nba-RL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Best Action Model\n",
    "\n",
    "This is a demo on a strategic next best action model using reinforcement leanring (fitted Q iteration).\n",
    "\n",
    "Adapted for telco usecase by Sergei Gromov and Sergei Bazhin.\n",
    "\n",
    "| Description | D002 (see [descriptions](https://github.com/ikatsov/tensor-house/blob/master/resources/descriptions.md)) |\n",
    "|--|:--|\n",
    "| Dataset | Generated internally, no external dependencies |\n",
    "| Papers | Theocharous2015, Riedmiller2005 (see [papers](https://github.com/ikatsov/tensor-house/blob/master/resources/papers.md)) | \n",
    "| Installation | NA |\n",
    "| Libs | Scikit-learn, Numpy |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Формализация событий/действий/целевых цепочек\n",
    "**События (events - states):**  \n",
    " - 0 - приближение к порогу отключения  \n",
    " - 1 - пересечение порога отключения  \n",
    " - 2 - зачисление платежа  \n",
    " - 3 - списание АП  \n",
    " - 4 - отключение пакета  \n",
    " - 5 - **подключение пакета (событие имеющее награду )**\n",
    " \n",
    "**Действия (offers - actions):**  \n",
    " - 1 - реклама\n",
    " - 2 - маленька скидка\n",
    " - 3 - большая скидка\n",
    " \n",
    "**Целевые цепочки (target sequence) :**\n",
    "- приближение к порогу отключения -> **(a)реклама** -> зачисление платежа -> **(a)большая скидка** -> подключение пакета  \n",
    "- пересечение порога отключения -> **(a)реклама** -> зачисление платежа -> **(a)маленькая скидка** -> подключение пакета  \n",
    "- отключение пакета -> **(a) реклама** -> подключение пакета   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Описание участка траектории\n",
    "Есть 100 временных отрезков, которые разбиваются на 4 участка общей траектории: [0 -(1)- 25 -(2)- 50 -(3)- 75 -(4)- 99].  \n",
    "Состояние формируется для участка траектории и награда так же считается для него.  \n",
    "Действие предпринимается так же на границе двух траекторий - три границы - три действия в арсенале.  \n",
    "\n",
    "``` \n",
    " #  начальное состояние  \n",
    " [кластер абонента   \n",
    "  время последнего события типа 0 - приближение к порогу отключения (100, если не было)\n",
    "  время последнего события типа 1 - пересечение порога отключения\n",
    "  время последнего события типа 2 - зачисление платежа\n",
    "  время последнего события типа 3 - списание АП\n",
    "  время последнего события типа 4 - отключение пакета\n",
    "  время действия 1 (100, если не было)\n",
    "  время действия 2\n",
    "  время действия 3]\n",
    " #  действие-награда\n",
    "  номер предложения/действия, которое мы даем на этом участке траектории (1,2,3)\n",
    "  награда\n",
    " [новое состояние]\n",
    " признак финального участка траектории\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Setting the parameters needed to export figures as PDFs \n",
    "# plt.rcParams.update({'font.size': 14, 'pdf.fonttype': 'truetype', \n",
    "#                     'font.family':'Candara', 'font.serif':['Candara']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Auxiliary functions\n",
    "#\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum(axis=0) # only difference\n",
    "\n",
    "def multinomial_int(p):\n",
    "    '''\n",
    "    p array holds probs that all must sum to 1\n",
    "    we draw one sample from 0..len(p) ints with probs of p\n",
    "    '''\n",
    "    return np.random.choice(list(range(len(p))), p=p)\n",
    "\n",
    "def count(ndarray, val):\n",
    "    return np.count_nonzero(ndarray == val)\n",
    "\n",
    "def index(ndarray, val, default):\n",
    "    try:\n",
    "        return ndarray.tolist().index(val)\n",
    "    except:\n",
    "        return default\n",
    "\n",
    "def find_offer_times(f):\n",
    "    return np.nonzero(f)[0]\n",
    "\n",
    "def offer_seq(f):\n",
    "    # just return array with positive eleme\n",
    "    return f[np.where(f > 0)]\n",
    "\n",
    "def get_p_events(num_events, no_action_p=0.7):\n",
    "    # сделаем массив вероятностей для событий с клиентом\n",
    "    # num_events - число событий\n",
    "    # no_action_p - вероятность события бездействия - самая большая\n",
    "\n",
    "    p_events = np.array([no_action_p] +\\\n",
    "                        list((1-no_action_p)*softmax(np.random.randn(num_events-1)))) # добиваем в остальные события случайные вероятности, чтобы в сумме была 1\n",
    "\n",
    "    # округлим верятности до 2 знаков и восстановим равенство единице суммы\n",
    "    p_events = np.round(p_events, 2)\n",
    "    delta = 1 - sum(p_events)\n",
    "    p_events[0] += delta\n",
    "    assert sum(p_events) == 1.0\n",
    "    return p_events\n",
    "\n",
    "\n",
    "def visualize_profiles(m):\n",
    "    fig, ax = plt.subplots(figsize=(10, 20))\n",
    "    chart = ax.imshow(m, cmap='viridis', interpolation='none')\n",
    "    ax.set_aspect(0.5 * m.shape[1] / m.shape[0])\n",
    "    ax.grid(False)\n",
    "    ax.set_xlabel('Time')\n",
    "    ax.set_ylabel('User ID')\n",
    "    plt.colorbar(chart, fraction=0.025, pad=0.04, ax=ax)\n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Environment parameters\n",
    "#\n",
    "# states\n",
    "events = [\n",
    "    0, # бездействие \n",
    "    1, # приближение к порогу отключения\n",
    "    2, # пересечение порога отключения \n",
    "    3, # зачисление платежа\n",
    "    4, # списание АП\n",
    "    5, # отключение пакета  \n",
    "    6  # подключение пакета (событие имеющее награду )\n",
    "]\n",
    "\n",
    "events_dict = {0 : 'бездействие', \n",
    "               1 : 'приближение к порогу отключения',\n",
    "               2 : 'пересечение порога отключения', \n",
    "               3 : 'зачисление платежа',\n",
    "               4 : 'списание АП',\n",
    "               5 : 'отключение пакета',  \n",
    "               6 : 'подключение пакета (событие имеющее награду )'}\n",
    "\n",
    "# actions\n",
    "offers = [\n",
    "    1, # advertisement\n",
    "    2, # small disocunt\n",
    "    3  # large discount\n",
    "]\n",
    "offers_dict = dict([\n",
    "    (1, 'advertisement'),\n",
    "    (2, 'small disocunt'),\n",
    "    (3, 'large discount')])\n",
    "\n",
    "# кластер абонента\n",
    "demogr = [\n",
    "    0,  \n",
    "    1,\n",
    "    2,\n",
    "    3\n",
    "]\n",
    "\n",
    "n = 1000 # users\n",
    "k = 100  # time intervals\n",
    "m = len(offers)    # offers\n",
    "p_offers = [1 / m] * m # probs of offers - uniform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Генерация профилей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Тестовая генерация\n",
    "она не учитывает целевые цепочки и просто создает события согласно распределению p_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# иногда барахлит округление и надо перезапустить генерацию\n",
    "while True:\n",
    "    try :\n",
    "        p_events = get_p_events(len(events), no_action_p=0.7)\n",
    "        break\n",
    "    except AssertionError:\n",
    "        print(\"rounding error - will try one more time...\")\n",
    "P = np.random.choice(events, size=(n,k), p=p_events)\n",
    "visualize_profiles(P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Генерация офферов/действий"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1000, 100)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# m, n, k \n",
    "# (3, 1000, 100)\n",
    "# вокруг временых точек t_offers c разборосом в t_offer_jit генерируем с вероятностью p_offers 3 предложения 1, 2 или 3\n",
    "# записываем результат в матрицу F - одной размерости с P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_offers = np.linspace(0, k, m + 2).astype(int).tolist()[1 : -1]    # offer campaing times\n",
    "t_offer_jit = 5                                           # offer time jitter, standard deviation in time units\n",
    "offers_jitter = np.rint(t_offer_jit * np.random.randn(n, len(t_offers))) # deviation from offer times\n",
    "offer_times = (offers_jitter + np.repeat([t_offers], n, axis=0)).astype(int) # time labels at which we make offers\n",
    "offers_sampled = np.random.choice(offers, size=offer_times.shape, p=p_offers) # offers that we make at offer times\n",
    "F = np.zeros_like(P)\n",
    "x_indices = np.array([[i]*offer_times.shape[-1] for i in range(n)]).flatten() # these are just [0,0,0], [1,1,1] ... [999,999,999] flattened\n",
    "F[x_indices, offer_times.flatten()] = offers_sampled.flatten()\n",
    "visualize_profiles(F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: 'бездействие',\n",
       "  1: 'приближение к порогу отключения',\n",
       "  2: 'пересечение порога отключения',\n",
       "  3: 'зачисление платежа',\n",
       "  4: 'списание АП',\n",
       "  5: 'отключение пакета',\n",
       "  6: 'подключение пакета (событие имеющее награду )'},\n",
       " {1: 'advertisement', 2: 'small disocunt', 3: 'large discount'})"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_dict, offers_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "1->(1)->3->(3)-> +\n",
    "2->(1)->3->(1)-> +\n",
    "5->(1)->(1) + \n",
    "0 : приближение к порогу отключения -> (a)реклама -> зачисление платежа -> (a)большая скидка -> подключение пакета\n",
    "[0.7 , 0.2, 0.02, 0.02, 0.02, 0.02, 0.02] -> [0.7 , 0.02, 0.02, 0.2, 0.02, 0.02, 0.02] -> [0.7 , 0.02, 0.02, 0.02, 0.02, 0.02, 0.2]\n",
    "\n",
    "1 : пересечение порога отключения -> (a)реклама -> зачисление платежа -> (a)маленькая скидка -> подключение пакета\n",
    "[0.7 , 0.02, 0.2, 0.02, 0.02, 0.02, 0.02] (a)реклама -> [0.7 , 0.02, 0.02, 0.2, 0.02, 0.02, 0.02] -> (a)маленькая скидка -> [0.7 , 0.02, 0.02, 0.02, 0.02, 0.02, 0.2] \n",
    "\n",
    "2 : отключение пакета -> (a) реклама ->*any event seq*-> (a)реклама -> подключение пакета\n",
    "[0.7 , 0.02, 0.02, 0.02, 0.02, 0.2, 0.02] -> [0.7 , 0.05, 0.05, 0.05, 0.05, 0.05, 0.05] -> [0.7 , 0.02, 0.02, 0.02, 0.02, 0.02, 0.2]\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Генерация событий "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95b3c22112c54b3290dce2901419d2a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего целевых цепочек - 549\n"
     ]
    }
   ],
   "source": [
    "target_events={0:0, 1:0, 2:0} # counter of target chains\n",
    "current_pattern = -1 # target reward pattern id from the memo above\n",
    "\n",
    "pos_1 = 0 # 1st occurence of target action in seq of F offers/actions\n",
    "pos_2 = 0 # 2nd occurence of target action in seq of F offers/actions\n",
    "\n",
    "# loop through offers and identify if it fits the pattern\n",
    "with tqdm(total=n) as pbar:\n",
    "    for i, (t, offer_ids) in enumerate(zip(offer_times, offers_sampled)):\n",
    "        # pattern 0: 1 -> 3\n",
    "        if (offer_ids[0]==1 and offer_ids[1]==3): #1->2\n",
    "            target_events[0] += 1\n",
    "            current_pattern = 0\n",
    "            pos_1, pos_2 = 0,1\n",
    "        elif  (offer_ids[0]==1 and offer_ids[2]==3): #1->3\n",
    "            target_events[0] += 1\n",
    "            current_pattern = 0\n",
    "            pos_1, pos_2 = 0,2\n",
    "        elif offer_ids[1]==1 and offer_ids[2]==3: #2->3\n",
    "            target_events[0] += 1\n",
    "            current_pattern = 0\n",
    "            pos_1, pos_2 = 1,2\n",
    "        # pattern 1: 1 -> 2\n",
    "        elif (offer_ids[0]==1 and offer_ids[1]==2): #1->2\n",
    "            target_events[1] += 1\n",
    "            current_pattern = 1\n",
    "            pos_1, pos_2 = 0,1\n",
    "        elif (offer_ids[0]==1 and offer_ids[2]==2): #1->3\n",
    "            target_events[1] += 1\n",
    "            current_pattern = 1\n",
    "            pos_1, pos_2 = 0,2\n",
    "        elif (offer_ids[1]==1 and offer_ids[2]==2): #2->3\n",
    "            target_events[1] += 1\n",
    "            current_pattern = 1\n",
    "            pos_1, pos_2 = 1,2\n",
    "        # pattern 2: 1 -> 1\n",
    "        elif (offer_ids[0]==1 and offer_ids[1]==1): # 1->2\n",
    "            target_events[2] += 1\n",
    "            current_pattern = 2\n",
    "            pos_1, pos_2 = 0,1\n",
    "        elif (offer_ids[1]==1 and offer_ids[2]==1): # 2->3\n",
    "            target_events[2] += 1\n",
    "            current_pattern = 2\n",
    "            pos_1, pos_2 = 1,2\n",
    "        else:\n",
    "            current_pattern = -1\n",
    "            pos_1, pos_2 = 0, 0\n",
    "\n",
    "        if current_pattern == 0:\n",
    "            p_events = ([0.7 , 0.2 , 0.02, 0.02, 0.02, 0.02, 0.02], # 1 - приближение к порогу отключения \n",
    "                        [0.7 , 0.02, 0.02, 0.2 , 0.02, 0.02, 0.02], # 3 - зачисление платежа \n",
    "                        [0.7 , 0.02, 0.02, 0.02, 0.02, 0.02, 0.2])  # 6 - подключение пакета\n",
    "        elif current_pattern == 1:\n",
    "            p_events = ([0.7 , 0.02, 0.2 , 0.02, 0.02, 0.02, 0.02], # 2 - пересечение порога отключения\n",
    "                        [0.7 , 0.02, 0.02, 0.2 , 0.02, 0.02, 0.02], # 3 - зачисление платежа\n",
    "                        [0.7 , 0.02, 0.02, 0.02, 0.02, 0.02, 0.2] ) # 6 - подключение пакета\n",
    "        elif current_pattern == 2:\n",
    "            p_events = ([0.7 , 0.02, 0.02, 0.02, 0.02, 0.2,  0.02], # 5 - отключение пакета\n",
    "                        [0.7 , 0.06, 0.06, 0.06, 0.06, 0.06, 0.00], # все равновероятно, кроме подключения пакера\n",
    "                        [0.7 , 0.02, 0.02, 0.02, 0.02, 0.02, 0.2]) # 6 - подключение пакета\n",
    "        else:\n",
    "            p_events = ([0.7 , 0.058, 0.058, 0.058, 0.058, 0.058,  0.01], # \n",
    "                        [0.7 , 0.058, 0.058, 0.058, 0.058, 0.058,  0.01], # все равновероятно, кроме подключения пакета\n",
    "                        [0.7 , 0.058, 0.058, 0.058, 0.058, 0.058,  0.01]) # \n",
    "            \n",
    "        # add some noise\n",
    "        if (current_pattern >= 0) and (np.random.randint(0,100) > 90): \n",
    "            p_events = ([0.7 , 0.058, 0.058, 0.058, 0.058, 0.058,  0.01], # \n",
    "            [0.7 , 0.058, 0.058, 0.058, 0.058, 0.058,  0.01], # все равновероятно, кроме подключения пакета\n",
    "            [0.7 , 0.058, 0.058, 0.058, 0.058, 0.058,  0.01]) # \n",
    "            \n",
    "        P[i, 0        : t[pos_1]] = np.random.choice(events, t[pos_1], p=p_events[0])\n",
    "        P[i, t[pos_1] : t[pos_2]] = np.random.choice(events, t[pos_2] - t[pos_1], p=p_events[1])\n",
    "        P[i, t[pos_2] :         ] = np.random.choice(events, k - t[pos_2], p=p_events[2])\n",
    "        \n",
    "        # if (np.random.randint(0, 100) > 90) and (pos_1 >0):\n",
    "        #     break\n",
    "            \n",
    "        pbar.update(1)\n",
    "        \n",
    "print(f\"Всего целевых цепочек - {sum(target_events.values())}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_profiles(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.random.choice(demogr, size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0.0: 86268, 1.0: 8085, 2.0: 5647})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(P.flatten()) # number of events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Debug zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_offers = [1 / m] * m                                    # offer probabilities (behavioral policy)\n",
    "t_offers = np.linspace(0, k, m + 2).tolist()[1 : -1]      # offer campaing times = [25.0, 50.0, 75.0]\n",
    "t_offer_jit = 5                                           # offer time jitter, standard deviation in time units "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# P = np.zeros((n, k)) # matrix of profile events\n",
    "# F = np.zeros((n, k)) # offer history\n",
    "# D = np.zeros((n, 1)) # demographic features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "u=0\n",
    "D[u, 0] = np.random.binomial(1, 0.5)\n",
    "\n",
    "# determine m time points to issue offers for customer u\n",
    "offer_times_u = np.rint(t_offer_jit * np.random.randn(len(t_offers)) + t_offers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3333333333333333, 0.3333333333333333, 0.3333333333333333]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_offers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.multinomial(2, p_offers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multinomial_int(p_offers) + 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3333333333333333, 0.3333333333333333, 0.3333333333333333]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1 / m] * m "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 100)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 2., 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.,\n",
       "       0., 0., 1., 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., 3., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F[45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 2., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 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.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 2., 1.,\n",
       "       0., 1., 0., 0., 2., 0., 2., 0., 0., 0., 0., 0., 0., 2., 0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P[45]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code condinue - cut the data into trajectories\n",
    "\n",
    "![trajectories](i09-transition-cutting.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1255/3762033957.py:48: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return np.array(T)\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Cut trajectories into transitions\n",
    "#\n",
    "def state_features(p, f, d, t_start, t_end):\n",
    "    p_frame = p[0:t_end]\n",
    "    f_frame = f[0:t_end]\n",
    "    f1 = index(f_frame, 1, k)  \n",
    "    f2 = index(f_frame, 2, k)\n",
    "    f3 = index(f_frame, 3, k)\n",
    "    return np.array([\n",
    "        d[0],                  # demographic features\n",
    "        count(p_frame, 1),     # number of visits\n",
    "        f1,                    # first time offer 1 was issued\n",
    "        f2,                    # first time offer 2 was issued\n",
    "        f3                     # first time offer 3 was issued\n",
    "    ])\n",
    "\n",
    "def frame_reward(p, t_start, t_end):\n",
    "    return count(p[t_start:t_end], 2)   # number of purchases in the time frame\n",
    "\n",
    "def offer_time_ranges(times):\n",
    "    rng = [-1] + times + [k]\n",
    "    return list(zip(map(lambda x: x + 1, rng), rng[1:]))\n",
    "    \n",
    "def prepare_trajectories(P, F, D):\n",
    "    T = []\n",
    "    for u in range(0, n):\n",
    "        offer_times = find_offer_times(F[u]).tolist()\n",
    "        ranges = offer_time_ranges(offer_times)\n",
    "        \n",
    "        T_u = []\n",
    "        for r in range(0, len(ranges)):\n",
    "            (t_start, t_end) = ranges[r]\n",
    "            state = state_features(P[u], F[u], D[u], 0, t_start)\n",
    "            reward = frame_reward(P[u], t_start, t_end)\n",
    "            state_new = state_features(P[u], F[u], D[u], t_start, min(t_end + 1, k))\n",
    "            is_end_of_trajectory = t_end >= k\n",
    "            \n",
    "            if(t_end in offer_times): \n",
    "                action = F[u, t_end]\n",
    "            else:\n",
    "                action = 1 # default action\n",
    "                \n",
    "            T_u.append([state, action, reward, state_new, is_end_of_trajectory])\n",
    "            \n",
    "        T.append(T_u) \n",
    "        \n",
    "    return np.array(T)\n",
    "\n",
    "T = prepare_trajectories(P, F, D)\n",
    "Tt = prepare_trajectories(Pt, Ft, Dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([  0.,   0., 100., 100., 100.]), 3.0, 0,\n",
       "       array([  0.,   2., 100., 100.,  21.]), False], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# demographic features\n",
    "# number of visits\n",
    "# first time offer 1 was issued\n",
    "# first time offer 2 was issued\n",
    "# first time offer 3 was issued\n",
    "\n",
    "T[0][0] # первая часть траектории по нулевому пользователю; общая структура элемента траектории : state, action, reward, state_new, is_end_of_trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(T[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 2., 0., 0., 0.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P[0][:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[array([  0.,   0., 100., 100., 100.]), 3.0, 0,\n",
       "        array([  0.,   2., 100., 100.,  21.]), False],\n",
       "       [array([  0.,   2., 100., 100.,  21.]), 3.0, 1,\n",
       "        array([  0.,   4., 100., 100.,  21.]), False],\n",
       "       [array([  0.,   4., 100., 100.,  21.]), 3.0, 2,\n",
       "        array([  0.,   5., 100., 100.,  21.]), False],\n",
       "       [array([  0.,   5., 100., 100.,  21.]), 1, 0,\n",
       "        array([  0.,   7., 100., 100.,  21.]), True]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Policy Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Debug zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n,m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1, 5)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0., 100., 100., 100.])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T[0][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   0., 100., 100., 100.]),\n",
       " 1,\n",
       " 0,\n",
       " array([  0.,   0., 100., 100., 100.]),\n",
       " True)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state, action, reward, state_new, is_end_of_trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1000)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X), len(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(T[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,4):\n",
    "    for item in T:\n",
    "        sample=item[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([  0.,   5., 100.,  23., 100.]), 1, 1,\n",
       "       array([  0.,   7., 100.,  23., 100.]), True], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([  0.,   5., 100.,  23., 100.]), 1, 1,\n",
       "       array([  0.,   7., 100.,  23., 100.]), True], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T[-1][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba1417d0a3a4401585abe76d9c220033",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/rapids/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:560: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5013597eef1e4429a1f1489c60210439",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/rapids/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:560: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "701b068a61ec496c993d7adbe165b9e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/rapids/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:560: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fabc8b75c3945059f760234cf93b905",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/rapids/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:560: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "#\n",
    "# Find the optimal action under a greedy policy and corresponding state value\n",
    "#\n",
    "def best_action(Q, state, actions):\n",
    "    v_best = 0\n",
    "    a_best = 0\n",
    "    for a in actions:\n",
    "        v = Q([np.append(state, a)])[0]\n",
    "        if(v > v_best):\n",
    "            v_best = v\n",
    "            a_best = a\n",
    "            \n",
    "    return a_best, v_best\n",
    "\n",
    "def Q_0(sa):\n",
    "    return [1]\n",
    "\n",
    "Q = Q_0\n",
    "score = []\n",
    "for i in range(1, 5): # FQI iterations\n",
    "    X = []\n",
    "    Y = []\n",
    "    for i in tqdm(range(0,4)):\n",
    "        for item in T:\n",
    "            sample=item[i]\n",
    "            state, action, reward, state_new, is_end_of_trajectory = sample\n",
    "            x = np.append(state, action) # feature vector consists of state-action pairs\n",
    "\n",
    "            a_best, v_best = best_action(Q, state_new, offers)\n",
    "\n",
    "            y = reward + v_best    # we ignore the initial conditions (ends of trajectories) for the sake of illustration      \n",
    "\n",
    "            X.append(x)\n",
    "            Y.append(y)\n",
    "    \n",
    "    regr = RandomForestRegressor(max_depth=4, random_state=0, n_estimators=10, oob_score=True)\n",
    "    regr.fit(X, Y)   \n",
    "    score.append(regr.oob_score_)\n",
    "    Q = regr.predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4352593380195454, 0.7517597022480891, 0.820727845193426, 0.8584319142079472]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   0., 100., 100., 100.,   2.])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[123]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.55230739]\n",
      "[6.26474602]\n",
      "[5.96279435]\n",
      "[13.52702846]\n",
      "[13.52702846]\n",
      "[17.90256729]\n"
     ]
    }
   ],
   "source": [
    "# demographic features\n",
    "# number of visits\n",
    "# first time offer 1 was issued\n",
    "# first time offer 2 was issued\n",
    "# first time offer 3 was issued\n",
    "\n",
    "# Values of possible actions for a user who did not get any offer yet  \n",
    "print(Q([[0, 0, 100, 100, 100, 1]]))\n",
    "print(Q([[0, 0, 100, 100, 100, 2]]))\n",
    "print(Q([[0, 0, 100, 100, 100, 3]]))\n",
    "\n",
    "# Values of possible actions for a user who got offer #1 at time step 20  \n",
    "print(Q([[0, 0, 20, 100, 100, 1]]))\n",
    "print(Q([[0, 0, 20, 100, 100, 2]]))\n",
    "print(Q([[0, 0, 20, 100, 100, 3]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.61321886]\n",
      "[5.82460587]\n",
      "[5.82460587]\n",
      "[13.93350275]\n",
      "[13.93350275]\n",
      "[18.20843831]\n"
     ]
    }
   ],
   "source": [
    "# Values of possible actions for a user who did not get any offer yet  \n",
    "print(Q([[0, 0, 100, 100, 100, 1]]))\n",
    "print(Q([[0, 0, 100, 100, 100, 2]]))\n",
    "print(Q([[0, 0, 100, 100, 100, 3]]))\n",
    "\n",
    "# Values of possible actions for a user who got offer #1 at time step 20  \n",
    "print(Q([[0, 0, 20, 100, 100, 1]]))\n",
    "print(Q([[0, 0, 20, 100, 100, 2]]))\n",
    "print(Q([[0, 0, 20, 100, 100, 3]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Use the test set to evaluate the policy\n",
    "#\n",
    "states = Tt[:, :, 0].flatten().tolist()\n",
    "\n",
    "values = []\n",
    "best_actions = []\n",
    "for s in states:\n",
    "    a_best, v_best = best_action(Q, s, offers)\n",
    "    values.append(v_best)\n",
    "    best_actions.append(a_best)\n",
    "    \n",
    "s_tsne = TSNE(learning_rate = 100).fit_transform(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segment ((-55, -45), (-30, -20)):\n",
      " [[100.  29.  48.]\n",
      " [100.  29.  48.]\n",
      " [100.  26.  51.]\n",
      " [100.  26.  51.]\n",
      " [100.  38.  52.]]\n",
      "Segment ((45, 55), (-30, -20)):\n",
      " [[ 20. 100. 100.]\n",
      " [ 20. 100. 100.]\n",
      " [ 20. 100. 100.]\n",
      " [ 21. 100. 100.]\n",
      " [ 20. 100. 100.]]\n",
      "Segment ((-20, -10), (-65, -55)):\n",
      " [[ 31.  54. 100.]\n",
      " [ 31.  54. 100.]\n",
      " [ 28.  52. 100.]\n",
      " [ 20.  43. 100.]\n",
      " [ 20.  43. 100.]]\n",
      "Segment ((0, 10), (45, 55)):\n",
      " [[100. 100.  25.]\n",
      " [100. 100.  25.]\n",
      " [100. 100.  25.]\n",
      " [100. 100.  26.]\n",
      " [100. 100.  26.]]\n",
      "Segment ((5, 15), (-50, -40)):\n",
      " [[ 27. 100.  54.]\n",
      " [ 27. 100.  54.]\n",
      " [ 21. 100.  42.]\n",
      " [ 21. 100.  42.]\n",
      " [ 14. 100.  49.]]\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Visualuze the Q-values and best action for individual users\n",
    "#\n",
    "\n",
    "#\n",
    "# Segment boxes need to be set manually because the tSNE\n",
    "# projection changes unpredictabley as the data changes  \n",
    "#\n",
    "segments = [\n",
    "    ( (-55, -45), (-30, -20) ),\n",
    "    ( (45, 55),   (-30, -20) ),\n",
    "    ( (-20, -10), (-65, -55) ),\n",
    "    ( (0, 10),    (45, 55) ),\n",
    "    ( (5, 15),    (-50, -40) )\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
    "scatter = ax[0].scatter(s_tsne[:, 0], s_tsne[:, 1], c=values, cmap='viridis')\n",
    "plt.colorbar(mappable=scatter, ax=ax[0])\n",
    "for s in segments:\n",
    "    rect = Rectangle((s[0][0], s[1][0]), s[0][1] - s[0][0], s[1][1] - s[1][0], linewidth=1, edgecolor='r', facecolor='none')\n",
    "    ax[0].add_patch(rect)\n",
    "\n",
    "\n",
    "scatter = ax[1].scatter(s_tsne[:, 0], s_tsne[:, 1], c=best_actions, cmap='viridis')\n",
    "plt.colorbar(mappable=scatter, ax=ax[1])\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "for s in segments:\n",
    "    segment = ((s_tsne[:, 0] > s[0][0]) & (s_tsne[:, 0] < s[0][1]) & (s_tsne[:, 1] > s[1][0]) & (s_tsne[:, 1] < s[1][1]))\n",
    "    print(f'Segment {s}:\\n', np.array(states)[segment, 2:][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Policy Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FQI provides a way to learn the Q function and derive some policy (e.g. epsilon-greedy) from it. \n",
    "If we change the policy, we need to evaluate its performance. It can be done using historical trajectories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(Q, eps):\n",
    "    def egreedy_policy(state, action):\n",
    "        a_best, v_best = best_action(Q, state, offers)\n",
    "\n",
    "        if(a_best == action):\n",
    "            return 1 - eps\n",
    "        else:\n",
    "            return eps / (m - 1)\n",
    "        \n",
    "    return egreedy_policy\n",
    "\n",
    "\n",
    "# Probability of action given state\n",
    "def behavioral_policy(state, action):\n",
    "    return 1 / m\n",
    "\n",
    "# Estimates the target policy return based on \n",
    "# the profiles (trajectories) P generated under the behavioral policy \n",
    "def evaluate_policy_return(T, behavioral_policy, target_policy):\n",
    "    returns = []\n",
    "    for trajectory in T:                                                   \n",
    "        importance_weight = 1\n",
    "        trajectory_reward = 0\n",
    "        for transition in trajectory:            \n",
    "            state, action, reward  = transition[0:3]\n",
    "            action_prob_b = behavioral_policy(state, action)\n",
    "            action_prob_t = target_policy(state, action)\n",
    "            \n",
    "            importance_weight *= (action_prob_t / action_prob_b) \n",
    "            trajectory_reward += reward\n",
    "            \n",
    "        returns.append(trajectory_reward * importance_weight)\n",
    "        \n",
    "    return np.mean(returns)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy_returns = []\n",
    "eps = np.linspace(0, 2/3, num = 10)\n",
    "for e in eps:\n",
    "    policy = make_epsilon_greedy_policy(Q, e)\n",
    "    policy_returns.append( evaluate_policy_return(T, behavioral_policy, policy) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-white')\n",
    "plt.grid(True)\n",
    "plt.xlabel('Epsilon')\n",
    "plt.ylabel('Return')\n",
    "plt.plot(eps, policy_returns, '-o', mfc='w')\n",
    "plt.show()"
   ]
  }
 ],
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