03b_Advanced_BPR_CellPhones_5core.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## procedure to load the data from the json format to rating matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "# load data from amazon reviews dataset csv\n",
    "def load_data_hybrid(data_path, min_items=1, min_users=1, sampling= True, sample_size = 0.5):\n",
    "    user_ratings = defaultdict(set)\n",
    "    item_ratings = defaultdict(set)\n",
    "    max_u_id = -1\n",
    "    max_i_id = -1\n",
    "    user_count = 0\n",
    "    item_count = 0\n",
    "    reviews = 0\n",
    "    users = {}  # aid to id LUT\n",
    "    items = {}  # asid to id LUT\n",
    "    brands = {}\n",
    "    prices = {}\n",
    "    prod_desc = {}\n",
    "    random.seed(0)\n",
    "    with open(data_path, 'r') as f:\n",
    "        for line in f.readlines()[1:]:\n",
    "            if (sampling and random.random()>sample_size):\n",
    "                continue\n",
    "            reviews += 1\n",
    "            if (len(line.split(\",\"))==6):\n",
    "                auid, asid, _, brand, price, product_desc = line.split(\",\")\n",
    "            else:\n",
    "                auid, asid, _, brand, price = line.split(\",\")\n",
    "\n",
    "            u, i = None, None\n",
    "\n",
    "            if auid in users:\n",
    "                u = users[auid]\n",
    "            else:\n",
    "                user_count += 1  # new user so increment\n",
    "                users[auid] = user_count\n",
    "                u = user_count\n",
    "\n",
    "            if asid in items:\n",
    "                i = items[asid]\n",
    "            else:\n",
    "                item_count += 1  # new i so increment\n",
    "                items[asid] = item_count\n",
    "                i = item_count\n",
    "                brands[i] = brand\n",
    "                if (price=='' or price=='\\r\\n' or price=='\\n'):\n",
    "                    prices[i] = 0\n",
    "                else:\n",
    "                    prices[i] = float(price.rstrip())\n",
    "                if (len(line.split(\",\")) == 6):\n",
    "                    prod_desc[i] = [int(el) for el in list(product_desc)[:-2][1:]]\n",
    "                    if (len(prod_desc[i])==0):\n",
    "                        prod_desc[i] = list(np.zeros(4525))\n",
    "\n",
    "            user_ratings[u].add(i)\n",
    "            item_ratings[i].add(u)\n",
    "            max_u_id = max(u, max_u_id)\n",
    "            max_i_id = max(i, max_i_id)\n",
    "\n",
    "    print \"max_u_id: \", max_u_id\n",
    "    print \"max_i_id: \", max_i_id\n",
    "    print \"reviews : \", reviews\n",
    "\n",
    "    # filter out users w/ less than X reviews\n",
    "    num_u_id = 0\n",
    "    num_i_id = 0\n",
    "    num_reviews = 0\n",
    "    user_ratings_filtered = defaultdict(set)\n",
    "    for u, ids in user_ratings.iteritems():\n",
    "        if len(ids) > min_items:\n",
    "            # keep\n",
    "            user_ratings_filtered[u] = ids\n",
    "            num_u_id += 1\n",
    "            num_reviews += len(ids)\n",
    "    item_ratings_filtered = defaultdict(set)\n",
    "    for ids, u in item_ratings.iteritems():\n",
    "        if len(u) > min_users:\n",
    "            # keep\n",
    "            item_ratings_filtered[ids] = u\n",
    "            num_i_id += 1\n",
    "\n",
    "\n",
    "    print \"u_id: \", num_u_id\n",
    "    print \"i_id: \", num_i_id\n",
    "    print \"reviews : \", num_reviews\n",
    "    return max_u_id, max_i_id, users, items, user_ratings_filtered, item_ratings_filtered, brands, prices, prod_desc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedure to load image features of the products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load image features for the given asin collection into dictionary\n",
    "def load_image_features(path, items):\n",
    "    count=0\n",
    "    image_features = {}\n",
    "    f = open(path, 'rb')\n",
    "    while True:\n",
    "        asin = f.read(10)\n",
    "        if asin == '': break\n",
    "        features_bytes = f.read(16384) # 4 * 4096 = 16KB, fast read, don't unpack\n",
    "  \n",
    "        if asin in items: #only unpack 4096 bytes if w need it -- big speed up\n",
    "            features = (np.fromstring(features_bytes, dtype=np.float32)/44.4722)\n",
    "            iid=items[asin]\n",
    "            if len(features)==0:\n",
    "                image_features[iid] = np.zeros(4096)\n",
    "            else:\n",
    "                image_features[iid] = features\n",
    "    \n",
    "    return image_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedure to uniformly sample the (u,i,j) triplet from the raw data for the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import cPickle as pickle\n",
    "import numpy\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def uniform_sample_batch(train_ratings, test_ratings, item_count, advanced_features):\n",
    "    neg_items = 2\n",
    "    for u in train_ratings.keys():\n",
    "        t = []\n",
    "        iv = []\n",
    "        jv = []\n",
    "        for i in train_ratings[u]:\n",
    "            if (u in test_ratings.keys()):\n",
    "                if (i != test_ratings[u]):  # make sure it's not in the test set\n",
    "                    for k in range(1,neg_items):\n",
    "                        j = random.randint(1, item_count)\n",
    "                        while j in train_ratings[u]:\n",
    "                            j = random.randint(1, item_count)\n",
    "                        # sometimes there will not be an image for given product\n",
    "                        try:\n",
    "                            advanced_features[i]\n",
    "                            advanced_features[j]\n",
    "                        except KeyError:\n",
    "                            continue\n",
    "                        iv.append(advanced_features[i])\n",
    "                        jv.append(advanced_features[j])\n",
    "                        t.append([u, i, j])\n",
    "            else:\n",
    "                for k in range(1,neg_items):\n",
    "                    j = random.randint(1, item_count)\n",
    "                    while j in train_ratings[u]:\n",
    "                        j = random.randint(1, item_count)\n",
    "                    # sometimes there will not be an image for given product\n",
    "                    try:\n",
    "                        advanced_features[i]\n",
    "                        advanced_features[j]\n",
    "                    except KeyError:\n",
    "                        continue\n",
    "                    iv.append(advanced_features[i])\n",
    "                    jv.append(advanced_features[j])\n",
    "                    t.append([u, i, j])\n",
    "\n",
    "        # block if queue is full\n",
    "        if len(iv)>1:\n",
    "            yield numpy.asarray(t), numpy.vstack(tuple(iv)), numpy.vstack(tuple(jv))\n",
    "        else:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedure to uniformly sample the (u,i,j) triplet from the raw data for the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_batch_generator_by_user(train_ratings, test_ratings, item_ratings, item_count, advanced_features, cold_start = False, cold_start_thresh = 5):\n",
    "    # using leave one cv\n",
    "    for u in random.sample(test_ratings.keys(), 4000):\n",
    "    #for u in test_ratings.keys():\n",
    "        i = test_ratings[u]\n",
    "        if (cold_start and len(item_ratings[i]) > cold_start_thresh-1):\n",
    "            continue\n",
    "        t = []\n",
    "        ilist = []\n",
    "        jlist = []\n",
    "        count = 0\n",
    "        for j in random.sample(range(item_count), 100):\n",
    "            # find item not in test[u] and train[u]\n",
    "            if j != test_ratings[u] and not (j in train_ratings[u]):\n",
    "                try:\n",
    "                    advanced_features[i]\n",
    "                    advanced_features[j]\n",
    "                except KeyError:\n",
    "                    continue\n",
    "\n",
    "                count += 1\n",
    "                t.append([u, i, j])\n",
    "                ilist.append(advanced_features[i])\n",
    "                jlist.append(advanced_features[j])\n",
    "\n",
    "        # print numpy.asarray(t).shape\n",
    "        # print numpy.vstack(tuple(ilist)).shape\n",
    "        # print numpy.vstack(tuple(jlist)).shape\n",
    "        if (len(ilist) == 0):\n",
    "            #print \"could not find neg item for user, count: \", count, u\n",
    "            continue\n",
    "        yield numpy.asarray(t), numpy.vstack(tuple(ilist)), numpy.vstack(tuple(jlist))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedure to generate the (u,i) pair for the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_test(user_ratings):\n",
    "    '''\n",
    "    for each user, random select one rating into test set\n",
    "    '''\n",
    "    user_test = dict()\n",
    "    for u, i_list in user_ratings.items():\n",
    "        user_test[u] = random.sample(user_ratings[u], 1)[0]\n",
    "    return user_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Personalized Ranking (BPR) Model and it's extensions (VBPR, NVBPR, Hybrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def abpr(user_count, item_count, advanced_features, bpr_extension= True, hidden_dim=10, hidden_img_dim=10,\n",
    "         l2_regulization=0.1,\n",
    "         bias_regulization=0.01,\n",
    "         embed_regulization = 0,\n",
    "         image_regulization =0.1,\n",
    "         visual_bias_regulization = 0.01):\n",
    "    \"\"\"\n",
    "    user_count: total number of users\n",
    "    item_count: total number of items\n",
    "    hidden_dim: hidden feature size of MF\n",
    "    hidden_img_dim: hidden visual/non-visual feature size of MF\n",
    "    \"\"\"\n",
    "    advanced_feat_dim = len(advanced_features[1])\n",
    "    u = tf.placeholder(tf.int32, [None])\n",
    "    i = tf.placeholder(tf.int32, [None])\n",
    "    j = tf.placeholder(tf.int32, [None])\n",
    "    iv = tf.placeholder(tf.float32, [None, advanced_feat_dim])\n",
    "    jv = tf.placeholder(tf.float32, [None, advanced_feat_dim])\n",
    "\n",
    "    # model parameters -- LEARN THESE\n",
    "    # latent factors\n",
    "    user_emb_w = tf.get_variable(\"user_emb_w\", [user_count + 1, hidden_dim],\n",
    "                                 initializer=tf.random_normal_initializer(0, 0.1))\n",
    "    item_emb_w = tf.get_variable(\"item_emb_w\", [item_count + 1, hidden_dim],\n",
    "                                 initializer=tf.random_normal_initializer(0, 0.1))\n",
    "\n",
    "    # UxD visual factors for users\n",
    "    user_img_w = tf.get_variable(\"user_img_w\", [user_count + 1, hidden_img_dim],\n",
    "                                 initializer=tf.random_normal_initializer(0, 0.1))\n",
    "    # this is E, the embedding matrix\n",
    "    img_emb_w = tf.get_variable(\"img_emb_w\", [hidden_img_dim, advanced_feat_dim],\n",
    "                                initializer=tf.random_normal_initializer(0, 0.1))\n",
    "\n",
    "    visual_bias = tf.get_variable(\"visual_bias\", [1, advanced_feat_dim], initializer=tf.random_normal_initializer(0, 0.1))\n",
    "\n",
    "    # biases\n",
    "    item_b = tf.get_variable(\"item_b\", [item_count + 1, 1], initializer=tf.constant_initializer(0.0))\n",
    "\n",
    "    # pull out the respective latent factor vectors for a given user u and items i & j\n",
    "    u_emb = tf.nn.embedding_lookup(user_emb_w, u)\n",
    "    i_emb = tf.nn.embedding_lookup(item_emb_w, i)\n",
    "    j_emb = tf.nn.embedding_lookup(item_emb_w, j)\n",
    "    # pull out the visual factor, 1 X D for user u\n",
    "    u_img = tf.nn.embedding_lookup(user_img_w, u)\n",
    "    # get the respective biases for items i & j\n",
    "    i_b = tf.nn.embedding_lookup(item_b, i)\n",
    "    j_b = tf.nn.embedding_lookup(item_b, j)\n",
    "\n",
    "\n",
    "    # MF predict: u_i > u_j\n",
    "    # MF predict: u_i > u_j\n",
    "    theta_i = tf.matmul(iv, img_emb_w, transpose_b=True)  # (f_i * E), eq. 3\n",
    "    theta_j = tf.matmul(jv, img_emb_w, transpose_b=True)  # (f_j * E), eq. 3\n",
    "    if bpr_extension:\n",
    "        xui = i_b + tf.reduce_sum(tf.multiply(u_emb, i_emb), 1, keep_dims=True) + tf.reduce_sum(tf.multiply(u_img, theta_i), 1, keep_dims=True) \\\n",
    "                                                                            + tf.reduce_sum(tf.multiply(visual_bias, iv), 1, keep_dims=True) \n",
    "        xuj = j_b + tf.reduce_sum(tf.multiply(u_emb, j_emb), 1, keep_dims=True) + tf.reduce_sum(tf.multiply(u_img, theta_j), 1, keep_dims=True) \\\n",
    "                                                                            + tf.reduce_sum(tf.multiply(visual_bias, jv), 1, keep_dims=True) \n",
    "    else:\n",
    "        xui = i_b + tf.reduce_sum(tf.multiply(u_emb, i_emb), 1, keep_dims=True)\n",
    "        xuj = j_b + tf.reduce_sum(tf.multiply(u_emb, j_emb), 1, keep_dims=True) \n",
    "    \n",
    "    xuij = xui - xuj\n",
    "\n",
    "    auc = tf.reduce_mean(tf.to_float(xuij > 0))\n",
    "\n",
    "    l2_norm = tf.add_n([\n",
    "        l2_regulization * tf.reduce_sum(tf.multiply(u_emb, u_emb)),\n",
    "        image_regulization * tf.reduce_sum(tf.multiply(u_img, u_img)),\n",
    "        l2_regulization * tf.reduce_sum(tf.multiply(i_emb, i_emb)),\n",
    "        l2_regulization * tf.reduce_sum(tf.multiply(j_emb, j_emb)),\n",
    "        embed_regulization * tf.reduce_sum(tf.multiply(img_emb_w, img_emb_w)),\n",
    "        bias_regulization * tf.reduce_sum(tf.multiply(i_b, i_b)),\n",
    "        bias_regulization * tf.reduce_sum(tf.multiply(j_b, j_b)),\n",
    "        visual_bias_regulization * tf.reduce_sum(tf.multiply(visual_bias, visual_bias))\n",
    "    ])\n",
    "\n",
    "    loss = l2_norm - tf.reduce_mean(tf.log(tf.sigmoid(xuij)))\n",
    "    train_op = tf.train.AdamOptimizer().minimize(loss)\n",
    "    return u, i, j, iv, jv, loss, auc, train_op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedure to run the end-to-end model (from loading data to training the model and to test the model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def session_run(num_iter, bpr_extension, advanced_features):\n",
    "    ### Loading and parsing the review matrix for Cell Phones 5-core dataset\n",
    "    auc_train = []\n",
    "    auc_test = []\n",
    "    auc_test_cs = []\n",
    "    data_path = os.path.join('', 'reviews_cellphone_scraped_cpp_fv_2.csv')\n",
    "    user_count, item_count, users, items, user_ratings, item_ratings, brands, prices, prod_desc = load_data_hybrid(data_path, min_items=4, min_users=0, sampling= True, sample_size = 0.8)\n",
    "    user_ratings_test = generate_test(user_ratings)\n",
    "    with tf.Graph().as_default(), tf.Session() as session:\n",
    "        with tf.variable_scope('abpr'):\n",
    "            u, i, j, iv, jv, loss, auc, train_op = abpr(user_count, item_count, advanced_features, bpr_extension=bpr_extension)\n",
    "\n",
    "        session.run(tf.global_variables_initializer())\n",
    "        \n",
    "\n",
    "        for epoch in range(1, num_iter+1):\n",
    "            print \"epoch \", epoch\n",
    "            _loss_train = 0.0\n",
    "            user_count = 0\n",
    "            auc_train_values = []\n",
    "            for d, _iv, _jv in uniform_sample_batch(user_ratings, user_ratings_test, item_count, advanced_features):\n",
    "                user_count += 1\n",
    "                _loss, _auc, _ = session.run([loss, auc, train_op], feed_dict={u:d[:,0], i:d[:,1], j:d[:,2], iv:_iv, jv:_jv})\n",
    "                _loss_train += _loss\n",
    "                auc_train_values.append(_auc)\n",
    "            print \"train_loss:\", _loss_train/user_count, \"train auc: \", numpy.mean(auc_train_values)\n",
    "            auc_train.append(numpy.mean(auc_train_values))\n",
    "\n",
    "\n",
    "            auc_values = []\n",
    "            _loss_test = 0.0\n",
    "            user_count = 0\n",
    "            for d, _iv, _jv in test_batch_generator_by_user(user_ratings, user_ratings_test, item_ratings, item_count, advanced_features, cold_start = False):\n",
    "                user_count += 1\n",
    "                _loss, _auc = session.run([loss, auc], feed_dict={u: d[:, 0], i: d[:, 1], j: d[:, 2], iv: _iv, jv: _jv})\n",
    "                _loss_test += _loss\n",
    "                auc_values.append(_auc)\n",
    "            print \"test_loss: \", _loss_test / user_count, \"test auc: \", numpy.mean(auc_values)\n",
    "            auc_test.append(numpy.mean(auc_values))\n",
    "\n",
    "            auc_values_cs = []\n",
    "            _loss_test_cs = 0.0\n",
    "            user_count = 0\n",
    "            for d, _iv, _jv in test_batch_generator_by_user(user_ratings, user_ratings_test, item_ratings, item_count, advanced_features, cold_start = True, cold_start_thresh = 10):\n",
    "                user_count += 1\n",
    "                _loss, _auc = session.run([loss, auc], feed_dict={u: d[:, 0], i: d[:, 1], j: d[:, 2], iv: _iv, jv: _jv})\n",
    "                _loss_test_cs += _loss\n",
    "                auc_values_cs.append(_auc)\n",
    "            print \"cold start test_loss: \", _loss_test_cs / user_count, \"cold start auc: \", numpy.mean(auc_values_cs)\n",
    "            auc_test_cs.append(numpy.mean(auc_values_cs))\n",
    "        return auc_train, auc_test, auc_test_cs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the image related feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n"
     ]
    }
   ],
   "source": [
    "data_path = os.path.join('', 'reviews_cellphone_scraped_cpp_fv_2.csv')\n",
    "user_count, item_count, users, items, user_ratings, item_ratings, brands, prices, prod_desc = load_data_hybrid(data_path, min_items=4, min_users=0, sampling= True, sample_size = 0.8)\n",
    "images_path = \"image_features_Cell_Phones_and_Accessories.b\"\n",
    "f = load_image_features(images_path, items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the base BPR model with no advaced features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.774432045883 train auc:  0.710456\n",
      "test_loss:  2.12412384774 test auc:  0.733454\n",
      "cold start test_loss:  2.14496888136 cold start auc:  0.324717\n",
      "epoch  2\n",
      "train_loss: 0.654387666477 train auc:  0.754382\n",
      "test_loss:  1.8064716123 test auc:  0.735381\n",
      "cold start test_loss:  1.76503518941 cold start auc:  0.302351\n",
      "epoch  3\n",
      "train_loss: 0.605238047745 train auc:  0.762096\n",
      "test_loss:  1.69689903816 test auc:  0.732722\n",
      "cold start test_loss:  1.62261208889 cold start auc:  0.297644\n",
      "epoch  4\n",
      "train_loss: 0.580253056792 train auc:  0.766196\n",
      "test_loss:  1.62869989565 test auc:  0.734842\n",
      "cold start test_loss:  1.62827654696 cold start auc:  0.300179\n",
      "epoch  5\n",
      "train_loss: 0.565641649604 train auc:  0.76977\n",
      "test_loss:  1.65993234013 test auc:  0.738847\n",
      "cold start test_loss:  1.74533574364 cold start auc:  0.298536\n",
      "epoch  6\n",
      "train_loss: 0.562258045624 train auc:  0.769056\n",
      "test_loss:  1.71125019058 test auc:  0.72808\n",
      "cold start test_loss:  1.85799070301 cold start auc:  0.303449\n",
      "epoch  7\n",
      "train_loss: 0.558597261139 train auc:  0.771948\n",
      "test_loss:  1.77522751657 test auc:  0.736032\n",
      "cold start test_loss:  2.0536215783 cold start auc:  0.280625\n",
      "epoch  8\n",
      "train_loss: 0.561112580014 train auc:  0.77016\n",
      "test_loss:  1.81738717814 test auc:  0.731873\n",
      "cold start test_loss:  2.14912623439 cold start auc:  0.294991\n",
      "epoch  9\n",
      "train_loss: 0.561721914956 train auc:  0.771614\n",
      "test_loss:  1.86674916439 test auc:  0.742867\n",
      "cold start test_loss:  2.27444506248 cold start auc:  0.290875\n",
      "epoch  10\n",
      "train_loss: 0.563689700818 train auc:  0.770041\n",
      "test_loss:  1.89723599514 test auc:  0.737665\n",
      "cold start test_loss:  2.36551910879 cold start auc:  0.29354\n",
      "epoch  11\n",
      "train_loss: 0.564545117905 train auc:  0.771155\n",
      "test_loss:  1.95265364762 test auc:  0.729012\n",
      "cold start test_loss:  2.48857866019 cold start auc:  0.28504\n",
      "epoch  12\n",
      "train_loss: 0.564682499616 train auc:  0.771193\n",
      "test_loss:  1.97798961747 test auc:  0.739155\n",
      "cold start test_loss:  2.51844893443 cold start auc:  0.29813\n",
      "epoch  13\n",
      "train_loss: 0.565905652675 train auc:  0.77021\n",
      "test_loss:  1.983535499 test auc:  0.737581\n",
      "cold start test_loss:  2.6147350198 cold start auc:  0.285732\n",
      "epoch  14\n",
      "train_loss: 0.565965362952 train auc:  0.774051\n",
      "test_loss:  2.01319029193 test auc:  0.735682\n",
      "cold start test_loss:  2.66542221212 cold start auc:  0.285706\n",
      "epoch  15\n",
      "train_loss: 0.567563817226 train auc:  0.771338\n",
      "test_loss:  2.01222785651 test auc:  0.742163\n",
      "cold start test_loss:  2.67170464449 cold start auc:  0.299147\n",
      "epoch  16\n",
      "train_loss: 0.570104882264 train auc:  0.770357\n",
      "test_loss:  2.04707931214 test auc:  0.735139\n",
      "cold start test_loss:  2.72452662984 cold start auc:  0.296094\n",
      "epoch  17\n",
      "train_loss: 0.569040572034 train auc:  0.77194\n",
      "test_loss:  2.06829915689 test auc:  0.729006\n",
      "cold start test_loss:  2.76163311931 cold start auc:  0.29153\n",
      "epoch  18\n",
      "train_loss: 0.571358219729 train auc:  0.770754\n",
      "test_loss:  2.0792560915 test auc:  0.732058\n",
      "cold start test_loss:  2.81793260522 cold start auc:  0.286481\n",
      "epoch  19\n",
      "train_loss: 0.571031739323 train auc:  0.769715\n",
      "test_loss:  2.09152664394 test auc:  0.727416\n",
      "cold start test_loss:  2.80048811021 cold start auc:  0.302685\n",
      "epoch  20\n",
      "train_loss: 0.571633186661 train auc:  0.769069\n",
      "test_loss:  2.09308305193 test auc:  0.733244\n",
      "cold start test_loss:  2.85603082808 cold start auc:  0.291257\n"
     ]
    }
   ],
   "source": [
    "bpr_auc_train, bpr_auc_test, bpr_auc_test_cold_start = session_run(20, False, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the Advanced BPR model with image features only (VBPR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.769213207876 train auc:  0.687115\n",
      "test_loss:  2.29788191317 test auc:  0.661085\n",
      "cold start test_loss:  2.33479332155 cold start auc:  0.439872\n",
      "epoch  2\n",
      "train_loss: 0.646217492796 train auc:  0.758917\n",
      "test_loss:  2.1122512837 test auc:  0.710022\n",
      "cold start test_loss:  2.12308064045 cold start auc:  0.401712\n",
      "epoch  3\n",
      "train_loss: 0.577286925481 train auc:  0.794209\n",
      "test_loss:  2.1451732841 test auc:  0.704026\n",
      "cold start test_loss:  2.13295278037 cold start auc:  0.42683\n",
      "epoch  4\n",
      "train_loss: 0.518185181861 train auc:  0.822456\n",
      "test_loss:  2.17154746803 test auc:  0.709536\n",
      "cold start test_loss:  2.25005653518 cold start auc:  0.466112\n",
      "epoch  5\n",
      "train_loss: 0.467741851756 train auc:  0.846441\n",
      "test_loss:  2.24900257157 test auc:  0.70816\n",
      "cold start test_loss:  2.37679085481 cold start auc:  0.473322\n",
      "epoch  6\n",
      "train_loss: 0.430949839682 train auc:  0.863032\n",
      "test_loss:  2.2801882119 test auc:  0.702879\n",
      "cold start test_loss:  2.45868776843 cold start auc:  0.514073\n",
      "epoch  7\n",
      "train_loss: 0.40257155698 train auc:  0.87616\n",
      "test_loss:  2.33469496823 test auc:  0.702438\n",
      "cold start test_loss:  2.59484316711 cold start auc:  0.520135\n",
      "epoch  8\n",
      "train_loss: 0.380119288309 train auc:  0.885216\n",
      "test_loss:  2.32891086058 test auc:  0.699099\n",
      "cold start test_loss:  2.54509542947 cold start auc:  0.547681\n",
      "epoch  9\n",
      "train_loss: 0.365153300214 train auc:  0.89034\n",
      "test_loss:  2.33648378134 test auc:  0.697766\n",
      "cold start test_loss:  2.58406682058 cold start auc:  0.5641\n",
      "epoch  10\n",
      "train_loss: 0.348818298862 train auc:  0.897396\n",
      "test_loss:  2.32671030271 test auc:  0.693945\n",
      "cold start test_loss:  2.61340890429 cold start auc:  0.550024\n",
      "epoch  11\n",
      "train_loss: 0.337594216081 train auc:  0.900378\n",
      "test_loss:  2.32705629417 test auc:  0.69249\n",
      "cold start test_loss:  2.56266760974 cold start auc:  0.570588\n",
      "epoch  12\n",
      "train_loss: 0.328311426701 train auc:  0.903584\n",
      "test_loss:  2.29708663586 test auc:  0.696233\n",
      "cold start test_loss:  2.51480534232 cold start auc:  0.585139\n",
      "epoch  13\n",
      "train_loss: 0.320925238553 train auc:  0.905861\n",
      "test_loss:  2.26922618072 test auc:  0.69996\n",
      "cold start test_loss:  2.54645476381 cold start auc:  0.569185\n",
      "epoch  14\n",
      "train_loss: 0.316013204897 train auc:  0.90774\n",
      "test_loss:  2.29882250486 test auc:  0.690066\n",
      "cold start test_loss:  2.57687390229 cold start auc:  0.572919\n",
      "epoch  15\n",
      "train_loss: 0.310775785546 train auc:  0.9083\n",
      "test_loss:  2.28067845709 test auc:  0.689066\n",
      "cold start test_loss:  2.51427947228 cold start auc:  0.588499\n",
      "epoch  16\n",
      "train_loss: 0.305972453153 train auc:  0.910578\n",
      "test_loss:  2.26548238345 test auc:  0.690052\n",
      "cold start test_loss:  2.54729200586 cold start auc:  0.58719\n",
      "epoch  17\n",
      "train_loss: 0.302538907911 train auc:  0.911206\n",
      "test_loss:  2.26783813361 test auc:  0.691738\n",
      "cold start test_loss:  2.4926086219 cold start auc:  0.588059\n",
      "epoch  18\n",
      "train_loss: 0.301357294632 train auc:  0.912406\n",
      "test_loss:  2.29246155839 test auc:  0.675601\n",
      "cold start test_loss:  2.48871023945 cold start auc:  0.59561\n",
      "epoch  19\n",
      "train_loss: 0.29678512866 train auc:  0.912869\n",
      "test_loss:  2.25285241828 test auc:  0.688519\n",
      "cold start test_loss:  2.42099483395 cold start auc:  0.606402\n",
      "epoch  20\n",
      "train_loss: 0.295285466789 train auc:  0.912899\n",
      "test_loss:  2.21378992684 test auc:  0.697595\n",
      "cold start test_loss:  2.39704360862 cold start auc:  0.605563\n"
     ]
    }
   ],
   "source": [
    "vbpr_auc_train, vbpr_auc_test, vbpr_auc_test_cold_start = session_run(20, True, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the price related feature and running the advanced BPR model with price features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.776521633032 train auc:  0.707053\n",
      "test_loss:  2.16207352903 test auc:  0.724254\n",
      "cold start test_loss:  2.13120220101 cold start auc:  0.346176\n",
      "epoch  2\n",
      "train_loss: 0.656959051539 train auc:  0.753644\n",
      "test_loss:  1.84112152597 test auc:  0.73083\n",
      "cold start test_loss:  1.78271951784 cold start auc:  0.321419\n",
      "epoch  3\n",
      "train_loss: 0.606363452042 train auc:  0.764772\n",
      "test_loss:  1.76503765453 test auc:  0.732067\n",
      "cold start test_loss:  1.68521881011 cold start auc:  0.314196\n",
      "epoch  4\n",
      "train_loss: 0.574265957633 train auc:  0.775519\n",
      "test_loss:  1.75004721513 test auc:  0.734917\n",
      "cold start test_loss:  1.76531928839 cold start auc:  0.340725\n",
      "epoch  5\n",
      "train_loss: 0.55306722507 train auc:  0.784944\n",
      "test_loss:  1.81011343494 test auc:  0.736671\n",
      "cold start test_loss:  1.8993082257 cold start auc:  0.332271\n",
      "epoch  6\n",
      "train_loss: 0.547706988952 train auc:  0.786854\n",
      "test_loss:  1.85709323202 test auc:  0.724281\n",
      "cold start test_loss:  2.0095375425 cold start auc:  0.342157\n",
      "epoch  7\n",
      "train_loss: 0.541725385908 train auc:  0.791418\n",
      "test_loss:  1.90259002477 test auc:  0.735769\n",
      "cold start test_loss:  2.18270211579 cold start auc:  0.330456\n",
      "epoch  8\n",
      "train_loss: 0.54341222323 train auc:  0.789771\n",
      "test_loss:  1.93479667814 test auc:  0.732236\n",
      "cold start test_loss:  2.25279431867 cold start auc:  0.327196\n",
      "epoch  9\n",
      "train_loss: 0.542388191707 train auc:  0.791395\n",
      "test_loss:  1.96854634866 test auc:  0.740699\n",
      "cold start test_loss:  2.36583498709 cold start auc:  0.334087\n",
      "epoch  10\n",
      "train_loss: 0.543377919314 train auc:  0.790947\n",
      "test_loss:  1.99051162797 test auc:  0.735209\n",
      "cold start test_loss:  2.43797067023 cold start auc:  0.329215\n",
      "epoch  11\n",
      "train_loss: 0.541577595043 train auc:  0.79243\n",
      "test_loss:  2.03325187162 test auc:  0.726707\n",
      "cold start test_loss:  2.53413385895 cold start auc:  0.318649\n",
      "epoch  12\n",
      "train_loss: 0.541860201241 train auc:  0.792008\n",
      "test_loss:  2.05148516208 test auc:  0.73268\n",
      "cold start test_loss:  2.54463390748 cold start auc:  0.330756\n",
      "epoch  13\n",
      "train_loss: 0.542447153397 train auc:  0.791476\n",
      "test_loss:  2.04767930204 test auc:  0.732101\n",
      "cold start test_loss:  2.61914352806 cold start auc:  0.315656\n",
      "epoch  14\n",
      "train_loss: 0.542267395881 train auc:  0.794725\n",
      "test_loss:  2.06358685407 test auc:  0.726877\n",
      "cold start test_loss:  2.64249786759 cold start auc:  0.320964\n",
      "epoch  15\n",
      "train_loss: 0.542896685599 train auc:  0.794324\n",
      "test_loss:  2.05615192279 test auc:  0.741105\n",
      "cold start test_loss:  2.6327005176 cold start auc:  0.336815\n",
      "epoch  16\n",
      "train_loss: 0.545161123511 train auc:  0.791058\n",
      "test_loss:  2.08963501909 test auc:  0.73227\n",
      "cold start test_loss:  2.69712033637 cold start auc:  0.330058\n",
      "epoch  17\n",
      "train_loss: 0.543400744232 train auc:  0.792958\n",
      "test_loss:  2.09902296883 test auc:  0.724713\n",
      "cold start test_loss:  2.70960927568 cold start auc:  0.323097\n",
      "epoch  18\n",
      "train_loss: 0.546359703082 train auc:  0.792125\n",
      "test_loss:  2.11239940059 test auc:  0.724662\n",
      "cold start test_loss:  2.75529365542 cold start auc:  0.31758\n",
      "epoch  19\n",
      "train_loss: 0.545054283918 train auc:  0.791491\n",
      "test_loss:  2.11361170268 test auc:  0.720861\n",
      "cold start test_loss:  2.73580272004 cold start auc:  0.338207\n",
      "epoch  20\n",
      "train_loss: 0.544682993786 train auc:  0.792497\n",
      "test_loss:  2.10604550776 test auc:  0.727949\n",
      "cold start test_loss:  2.76362837036 cold start auc:  0.324861\n"
     ]
    }
   ],
   "source": [
    "user_count, item_count, users, items, user_ratings, item_ratings, brands, prices, prod_desc = load_data_hybrid(data_path, min_items=4, min_users=0, sampling= True, sample_size = 0.8)\n",
    "prices_features= {}\n",
    "prices_all = list(set(prices.values()))\n",
    "price_quant_level = 10\n",
    "price_max = float(max(prices.values()))\n",
    "for key, value in prices.iteritems():\n",
    "    prices_vec = numpy.zeros(price_quant_level+1)\n",
    "    idx = int(numpy.ceil(float(value)/(price_max/price_quant_level)))\n",
    "    prices_vec[idx]=1\n",
    "    prices_features[key] = prices_vec\n",
    "    \n",
    "pbpr_auc_train, pbpr_auc_test, pbpr_auc_test_cold_start = session_run(20, True, prices_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the brand related feature and running the advanced BPR model with brand features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.772491443207 train auc:  0.709362\n",
      "test_loss:  2.19494243532 test auc:  0.714503\n",
      "cold start test_loss:  2.18027051008 cold start auc:  0.368037\n",
      "epoch  2\n",
      "train_loss: 0.653070814381 train auc:  0.765395\n",
      "test_loss:  1.89966562456 test auc:  0.729683\n",
      "cold start test_loss:  1.87026472627 cold start auc:  0.330427\n",
      "epoch  3\n",
      "train_loss: 0.59885170506 train auc:  0.781351\n",
      "test_loss:  1.87980972096 test auc:  0.732331\n",
      "cold start test_loss:  1.8352484957 cold start auc:  0.332897\n",
      "epoch  4\n",
      "train_loss: 0.557746093631 train auc:  0.803984\n",
      "test_loss:  1.92497740795 test auc:  0.740968\n",
      "cold start test_loss:  1.98708815039 cold start auc:  0.349654\n",
      "epoch  5\n",
      "train_loss: 0.513590308268 train auc:  0.832035\n",
      "test_loss:  2.0576493348 test auc:  0.744864\n",
      "cold start test_loss:  2.20378710893 cold start auc:  0.389713\n",
      "epoch  6\n",
      "train_loss: 0.477185853433 train auc:  0.848966\n",
      "test_loss:  2.17493322597 test auc:  0.732683\n",
      "cold start test_loss:  2.39205922839 cold start auc:  0.422478\n",
      "epoch  7\n",
      "train_loss: 0.447370350189 train auc:  0.865276\n",
      "test_loss:  2.24742695405 test auc:  0.738825\n",
      "cold start test_loss:  2.60071828058 cold start auc:  0.446487\n",
      "epoch  8\n",
      "train_loss: 0.426046132907 train auc:  0.876904\n",
      "test_loss:  2.31709906544 test auc:  0.733224\n",
      "cold start test_loss:  2.68752633681 cold start auc:  0.466362\n",
      "epoch  9\n",
      "train_loss: 0.4067920849 train auc:  0.885139\n",
      "test_loss:  2.34777556559 test auc:  0.73645\n",
      "cold start test_loss:  2.73739892953 cold start auc:  0.481807\n",
      "epoch  10\n",
      "train_loss: 0.393577778819 train auc:  0.890044\n",
      "test_loss:  2.37071582271 test auc:  0.729933\n",
      "cold start test_loss:  2.79003539897 cold start auc:  0.486443\n",
      "epoch  11\n",
      "train_loss: 0.378851809761 train auc:  0.898593\n",
      "test_loss:  2.39616371989 test auc:  0.726734\n",
      "cold start test_loss:  2.79493424832 cold start auc:  0.510341\n",
      "epoch  12\n",
      "train_loss: 0.366100278758 train auc:  0.902692\n",
      "test_loss:  2.3931162722 test auc:  0.725328\n",
      "cold start test_loss:  2.79727708146 cold start auc:  0.515431\n",
      "epoch  13\n",
      "train_loss: 0.359045201655 train auc:  0.904987\n",
      "test_loss:  2.39448687267 test auc:  0.71889\n",
      "cold start test_loss:  2.78496131281 cold start auc:  0.523278\n",
      "epoch  14\n",
      "train_loss: 0.348506599454 train auc:  0.908597\n",
      "test_loss:  2.41077230915 test auc:  0.718908\n",
      "cold start test_loss:  2.78327669185 cold start auc:  0.531768\n",
      "epoch  15\n",
      "train_loss: 0.341705054632 train auc:  0.910882\n",
      "test_loss:  2.39137797305 test auc:  0.721149\n",
      "cold start test_loss:  2.73161651559 cold start auc:  0.547603\n",
      "epoch  16\n",
      "train_loss: 0.336785312936 train auc:  0.91101\n",
      "test_loss:  2.3692265632 test auc:  0.723758\n",
      "cold start test_loss:  2.79276414074 cold start auc:  0.530035\n",
      "epoch  17\n",
      "train_loss: 0.330785131852 train auc:  0.914111\n",
      "test_loss:  2.37975212489 test auc:  0.716041\n",
      "cold start test_loss:  2.70622424589 cold start auc:  0.557938\n",
      "epoch  18\n",
      "train_loss: 0.326695735379 train auc:  0.914294\n",
      "test_loss:  2.35892401005 test auc:  0.714924\n",
      "cold start test_loss:  2.67893309858 cold start auc:  0.565907\n",
      "epoch  19\n",
      "train_loss: 0.321508979536 train auc:  0.916631\n",
      "test_loss:  2.33790847448 test auc:  0.720373\n",
      "cold start test_loss:  2.67834270289 cold start auc:  0.559869\n",
      "epoch  20\n",
      "train_loss: 0.317571736247 train auc:  0.917741\n",
      "test_loss:  2.31684064944 test auc:  0.723086\n",
      "cold start test_loss:  2.64987867504 cold start auc:  0.563251\n"
     ]
    }
   ],
   "source": [
    "brands_features = {}\n",
    "brands_all = list(set(brands.values()))\n",
    "for key, value in brands.iteritems():\n",
    "    brands_vec = numpy.zeros(len(brands_all))\n",
    "    brands_vec[brands_all.index(value)] = 1\n",
    "    brands_features[key] = brands_vec\n",
    "bbpr_auc_train, bbpr_auc_test, bbpr_auc_test_cold_start = session_run(20, True, brands_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the product description related feature and running the advanced BPR model with product description generated features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.763333295287 train auc:  0.705536\n",
      "test_loss:  2.31917500034 test auc:  0.654612\n",
      "cold start test_loss:  2.33877206174 cold start auc:  0.432985\n",
      "epoch  2\n",
      "train_loss: 0.636424841603 train auc:  0.778064\n",
      "test_loss:  2.09640703131 test auc:  0.689711\n",
      "cold start test_loss:  2.15185072101 cold start auc:  0.405122\n",
      "epoch  3\n",
      "train_loss: 0.562867354866 train auc:  0.816657\n",
      "test_loss:  2.09652371746 test auc:  0.719816\n",
      "cold start test_loss:  2.14631537721 cold start auc:  0.429709\n",
      "epoch  4\n",
      "train_loss: 0.496305924519 train auc:  0.848011\n",
      "test_loss:  2.11712079105 test auc:  0.732295\n",
      "cold start test_loss:  2.23921195035 cold start auc:  0.475292\n",
      "epoch  5\n",
      "train_loss: 0.43870662286 train auc:  0.872288\n",
      "test_loss:  2.15012197328 test auc:  0.750069\n",
      "cold start test_loss:  2.3266923425 cold start auc:  0.522714\n",
      "epoch  6\n",
      "train_loss: 0.398132274658 train auc:  0.886851\n",
      "test_loss:  2.14458031423 test auc:  0.750016\n",
      "cold start test_loss:  2.37179722607 cold start auc:  0.570656\n",
      "epoch  7\n",
      "train_loss: 0.369468948973 train auc:  0.897922\n",
      "test_loss:  2.12390663679 test auc:  0.757357\n",
      "cold start test_loss:  2.42430597005 cold start auc:  0.577955\n",
      "epoch  8\n",
      "train_loss: 0.348763096991 train auc:  0.904632\n",
      "test_loss:  2.12678131725 test auc:  0.751797\n",
      "cold start test_loss:  2.3852470802 cold start auc:  0.604021\n",
      "epoch  9\n",
      "train_loss: 0.331041012061 train auc:  0.910862\n",
      "test_loss:  2.10173919323 test auc:  0.758493\n",
      "cold start test_loss:  2.37176623447 cold start auc:  0.624034\n",
      "epoch  10\n",
      "train_loss: 0.318138290401 train auc:  0.914792\n",
      "test_loss:  2.06345673519 test auc:  0.762507\n",
      "cold start test_loss:  2.36641682767 cold start auc:  0.612778\n",
      "epoch  11\n",
      "train_loss: 0.30736957479 train auc:  0.917684\n",
      "test_loss:  2.04922391337 test auc:  0.752135\n",
      "cold start test_loss:  2.31881951083 cold start auc:  0.631548\n",
      "epoch  12\n",
      "train_loss: 0.297364712264 train auc:  0.920012\n",
      "test_loss:  2.03054672787 test auc:  0.760955\n",
      "cold start test_loss:  2.2423838094 cold start auc:  0.64921\n",
      "epoch  13\n",
      "train_loss: 0.290689403922 train auc:  0.921965\n",
      "test_loss:  2.01200868703 test auc:  0.760066\n",
      "cold start test_loss:  2.27130887885 cold start auc:  0.63618\n",
      "epoch  14\n",
      "train_loss: 0.282601252843 train auc:  0.924993\n",
      "test_loss:  1.98265265077 test auc:  0.760238\n",
      "cold start test_loss:  2.22977667201 cold start auc:  0.644513\n",
      "epoch  15\n",
      "train_loss: 0.276710997722 train auc:  0.925719\n",
      "test_loss:  1.98997997344 test auc:  0.755372\n",
      "cold start test_loss:  2.21205326788 cold start auc:  0.656817\n",
      "epoch  16\n",
      "train_loss: 0.271738980288 train auc:  0.926295\n",
      "test_loss:  1.96450253592 test auc:  0.755463\n",
      "cold start test_loss:  2.19779172199 cold start auc:  0.643194\n",
      "epoch  17\n",
      "train_loss: 0.265175691803 train auc:  0.930026\n",
      "test_loss:  1.96799918193 test auc:  0.755075\n",
      "cold start test_loss:  2.16231485674 cold start auc:  0.661503\n",
      "epoch  18\n",
      "train_loss: 0.265108666616 train auc:  0.928076\n",
      "test_loss:  1.92728851864 test auc:  0.760904\n",
      "cold start test_loss:  2.13259377621 cold start auc:  0.667928\n",
      "epoch  19\n",
      "train_loss: 0.26240346726 train auc:  0.928319\n",
      "test_loss:  1.94134374329 test auc:  0.753765\n",
      "cold start test_loss:  2.15348863394 cold start auc:  0.656483\n",
      "epoch  20\n",
      "train_loss: 0.25537353558 train auc:  0.931154\n",
      "test_loss:  1.90556862371 test auc:  0.758332\n",
      "cold start test_loss:  2.09161580037 cold start auc:  0.669339\n"
     ]
    }
   ],
   "source": [
    "d = prod_desc\n",
    "pdbpr_auc_train, pdbpr_auc_test, pdbpr_auc_test_cold_start = session_run(20, True, prod_desc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the advanced BPR model with the hybrid of all non-visual features:  price+brand+product description features (NVBPR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.761333127331 train auc:  0.709857\n",
      "test_loss:  2.30716349129 test auc:  0.646151\n",
      "cold start test_loss:  2.36630563262 cold start auc:  0.439402\n",
      "epoch  2\n",
      "train_loss: 0.633368142006 train auc:  0.781037\n",
      "test_loss:  2.10744990072 test auc:  0.680491\n",
      "cold start test_loss:  2.15810299692 cold start auc:  0.415071\n",
      "epoch  3\n",
      "train_loss: 0.560849658853 train auc:  0.820517\n",
      "test_loss:  2.11520813595 test auc:  0.701808\n",
      "cold start test_loss:  2.1865384234 cold start auc:  0.425034\n",
      "epoch  4\n",
      "train_loss: 0.495772151471 train auc:  0.850713\n",
      "test_loss:  2.13325128587 test auc:  0.726826\n",
      "cold start test_loss:  2.25112458201 cold start auc:  0.466069\n",
      "epoch  5\n",
      "train_loss: 0.436103345078 train auc:  0.876728\n",
      "test_loss:  2.16464319006 test auc:  0.739841\n",
      "cold start test_loss:  2.35583786366 cold start auc:  0.506094\n",
      "epoch  6\n",
      "train_loss: 0.390503824818 train auc:  0.892946\n",
      "test_loss:  2.1676802763 test auc:  0.738676\n",
      "cold start test_loss:  2.41391513714 cold start auc:  0.557546\n",
      "epoch  7\n",
      "train_loss: 0.356168600066 train auc:  0.904639\n",
      "test_loss:  2.14054545695 test auc:  0.753172\n",
      "cold start test_loss:  2.4482945987 cold start auc:  0.584929\n",
      "epoch  8\n",
      "train_loss: 0.331695199587 train auc:  0.912697\n",
      "test_loss:  2.09967011286 test auc:  0.753371\n",
      "cold start test_loss:  2.41452878165 cold start auc:  0.5918\n",
      "epoch  9\n",
      "train_loss: 0.311816743024 train auc:  0.917637\n",
      "test_loss:  2.07176796649 test auc:  0.756773\n",
      "cold start test_loss:  2.29073273623 cold start auc:  0.646146\n",
      "epoch  10\n",
      "train_loss: 0.298649736436 train auc:  0.921208\n",
      "test_loss:  2.03532569382 test auc:  0.755623\n",
      "cold start test_loss:  2.33039393591 cold start auc:  0.627755\n",
      "epoch  11\n",
      "train_loss: 0.287128367789 train auc:  0.924379\n",
      "test_loss:  1.98694173738 test auc:  0.756151\n",
      "cold start test_loss:  2.25087451573 cold start auc:  0.646735\n",
      "epoch  12\n",
      "train_loss: 0.275334438533 train auc:  0.927948\n",
      "test_loss:  1.97551634349 test auc:  0.757635\n",
      "cold start test_loss:  2.21186406548 cold start auc:  0.657505\n",
      "epoch  13\n",
      "train_loss: 0.268146607518 train auc:  0.929885\n",
      "test_loss:  1.95724547301 test auc:  0.761144\n",
      "cold start test_loss:  2.16907437021 cold start auc:  0.660272\n",
      "epoch  14\n",
      "train_loss: 0.260126552444 train auc:  0.931536\n",
      "test_loss:  1.93323404284 test auc:  0.758619\n",
      "cold start test_loss:  2.16035756217 cold start auc:  0.661801\n",
      "epoch  15\n",
      "train_loss: 0.256270017664 train auc:  0.932446\n",
      "test_loss:  1.90384077262 test auc:  0.76328\n",
      "cold start test_loss:  2.10122718483 cold start auc:  0.673783\n",
      "epoch  16\n",
      "train_loss: 0.249718757843 train auc:  0.934736\n",
      "test_loss:  1.89718417108 test auc:  0.759567\n",
      "cold start test_loss:  2.12274080341 cold start auc:  0.660172\n",
      "epoch  17\n",
      "train_loss: 0.243681674297 train auc:  0.936808\n",
      "test_loss:  1.88994380623 test auc:  0.758451\n",
      "cold start test_loss:  2.10264003239 cold start auc:  0.662912\n",
      "epoch  18\n",
      "train_loss: 0.243531597072 train auc:  0.934838\n",
      "test_loss:  1.85901134557 test auc:  0.766067\n",
      "cold start test_loss:  2.02499885417 cold start auc:  0.6836\n",
      "epoch  19\n",
      "train_loss: 0.239887734765 train auc:  0.936593\n",
      "test_loss:  1.84280104548 test auc:  0.761856\n",
      "cold start test_loss:  2.05042418589 cold start auc:  0.67265\n",
      "epoch  20\n",
      "train_loss: 0.236490383729 train auc:  0.937531\n",
      "test_loss:  1.83266161118 test auc:  0.762694\n",
      "cold start test_loss:  1.98175700791 cold start auc:  0.681719\n"
     ]
    }
   ],
   "source": [
    "a = prices_features\n",
    "b = brands_features\n",
    "c = dict([(k, numpy.append(a[k],b[k])) for k in set(b) & set(a)])\n",
    "\n",
    "e = dict([(k, numpy.append(c[k],d[k])) for k in set(c) & set(d)])\n",
    "\n",
    "hybrid_non_visual_features = e\n",
    "nvbpr_auc_train, nvbpr_auc_test, nvbpr_auc_test_cold_start = session_run(20, True, hybrid_non_visual_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the base advanced BPR model with the hybrid of all visual and non-visual features: image+price+brand+product description features (HBPR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_u_id:  27876\n",
      "max_i_id:  10429\n",
      "reviews :  155429\n",
      "u_id:  17101\n",
      "i_id:  10429\n",
      "reviews :  117077\n",
      "epoch  1\n",
      "train_loss: 0.760003733238 train auc:  0.705554\n",
      "test_loss:  2.35651715919 test auc:  0.633749\n",
      "cold start test_loss:  2.42610215491 cold start auc:  0.438026\n",
      "epoch  2\n",
      "train_loss: 0.629862880703 train auc:  0.777061\n",
      "test_loss:  2.15230958718 test auc:  0.668541\n",
      "cold start test_loss:  2.23124853423 cold start auc:  0.419495\n",
      "epoch  3\n",
      "train_loss: 0.555777549144 train auc:  0.818691\n",
      "test_loss:  2.17976698431 test auc:  0.683159\n",
      "cold start test_loss:  2.25805919229 cold start auc:  0.426859\n",
      "epoch  4\n",
      "train_loss: 0.49341323552 train auc:  0.849167\n",
      "test_loss:  2.20214743558 test auc:  0.692565\n",
      "cold start test_loss:  2.378083235 cold start auc:  0.447088\n",
      "epoch  5\n",
      "train_loss: 0.441483307364 train auc:  0.871616\n",
      "test_loss:  2.2456486308 test auc:  0.704376\n",
      "cold start test_loss:  2.46543730399 cold start auc:  0.482276\n",
      "epoch  6\n",
      "train_loss: 0.397100627143 train auc:  0.890297\n",
      "test_loss:  2.25691217982 test auc:  0.704009\n",
      "cold start test_loss:  2.51266899553 cold start auc:  0.521163\n",
      "epoch  7\n",
      "train_loss: 0.362577321266 train auc:  0.901569\n",
      "test_loss:  2.22331565403 test auc:  0.71984\n",
      "cold start test_loss:  2.5717457792 cold start auc:  0.538597\n",
      "epoch  8\n",
      "train_loss: 0.334868463851 train auc:  0.912541\n",
      "test_loss:  2.20087105077 test auc:  0.719898\n",
      "cold start test_loss:  2.45980589448 cold start auc:  0.576807\n",
      "epoch  9\n",
      "train_loss: 0.313208295628 train auc:  0.917934\n",
      "test_loss:  2.15832301128 test auc:  0.728415\n",
      "cold start test_loss:  2.41326009334 cold start auc:  0.614493\n",
      "epoch  10\n",
      "train_loss: 0.29716926194 train auc:  0.922321\n",
      "test_loss:  2.12620024665 test auc:  0.722445\n",
      "cold start test_loss:  2.38351131112 cold start auc:  0.615509\n",
      "epoch  11\n",
      "train_loss: 0.281004818131 train auc:  0.927213\n",
      "test_loss:  2.08591378466 test auc:  0.725563\n",
      "cold start test_loss:  2.3658730033 cold start auc:  0.620725\n",
      "epoch  12\n",
      "train_loss: 0.267333007848 train auc:  0.932194\n",
      "test_loss:  2.03723239519 test auc:  0.730452\n",
      "cold start test_loss:  2.26797546714 cold start auc:  0.63032\n",
      "epoch  13\n",
      "train_loss: 0.257399083253 train auc:  0.932774\n",
      "test_loss:  1.98707241473 test auc:  0.742235\n",
      "cold start test_loss:  2.23887591922 cold start auc:  0.638963\n",
      "epoch  14\n",
      "train_loss: 0.249640544263 train auc:  0.934933\n",
      "test_loss:  1.96730994793 test auc:  0.737856\n",
      "cold start test_loss:  2.14627945001 cold start auc:  0.660756\n",
      "epoch  15\n",
      "train_loss: 0.243011651982 train auc:  0.936647\n",
      "test_loss:  1.92967347844 test auc:  0.747826\n",
      "cold start test_loss:  2.14737057646 cold start auc:  0.655597\n",
      "epoch  16\n",
      "train_loss: 0.238748793993 train auc:  0.937684\n",
      "test_loss:  1.91607176707 test auc:  0.746183\n",
      "cold start test_loss:  2.1713462743 cold start auc:  0.646884\n",
      "epoch  17\n",
      "train_loss: 0.233747040222 train auc:  0.938745\n",
      "test_loss:  1.87118831448 test auc:  0.752057\n",
      "cold start test_loss:  2.09191026369 cold start auc:  0.657695\n",
      "epoch  18\n",
      "train_loss: 0.226345288265 train auc:  0.941281\n",
      "test_loss:  1.85689478951 test auc:  0.748691\n",
      "cold start test_loss:  2.06644561844 cold start auc:  0.67434\n",
      "epoch  19\n",
      "train_loss: 0.223567072473 train auc:  0.941544\n",
      "test_loss:  1.83446054229 test auc:  0.751094\n",
      "cold start test_loss:  2.00152917274 cold start auc:  0.675097\n",
      "epoch  20\n",
      "train_loss: 0.220909437147 train auc:  0.942711\n",
      "test_loss:  1.7837339146 test auc:  0.76081\n",
      "cold start test_loss:  1.96302814634 cold start auc:  0.678095\n"
     ]
    }
   ],
   "source": [
    "hybrid_all_features = dict([(k, numpy.append(e[k],f[k])) for k in set(e) & set(f)])\n",
    "hbpr_auc_train, hbpr_auc_test, hbpr_auc_test_cold_start = session_run(20, True, hybrid_all_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## comparing Test AUC vs. number of iterations for different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x117f38e90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kq1bj3bkTVBWtzYZpyBBMw4YS07cfJW+/RekHHxJ9+eWk/eVlDOnpjd10IUQdKztWURks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eHg7AdWFzGxa8yHlfOltujz/gcJN/7hpg1tGh5Zc4qjJXIWDAnh5Zn9TN7SPF+ez4O7H6RN18Y/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\nllDQWIC/zp9Lki/hyvQrGRE3QobmdzG+sFxOjzr4ewJQjjr4+yZFUXb/4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okezeNwUPfO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kblwGjftr+Md/fpuZdz/AhEVL0HQ6T4cshBBCXBBX9vwcBI7/GGbUwH2ns5SvWuUu6Fql1DJg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\nI5K1uv+hf1TRgkUTXygwgvhIATCuejva1Hq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139a8a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(bpr_auc_test, label = \"BPR\")\n",
    "plt.plot(pbpr_auc_test, label = \"Price-BPR\")\n",
    "plt.plot(bbpr_auc_test, label = \"Brand-BPR\")\n",
    "plt.plot(pdbpr_auc_test, label = \"Product Description-BPR\")\n",
    "plt.plot(nvbpr_auc_test, label = \"NonVisual-BPR\")\n",
    "plt.plot(vbpr_auc_test, label = \"Visual-BPR\")\n",
    "plt.plot(hbpr_auc_test, label = \"Hybrid-BPR\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Test AUC\")\n",
    "plt.xlabel(\"Number of Iterations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## comparing Cold Start Test AUC vs. number of iterations for different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115a32b50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MCzC49Q8x7ZpbH3LCISeBTw9Rd5rnqJn1HUIgY7IVzWIgpNNweYI0twRoaPBRe9iD2xck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3DjesytLURPTCF10VJC40fZpR4hhDgXGUwGnt77NB9kfUCwezDPzXmO6eHT7V2WEOck6WQSQoh+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TMzqi0sy2zfLFTdVPBZ7wqVVVlKuh5fMCgk2pfevwyfdyRIL+xnuC2jJz0ED/1/JDbNssfvLV\nvzx9lkrM58TIc024lDaua06ncTaag2lNlU85zUFUQ0Xz44LCzVS7xCEtp+AlZEBIhP/GL12fs9FU\nAq75o6mIjh8MU35t+pd19GI936ehCnI/9vRx+tBsfwSGwMBpJnAaeobZjpAfpJBJjpxtm7T3o9ug\nLBcyZsFp90HysKP+kdkHsrlq2VWUN5STEZfRIhyqc9b94PMDrcAWQU9UcNRB4Y/vfS2CJJ9AyS+9\nkdpb2U4TLmV/CMnHmalx/Sf6e1Qih6eqyKxY5125rrrI3B4/GOIHmhLriARTXh3ey3Meb84jEszl\nnrxx7GwwPTx8QyRvbw/fqWrhvcyBA2+I5J3uFpfe+aem2baZJuI7Da91VVTlXnN00ldgiNlYbBE+\ntZqepyXFu57GWti12kzFyFlmKjti+sCIeTByweE1ZJaepTzf9Kjc/C/z+RgYanYox5xntnG747Yh\nmM/O6uJW1U+e8/J88K3cj+3Xqum457siKkV/T/LdGmvMLJi1fzbfxamjYOqNMPyszr9t4XLC7k89\nfZzea5710HssDDvTfEakjNTv/3dQyCRHz9kIG56HFQ9BYzWMvxSm33rUq2ntr93PQxseoqqx6pDV\nRC2qilpVE4UHhfe4Vep+kLMB1vwJPnnUTC+ZfitMuLz7bixJ92fbsP9bEzjt+sQEB3VlUHugeXXB\nQwkKaw6ewnv5hFLxhz6PiIfQ2K5V+VB3wKcqyed0YBfY7ubHxaW3DJG80926e1Wj222qEr5rSl7l\nHqgsbNlQF8zvTkxaq/DJE0ylHGf+PcW/bNtUPOYsM8FS3loTKAaFwYAppjK14HOzypDbYQLqkQvM\nKmBJQ/09evGX+gr45r+mailvtbmt/2RTWXHc2V1zEZS25KjzHKDY3qoXXw44apofFxrrOTgxtOW5\nGo/3bHUHYP3z8OkzZjut/2SYcoOZetYV99ds2xyky3rPhE4FGwEbYtNN2DTsDPMetY/VRCGTHLua\nUljxAGx8EUKj4JSbzUplWmXLf3I/hvduMpVmx50DkxfUvwAAIABJREFUp99vdo5Euitno9moqSsz\nVS21ZZ7LZc1BVG1py9vqDrQMYHxZASaQal0VFdGrVSjVKrBqzw0Mt9uEIS2qkjz9kmr2Nz8uMNQz\nzSGzZc8kTXn4fm63+XdsMSWv1fS8yr0te5vE9oP+kzynKabRblfcgO5qasvM95z3VFVobk8abnZi\nBp9q/k98p3TWlsG378KW101IbbtNr7eR803o1Bl77EjbcjnM78tX/zQNf5315nNx9HkwupP2Weps\nbNt8DpZkeQ5sZDUf4Kgpbn5c01TrzJYBVEIGBIf5b/zSvqqKYN1TZp+wsdr0NZp6gwn7u5OqfWY1\nvKzFprWDs94ErpmzTeiUObtjFwTohBQySdsp/hY++r05Whg/GE671/yhaYO741QWmp5ZW9+E+EFw\nxiOm1FtEDuZ2m34UtWWHCKXKWgVWB5pva92I2ldItAmimkKp76iU8r0tJLLl56Sz0QTEviHSoY4g\nh8V5QiTPRry3Z5KOILcft8tML6koMCvS5K02lTPekC8y2RM4TTbnycd1rYq4zsrlhD0bm6uV9mwC\nbPM3MHgGDPYES4d7MKVqH2xdBF+/DgUbzG19TzLVTSPmmRUMpXuwbfO3uvlfJmCsLTGfu6PONeFS\nny7aZ6kzqi1r/s7yTs3en9Vy6p0VYL6jvAdAkoY2h1A9fKe8SyvbCWsWwpf/MBWjI+bDlOshdaS/\nR9b+GmtMO4dv3zfBU22JmUEyYAoMPROGzumRVc8KmaTtZS+BD39vvmQGnmKqaHrCh4w/uZyw/jlY\nfr+ZJjD1Rph8nY4WibSHxtqDq6J8g6iD7jvQsrlqa4EhzVVRzvqDVwKK7Xdw4+3EoWaKm3aO/M+2\nzXSSvDUmcNq1xlRAgQlB0ieawGnAZEgdo2WRD1f57ua+SjtWmr8hKwD6jPdUK800AcGxBqoHdsHX\nb5pGz/u+Nq8xYKoJIYb/2FQ0StdTvhu2/NtULZVsN5+zQ+eaYCljlqrtO1JjbfOUu/1ZzdVPpTkt\npyhHpfpMufM5gBKdqu+6zmrfVlj9hPn8DAiCsefDpGtNVW9P5HaZAxdZ75vQqTTb3J4yykypGzrX\n9HTqAb/PCpmkfbgcsPFvsOJ+M+993EUw4zaISvL3yLqf/M/gvRvMxnHGLFO9FD/I36MSEV8uR8tq\nKN/zplDqgAkgvIGSt8Gqv5b1laNXnu8JnDyVTmW55vbgSEif0FztlDZOBwO8GmtNUOetVirZbm6P\n6WOqlDJmwqDp7Rv6FH9rqpu2vG6avAYEm2kPIxeYnQP9LXZu9ZWmz9Lmf5kpkWBC3jHnmdYBPb3P\nUmfjcpqQt2T7wdPvfPsshsa2nP7tPe81QFW7/rJ7PXzyOGxfbL7Xxl8CE682q7pKs5JsEzhlLYb8\nTwEbotOa+zgNmNq5VtdrQwqZpH3VHYCVD5sqm6BwmHYTnPyrbvsH1aFqSmHpHfDFK2YjfM6D5qhr\nD0jHRUS6lKoiEzZ5T8Vbze2BodB3fHNfp34Tek6QYdtQvK25WilvLbgaTMPu/pObq5WShnb895p3\nitXXb5gqp6q9EBxhdgxGnmvGpu2YzsHlNH2WNv/TrADlrDctG8Z4+ywN8PcI5UjZtumz5jvlznve\nuu9TQkarxuNDICFT4X17sG3zef3JE2aqeHgvmPArOOkXZvq/fL+aEtj+oQmdcj82K/yGRJvvk6Fn\nmAMa3ejfUSFTF7Vy+35e+GQHN8wewvHpXaCUuyQbPrrdJN69BsDsu83ylQpEjpzbbYKlpXdAQxWc\nfKVpth4a5e+RiYjI4agtM0c189aYU+FXphF1QJAppfdWOqVP6F7TtWrLTO+KnGWeht17ze1Jw0yg\nlHGqed++Dbv9ze2G/LUmcNr6lqk6DIs1B3VGngsDp6maoqPZNhR+aVaG+/p10xMtvJf5/xhzHvQ5\nQduX3VWLlVR9qp8O5NHU9wnLNHFvseKdp/pJ1WxHzu2Cbe/A6sfNd1V0Gky6GsZdrH2Po+WoM9PA\nvVVONcVgBZpCjNPv8/fo2oRCpi7q7a/2cufbWymraWTmsGSunz2EkX26QMO83OWmMXXxN2ZD8vT7\nIW2sv0fVdRRuNlPjCjZA+iQ48zGzjLaIiHRdDVWw+7PmSqc9n5v+eliQMtJnBbvJXWvauctp3ou3\nWmnvJhOmhcWaqW+DZ5qjuLF9/T3Sw+NymJBsy+umaqaxyjR7HzHP9HDqe6LCjfZUUQCbvX2Wskyf\npSFzTLCUMVt9lnoyR53p8dS6+qk0x/NZ6hGVcnDDcfV9OjRno5l6uuaP5t8xfjBM+TWM/okqOduS\n222+G799D1JGmO+SbkAhUxdW3eDkpbW7eHZlLpX1TuaOTOX62UMYkhLt76F9P5cTvngZPr7P9CIZ\newHMvN18wMuh1Veapt7rnzUNgk+712xU6QtRRKT7cdSZcMbb12n3enDWmfsSh7Rcwa6zBTQVBc19\nlXasMH0ZrQBTXeINldLGdf0G6I46yP7IBE7bPzRT/WLTYeR8s5OQMlLf0W2hvhK2vW2CpV2rARv6\nnQxjfmLCve5U6Sdtz+WE8rzmhuMl2c0BVENl8+NCY0zfp14DTRVUXLpZBS8u3Sy+0ZMCzMYa2PQy\nrP0zVO6B1FEw5QY47mxVbcphU8jUDVTUOfjr6p28uHonNY1OzhqTxq9nDWFgYifv61BfAasehU+f\nMUejpl5vmsZ1pjJ5f7NtU6L/4e+heh+Mv9QEctqoEhHpOZyNZpqCdwW7/E+bVyyMS28OnPpPNgs/\ndGS44agzK+p5q5VKsszt0Wlm+ttgT8PubtRr4iD1leYo9NdvmGmAtstUR4w61zQN76krLR0tlxN2\nLIev/s+s0OSsM7/Xoz19luIH+nuE0tXZtumV5zvlrmS7aUResaflCq9YEJPWMnjyDaJi+nT90BzM\nVMT1L8Bnz5gigP6TTbiUMVOBuRwxhUzdyIGaRp5dtYOX1u6i0eVmwbg+XHNqJv3iI/w9tO9XtgOW\n/MHM943tB8PONP0ZkoZB8rCeG6iUZMN7N8LOlaZHx48eN0eCRUSkZ3O7zNLReWtNA9a8tWanAMwy\n4L7T65KGQUBA2722bcP+b5urlfLWmmbLgaHmNb0Nu5OH98wdk5pS+OYtEzjlrQVs8x0+6lwYMR9i\n+/h7hJ2Ts8H8Tm/5j6kOqyk2238j5sOYn5oG+T3x90k6nstp+sUdyDMrhZZ7zr3XK/fQ3P8J00sn\nps/BFVDe69G9O3cFUFURrHsKNr4IjdWQeTpMvQHST/b3yKQLU8jUDe2vauCZFbm8+lketm3zP+P7\ncfWpGfSO7eQVQrtWw/IHzIoujprm26NSWoZO3svd9ahoYy188hisWWhWs5l5u6lg6sxfUCIi4j+2\nbY7Ceyuddq1pbqod3sv08PMGT6mjj/yoe92Blg27K/eY2xOHQMYsEyr1nwQhnfygVker2ANbF5nm\n1Hu/MLelT4JRC+C4cyAy0b/j60gN1VCxG8p3Q0W+OS/Pb76tusg8LjAEhpxuqpYyT+tZ05Ska3A2\nQmVBy+DJG0SV55uV8XwFBJtpzS0qoAY0X49MbtsDAYerbCes/RN88Rq4HWb66ZTrzfQ4kWOkkKkb\nK6yo46nlOfxrw24sy+KCCelcOT2DpOhO3qzN7TYf3sXfmqOlTacsk7B7RSa3DJ2Shpkjp105fMr6\nABb/xnxJjT4PTrsHopL9PSoREelKbNvs9OStbQ6eynaY+0Kizap13kqntOMPbuLqdpmeUN5qpT2f\nm4bdobEw6JTmaqW4fh3/3rqq0lxT3bTldTM1xwqEwTPMdLphP4KwGH+P8OjZtgkivYFRU3jkEyLV\nlbV8TtOOdz/Tyyqun+mH082W8ZYeyFFvetOV72oVRHnCqJr9LR8fGOoJntIPHURFJrZtFd++rbD6\nCfj6TXMAe+z5MOlaTeuVNqWQqQfYXVbLnz/O5o1NewgJDODiSQO4fNogekV2saNDtm0+tL2hU7Fv\n+FTV/LjIJJ/gaagJnpKGde4jhuX5sPgWyHrPjPXMx2DAFH+PSkREuovKvc2r1+Wthf3bzO1BYWZV\ntP6TzAIcO1Z6GnaXAxb0GdfcsLvP+O7Re8SfbNvs5H39ugmdyvPNTuaQ02DkuaaKp7P1prRtqC72\nBEZ5nmqk3T7n+S0PAoKpxI7t5wmR+jXvQHtvi0r1T/WGiL811rYMnVpPx2sdyAZHtJyG17onVHiv\nwwuhdq+HTx6H7YshOBLGX2J64cb0bp/3KT2aQqYeZGdJDQuXbue/X+0lMiSISycP4OdTBxEbHuzv\noR0b2zal+y2CJ0/45LtyRETiwVPukob5dzloZyOsexJWPmy+IE65GU6+UuXhIiLSvmpKIX9dc7VT\n0WZTrRSVaqbAZZwKg2aoqqQ92TYUbPQETm+aPkQhUaY35chzTaVTYAdso7mcZoqPb+VRiyltBWb1\nPF9hsZ4KpHSfIMl73t/83qiHksiRq680f4etK6DK8+BAfvOiD14h0Qf3g/INogo2msqlXZ+YQGrC\nr+CkX+izXdqVQqYeaPu+Kv64dDvvbykiJiyIX04bxM8mDyQqtJsdnbRtc+TWd8pdsTd88vmAjkho\nrnpKGt5c/RSZ1L4bSDtWwvs3mT4aw34Ecx7U1AMREfGP+kozjaOjV6cTw+0yO4FbXodtb5sVeMPj\nzbLho841vZyOtvLH2eCZvpN/cAVS+W5zoK7FalqYlgQtwqNWgVJXnt4n0pXVlbfsAeXbF+pAXsu+\ntl7RaTDpahh3MYRGdfyYpcdRyNSDbd1bwRNLtrN0WzHxkSH86pTBXHhyf8JDunmDae+ypfu3mcCp\n2HO+f5vZqPMK79UydPKGUFHJx7YBXrUPProNtvwbeg2AuY+YMnkRERERZ6PphbXldch6Hxy1ZoWq\nEfNN0/C0cS23Qw63qbaXFWB2OltMZfO5HNu3803ZE5EfZttQW+YTQuWZwHjk/IN774m0I4VMwhf5\nB3h8yXY+yS4hKTqUq2dkcN5J/QgN6uZhU2u2DdX7WoZO3hCqvrz5cWFxh552F536/eGT2wUb/gof\n32OWe55yvTlpQ05EREQOpbEGtn8AW96AnCXgajQNslNGHEZT7XRaNNb2hkgxaR0zDU9ERHokhUzS\nZP3OMh79KIv1O8tIiw3jmpmZnHtCX4IDe3hjRm/Dy4Om3W0zq6l4hcW2WunOGz71NivzvHu96Xkx\naIZp7K1VHERERORw1ZXDtndMw/Cqwu9urB2VoqbaIiLiNwqZpAXbtlmTU8pjS7L4Ir+c9PgIrpuZ\nyTnH9yEwQD0aWrBt07/C22TctwKqtrT5caGxpgF5dCrMeQCOO0f9LkRERERERKTbUcgkh2TbNsuz\ninnso+1s3VvJoKRIrp81hDNH9SZAYdMPqynxhE6eyqfweJh0jRplioiIiIiISLelkEm+l23bfLh1\nH48vyWL7vmqGpUZz/ewhnHZcCpaqcURERERERETE43BDJk3s7qEsy2LOyFQWXzeNheeNpdHp5vJX\nPuesJ9ewPKuY7hQ+ioiIiIiIiEj7U8jUwwUGWJw9tg8fXT+NR84dTXldI5f8bQMLnlnL2pwSfw9P\nRERERERERLoITZeTFhqdbv7z+W6e/DiHwop6Th4Uz42nDeXEAfH+HpqIiIiIiIiI+IF6MskxqXe4\n+L/1+Ty1PJeS6gamDUnixtlDGNMvzt9DExEREREREZEOpJBJ2kRdo4tXPt3FMytyOVDrYNbwFG6Y\nPYTj0rSamoiIiIiIiEhPoJBJ2lR1g5O/rd7Jc5/soKreyZmjenP97EwykqP9PTQRERERERERaUcK\nmaRdVNQ6eGH1Dl5cvZM6h4uzx/bhupmZDEiM9PfQRERERERERKQdKGSSdlVW08izq3J5ae0uHC6b\n/3dCX64+NYO+vSL8PTQRERERERERaUMKmaRDFFfV8/TyXP7xWT42NuedmM7Vp2aQEhPm76GJiIiI\niIiISBtQyCQdam95HU8uz+HfG3YTGGBx4cn9+dX0wSRGhfp7aCIiIiIiIiJyDBQyiV/sLqtl4bJs\n3txUQFhwID+bNIBfThtEXESIv4cm0mU5XW72VTVQWF7H3op6CsvrKKyox7LgkkkDSU/QNFURERER\nEWk/CpnEr3L3V7NwaTbvbN5LVEgQl04ZyLQhSfSODSM5OpSgwAB/D1GkU3C5bUqqG9jrCY6854UV\nnvPyeoqr6nG3+qiOCg2i0eXG7bY576R+XHNqpqapioiIiEiXUl7byDubC/lsRykj0mKZnJHAiLRY\nAgMsfw9NWukUIZNlWXOAhUAg8IJt2w+2uv9s4B7ADTiBX9u2vfpwnnsoCpk6n6yiKv64dDuLvy5q\nui3AgsSoUHrHhpEaG0bv2HBSYsKarqfGmPOw4EA/jlzk2Nm2TWlNI4Xl9eytqGuqQNpbUU9RRR17\ny+vZV1mPs1WCFBYcQFpsOL3jzN9HWmwYvePC6R0bRprnPDosmH2V9Tz5cQ7/tz6fwACLn00awBWn\nDKZXpCoHRURERKRzcrrcrNy+nzc2FbD0m2IaXW4So0IpqW4AIDY8mImDEpickcCkjEQGJUZiWQqd\n/M3vIZNlWYHAdmA2UABsAH5q2/Y3Po+JAmps27YtyxoN/Nu27WGH89xDUcjUeeWV1rCjpIaiivqm\nU2Gl2dEuqqinst550HN6RQT7hE/hpPoGUZ5TdGiQPnDEL2zbpqLOwd5yU3XkO42tqQqpop5Gp7vF\n80ICAzzhanNg1DvOEyTFhpMWF0ZsePAR/V7nl9byx2XbWfTFHqJCgvjFtEFcOmUgUaFBbf22RURE\nRESOyrbCSt74vIC3vtxLSXUD8ZEhnD02jQXj+jIiLYb91Q2syy1lTU4Ja3JK2VNeB0BqTBiTMhKY\nkpHI5IxEVe/7SWcImSYCd9q2fbrn+q0Atm0/8D2Pf9G27eFH+lwvhUxdV02Dk6LKevZ5dsyLKj1B\nVEU9RZV1FFU0NCXbviJDAknx7LCnxoSTGhtKamw4vWOag6j4iBACeni5pdttU9XgpKreQXWDk6p6\nc7mq3kmlz+Wqege1jS5CgwIIDQokPCSQ8OBAwoIDPOfmFB5s7gsLDmi67nt7aFBAlw//quodLaev\nefohFVV4q5LqqXO4WjwnMMBqCkObgyPvZVOZ1J6/j9v3VfH4R9v5YGsR8ZEhXDl9MBee3F9VgSIi\nIiLiF6XVDfz3y728/nkB3xRWEhxoceqwZBaM68v0ocmEBB26jYpt2+SV1rImt4S1OaWszS3hQK0D\ngMFJkUzOSGTS4EQmDkogNiK4I99Sj9UZQqZzgTm2bV/muf6/wATbtq9u9bh5wANAMnCmbdvrDve5\nnvt+CfwSID09/YS8vLx2eT/if41ON/sqzfSiQm9FVFMYVce+yoZDTj0KCQwgOSbUpyLKE0TFhjVV\nSnXmPlFOl7spGKpsCoNaBkNV9U5PiHSI2+udVDccXCnWWlCARXRYEBEhQThcbuocLuodLhyuo/uM\n8A2mmgOqgKbgKjS4ZYDle5s3wDpksBUUSFhI833BR/H/VtfoagqKvOdFlXVNVUmF5fVUtfo3syxI\njg5tqjbqHdty+lpaXDiJUaGdYv74V7vLefSjLD7JLqF3bBjXzszk3BP6HtW/lYiIiDSrd7hYt6OU\n2gYXcRHBxIYHExcRTFxECJEhgV3+IJtIW2h0uvn42328/vkeVmQV43TbjOoTy4JxfThrbB/ij6K1\ng9tts62osqnKaf3OMuocLgIsGNUnlkkZiUwenMj4Ab10gLWddJmQyefx04A/2LY960if66VKJnG5\nbUqrGyj6jiDKe73e0XIKU+s+UaYvVMsg6mj6RDU63S3Cnqp6B5We0OdQYVBlq9uqG5zUNrp+8HVC\nggKICQsiOiyY6LAgcwoNJsp7OSzYc7/vYzznoeZyWPChq4+cLjf1Tjd1jSZ0qne4PAFUcxBV73A1\n3V/nub3B87i6Rtchn28e4/Z5jIuj+TgKCrCaA6qQg4Mpb7VVdb3TTGmrqKPccxTEV2JUyEHBUVM1\nUlw4ydGhXS6kWZtbwiMfZvFFfjkDEyO5fvYQfjSqd4+v7BORrq+i1kF0WJA+z6RDlNU0smzbPpZ8\ns49PsksOqmT2CgqwfIKnEOLCg4mNCCYuPMQnjGp5f1xEMNFhwZ3iIJXIsbBtmy17Knjj8wLe/mov\nB2odJEWHMv/4Piw4oS9DUqLb9PUanW6+3F3uCZ1K+HJ3OU63TUhQAOP79/JUOiUwqk9spy0m6Go6\nQ8h0xFPeLMvaAZwEZB7pc0Ehkxweby+d1tPy9h1Gn6i4iOAWvaHCg4NaBEKtp6A1tOrHcyjhwYFN\nwVCUbxgU2ioM8gmLolrdHhrU9dN627ZpcLo9QVRzgFXncFHvCaHqGg8RbPnc3uATYHkDr/pGcz0y\nNMgTGIW1qEZKiw0nJTa0W/wbHopt23z8bTGPfJjFt0VVDEuN5jenD+XUYck62ioiXYbbbbN5TwXL\ntu1j6bZithVWkhAZwrQhSUwfmsTUzKSjOjIu8l12ldSw5BsTLG3MK8NtQ+/YMGYNT2H2cSmkxoZR\nXuugvLaR8joHFbUOyusazW2trlfUOg6qkPZlWRAT5gmhwoOJ9QmgDroeEUxseEhTWNXVDoBJ91Nc\nWc+iL/bw+ucFZBdXExIUwGnHpbDghL5MzUjssICnusHJhp1lJnTKLWVbYSUA0aFBTBiUwJSMBCZn\nJJKRHKVt4KPUGUKmIEzz7pnAHkzz7vNt297q85gMINfT+Hsc8A7QF7Oi3Pc+91AUMklbOpw+UXWN\nzoNCoENVDkWFtrw/xnMeFRakjQPpEG63zTub9/LEku3sKq1lXHocvzl9GBMHJ/h7aCIih1Tb6GR1\ndgnLthXzcVYx+6saCLBgfP94pmQmsmN/NauySyiracSyYHTfOKZ7QqfRfeNUGSJHxO22+aqgvClY\nyi6uBmB47xhmD09m9nGpjOwTc9Q7pw6Xm8o6E0CV1zqo8AZSTaFUIxUt7jcBVkWdA/f37K5FhQYR\nGx7cqlIqpCmganHdU1UVFxGs6URyTOodLpZ8s4/XPy/gk+z9uG0Ylx7HghP68qPRacSG+79HUomn\nifjaXDO9Lr+sFjDtLyYNNqvWTc5IpE9cuJ9H2nX4PWTyDOIM4I+Y0OhF27bvsyzrCgDbtv9iWdbN\nwEWAA6gDfmPb9urveu4PvZ5CJhGR7+dwuXn98wIWLs2mqLKeqZmJ/Ob0oYzuG+fvoYmIUFhRx7Jt\nxSzbto+1uaU0ON1EhwYxbWgSs4YnM31IMr18KpZcbjM9Y0VWMSuy9vNVQTm2bVao9VY5TctMIiEq\n1I/vSjqreoeLdbmlfPTNPpZt20dxVQOBARYnDYhn9nGmYqlffIRfx+hdvOXgSqnmyy1CK5/r39dX\nMzQooCl0ivUEUvGRISTHhJESE0pqjGkZoUV0xMu2bTbll/P65wW8u3kvVfVO0mLDmD+uL/PH9WFQ\nUpS/h/i9dpfVsja3hNU5pazLLaGkuhGAgYmRTBpsqpwmDkpo8R0jLXWKkKmjKWQSETk89Q4Xr36a\nx1PLczhQ62DOiFRuPG0ImW08X17MRs3e8jqSokNJig4lKjRIZdoiHm63zdd7K1jqCZa27jXTG9Lj\nI5g5PJlZw1M4cUD8d64+1NqBmkZWZe9nZdZ+Vm7fT6m3yqlPLKcMTeaUIUmM7acqp56svLaRj78t\nZsk3+1i1fT81jS4iQwI5ZWgSs49LYcbQZOIiuv5Opm3b1Da6Wkzdq/AJoZqu+4RXpTWNlFQ3HNQj\nMzjQIjnaBE4pMaEmfIrxXg9ruh4eouqo7mhPeR2LNhXwxqY97CypITw4kLkjU1lwQl8mDkrokgGk\nbdtk7atiTU4pa3NK+HRHKTWNLiwLRqTFMHlwIpMyEjlxQC8iQoL8PdxOQyGTiIj8oKp6By+u3sXz\nn+ygttHJOcf34fpZQ/x+5Laryyut4f0tRby/pZAteypa3BceHNgUOCVFhTZdTo5uvpwUHUpiVNdr\nNi9yOOoaXazJKWHZt/tYtq2YYs80uHHpvZg5PIVZw5PbpGeGN8BakbWfFVnFfLm7HLdt+itOzUxi\n+pAkpg1JIilaVU7d3e6yWj76Zh9Lviliw64DuNw2ydGhzPJUK00anNBtezMeKafLzf7qBooq6ptW\nbva2jyjyXC6ubDjkysUxYUFNwZO3EirFu6BOTBgpsaEkRoZ2yVCip6ltdPLB10W8samAtbml2DZM\nGBjPghP6csao3kSFdq/gxeFys7mgnDU5pazJKWFT/gEcLpvgQIvj03sxJSORyRkJjO4b16O3zRQy\niYjIYTtQ08gzK3N5ae0u3LbNeSemc82pGSTHhPl7aF3Gjv3VLP66iPc2F/KNp9nkmH5xnDkqlWGp\nMZTWNLC/qvlU7L1c3XDIFQ8B4iNDvjuIivLeFkZMuKqjDodt29Q0usxKn3XeFT19LztJiAxhZJ9Y\nMlOitNPZhvZV1jdNg1udU0KD001UaBDThiQyc1gKM4Ylt3vj7vLaRlZllzRVOZVUNwBm6ev/3969\nB8d13ucd//5wB3EliTt4E28gKdCiKNmMSIoixYsk2pVsSUmsauIozjRWYqtyOnbrtJ3U485kbCtK\nUnuatInsxJ64qVzLitWxaF4kUVEp05JISwJAEuJFpERcCZDAAiAWwO6+/eMcgIvlLkhqSSyAfT4z\nGOzuOXv2Bfni7OI5v/d9N9d5Q+tumVeqVYhmgNFVrkbnVzrW3gfA8spCfxhcFR+rLVHYkYT+oZAf\nRF1azbkzMBpEDdHRG+Rc/xDhmAmlsjKM8qLLq6GqSnKpLLoUShXMsBBjOohEHG+cPs9PDp1lV0Mb\nA8NhFsyZxYNra3lo7by0ugB5cTjEm6cv8PqJLg6c7KKpNYBzUJCTybrF3tC6DUvnUldZlFafvxQy\niYjINWvvDfLdl4/z7JsfkpVpPLb+Jh6/a/EtsE7JAAAZXElEQVSMGDpwI5zo7BurWBr9I2btglJ2\nrq7m3voq5s2+ug9kQ6Ew3f3Dl4KnsQDKu2J8rv9SMDUcZ9XKnMwMr/rJD58qisdXSY0GVGWFudN6\nstdQOEL/UGgsFAr4AVFfcIRAMERgcHSFz5Hxt0dX/rzCBLrRsjONZRVF1NcWU19bws01JayqLtZw\nkKvknKOxJTBWrTRa0Tdvdj7bVlaydWUF626ae9XD4K63SMRxpC0wNpfT4Q8uEHFQkp/NxmVlbF5e\nzl115VQUKWifLoZDEQ6e6mbPkXb2HemkPRD0JopfNIcdfsXSwrkFqW5mWglHHN39Q2OL54xWRbX3\nDtHZdymc6ouzonNRbhaVCYbnjd4uK8zV0Nfr4Ez3AM8dbuGnh89y9sIghblZ7FxdxcO3zefji2an\nVYiSyIWBYQ6e6uaAP4n4+10DAJQV5nDHkjI2+HM6zfQgTiGTiIh8ZGe6B/irfcf557dbKMzJ4g82\nLebzG29K+yuLzjmOd/bz83fb2NXYxnsd/ZjB7Qtnc199NfetrqK65MatUuKcNwHsub6hceHTpeqo\nIOf6hujqH6J7YPiyeTXAG85wKXjKu7wyyg+oZt+AiV6DI+H4IdBYUDRxaDQwHL7iaxTmZlGcl0Vx\n/qXVPMffHl0BNPq2t39hbhYdgSCNLQEaW3tpbPG+LviVZhkGS8oL/dDJC59W1RRTnJf6VXSmguCI\nNwxu39FOXj7WQUdgCBsbBlfB1hWVLK+cmktH914c4bUT59jvVzmd6/OqnG6uKfarnCq4db6qnKaa\n3sER9jd78yu92nyOvqEQ+dmZbFpexvZVVdw9CRVykryLw6GoaqjLQ6mO3iCdfUOEYq4SZBiUF42f\npDx6jqiqEi+gKtI5+jJ9wRFebGjjuUMtvHH6PGawcWkZD62dxz03V+mCyhW09gxy4EQXr5/0htd1\n+u8ZC+bMYsPSuaxfUsb6JXNn3KITCplERCRpze19PL2nmT1HOphbkMMXtyzlX69bMK2rYa6Vc45j\n7X3samjj5w1tnDw3gBl8fNEcPulXLFVOwWGFoXCE8wMx1VH948Oo0a944U1mhlFWmHMpjIqpjJo9\nK4fBkdC4oWaBwUtBUcAPivoG/cAoOBK3Civ2NYvz/OAn3wuF4gdF40OjYj80KszLuu5XtZ1ztPUG\nvcCpNUBTSy+Nrb10BIbG9lk0dxY315ZQX1PiVT7VlKTN6jSdgSAvHbs0DC44EqEgJ5NNy8vZurKS\nLXXTb2U350arnLwJxA994M3hU5yXxZ3LvAqnu5aXT8nf+3TQ0jPIPn8Y3MFT3YQijrLCHLau8KqV\nNi4rS6v3qHQRiTi6B4bHDc/rCIwGUUNjc0b1Dl4+/LwoN4ua0nyqS/OoKc2ntjSf6pJLtyuL81JW\nVTmZwhHH6ye7+Mmhs+xuaic4EmFxeQEPrZ3HZ26tpab0xl0km8mcc5zo7OfAiS4OnOzm4Knuseq8\nFVVFPLpuAb9zx6LUNvI6UcgkIiLXza8/uMCf72nmwIluakryeHLbMh5aO2/GXtV3ztHUGmBXYxu7\nGto51TVAhsG6m+ayc3UV99RXzahhNAN+dVRsZdRYIOU/3tU/fNn8GtFyszIozs+OCoqyLwuNRquG\nxgKkqNuzcjKnZJVLPJ19QZpGQye/8unshcGx7bWl+WPVTqPB00yY42z0d+Olo528dKyDd896w+Bq\nS/PZtrKCrSsrWbd4zoyaz6p3cIQDJ7rY39zJq++dGwsYV1b7VU7Ly1m7cHZaTwZ7I42GfqPzK42u\nQLi4vIDtqyrZsaqSNfNna9iUAN7CApfCJ+97a0+Q1p5BWnsHaesJ0j0wPO45ZlBemHtZAOV9ebfn\nFuRMm/enWCc6+3nu8FmeP9xCeyBIcV4W/+qWGh6+bR5r5pdO259rqgqFIzS2BrzQ6UQXG5aW8cUt\nS1PdrOtCIZOIiFx3B0508e3dzbzzYQ+Lywr44+3L+eTq6hkxeeroHDI/b/CGwp3pvkiGwR1L5rJz\ndTU7VlWl/SpUkYjjwkWvOurCxWEKcrLGgqKivKwZFSx8FD0Xh2lqDYyrejrlz9sA3rCO+ppLczzV\n1xZTW5o/5T/gB0fC/PJkN/uOdvDysU7aeoOYwZr5pWPzK6XL5KejlY2jK9YdOnOBUMRRlJvlzeVU\nV85dyyuoKpn+gWIqjYQjvPH++bFgqaVncGzo5XZ/fqUl5YWpbqZMU8GRMK09g7T1BmnpGfQCqJj7\nwZHxlbc5WRnURIdPcYKoqbTUfe/FEV54t5XnDp3l7Q97yMwwNi0r4+Hb5rN1ZYWq/eQjUcgkIiI3\nhHOOvUc6eHrPezR39LGqupiv3lPH5rryafdHpnOOd872squhjRcb2/jw/CCZGcb6sWCpctoN9ZGp\npS84wtG2Pj946qWpJcDxzr6xCchLZ2VTX1PCzX61U31tCQvnzEp5cNvZF+SVY53sO9rJ/zvexeBI\nmFk5mdy5rIytKyu5e0UFZfrdIBAc4fUTXX7odI72QBDwhkhsrqvgruXl3L5IVU5Xoy84wqvvnWPv\nkQ5eOdZJIBgiNyuDO5eVsX1VJXevqEz7oF8mh3OOnosjY4FTW69XCRV9vyMQvGwhidJZ2dSUXAqd\navyqqFr/dkVR7g2tAA+FI/zL8XM8d6iFvUc6GA5HqKss4uHb5vHArTUzqgJbUkMhk4iI3FDhiOP/\nvtPKX+x9jw/OX+T2hbP56j11rFs8N9VNm1Ak4nj7bA8vvtvGrsZ2WnoGycowNi4rY2d9NdtXVabN\nfDqSGoPDYY61B8bN8dTc3sdI2PtMVpibxaqa4ktzPNWWsLis4Ib+ceKc42hbHy8d7WDfsU7e+bAH\ngJqSPLb61Uq/sXiurn5PwDlHc0cfr/qB01tnzjMSdhTmZrFh6Vw211Wwua78hi4OMN209wbZe9Sr\nVvrlyS5Gwo45BTncvaKC7asquXNZ2ZSqDhEZNRKO0BEIjgug2nrGh1GBmFXzMjOMyqJcf34oL4zy\nhuddul2Sn33NF+yOtQd47tBZnv91K139Q8wpyOF+fzjczTXF0+4CoExdCplERGRSjIQjPPvmh3zn\npeN09g2xaXk5X91Rx+p5Jalu2phIxHH4gwv8vKGNXzS209YbJDvTuHNZOTtXV7N9ZSUls7T6jKTO\ncCjCex19NLX2jg25O9IWGBuykZedwcrqS8HTzTUlLK8sSmqy2uBImIOnur35lY520OoPg7tlXinb\nVlZw94pKVlanxzC4G6F/KOTP5XSOV5s7ae31qpzqKou8YXV15dy+cE5aTDg8ajSI29vUwd6jl+b0\nWjR3lj8MrorbFmp+JZkZ+odCtI0GUHGqodp6ggyHxw/Lm5WTOW5S8ugAqtqvjMrLzqS7f4ifvd3K\nc4fP0tQaICvDuHtFBQ/dNo8tdRVpdV6RyaOQSUREJlVwJMwPf3mav95/kp6LI+xcXcW/276cpRVF\nKWlPOOJ46/R5djW2s6uxjY7AEDmZGWxaXs7O1VVsXVlJSb6CJZm6QuEI73cN0NjqTy7e4gVQ/UPe\n1fHsTKOuqsgfbldCfU0xK6uLJ6w26uof4mV/NbjXjndxcThMfrY3DG7bykq2rKjQkKQbwDnH8c7+\nscnD33jfq3IqyMlk/VJvLqfNdRXUzsDVnULhCG+evuDNr3S0nQ/PexPkr5lfOjZx99KKQoWZknYi\nEUfXwNC4CqjRMMq7H6Srf+iy580tyKF3cIRQxFFfW8zDa+dx/5pa5qgKW24whUwiIpISfcERnnnt\nfZ557RSDI2EeXDuPJ7cuY/6cWTf8tcMRxxvvn+fFhjZ+0dTOub4hcrIy2Ly8nE9+rJq7V1RQlKdg\nSaavSMTxwfmLY8FTU2svDS299Fz0lu3OzDCWlheOm+OpIDeT/c3eXDfvnO3BOaguyWOrvxrcHRoG\nN+kGhkK8frKb/c2d7G8+R0uPF7yU5GeTqIhnohBmongm8dMmON4EB0y0KdFzBobC9A+FyMnMYP3S\nuexYVcW2lRUzYrVFkRttKBSm3Z+QvC1qlbzi/GwevHUedVWpuZAn6Ukhk4iIpFR3/xB/s/8kPzx4\nBuccj65byB9tWXLdJ54MhSP8yg+Wdje109U/TF52BlvqKrhvtRcsFeZqTg+ZuZxztPYGvUonf2W7\nxpZeOvvGXwG/ZV7J2PxKq6o1T8dU4Zzj5Ll+9jef44PzFxPsM8HzSbwx0fMm+vQ/8Z8G8TdO9Jzs\nzAzuWDKXTcvLdS4WEZnGFDKJiMiU0NY7yHdeOsGP3/qQnMwMfm/DIr6waUlScyCNhCP88mQ3uxrb\n2N3UwfmBYfKzM7l7RQU7V1ezua6cAv0xI2muMxCkqTVAz+AwG5aUqXJEREREPjKFTCIiMqWc7hrg\nL/e9xwvvtFKYm8Xjdy3hsfWLrjoMGg5FeP1kFy82tLHnSAc9F0eYlZPJ1pWV7KyvYnNdBfk5GvIj\nIiIiInK9KWQSEZEp6WhbgKf3NLPvaCdlhTl8actSHlm3gNysywOioVCYAye6eLGhnT1N7QSCIQpz\ns9i20hsKd9fycs0lIyIiIiJygylkEhGRKe3QmQs8tfsYB0+dp7Y0nye3LePBW2sJRRyvHe9iV0Mb\ne4920BcMUZSbxfZVlexcXc3GZWUKlkREREREJpFCJhERmfKccxw40c1Tu4/xztle5s3Op+fiCP1D\nIYrzsthxcxU7V1exYWlZ3EonERERERG58a42ZNKsqCIikjJmxsZlZWxYuoHdTR384PXTrF+Sz87V\n1axfUkZOVkaqmygiIiIiIldJIZOIiKScmXFvfRX31leluikiIiIiIvIR6RKxiIiIiIiIiIgkTSGT\niIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgk\nTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiI\niIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiI\niIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGT\niIiIiIiIiIgkTSGTiIiIiIiIiIgkTSGTiIiIiIiIiIgkzZxzqW7DdWNm54AzqW7HdVAGdKW6ETLl\nqF9ILPUJiUf9QmKpT0g86hcSS31C4lG/kFELnXPlV9ppRoVMM4WZveWcuz3V7ZCpRf1CYqlPSDzq\nFxJLfULiUb+QWOoTEo/6hVwrDZcTEREREREREZGkKWQSEREREREREZGkKWSamv421Q2QKUn9QmKp\nT0g86hcSS31C4lG/kFjqExKP+oVcE83JJCIiIiIiIiIiSVMlk4iIiIiIiIiIJE0hUwqZ2b1m1mxm\nJ8zsa3G2m5l9x9/+rpmtTUU7ZfKY2Xwze8XMjphZk5k9GWefzWbWa2Zv+19/moq2yuQxs9Nm1uD/\nf78VZ7vOFWnGzOqizgFvm1nAzL4cs4/OFTOcmX3fzDrNrDHqsTlmttfMjvvfZyd47oSfQWT6StAv\nnjKzY/57xPNmVprguRO+38j0lKBPfN3MWqLeI3YmeK7OFTNUgn7xbFSfOG1mbyd4rs4VkpCGy6WI\nmWUC7wHbgbPAm8AjzrkjUfvsBJ4AdgLrgP/mnFuXgubKJDGzaqDaOXfYzIqAQ8CnY/rFZuArzrlP\npaiZMsnM7DRwu3OuK8F2nSvSmP9+0gKsc86diXp8MzpXzGhmtgnoB37onKv3H/s2cN45903/D8LZ\nzrn/EPO8K34GkekrQb/YAbzsnAuZ2bcAYvuFv99pJni/kekpQZ/4OtDvnPvzCZ6nc8UMFq9fxGx/\nGuh1zn0jzrbT6FwhCaiSKXU+AZxwzp1yzg0D/xt4IGafB/B+6Z1z7iBQ6ocQMkM559qcc4f9233A\nUaA2ta2SaUDnivS2FTgZHTBJenDO/QtwPubhB4Af+Ld/AHw6zlOv5jOITFPx+oVzbo9zLuTfPQjM\nm/SGScokOFdcDZ0rZrCJ+oWZGfBbwD9NaqNkRlDIlDq1wIdR989yeZhwNfvIDGVmi4BbgV/F2bze\nL3nfZWY3T2rDJBUcsM/MDpnZH8TZrnNFevssiT8E6lyRfiqdc23+7XagMs4+Omekt88DuxJsu9L7\njcwsT/jvEd9PMLRW54r0dSfQ4Zw7nmC7zhWSkEImkSnIzAqB54AvO+cCMZsPAwuccx8Dvgv882S3\nTybdRufcGuA+4It+ebMIZpYD3A/8nzibda5Ic86bE0HzIsgYM/tPQAj4UYJd9H6TPv4GWAysAdqA\np1PbHJliHmHiKiadKyQhhUyp0wLMj7o/z3/sWveRGcbMsvECph85534au905F3DO9fu3XwSyzaxs\nkpspk8g51+J/7wSexytfj6ZzRfq6DzjsnOuI3aBzRdrqGB0u63/vjLOPzhlpyMweAz4FPOoSTMp6\nFe83MkM45zqcc2HnXAT4O+L/X+tckYbMLAt4EHg20T46V8hEFDKlzpvAMjO7yb8S/VnghZh9XgA+\n568c9Rt4E6+1xR5IZg5//PP3gKPOub9IsE+Vvx9m9gm83+PuyWulTCYzK/AngcfMCoAdQGPMbjpX\npK+EVxp1rkhbLwC/69/+XeBncfa5ms8gMoOY2b3Avwfud85dTLDP1bzfyAwRM3fjZ4j/f61zRXra\nBhxzzp2Nt1HnCrmSrFQ3IF35q3t8CdgNZALfd841mdnj/vb/AbyIt1rUCeAi8Hupaq9Mmg3A7wAN\nUUuG/kdgAYz1i4eBPzSzEDAIfDbRFUmZESqB5/2sIAv4X865X+hcIf4Hu+3AF6Iei+4XOlfMcGb2\nT8BmoMzMzgL/Bfgm8GMz+33gDN7ErZhZDfCMc25nos8gqfgZ5PpL0C/+BMgF9vrvJwedc49H9wsS\nvN+k4EeQ6yxBn9hsZmvwhtSexn8v0bkifcTrF8657xFnrkedK+RamD5vioiIiIiIiIhIsjRcTkRE\nREREREREkqaQSUREREREREREkqaQSUREREREREREkqaQSUREREREREREkqaQSUREREREREREkqaQ\nSURERKYNM3Nm9nTU/a+Y2dev07H/wcwevh7HusLr/KaZHTWzV2IeX2Rmjf7tNWa28zq+ZqmZ/VHU\n/Roz+8n1Or6IiIgIKGQSERGR6WUIeNDMylLdkGhmlnUNu/8+8G+cc1sm2GcNcE0h0xXaUAqMhUzO\nuVbn3A0P1ERERCS9KGQSERGR6SQE/C3wx7EbYiuRzKzf/77ZzF41s5+Z2Skz+6aZPWpmb5hZg5kt\niTrMNjN7y8zeM7NP+c/PNLOnzOxNM3vXzL4QddzXzOwF4Eic9jziH7/RzL7lP/anwEbge2b2VLwf\n0MxygG8Av21mb5vZb5tZgZl932/zr83sAX/fx8zsBTN7GXjJzArN7CUzO+y/9gP+Yb8JLPGP91RM\n1VSemf29v/+vzWxL1LF/ama/MLPjZvbtqH+Pf/B/rgYzu+z/QkRERNLTtVx1ExEREZkK/jvw7mjo\ncZVuAVYC54FTwDPOuU+Y2ZPAE8CX/f0WAZ8AlgCvmNlS4HNAr3Pu42aWCxwwsz3+/muBeufc+9Ev\nZmY1wLeA24ALwB4z+7Rz7htmdjfwFefcW/Ea6pwb9sOo251zX/KP92fAy865z5tZKfCGme2LasPH\nnHPn/WqmzzjnAn6110E/BPua3841/vEWRb3kF72XdavNbIXf1uX+tjXArXgVZM1m9l2gAqh1ztX7\nxyq9wr+9iIiIpAlVMomIiMi04pwLAD8E/u01PO1N51ybc24IOAmMhkQNeMHSqB875yLOueN4YdQK\nYAfwOTN7G/gVMBdY5u//RmzA5Ps4sN85d845FwJ+BGy6hvbG2gF8zW/DfiAPWOBv2+ucO+/fNuDP\nzOxdYB9QC1Re4dgbgX8EcM4dA84AoyHTS865XudcEK9aayHev8tiM/uumd0LBJL4uURERGQGUSWT\niIiITEd/BRwG/j7qsRD+BTQzywByorYNRd2ORN2PMP7zkIt5HYcX3DzhnNsdvcHMNgMDH63518yA\nh5xzzTFtWBfThkeBcuA259yImZ3GC6Q+quh/tzCQ5Zy7YGa3APcAjwO/BXw+idcQERGRGUKVTCIi\nIjLt+JU7P8abRHvUabzhaQD3A9kf4dC/aWYZ/jxNi4FmYDfwh2aWDWBmy82s4ArHeQO4y8zKzCwT\neAR49Rra0QcURd3fDTxhZua34dYEzysBOv2AaQte5VG840V7DS+cwh8mtwDv547LH4aX4Zx7DvjP\neMP1RERERBQyiYiIyLT1NBC9ytzf4QU77wB38NGqjD7AC4h2AY/7w8SewRsqdtifLPt/coVqcOdc\nG948SK8A7wCHnHM/u4Z2vAKsGp34G/iveKHZu2bW5N+P50fA7WbWgDeX1DG/Pd14c0k1xplw/K+B\nDP85zwKP+cMKE6kF9vtD9/4R+JNr+LlERERkBjPnYqvCRUREREREREREro0qmUREREREREREJGkK\nmUREREREREREJGkKmUREREREREREJGkKmUREREREREREJGkKmUREREREREREJGkKmURERERERERE\nJGkKmUREREREREREJGkKmUREREREREREJGn/H21wtlxouFZ4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139a8f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(bpr_auc_test_cold_start, label = \"BPR\")\n",
    "plt.plot(pbpr_auc_test_cold_start, label = \"Price-BPR\")\n",
    "plt.plot(bbpr_auc_test_cold_start, label = \"Brand-BPR\")\n",
    "plt.plot(pdbpr_auc_test_cold_start, label = \"Product Description-BPR\")\n",
    "plt.plot(nvbpr_auc_test_cold_start, label = \"NonVisual-BPR\")\n",
    "plt.plot(vbpr_auc_test_cold_start, label = \"Visual-BPR\")\n",
    "plt.plot(hbpr_auc_test_cold_start, label = \"Hybrid-BPR\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cold Start Test AUC\")\n",
    "plt.xlabel(\"Number of Iterations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing the results to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "rows = zip(bpr_auc_test, pbpr_auc_test, bbpr_auc_test, pdbpr_auc_test, nvbpr_auc_test, vbpr_auc_test, \n",
    "           hbpr_auc_test, bpr_auc_test_cold_start, pbpr_auc_test_cold_start, bbpr_auc_test_cold_start,\n",
    "          pdbpr_auc_test_cold_start, nvbpr_auc_test_cold_start, vbpr_auc_test_cold_start, hbpr_auc_test_cold_start)\n",
    "\n",
    "with open(\"AUC_5core_CellPhones.csv\", \"wb\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([\"bpr_auc_test\", \"pbpr_auc_test\", \"bbpr_auc_test\", \"pdbpr_auc_test\", \"nvbpr_auc_test\", \n",
    "                     \"vbpr_auc_test\", \"hbpr_auc_test\", \"bpr_auc_test_cold_start\", \"pbpr_auc_test_cold_start\",\n",
    "                    \"bbpr_auc_test_cold_start\", \"pdbpr_auc_test_cold_start\", \"nvbpr_auc_test_cold_start\",\n",
    "                    \"vbpr_auc_test_cold_start\", \"hbpr_auc_test_cold_start\"])\n",
    "    for row in rows:\n",
    "        writer.writerow(row)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}