Collaborative Filtering RecSys with Implicit Data_Hotel booking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.sparse as sparse\n",
    "import numpy as np\n",
    "from scipy.sparse.linalg import spsolve\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import random\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "booking = pd.read_csv('hotel_booking_update.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>consumer_id</th>\n",
       "      <th>total_including_tax</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>order_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>345</td>\n",
       "      <td>251.00</td>\n",
       "      <td>Super8 By Wyndham West Kelowna Bc</td>\n",
       "      <td>1631</td>\n",
       "      <td>6801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>379</td>\n",
       "      <td>333.60</td>\n",
       "      <td>The Sutton Place Hotel - Edmonton</td>\n",
       "      <td>1636</td>\n",
       "      <td>6838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>379</td>\n",
       "      <td>312.45</td>\n",
       "      <td>The Sutton Place Hotel - Edmonton</td>\n",
       "      <td>1636</td>\n",
       "      <td>6844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>387</td>\n",
       "      <td>615.23</td>\n",
       "      <td>Hotel Alma</td>\n",
       "      <td>1639</td>\n",
       "      <td>6847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>389</td>\n",
       "      <td>110.00</td>\n",
       "      <td>Quality Inn&amp; Conference Centre Downtown</td>\n",
       "      <td>1641</td>\n",
       "      <td>6850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   consumer_id  total_including_tax                                     name  \\\n",
       "0          345               251.00        Super8 By Wyndham West Kelowna Bc   \n",
       "1          379               333.60        The Sutton Place Hotel - Edmonton   \n",
       "2          379               312.45        The Sutton Place Hotel - Edmonton   \n",
       "3          387               615.23                               Hotel Alma   \n",
       "4          389               110.00  Quality Inn& Conference Centre Downtown   \n",
       "\n",
       "     id  order_id  \n",
       "0  1631      6801  \n",
       "1  1636      6838  \n",
       "2  1636      6844  \n",
       "3  1639      6847  \n",
       "4  1641      6850  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Best Western Plus Meridian Hotel, Lloydminster    4\n",
       "Hotel Signature Quebec                            3\n",
       "Delta Hotels Ottawa City Centre                   3\n",
       "Holiday Inn Ottawa Parliament Hill                3\n",
       "Econo Lodge Inn & Suites University               3\n",
       "                                                 ..\n",
       "Four Points By Sheraton Vancouver Airport         1\n",
       "DoubleTree by Hilton Montreal                     1\n",
       "Days Inn by Wyndham Moose Jaw                     1\n",
       "BEST WESTERN WARREN HOTEL                         1\n",
       "HOLIDAY INN WEST                                  1\n",
       "Name: name, Length: 379, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booking.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most booked hotel has only been booked 4 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "493     22\n",
       "685      5\n",
       "1764     4\n",
       "3020     3\n",
       "718      3\n",
       "        ..\n",
       "2427     1\n",
       "865      1\n",
       "2914     1\n",
       "1379     1\n",
       "514      1\n",
       "Name: consumer_id, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booking.consumer_id.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequent customers has made 22 bookings And below are his (or her) bookings.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>consumer_id</th>\n",
       "      <th>total_including_tax</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>order_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>493</td>\n",
       "      <td>560.16</td>\n",
       "      <td>Podollan Inn &amp; Spa Grande Prairie</td>\n",
       "      <td>2211</td>\n",
       "      <td>8771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>493</td>\n",
       "      <td>114.44</td>\n",
       "      <td>Holiday Inn Express Hotel &amp; Suites Edmonton South</td>\n",
       "      <td>2209</td>\n",
       "      <td>8769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>493</td>\n",
       "      <td>144.40</td>\n",
       "      <td>Hampton Inn by Hilton Lloydminster</td>\n",
       "      <td>2210</td>\n",
       "      <td>8770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>493</td>\n",
       "      <td>161.59</td>\n",
       "      <td>Home2 Suites by Hilton Fort St. John, Fort St....</td>\n",
       "      <td>2616</td>\n",
       "      <td>10393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>493</td>\n",
       "      <td>420.74</td>\n",
       "      <td>Best Western Plus Meridian Hotel, Lloydminster</td>\n",
       "      <td>2619</td>\n",
       "      <td>10397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>206</td>\n",
       "      <td>493</td>\n",
       "      <td>163.49</td>\n",
       "      <td>Holiday Inn Express Hotel &amp; Suites Edson, Edson</td>\n",
       "      <td>2622</td>\n",
       "      <td>10401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>207</td>\n",
       "      <td>493</td>\n",
       "      <td>327.01</td>\n",
       "      <td>Best Western Plus Hinton Inn &amp; Suites</td>\n",
       "      <td>2625</td>\n",
       "      <td>10405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>493</td>\n",
       "      <td>980.94</td>\n",
       "      <td>Best Western Plus Sherwood Park Inn &amp; Suites</td>\n",
       "      <td>2620</td>\n",
       "      <td>10398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>493</td>\n",
       "      <td>182.76</td>\n",
       "      <td>Podollan Inn &amp; Spa Grande Prairie, Grande Prairie</td>\n",
       "      <td>2623</td>\n",
       "      <td>10402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>493</td>\n",
       "      <td>248.92</td>\n",
       "      <td>Best Western Plus Fox Creek, Fox Creek</td>\n",
       "      <td>2626</td>\n",
       "      <td>10406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>255</td>\n",
       "      <td>493</td>\n",
       "      <td>398.84</td>\n",
       "      <td>Podollan Inn &amp; Spa Grande Prairie, Grande Prairie</td>\n",
       "      <td>2619</td>\n",
       "      <td>10399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>294</td>\n",
       "      <td>493</td>\n",
       "      <td>980.94</td>\n",
       "      <td>Best Western Plus Sherwood Park Inn &amp; Suites</td>\n",
       "      <td>2620</td>\n",
       "      <td>10396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>295</td>\n",
       "      <td>493</td>\n",
       "      <td>365.52</td>\n",
       "      <td>Podollan Inn &amp; Spa Grande Prairie, Grande Prairie</td>\n",
       "      <td>2623</td>\n",
       "      <td>10404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>378</td>\n",
       "      <td>493</td>\n",
       "      <td>606.04</td>\n",
       "      <td>Hampton Inn by Hilton Edmonton/Sherwood Park, ...</td>\n",
       "      <td>2986</td>\n",
       "      <td>11623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>379</td>\n",
       "      <td>493</td>\n",
       "      <td>155.47</td>\n",
       "      <td>Paradise Inn &amp; Suites Valleyview, Valleyview</td>\n",
       "      <td>2987</td>\n",
       "      <td>11627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>380</td>\n",
       "      <td>493</td>\n",
       "      <td>280.13</td>\n",
       "      <td>Best Western Plus Meridian Hotel, Lloydminster</td>\n",
       "      <td>2619</td>\n",
       "      <td>11629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>381</td>\n",
       "      <td>493</td>\n",
       "      <td>106.93</td>\n",
       "      <td>Microtel Inn &amp; Suites By Wyndham Whitecourt, W...</td>\n",
       "      <td>2989</td>\n",
       "      <td>11630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>382</td>\n",
       "      <td>493</td>\n",
       "      <td>150.11</td>\n",
       "      <td>Towneplace Suites by Marriott Red Deer, Red Deer</td>\n",
       "      <td>2990</td>\n",
       "      <td>11632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>493</td>\n",
       "      <td>280.13</td>\n",
       "      <td>Best Western Plus Meridian Hotel, Lloydminster</td>\n",
       "      <td>2619</td>\n",
       "      <td>11637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>384</td>\n",
       "      <td>493</td>\n",
       "      <td>703.05</td>\n",
       "      <td>Best Western Plus Meridian Hotel, Lloydminster</td>\n",
       "      <td>2619</td>\n",
       "      <td>11639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>385</td>\n",
       "      <td>493</td>\n",
       "      <td>612.85</td>\n",
       "      <td>Crowne Plaza Houston River Oaks, Houston</td>\n",
       "      <td>2994</td>\n",
       "      <td>11642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>386</td>\n",
       "      <td>493</td>\n",
       "      <td>754.50</td>\n",
       "      <td>Embassy Suites by Hilton Portland Downtown, Po...</td>\n",
       "      <td>2999</td>\n",
       "      <td>11662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     consumer_id  total_including_tax  \\\n",
       "51           493               560.16   \n",
       "193          493               114.44   \n",
       "198          493               144.40   \n",
       "204          493               161.59   \n",
       "205          493               420.74   \n",
       "206          493               163.49   \n",
       "207          493               327.01   \n",
       "217          493               980.94   \n",
       "218          493               182.76   \n",
       "219          493               248.92   \n",
       "255          493               398.84   \n",
       "294          493               980.94   \n",
       "295          493               365.52   \n",
       "378          493               606.04   \n",
       "379          493               155.47   \n",
       "380          493               280.13   \n",
       "381          493               106.93   \n",
       "382          493               150.11   \n",
       "383          493               280.13   \n",
       "384          493               703.05   \n",
       "385          493               612.85   \n",
       "386          493               754.50   \n",
       "\n",
       "                                                  name    id  order_id  \n",
       "51                   Podollan Inn & Spa Grande Prairie  2211      8771  \n",
       "193  Holiday Inn Express Hotel & Suites Edmonton South  2209      8769  \n",
       "198                 Hampton Inn by Hilton Lloydminster  2210      8770  \n",
       "204  Home2 Suites by Hilton Fort St. John, Fort St....  2616     10393  \n",
       "205     Best Western Plus Meridian Hotel, Lloydminster  2619     10397  \n",
       "206    Holiday Inn Express Hotel & Suites Edson, Edson  2622     10401  \n",
       "207              Best Western Plus Hinton Inn & Suites  2625     10405  \n",
       "217       Best Western Plus Sherwood Park Inn & Suites  2620     10398  \n",
       "218  Podollan Inn & Spa Grande Prairie, Grande Prairie  2623     10402  \n",
       "219             Best Western Plus Fox Creek, Fox Creek  2626     10406  \n",
       "255  Podollan Inn & Spa Grande Prairie, Grande Prairie  2619     10399  \n",
       "294       Best Western Plus Sherwood Park Inn & Suites  2620     10396  \n",
       "295  Podollan Inn & Spa Grande Prairie, Grande Prairie  2623     10404  \n",
       "378  Hampton Inn by Hilton Edmonton/Sherwood Park, ...  2986     11623  \n",
       "379       Paradise Inn & Suites Valleyview, Valleyview  2987     11627  \n",
       "380     Best Western Plus Meridian Hotel, Lloydminster  2619     11629  \n",
       "381  Microtel Inn & Suites By Wyndham Whitecourt, W...  2989     11630  \n",
       "382   Towneplace Suites by Marriott Red Deer, Red Deer  2990     11632  \n",
       "383     Best Western Plus Meridian Hotel, Lloydminster  2619     11637  \n",
       "384     Best Western Plus Meridian Hotel, Lloydminster  2619     11639  \n",
       "385           Crowne Plaza Houston River Oaks, Houston  2994     11642  \n",
       "386  Embassy Suites by Hilton Portland Downtown, Po...  2999     11662  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booking.loc[booking['consumer_id'] == 493]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = booking.groupby(['consumer_id', 'name', 'id'])['total_including_tax'].sum().reset_index()\n",
    "df['count'] = df['name'].map(booking['name'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotel_lookup = df[['id', 'name']].drop_duplicates() \n",
    "hotel_lookup['id'] = hotel_lookup.id.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['consumer_id'] = df.consumer_id.astype(int) \n",
    "df = df[['id', 'count', 'consumer_id']] \n",
    "grouped_purchased = df.groupby(['consumer_id', 'id']).sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = list(np.sort(grouped_purchased['consumer_id'].unique()))\n",
    "hotels = list(grouped_purchased['id'].unique())\n",
    "quantity = list(grouped_purchased['count']) \n",
    "rows = grouped_purchased['consumer_id'].astype(CategoricalDtype(categories = customers)).cat.codes \n",
    "cols = grouped_purchased['id'].astype(CategoricalDtype(categories = hotels)).cat.codes\n",
    "purchases_sparse = sparse.csr_matrix((quantity, (rows, cols)), shape=(len(customers), len(hotels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<368x379 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 405 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.70961913502352"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_size = purchases_sparse.shape[0]*purchases_sparse.shape[1] \n",
    "num_purchases = len(purchases_sparse.nonzero()[0]) \n",
    "sparsity = 100*(1 - (num_purchases/matrix_size))\n",
    "sparsity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I learned that in generally, for collaborative filtering to work, the maximum sparsity you could get away with would probably be around 99.5% or so. We are at over 99.7%. So we exceed this limit. So we should not expect to get decent results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train(ratings, pct_test = 0.2):\n",
    "    \n",
    "    test_set = ratings.copy() \n",
    "    test_set[test_set != 0] = 1 \n",
    "    training_set = ratings.copy()  \n",
    "    nonzero_inds = training_set.nonzero() \n",
    "    nonzero_pairs = list(zip(nonzero_inds[0], nonzero_inds[1])) \n",
    "    random.seed(0) \n",
    "    num_samples = int(np.ceil(pct_test*len(nonzero_pairs))) \n",
    "    samples = random.sample(nonzero_pairs, num_samples) \n",
    "    user_inds = [index[0] for index in samples] \n",
    "    item_inds = [index[1] for index in samples] \n",
    "    training_set[user_inds, item_inds] = 0 \n",
    "    training_set.eliminate_zeros() \n",
    "    return training_set, test_set, list(set(user_inds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hotel_train, hotel_test, hotel_users_altered = make_train(purchases_sparse, pct_test = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def implicit_weighted_ALS(training_set, lambda_val = 0.1, alpha = 40, iterations = 10, rank_size = 20, seed = 0):\n",
    "    \n",
    "    conf = (alpha*training_set) \n",
    "    num_user = conf.shape[0]\n",
    "    num_hotel = conf.shape[1] \n",
    "    \n",
    "    rstate = np.random.RandomState(seed)\n",
    "    \n",
    "    X = sparse.csr_matrix(rstate.normal(size = (num_user, rank_size))) \n",
    "    Y = sparse.csr_matrix(rstate.normal(size = (num_hotel, rank_size))) \n",
    "                                                                 \n",
    "    X_eye = sparse.eye(num_user)\n",
    "    Y_eye = sparse.eye(num_hotel)\n",
    "    lambda_eye = lambda_val * sparse.eye(rank_size) \n",
    "   \n",
    "    for iter_step in range(iterations): \n",
    "        \n",
    "        yTy = Y.T.dot(Y)\n",
    "        xTx = X.T.dot(X)\n",
    "        \n",
    "        for u in range(num_user):\n",
    "            conf_samp = conf[u,:].toarray() \n",
    "            pref = conf_samp.copy() \n",
    "            pref[pref != 0] = 1 \n",
    "            CuI = sparse.diags(conf_samp, [0]) \n",
    "            yTCuIY = Y.T.dot(CuI).dot(Y) \n",
    "            yTCupu = Y.T.dot(CuI + Y_eye).dot(pref.T)                                                  \n",
    "            X[u] = spsolve(yTy + yTCuIY + lambda_eye, yTCupu) \n",
    "            \n",
    "        for i in range(num_hotel):\n",
    "            conf_samp = conf[:,i].T.toarray() \n",
    "            pref = conf_samp.copy()\n",
    "            pref[pref != 0] = 1 \n",
    "            CiI = sparse.diags(conf_samp, [0]) \n",
    "            xTCiIX = X.T.dot(CiI).dot(X) \n",
    "            xTCiPi = X.T.dot(CiI + X_eye).dot(pref.T) \n",
    "            Y[i] = spsolve(xTx + xTCiIX + lambda_eye, xTCiPi)\n",
    "            \n",
    "    return X, Y.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_vecs, hotel_vecs = implicit_weighted_ALS(hotel_train, lambda_val = 0.1, alpha = 15, iterations = 1,\n",
    "                                            rank_size = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_vecs[0,:].dot(hotel_vecs).toarray()[0,:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This method is deprecated. Please use the AlternatingLeastSquares class instead\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bc89c4642aa435f9abae1f99fdefa66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=50), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "alpha = 15\n",
    "user_vecs, hotel_vecs = implicit.alternating_least_squares((hotel_train*alpha).astype('double'), \n",
    "                                                          factors=20, \n",
    "                                                          regularization = 0.1, \n",
    "                                                         iterations = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def auc_score(predictions, test):\n",
    "    \n",
    "    fpr, tpr, thresholds = metrics.roc_curve(test, predictions)\n",
    "    return metrics.auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_mean_auc(training_set, altered_users, predictions, test_set):\n",
    "    \n",
    "    store_auc = [] \n",
    "    popularity_auc = [] \n",
    "    pop_hotels = np.array(test_set.sum(axis = 0)).reshape(-1) \n",
    "    hotel_vecs = predictions[1]\n",
    "    for user in altered_users: \n",
    "        training_row = training_set[user,:].toarray().reshape(-1) \n",
    "        zero_inds = np.where(training_row == 0) \n",
    "        user_vec = predictions[0][user,:]\n",
    "        pred = user_vec.dot(hotel_vecs).toarray()[0,zero_inds].reshape(-1)\n",
    "        actual = test_set[user,:].toarray()[0,zero_inds].reshape(-1) \n",
    "        pop = pop_hotels[zero_inds] \n",
    "        store_auc.append(auc_score(pred, actual)) \n",
    "        popularity_auc.append(auc_score(pop, actual)) \n",
    "    \n",
    "    return float('%.3f'%np.mean(store_auc)), float('%.3f'%np.mean(popularity_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.496, 0.51)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_mean_auc(hotel_train, hotel_users_altered, \n",
    "              [sparse.csr_matrix(user_vecs), sparse.csr_matrix(hotel_vecs.T)], hotel_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our RecSys did not beat popularity. Our RecSys had a mean AUC of 0.496, while the popular hotel benchmark had a higher AUC of 0.51. So, simply put, at this moment, we are better off just recommend popular hotels to every one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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