254, -0.00377397,  0.00614944,  0.00133733,\n",
       "        -0.34703091, -0.5872998 , -0.2295926 , -0.73973899, -0.09535172,\n",
       "        -0.14542796, -0.76193027, -0.60896858, -0.44172883, -0.13473659]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_freq.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Mark of the Presence of an _`Action`_ in _`Views`_\n",
    "\n",
    "So far, the features were based on _`Views`_ and _`Actions`_ _irrespectively_ to the presence of any _`View`_ that corresponds to the _`Action`_.\n",
    "\n",
    "At this time, we will introduce a new mark into features: _the Marker of the Presence of an `Action` in `Views`_ and set it as follows:\n",
    "* _**`1`**_ if an _`Action`_ that corresponds to a certain _`Product`_ was present at least once in preceding _Organic_ _`Events`_.\n",
    "* _**`0`**_ in another case.\n",
    "\n",
    "\n",
    "**E.G.:**\n",
    "For previously considered data, we will have the following:\n",
    "* At time _`8`_:\n",
    "    * **Input:**\n",
    "        * _`Views`:_ _`0 0 0 0`_ _**`1`**_ _`0 0 `_ _**`1`**_ _**`3`**_ _`0`_\n",
    "        * _`Action`:_ _`0 0 0 0`_ _**`1`**_ _`0 0 0 0 0`_\n",
    "        * _`Mark`:_ _**`1`**_ (there is _`1`_ in _`View`_ for _`Product`_ = _`4`_)\n",
    "    * **Output:** _`0`_\n",
    "* At time _`9`_:\n",
    "    * **Input:**\n",
    "        * _`Views`:_ _`0 0 0 0`_ _**`1`**_ _`0 0 `_ _**`1`**_ _**`3`**_ _`0`_\n",
    "        * _`Action`:_ _`0 0 0 0 0 0`_ _**`1`**_ _`0 0 0`_\n",
    "        * _`Mark`:_ _**`0`**_ (there is **no** any _`View`_ for _`Product`_ = _`6`_)\n",
    "    * **Output:** _`0`_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fa1c780>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 939
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_features03, test_outs03 = build_train_data(data, True)\n",
    "\n",
    "logreg_with_mark = LogisticRegression(\n",
    "    solver = 'lbfgs',\n",
    "    max_iter = 2000,\n",
    "    random_state = RandomSeed\n",
    ")\n",
    "\n",
    "lr_with_mark = logreg_with_mark.fit(test_features03, test_outs03)\n",
    "\n",
    "figs, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "for product_id in range(NumberOfProducts):\n",
    "    counts = []\n",
    "    prob1s = []\n",
    "    test_data = np.zeros((1, 2 * NumberOfProducts + 1)) # The lattest column is the marker.\n",
    "    test_data[:, NumberOfProducts + product_id] = 1\n",
    "    test_data[:, 2 * NumberOfProducts] = 1\n",
    "    for count in range(1, 100):\n",
    "        test_data[:, product_id] = count\n",
    "        test_out = logreg_with_mark.predict_proba(test_data)\n",
    "        counts.append(count)\n",
    "        prob1s.append(test_out[0][1])\n",
    "\n",
    "    axs[0].loglog(counts, prob1s)\n",
    "    axs[1].plot(counts, prob1s)\n",
    "\n",
    "axs[0].set_title(f'LogLog Scale')\n",
    "axs[0].legend(labels)\n",
    "axs[1].set_title(f'Normal Scale')\n",
    "axs[1].legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.94091255e-03, -9.63213855e-03, -2.01240085e-02,\n",
       "         1.25781254e-02,  1.44769955e-03, -5.34697611e-03,\n",
       "        -3.29749666e-04, -5.10838484e-03,  4.87405197e-05,\n",
       "         6.73808673e-04, -3.52893507e-01, -5.26056282e-01,\n",
       "        -1.42539481e-01, -7.82688828e-01, -1.98734015e-01,\n",
       "        -2.31495981e-01, -6.85250876e-01, -5.71444832e-01,\n",
       "        -4.33515367e-01, -2.32977510e-01,  2.91381971e-01]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_with_mark.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Feature Set\n",
    "\n",
    "Previously, a linear set of features was considered. Now we are going to analyse a polynomial set of features, i.e. the set of the features those are either in the second degree or those are in a specific combination with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "test_features04, test_outs04 = build_train_data(data)\n",
    "\n",
    "# Instantiate Polynomial feature of the 2nd degree.\n",
    "poly2 = PolynomialFeatures(2)\n",
    "test_features_poly = poly2.fit_transform(test_features04)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the set of _features_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features of Training Data in 2nd Polinomial Degree:\n",
      " [1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Features of Training Data in 2nd Polinomial Degree Shape: (788399, 231)\n",
      "Feature of Training Data Selection Flags:  [False  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False False False\n",
      " False False False False False False False  True  True  True  True  True\n",
      "  True  True  True  True  True False False False False False False False\n",
      " False False  True  True  True  True  True  True  True  True  True  True\n",
      " False False False False False False False False  True  True  True  True\n",
      "  True  True  True  True  True  True False False False False False False\n",
      " False  True  True  True  True  True  True  True  True  True  True False\n",
      " False False False False False  True  True  True  True  True  True  True\n",
      "  True  True  True False False False False False  True  True  True  True\n",
      "  True  True  True  True  True  True False False False False  True  True\n",
      "  True  True  True  True  True  True  True  True False False False  True\n",
      "  True  True  True  True  True  True  True  True  True False False  True\n",
      "  True  True  True  True  True  True  True  True  True False  True  True\n",
      "  True  True  True  True  True  True  True  True False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False]\n",
      "Features of Training Data in 2nd Polinomial Degree with Selection:\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Features of Training Data in 2nd Polinomial Degree with Selection Shape: (788399, 120)\n"
     ]
    }
   ],
   "source": [
    "print(\"Features of Training Data in 2nd Polinomial Degree:\\n\", test_features_poly[1, :])\n",
    "print(\"Features of Training Data in 2nd Polinomial Degree Shape:\", test_features_poly.shape)\n",
    "\n",
    "only_first_degree = np.sum(poly2.powers_, axis=1) == 1\n",
    "only_with_actions = np.sum(poly2.powers_[:, NumberOfProducts:], axis=1) == 1\n",
    "feature_selection_flags = only_first_degree | only_with_actions\n",
    "print(\"Feature of Training Data Selection Flags: \", feature_selection_flags)\n",
    "\n",
    "print(\"Features of Training Data in 2nd Polinomial Degree with Selection:\\n\", test_features_poly[1, feature_selection_flags])\n",
    "print(\"Features of Training Data in 2nd Polinomial Degree with Selection Shape:\", test_features_poly[:, feature_selection_flags].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, there are _`231`_ features in the second polynomial degree. However, we do not use all of them. Instead, we use the following features:\n",
    "* all features in the 1st degree (_`Views`_ and _`Action`_ as a vector)\n",
    "* all cross features of _`Views`_ with _`Action`_ as a vector\n",
    "\n",
    "\n",
    "That _explicit_ features selection reduces the number of features to _`120`_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression(\n",
    "    solver = 'lbfgs',\n",
    "    max_iter = 5000,\n",
    "    random_state = RandomSeed\n",
    ")\n",
    "\n",
    "lr_poly = logreg.fit(test_features_poly[:, feature_selection_flags], test_outs04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 939
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.seterr(over = 'ignore')\n",
    "\n",
    "figs, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "for product_id in range(NumberOfProducts):\n",
    "    counts = []\n",
    "    prob1s = []\n",
    "    test_data = np.zeros((1, 2 * NumberOfProducts))\n",
    "    test_data[:, NumberOfProducts + product_id] = 1\n",
    "    for count in range(1, 100):\n",
    "        test_data[:, product_id] = count\n",
    "        test_out = lr_poly.predict_proba(poly2.fit_transform(test_data)[:, feature_selection_flags])\n",
    "        counts.append(count)\n",
    "        prob1s.append(test_out[0][1])\n",
    "\n",
    "    axs[0].loglog(counts, prob1s)\n",
    "    axs[1].plot(counts, prob1s)\n",
    "\n",
    "axs[0].set_title(f'LogLog Scale')\n",
    "axs[0].legend(labels)\n",
    "axs[1].set_title(f'Normal Scale')\n",
    "axs[1].legend(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.54673507e-03, -9.32772316e-03, -5.64658514e-03,\n",
       "         1.12376701e-02,  1.52924919e-03, -3.29362400e-03,\n",
       "        -1.06978876e-02, -4.36140699e-03,  5.03189810e-03,\n",
       "         7.82517883e-04, -3.27612906e-01, -6.10126989e-01,\n",
       "        -1.54123810e-01, -7.58031733e-01, -7.56703022e-02,\n",
       "        -1.43255356e-01, -7.98906986e-01, -6.15861069e-01,\n",
       "        -3.69580116e-01, -1.55612487e-01,  9.75110156e-03,\n",
       "         2.96442126e-03, -2.27697314e-02, -1.41293508e-02,\n",
       "        -7.39748365e-03,  7.09002325e-03,  1.11376053e-02,\n",
       "        -6.76988147e-04,  6.19104070e-03,  2.92626859e-04,\n",
       "        -2.15827940e-02,  4.23523949e-02,  2.63088404e-03,\n",
       "         3.69744511e-02, -1.31717859e-02, -1.08039981e-02,\n",
       "         4.57799146e-02, -2.10745138e-02, -5.63733245e-02,\n",
       "        -1.40589516e-02, -5.21314809e-03,  1.49326378e-01,\n",
       "        -5.09450193e-02, -8.44324482e-02,  1.24501476e-01,\n",
       "        -1.18678055e-01, -2.86188348e-02,  1.76631572e-02,\n",
       "        -5.48148703e-03, -3.76860324e-03, -3.19317130e-02,\n",
       "        -3.99346432e-02,  2.02421019e-02,  4.79415133e-02,\n",
       "        -1.32941647e-02,  2.06582201e-03,  1.56549870e-03,\n",
       "         3.77863847e-03,  5.50264946e-03,  1.53019673e-02,\n",
       "        -1.76968322e-03,  4.32812870e-03, -3.88936707e-03,\n",
       "         5.29718168e-03,  7.61500881e-03,  8.91824494e-04,\n",
       "         8.57220083e-04, -7.99643722e-03,  2.51995116e-03,\n",
       "        -6.32457822e-03,  1.14659650e-02,  2.46269728e-03,\n",
       "        -1.06583170e-02, -1.70880216e-02, -2.33163375e-04,\n",
       "         1.18596607e-02, -9.12335954e-04,  1.68925570e-02,\n",
       "        -1.97887840e-02,  2.70611790e-03,  9.29053158e-03,\n",
       "         2.08942903e-02, -2.00485309e-02, -1.15192840e-01,\n",
       "         8.76889515e-03,  1.15629568e-02,  4.18068952e-02,\n",
       "         9.98954769e-03, -5.19113861e-03,  2.74215056e-02,\n",
       "        -2.23143946e-02,  8.00106526e-03, -3.41343721e-03,\n",
       "        -1.37507191e-03, -9.15502168e-03, -5.75611070e-03,\n",
       "         1.01200575e-02,  2.70692677e-02, -3.04051213e-03,\n",
       "        -4.49724918e-03,  9.08544025e-03,  1.58498106e-03,\n",
       "         1.01546859e-02,  1.76756044e-04,  1.78474316e-02,\n",
       "        -1.72648269e-02, -2.11642507e-02,  2.87476868e-02,\n",
       "        -3.39740084e-02,  9.83800247e-03,  5.00670217e-03,\n",
       "         1.07078127e-03,  1.15433744e-03,  6.53773968e-04,\n",
       "        -4.73271304e-03, -2.01155855e-03, -2.05793390e-03,\n",
       "        -7.87734547e-03,  3.37953986e-03,  6.19693414e-03]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_poly.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, all _`Actions`_ uniformly react on the increase of _`Views`_: the _the probability of the click_ grows as grows the amount of _`Views`_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agent Comparision\n",
    "\n",
    "In this section, we are going to compare the performance of different _`Agents`_ those apply different policies to select an appropriate _`Action`_ for a _Bandit_ _`Event`_.\n",
    "\n",
    "As a measurement of the _`Agent`_ performance, we will use _Click Trhough Rate_ (_CTR_) that is defined as following:\n",
    "$$ CTR = \\frac{\\sum_{B}{\\mathbb{1}_c}}{\\big|B\\big|} $$\n",
    "\n",
    "Here\n",
    "* $\\sum_{B}{\\mathbb{1}_c}$ is a total amount of clicks for all _Bandit_ _`Events`_\n",
    "* $\\big|B\\big|$ is a total amount of all _Bandit_ _`Events`_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent based on Organic Events Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have estimated _Logistig Regression_ models and evaluated different feature sets:\n",
    "* _`Views`_ and _`Action`_\n",
    "* _`Views`_ **as frequencies** and _`Action`_ \n",
    "* _`Views`_, _`Action`_, and _**the mark of the presence of an `Action` in `Views`**_ \n",
    "* _`Views`_ and _`Action`_ _**as a polynomial set**_\n",
    "\n",
    "Now, we consider a so-called _Organic Agent_. The idea of the _`Agent`_ is following:\n",
    "* whenever a new _Organic_ _`Event`_ happens, the _`View`_ for a particular _Product_ is incremented\n",
    "* whenever a new _Bandit_ _`Event`_ happens, we provide an _`Action`_ that corresponds to the most frequently shown _`Product`_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a new _`Agent`_ that incorporates that logic explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.random.mtrand import RandomState\n",
    "from reco_gym import Configuration\n",
    "from recogym.agents import Agent\n",
    "\n",
    "class OrganicEventCounterAgent(Agent):\n",
    "    \"\"\"\n",
    "    Organic Event Counter Agent\n",
    "    \n",
    "    The Agent that counts Organic views of Products\n",
    "    and selects an Action for the most frequently shown Product.\n",
    "    \"\"\"\n",
    "    def __init__(self, config):\n",
    "        super(OrganicEventCounterAgent, self).__init__(config)\n",
    "        self.rng = RandomState(self.config.random_seed)\n",
    "        self.organic_views = np.zeros(self.config.num_products)\n",
    "\n",
    "    def observe(self, observation):\n",
    "        for session in observation.sessions():\n",
    "            self.organic_views[session['v']] += 1\n",
    "\n",
    "    def act(self, observation, reward, done):\n",
    "        \"\"\"Act method returns an Action based on current observation and past history\"\"\"\n",
    "        self.observe(observation)\n",
    "        prob = self.organic_views / sum(self.organic_views)\n",
    "        action = self.rng.choice(self.config.num_products, p = prob)\n",
    "        return {\n",
    "            **super().act(observation, reward, done),\n",
    "            **{\n",
    "                'a': action,\n",
    "                'ps': prob[action],\n",
    "                'ps-a': prob,\n",
    "            },\n",
    "        }\n",
    "\n",
    "    def reset(self):\n",
    "        self.organic_views = np.zeros(self.config.num_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "abtest_data = deepcopy(env).generate_logs(ABTestNumberOfUsers, OrganicEventCounterAgent(Configuration(env_1_args)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is evident in logs; the _Agent_ provides an _`Action`_ that corresponds to the _`Product`_ that was most frequently shown in _Organic_ _`Events`_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent Based on Logistic Regression Model\n",
    "\n",
    "Let's create _`Agents`_ those use _Logistic Regression_ models based on the set of features we have already examined and compare their performance with _Organic_ _`Agent`_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from recogym.agents import FeatureProvider\n",
    "\n",
    "class ViewsFeatureProvider(FeatureProvider):\n",
    "    \"\"\"Feature provider as an abstract class that defined interface of setting/getting features\"\"\"\n",
    "\n",
    "    def __init__(self, config):\n",
    "        super(ViewsFeatureProvider, self).__init__(config)\n",
    "        self.feature_data = None\n",
    "\n",
    "    def observe(self, observation):\n",
    "        \"\"\"Consider an Organic Event for a particular user\"\"\"\n",
    "        for session in observation.sessions():\n",
    "            self.feature_data[:, session['v']] += 1\n",
    "\n",
    "    def features(self, observation):\n",
    "        \"\"\"Provide feature values adjusted to a particular feature set\"\"\"\n",
    "        return self.feature_data\n",
    "\n",
    "\n",
    "class ViewsAndActionAsVectorFeatureProvider(ViewsFeatureProvider):\n",
    "    \"\"\"Feature Provider for feature set defined as Views and an Action as a Vector\"\"\"\n",
    "\n",
    "    def __init__(self, config):\n",
    "        super(ViewsAndActionAsVectorFeatureProvider, self).__init__(config)\n",
    "\n",
    "    def reset(self):\n",
    "        self.feature_data = np.zeros((self.config.num_products, 2 * self.config.num_products))\n",
    "        for ix in range(self.config.num_products):\n",
    "            self.feature_data[ix, self.config.num_products + ix] = 1\n",
    "\n",
    "\n",
    "class ViewFrequenciesAndActionAsVectorFeatureProvider(ViewsAndActionAsVectorFeatureProvider):\n",
    "    def __init__(self, env):\n",
    "        super().__init__(env)\n",
    "\n",
    "    def features(self, observation):\n",
    "        base_features = super().features(observation)\n",
    "        views = base_features[:, :self.config.num_products]\n",
    "        views = views / np.sum(views, axis = 1)\n",
    "        base_features[:, :self.config.num_products] = views\n",
    "        return base_features\n",
    "\n",
    "\n",
    "class ViewsAndActionAsVectorWithMarkFeatureProvider(ViewsAndActionAsVectorFeatureProvider):\n",
    "    def __init__(self, config):\n",
    "        super(ViewsAndActionAsVectorWithMarkFeatureProvider, self).__init__(config)\n",
    "\n",
    "    def features(self, observation):\n",
    "        base_features = super().features(observation)\n",
    "        marks = np.zeros((self.config.num_products, 1))\n",
    "        for product_id in range(self.config.num_products):\n",
    "            if base_features[product_id, product_id] > 0:\n",
    "                marks[product_id, 0] = 1\n",
    "\n",
    "        base_features = np.append(base_features, marks, axis = 1)\n",
    "        return base_features\n",
    "\n",
    "\n",
    "class ViewsAndActionPolynomialFeatureProvider(ViewsAndActionAsVectorFeatureProvider):\n",
    "    \"\"\"Feature Provider for polynomial feature set defined as Views and an Action\"\"\"\n",
    "\n",
    "    def __init__(self, env, flags, degree = 2):\n",
    "        super().__init__(env)\n",
    "        self.poly = PolynomialFeatures(degree)\n",
    "        self.flags = flags\n",
    "\n",
    "    def features(self, observation):\n",
    "        return self.poly.fit_transform(super().features(observation))[:, self.flags]\n",
    "\n",
    "\n",
    "class ModelBasedAgent(Agent):\n",
    "    def __init__(self, env, feature_provider, model):\n",
    "        # Set environment as an attribute of Agent.\n",
    "        self.env = env\n",
    "        self.feature_provider = feature_provider\n",
    "        self.model = model\n",
    "        self.reset()\n",
    "\n",
    "    def act(self, observation, reward, done):\n",
    "        \"\"\"Act method returns an action based on current observation and past history\"\"\"\n",
    "        self.feature_provider.observe(observation)\n",
    "        prob = self.model.predict_proba(self.feature_provider.features(observation))[:, 1]\n",
    "        action = np.argmax(prob)\n",
    "        return {\n",
    "            **super().act(observation, reward, done),\n",
    "            **{\n",
    "                'a': action,\n",
    "                'ps': prob[action],\n",
    "                'ps-a': prob,\n",
    "            }\n",
    "        }\n",
    "\n",
    "    def reset(self):\n",
    "        self.feature_provider.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, there is the definition of the function that applies A/B tests for different _`Agents`_ and calculates _Click-Through Rate_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats.distributions import beta\n",
    "\n",
    "def verify_agents(env, number_of_users, agents):\n",
    "    stat = {\n",
    "        'Agent': [],\n",
    "        '0.025': [],\n",
    "        '0.500' : [],\n",
    "        '0.975': [],\n",
    "    }\n",
    "\n",
    "    for agent_id in agents:\n",
    "        stat['Agent'].append(agent_id)\n",
    "        data = deepcopy(env).generate_logs(number_of_users, agents[agent_id])\n",
    "        bandits = data[data['z'] == 'bandit']\n",
    "        successes = bandits[bandits['c'] == 1].shape[0]\n",
    "        failures = bandits[bandits['c'] == 0].shape[0]\n",
    "        stat['0.025'].append(beta.ppf(0.025, successes + 1, failures + 1))\n",
    "        stat['0.500'].append(beta.ppf(0.500, successes + 1, failures + 1))\n",
    "        stat['0.975'].append(beta.ppf(0.975, successes + 1, failures + 1))\n",
    "        \n",
    "    return pd.DataFrame().from_dict(stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from reco_gym import env_1_args\n",
    "\n",
    "agen_stats00 = verify_agents(\n",
    "    env,\n",
    "    ABTestNumberOfUsers,\n",
    "    {\n",
    "        'Organic User Event Counter': OrganicEventCounterAgent(Configuration(env_1_args)),\n",
    "        'LogReg: Views & Action':\n",
    "            ModelBasedAgent(\n",
    "                env_1_args,\n",
    "                ViewsAndActionAsVectorFeatureProvider(Configuration(env_1_args)),\n",
    "                lr_base\n",
    "            ),\n",
    "        'LogReg: Views as Frequencies & Action':\n",
    "            ModelBasedAgent(\n",
    "                env_1_args,\n",
    "                ViewFrequenciesAndActionAsVectorFeatureProvider(Configuration(env_1_args)),\n",
    "                lr_freq\n",
    "            ),\n",
    "        'LogReg: Views & Action & Mark of Presence':\n",
    "            ModelBasedAgent(\n",
    "                env_1_args,\n",
    "                ViewsAndActionAsVectorWithMarkFeatureProvider(Configuration(env_1_args)),\n",
    "                lr_with_mark\n",
    "            ),\n",
    "        'LogReg: Views & Action (as 2nd Polynom)':\n",
    "            ModelBasedAgent(\n",
    "                env_1_args,\n",
    "                ViewsAndActionPolynomialFeatureProvider(Configuration(env_1_args), feature_selection_flags),\n",
    "                lr_poly\n",
    "            )\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agent</th>\n",
       "      <th>0.025</th>\n",
       "      <th>0.500</th>\n",
       "      <th>0.975</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic User Event Counter</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>0.016620</td>\n",
       "      <td>0.016906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogReg: Views &amp; Action</td>\n",
       "      <td>0.014841</td>\n",
       "      <td>0.015112</td>\n",
       "      <td>0.015386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogReg: Views as Frequencies &amp; Action</td>\n",
       "      <td>0.014841</td>\n",
       "      <td>0.015112</td>\n",
       "      <td>0.015386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogReg: Views &amp; Action &amp; Mark of Presence</td>\n",
       "      <td>0.016095</td>\n",
       "      <td>0.016377</td>\n",
       "      <td>0.016662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LogReg: Views &amp; Action (as 2nd Polynom)</td>\n",
       "      <td>0.016574</td>\n",
       "      <td>0.016860</td>\n",
       "      <td>0.017149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Agent     0.025     0.500     0.975\n",
       "0                 Organic User Event Counter  0.016336  0.016620  0.016906\n",
       "1                     LogReg: Views & Action  0.014841  0.015112  0.015386\n",
       "2      LogReg: Views as Frequencies & Action  0.014841  0.015112  0.015386\n",
       "3  LogReg: Views & Action & Mark of Presence  0.016095  0.016377  0.016662\n",
       "4    LogReg: Views & Action (as 2nd Polynom)  0.016574  0.016860  0.017149"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agen_stats00"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, the _`Agent`_ that utilises a model based on a feature set with 2nd-degree polynomial has the best performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## History of _Organic_ _`Events`_\n",
    "\n",
    "In previous models, all _`Views`_ of _`Products`_ in _Organic_ _`Events`_ were considered equally _irrespectively_ to the history when a _`Product`_ was shown.\n",
    "\n",
    "At this time, we are going to compare models with different treating of the notion of the _`View`_ _history_.\n",
    "\n",
    "The model based on the feature set of the 2nd-degree polynomial as it has revealed the best performance.\n",
    "\n",
    "For each _`View`_ a weight will be assigned.\n",
    "\n",
    "The following cases are considered:\n",
    "* $w(t) = \\frac{1}{1 + t}$\n",
    "* $w(t) = \\frac{1}{1 + \\ln(1 + t)}$\n",
    "* $w(t) = \\begin{cases}\n",
    "    1,& \\text{if } t < L\\\\\n",
    "    0,              & \\text{otherwise}\n",
    "\\end{cases}$\n",
    "\n",
    "Time $t$ will be counted from _`0`_ from the latest _Organic_ _`Event`_ to the past, each time incremented by _`+1`_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WeightedViewsAndActionPolynomialFeatureProvider(FeatureProvider):\n",
    "    \"\"\"Feature Provider for polynomial feature set defined as Views and an Action\"\"\"\n",
    "\n",
    "    def __init__(self, config, flags, weight_history_function, degree = 2):\n",
    "        super().__init__(config)\n",
    "        self.poly = PolynomialFeatures(degree)\n",
    "        self.flags = flags\n",
    "        self.weight_history_function = weight_history_function\n",
    "        self.views = np.zeros((0, self.config.num_products))\n",
    "\n",
    "    def observe(self, observation):\n",
    "        \"\"\"Consider an Organic Event for a particular user\"\"\"\n",
    "        for session in observation.sessions():\n",
    "            tmp_view = np.zeros(self.config.num_products)\n",
    "            tmp_view[session['v']] = 1\n",
    "\n",
    "            self.views = np.append(tmp_view[np.newaxis, :], self.views, axis = 0)\n",
    "\n",
    "    def reset(self):\n",
    "        self.views = np.zeros((0, self.config.num_products))\n",
    "\n",
    "    def features(self, observation):\n",
    "        base_features = np.zeros((self.config.num_products, 2 * self.config.num_products))\n",
    "        for ix in range(self.config.num_products):\n",
    "            base_features[ix, self.config.num_products + ix] = 1\n",
    "\n",
    "        history = np.array(range(self.views.shape[0])).reshape(self.views.shape[0], 1)\n",
    "        weights = self.weight_history_function(history)\n",
    "        weighted_views = np.sum(self.views * weights, axis = 0)\n",
    "        base_features[:, :self.config.num_products] = weighted_views\n",
    "        return self.poly.fit_transform(base_features)[:, self.flags]\n",
    "\n",
    "\n",
    "def build_history_agent(env_args, data, weight_fynction):\n",
    "    features, outs = build_train_data(data, False, False, weight_fynction)\n",
    "\n",
    "    config = Configuration(env_args)\n",
    "    logreg = LogisticRegression(\n",
    "        solver = 'lbfgs',\n",
    "        max_iter = 5000,\n",
    "        random_state = config.random_seed\n",
    "    )\n",
    "\n",
    "    poly = PolynomialFeatures(2)\n",
    "    features_poly = poly.fit_transform(features)\n",
    "    \n",
    "    only_first_degree = np.sum(poly.powers_, axis=1) == 1\n",
    "    only_with_actions = np.sum(poly.powers_[:, NumberOfProducts:], axis=1) == 1\n",
    "    flags = only_first_degree | only_with_actions\n",
    "\n",
    "    lr_weight_poly = logreg.fit(features_poly[:, flags], outs)\n",
    "\n",
    "    return ModelBasedAgent(\n",
    "        config,\n",
    "        WeightedViewsAndActionPolynomialFeatureProvider(config, flags, weight_fynction),\n",
    "        lr_weight_poly\n",
    "    )\n",
    "\n",
    "\n",
    "def slide_window(t, L):\n",
    "    weights = np.zeros((t.shape[0], 1))\n",
    "    weights[:L, :] = 1\n",
    "    return weights\n",
    "\n",
    "\n",
    "def apply_new_environment(new_env_args):\n",
    "    new_env_1_args = {\n",
    "        **env_1_args,\n",
    "        **new_env_args,\n",
    "    }\n",
    "\n",
    "    new_env = deepcopy(env)\n",
    "    new_env.init_gym(new_env_1_args)\n",
    "    data = deepcopy(new_env).generate_logs(ABTestNumberOfUsers) \n",
    "    stat = verify_agents(\n",
    "        new_env,\n",
    "        ABTestNumberOfUsers,\n",
    "        {\n",
    "            'Organic User Event Counter': OrganicEventCounterAgent(Configuration({\n",
    "                **new_env_1_args\n",
    "            })),\n",
    "            'LogReg Poly & w ~ 1/(1 + t)':\n",
    "                build_history_agent(new_env_1_args, data, lambda t: 1.0 / (1 + t)),\n",
    "            'LogReg Poly & w ~ 1/(1 + ln(1 + t))':\n",
    "                build_history_agent(new_env_1_args, data, lambda t: 1.0 / (1 + np.log(1 + t))),\n",
    "            'LogReg Poly & w is Latest 20 Organic Events':\n",
    "                build_history_agent(new_env_1_args, data, lambda t: slide_window(t, 20)),\n",
    "            'LogReg Poly & w is the latest Organic Event':\n",
    "                build_history_agent(new_env_1_args, data, lambda t: slide_window(t, 1)),\n",
    "        })\n",
    "    return stat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case #1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "agent_stats01 = apply_new_environment({\n",
    "    'num_products': 20,\n",
    "    'number_of_flips': 10,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agent</th>\n",
       "      <th>0.025</th>\n",
       "      <th>0.500</th>\n",
       "      <th>0.975</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic User Event Counter</td>\n",
       "      <td>0.007956</td>\n",
       "      <td>0.008153</td>\n",
       "      <td>0.008354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogReg Poly &amp; w ~ 1/(1 + t)</td>\n",
       "      <td>0.023151</td>\n",
       "      <td>0.023490</td>\n",
       "      <td>0.023832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogReg Poly &amp; w ~ 1/(1 + ln(1 + t))</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>0.023205</td>\n",
       "      <td>0.023545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogReg Poly &amp; w is Latest 20 Organic Events</td>\n",
       "      <td>0.022930</td>\n",
       "      <td>0.023266</td>\n",
       "      <td>0.023607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LogReg Poly &amp; w is the latest Organic Event</td>\n",
       "      <td>0.023055</td>\n",
       "      <td>0.023393</td>\n",
       "      <td>0.023735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Agent     0.025     0.500     0.975\n",
       "0                   Organic User Event Counter  0.007956  0.008153  0.008354\n",
       "1                  LogReg Poly & w ~ 1/(1 + t)  0.023151  0.023490  0.023832\n",
       "2          LogReg Poly & w ~ 1/(1 + ln(1 + t))  0.022869  0.023205  0.023545\n",
       "3  LogReg Poly & w is Latest 20 Organic Events  0.022930  0.023266  0.023607\n",
       "4  LogReg Poly & w is the latest Organic Event  0.023055  0.023393  0.023735"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_stats01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case #2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_stats02 = apply_new_environment({\n",
    "    'num_products': 10,\n",
    "    'number_of_flips': 5,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agent</th>\n",
       "      <th>0.025</th>\n",
       "      <th>0.500</th>\n",
       "      <th>0.975</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic User Event Counter</td>\n",
       "      <td>0.012743</td>\n",
       "      <td>0.012994</td>\n",
       "      <td>0.013247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogReg Poly &amp; w ~ 1/(1 + t)</td>\n",
       "      <td>0.017302</td>\n",
       "      <td>0.017594</td>\n",
       "      <td>0.017889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogReg Poly &amp; w ~ 1/(1 + ln(1 + t))</td>\n",
       "      <td>0.017215</td>\n",
       "      <td>0.017506</td>\n",
       "      <td>0.017801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogReg Poly &amp; w is Latest 20 Organic Events</td>\n",
       "      <td>0.017308</td>\n",
       "      <td>0.017600</td>\n",
       "      <td>0.017895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LogReg Poly &amp; w is the latest Organic Event</td>\n",
       "      <td>0.016900</td>\n",
       "      <td>0.017189</td>\n",
       "      <td>0.017481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Agent     0.025     0.500     0.975\n",
       "0                   Organic User Event Counter  0.012743  0.012994  0.013247\n",
       "1                  LogReg Poly & w ~ 1/(1 + t)  0.017302  0.017594  0.017889\n",
       "2          LogReg Poly & w ~ 1/(1 + ln(1 + t))  0.017215  0.017506  0.017801\n",
       "3  LogReg Poly & w is Latest 20 Organic Events  0.017308  0.017600  0.017895\n",
       "4  LogReg Poly & w is the latest Organic Event  0.016900  0.017189  0.017481"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_stats02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Different feature sets were investigated for _Likelihood_ model and it was concluded that the polynomial feature set is the best one.\n",
    "\n",
    "It was also investigated how a history of _Organic_ _`Events`_ influences on the performance of the likelihood model."
   ]
  }
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