{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommendation engine using collaborating filtering on Movielens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "from fastai.learner import *\n",
    "from fastai.column_data import *\n",
    "from fastai.imports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collaborating filter.ipynb  ml-latest-small.zip  movielens.ipynb    tmp\r\n",
      "ml-latest-small\t\t    models\t\t ratings_small.csv\r\n"
     ]
    }
   ],
   "source": [
    "! ls ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv('ratings_small.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100004, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " There are no NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_users=int(ratings.userId.nunique())\n",
    "n_movies=int(ratings.movieId.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_users =  671 || n_movies =  9066\n"
     ]
    }
   ],
   "source": [
    "print(\"n_users = \",n_users, \"||\", \"n_movies = \", n_movies )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a cross-tab for better visualization of user ids and item ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = ratings.groupby('userId')['rating'].count()\n",
    "topg = g.sort_values(ascending = False)[:15]\n",
    "\n",
    "i = ratings.groupby('movieId')['rating'].count()\n",
    "topi = i.sort_values(ascending = False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1     110   260   296   318   356   480   527   589   593   608   \\\n",
       "userId                                                                      \n",
       "15        2.0   3.0   5.0   5.0   2.0   1.0   3.0   4.0   4.0   5.0   5.0   \n",
       "30        4.0   5.0   4.0   5.0   5.0   5.0   4.0   5.0   4.0   4.0   5.0   \n",
       "73        5.0   4.0   4.5   5.0   5.0   5.0   4.0   5.0   3.0   4.5   4.0   \n",
       "212       3.0   5.0   4.0   4.0   4.5   4.0   3.0   5.0   3.0   4.0   NaN   \n",
       "213       3.0   2.5   5.0   NaN   NaN   2.0   5.0   NaN   4.0   2.5   2.0   \n",
       "294       4.0   3.0   4.0   NaN   3.0   4.0   4.0   4.0   3.0   NaN   NaN   \n",
       "311       3.0   3.0   4.0   3.0   4.5   5.0   4.5   5.0   4.5   2.0   4.0   \n",
       "380       4.0   5.0   4.0   5.0   4.0   5.0   4.0   NaN   4.0   5.0   4.0   \n",
       "452       3.5   4.0   4.0   5.0   5.0   4.0   5.0   4.0   4.0   5.0   5.0   \n",
       "468       4.0   3.0   3.5   3.5   3.5   3.0   2.5   NaN   NaN   3.0   4.0   \n",
       "509       3.0   5.0   5.0   5.0   4.0   4.0   3.0   5.0   2.0   4.0   4.5   \n",
       "547       3.5   NaN   NaN   5.0   5.0   2.0   3.0   5.0   NaN   5.0   5.0   \n",
       "564       4.0   1.0   2.0   5.0   NaN   3.0   5.0   4.0   5.0   5.0   5.0   \n",
       "580       4.0   4.5   4.0   4.5   4.0   3.5   3.0   4.0   4.5   4.0   4.5   \n",
       "624       5.0   NaN   5.0   5.0   NaN   3.0   3.0   NaN   3.0   5.0   4.0   \n",
       "\n",
       "movieId  1196  1198  1270  2571  \n",
       "userId                           \n",
       "15        5.0   4.0   5.0   5.0  \n",
       "30        4.0   5.0   5.0   3.0  \n",
       "73        5.0   5.0   5.0   4.5  \n",
       "212       NaN   3.0   3.0   5.0  \n",
       "213       5.0   3.0   3.0   4.0  \n",
       "294       4.0   4.5   4.0   4.5  \n",
       "311       3.0   4.5   4.5   4.0  \n",
       "380       4.0   NaN   3.0   5.0  \n",
       "452       4.0   4.0   4.0   2.0  \n",
       "468       3.0   3.5   3.0   3.0  \n",
       "509       5.0   5.0   3.0   4.5  \n",
       "547       2.5   2.0   3.5   3.5  \n",
       "564       5.0   5.0   3.0   3.0  \n",
       "580       4.0   3.5   3.0   4.5  \n",
       "624       5.0   5.0   5.0   2.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gettings ratings of top users and top items\n",
    "\n",
    "join1 = ratings.join(topg, on='userId', how = 'inner', rsuffix='_r')\n",
    "join1 = join1.join(topi, on='movieId', how = 'inner', rsuffix = '_r')\n",
    "\n",
    "pd.crosstab(join1.userId, join1.movieId, join1.rating, aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_indx = get_cv_idxs(len(ratings))  # index for validation set\n",
    "wd = 2e-4 # weight decay\n",
    "n_factors = 50 # n_factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data loader\n",
    "cf = CollabFilterDataset.from_csv(path, 'ratings_small.csv', 'userId', 'movieId', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cf.get_learner(n_factors, val_indx, bs=64, opt_fn=optim.Adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "065d1fd5a51945359526689db634fd06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.7727   0.80396]                                    \n",
      "[ 1.       0.77782  0.77585]                                    \n",
      "[ 2.       0.58389  0.76542]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(1e-2,2, wds = wd, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got .76"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collaborating filter from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "u_uniq = ratings.userId.unique()\n",
    "user2idx = {o:i for i,o in enumerate(u_uniq)}\n",
    "ratings.userId = ratings.userId.apply(lambda x: user2idx[x])\n",
    "\n",
    "m_uniq = ratings.movieId.unique()\n",
    "movie2idx = {o:i for i,o in enumerate(m_uniq)}\n",
    "ratings.movieId = ratings.movieId.apply(lambda x: movie2idx[x])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_users, n_movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`nn.Embedding` creates a lookup table that stores embeddings of a fixed dictionary and size. So word embeddings once stored can be retrieved using indices. After making `embeddings`, we get free `u.weights` which are correspondings weights of ebeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_indx = get_cv_idxs(len(ratings))  # index for validation set\n",
    "wd = 2e-4 # weight decay\n",
    "n_factors = 50 # n_factors i.e. 1 dimension of embeddings (random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 5.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_rating,max_rating = ratings.rating.min(),ratings.rating.max()\n",
    "min_rating,max_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_emb(ni,nf):\n",
    "    e = nn.Embedding(ni, nf)\n",
    "    e.weight.data.uniform_(-0.01,0.01)\n",
    "    #e.weight.data.normal_(0,0.003)\n",
    "\n",
    "    return e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ratings.drop(['rating'],axis=1)\n",
    "y = ratings['rating'].astype(np.float32)\n",
    "\n",
    "data = ColumnarModelData.from_data_frame(path, val_indx, x, y, ['userId', 'movieId'], 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nh = dimension of hidden linear layer\n",
    "# p1 = dropout1\n",
    "# p2 = dropout2\n",
    "\n",
    "class EmbeddingNet(nn.Module):\n",
    "    def __init__(self, n_users, _n_movies, nh = 10, p1 = 0.05, p2= 0.5):\n",
    "        super().__init__()\n",
    "        (self.u, self.m, self.ub, self.mb) = [get_emb(*o) for o in [\n",
    "            (n_users, n_factors), (n_movies, n_factors),\n",
    "            (n_users,1), (n_movies,1)\n",
    "        ]]\n",
    "        \n",
    "        self.lin1 = nn.Linear(n_factors*2, nh)  # bias is True by default\n",
    "        self.lin2 = nn.Linear(nh, 1)\n",
    "        self.drop1 = nn.Dropout(p = p1)\n",
    "        self.drop2 = nn.Dropout(p = p2)\n",
    "    \n",
    "    def forward(self, cats, conts): # forward pass i.e.  dot product of vector from movie embedding matrixx\n",
    "                                    # and vector from user embeddings matrix\n",
    "        \n",
    "        # torch.cat : concatenates both embedding matrix to make more columns, same rows i.e. n_factors*2, n : rows\n",
    "        # u(users) is doing lookup for indexed mentioned in users\n",
    "        # users has indexes to lookup in embedding matrix. \n",
    "        \n",
    "        users,movies = cats[:,0],cats[:,1]\n",
    "        u2,m2 = self.u(users) , self.m(movies)\n",
    "       \n",
    "        x = self.drop1(torch.cat([u2,m2], 1)) # drop initialized weights\n",
    "        x = self.drop2(F.relu(self.lin1(x))) # drop 1st linear + nonlinear wt\n",
    "        r = F.sigmoid(self.lin2(x)) * (max_rating - min_rating) + min_rating               \n",
    "        return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd=1e-5\n",
    "model = EmbeddingNet(n_users, n_movies)\n",
    "model = model.cuda()\n",
    "opt = optim.Adam(model.parameters(), 1e-3, weight_decay=wd) # got parameter() for free , lr = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingNet (\n",
       "  (u): Embedding(671, 50)\n",
       "  (m): Embedding(9066, 50)\n",
       "  (ub): Embedding(671, 1)\n",
       "  (mb): Embedding(9066, 1)\n",
       "  (lin1): Linear (100 -> 10)\n",
       "  (lin2): Linear (10 -> 1)\n",
       "  (drop1): Dropout (p = 0.05)\n",
       "  (drop2): Dropout (p = 0.5)\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f45cf8456bc4101a27367f9c26d9f6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.74293  0.79247]                                    \n",
      "[ 1.       0.74748  0.79483]                                    \n",
      "[ 2.       0.75364  0.79638]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                        \r"
     ]
    }
   ],
   "source": [
    "# from tqdm import tqdm as tqdm_cls\n",
    "\n",
    "# inst = tqdm_cls._instances\n",
    "# for i in range(len(inst)): inst.pop().close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_lrs(opt, 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea1cd58e12574699969fd0040c18f1c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.79631  0.78994]                                    \n",
      "[ 1.       0.78677  0.79127]                                    \n",
      "[ 2.      0.7614  0.7906]                                       \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surprise package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collaborating filter.ipynb  ml-latest-small.zip  movielens.ipynb    tmp\r\n",
      "ml-latest-small\t\t    models\t\t ratings_small.csv\r\n"
     ]
    }
   ],
   "source": [
    "! ls ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196\t242\t3\t881250949\r\n",
      "186\t302\t3\t891717742\r\n",
      "22\t377\t1\t878887116\r\n",
      "244\t51\t2\t880606923\r\n",
      "166\t346\t1\t886397596\r\n"
     ]
    }
   ],
   "source": [
    "! head -5 '/home/ubuntu/.surprise_data/ml-100k/ml-100k/u.data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId,movieId,rating,timestamp\r",
      "\r\n",
      "1,31,2.5,1260759144\r",
      "\r\n",
      "1,1029,3.0,1260759179\r",
      "\r\n",
      "1,1061,3.0,1260759182\r",
      "\r\n",
      "1,1129,2.0,1260759185\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! head -5 'ratings_small.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import Reader, Dataset\n",
    "# Define the format\n",
    "\n",
    "reader = Reader(line_format='user item rating timestamp', sep='\\t')\n",
    "# Load the data from the file using the reader format\n",
    "\n",
    "data = Dataset.load_from_file('/home/ubuntu/.surprise_data/ml-100k/ml-100k/u.data', reader=reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_dict = {'itemID': list(ratings.movieId),\n",
    "                'userID': list(ratings.userId),\n",
    "                'rating': list(ratings.rating)}\n",
    "df = pd.DataFrame(ratings_dict)\n",
    "\n",
    "# A reader is still needed but only the rating_scale param is requiered.\n",
    "reader = Reader(rating_scale=(0.5, 5.0))\n",
    "# The columns must correspond to user id, item id and ratings (in that order).\n",
    "data = Dataset.load_from_df(df[['userID', 'itemID', 'rating']], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into 5 folds\n",
    "\n",
    "data.split(n_folds=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import SVD, evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'lr_all': 0.002, 'reg_all': 0.4}, {'lr_all': 0.002, 'reg_all': 0.6}, {'lr_all': 0.005, 'reg_all': 0.4}, {'lr_all': 0.005, 'reg_all': 0.6}]\n",
      "------------\n",
      "Parameters combination 1 of 4\n",
      "params:  {'lr_all': 0.002, 'reg_all': 0.4}\n",
      "------------\n",
      "Mean RMSE: 0.9133\n",
      "------------\n",
      "------------\n",
      "Parameters combination 2 of 4\n",
      "params:  {'lr_all': 0.002, 'reg_all': 0.6}\n",
      "------------\n",
      "Mean RMSE: 0.9214\n",
      "------------\n",
      "------------\n",
      "Parameters combination 3 of 4\n",
      "params:  {'lr_all': 0.005, 'reg_all': 0.4}\n",
      "------------\n",
      "Mean RMSE: 0.9031\n",
      "------------\n",
      "------------\n",
      "Parameters combination 4 of 4\n",
      "params:  {'lr_all': 0.005, 'reg_all': 0.6}\n",
      "------------\n",
      "Mean RMSE: 0.9121\n",
      "------------\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'lr_all': [0.002, 0.005],\n",
    "              'reg_all': [0.4, 0.6]}\n",
    "grid_search = GridSearch(SVD, param_grid, measures=['RMSE'])\n",
    "grid_search.evaluate(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE of algorithm SVD.\n",
      "\n",
      "------------\n",
      "Fold 1\n",
      "RMSE: 0.8990\n",
      "------------\n",
      "Fold 2\n",
      "RMSE: 0.8983\n",
      "------------\n",
      "Fold 3\n",
      "RMSE: 0.8941\n",
      "------------\n",
      "Fold 4\n",
      "RMSE: 0.8962\n",
      "------------\n",
      "Fold 5\n",
      "RMSE: 0.8962\n",
      "------------\n",
      "------------\n",
      "Mean RMSE: 0.8967\n",
      "------------\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CaseInsensitiveDefaultDict(list,\n",
       "                           {'rmse': [0.89895181594737417,\n",
       "                             0.89831051013903251,\n",
       "                             0.89405859774725671,\n",
       "                             0.89621812893141306,\n",
       "                             0.89617318551492264]})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = SVD()\n",
    "evaluate(algo, data, measures=['RMSE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As noticed above best RMSE from SVD is still higher than default result from fast.ai's neural net version for collaborative filtering using embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try `KNN` algorithm also. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import KNNBasic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm KNNBasic.\n",
      "\n",
      "------------\n",
      "Fold 1\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.9662\n",
      "MAE:  0.7645\n",
      "------------\n",
      "Fold 2\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.9834\n",
      "MAE:  0.7787\n",
      "------------\n",
      "Fold 3\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.9802\n",
      "MAE:  0.7744\n",
      "------------\n",
      "Fold 4\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.9812\n",
      "MAE:  0.7728\n",
      "------------\n",
      "Fold 5\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.9804\n",
      "MAE:  0.7735\n",
      "------------\n",
      "------------\n",
      "Mean RMSE: 0.9783\n",
      "Mean MAE : 0.7728\n",
      "------------\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CaseInsensitiveDefaultDict(list,\n",
       "                           {'mae': [0.76447687302283862,\n",
       "                             0.77871336218916276,\n",
       "                             0.77444253761129189,\n",
       "                             0.77277756247233054,\n",
       "                             0.77353073380751081],\n",
       "                            'rmse': [0.96618541819639647,\n",
       "                             0.98337516247695278,\n",
       "                             0.98018440899082937,\n",
       "                             0.98120591146396685,\n",
       "                             0.98038668816669572]})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = KNNBasic()\n",
    "evaluate(algo, data, measures=['RMSE', 'MAE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE of algorithm NMF.\n",
      "\n",
      "------------\n",
      "Fold 1\n",
      "RMSE: 0.9476\n",
      "------------\n",
      "Fold 2\n",
      "RMSE: 0.9449\n",
      "------------\n",
      "Fold 3\n",
      "RMSE: 0.9479\n",
      "------------\n",
      "Fold 4\n",
      "RMSE: 0.9494\n",
      "------------\n",
      "Fold 5\n",
      "RMSE: 0.9450\n",
      "------------\n",
      "------------\n",
      "Mean RMSE: 0.9469\n",
      "------------\n",
      "------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CaseInsensitiveDefaultDict(list,\n",
       "                           {'rmse': [0.9475771765522677,\n",
       "                             0.94487435132530351,\n",
       "                             0.94786484545358385,\n",
       "                             0.94936598409066575,\n",
       "                             0.94501542053063314]})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = NMF()\n",
    "evaluate(algo, data, measures=['RMSE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cosine distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv('ratings_small.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings2 = ratings.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['movieId', 'userId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in col:\n",
    "    ratings2[c].replace({val: i for i, val in enumerate(ratings2[c].unique())}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       0        0     2.5  1260759144\n",
       "1       0        1     3.0  1260759179"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_users=int(ratings2.userId.nunique())\n",
    "n_items=int(ratings2.movieId.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_users =  671 || n_items =  9066\n"
     ]
    }
   ],
   "source": [
    "print(\"n_users = \",n_users, \"||\", \"n_items = \", n_items )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cross_validation as cv\n",
    "train_data, test_data = cv.train_test_split(ratings2, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create two user-item matrices, one for training and another for testing\n",
    "train_data_matrix = np.zeros((n_users, n_items))\n",
    "for line in train_data.itertuples():\n",
    "    train_data_matrix[line[1]-1, line[2]-1] = line[3]\n",
    "    \n",
    "test_data_matrix = np.zeros((n_users, n_items))\n",
    "for line in test_data.itertuples():\n",
    "    test_data_matrix[line[1]-1, line[2]-1] = line[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(ratings, similarity, type='user'):\n",
    "    if type == 'user':\n",
    "        mean_user_rating = ratings.mean(axis=1)\n",
    "        #You use np.newaxis so that mean_user_rating has same format as ratings\n",
    "        ratings_diff = (ratings - mean_user_rating[:, np.newaxis])\n",
    "        pred = mean_user_rating[:, np.newaxis] + similarity.dot(ratings_diff) / np.array([np.abs(similarity).sum(axis=1)]).T\n",
    "    elif type == 'item':\n",
    "        pred = ratings.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "user_similarity = pairwise_distances(train_data_matrix, metric='cosine')\n",
    "item_similarity = pairwise_distances(train_data_matrix.T, metric='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_prediction = predict(train_data_matrix, item_similarity, type='item')\n",
    "user_prediction = predict(train_data_matrix, user_similarity, type='user')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "def mse(prediction, ground_truth):\n",
    "    prediction = prediction[ground_truth.nonzero()].flatten()\n",
    "    ground_truth = ground_truth[ground_truth.nonzero()].flatten()\n",
    "    return mean_squared_error(prediction, ground_truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User-based CF MSE:  11.3668710905\n",
      "Item-based CF MSE:  12.8400786831\n"
     ]
    }
   ],
   "source": [
    "print('User-based CF MSE: ' , str(mse(user_prediction, test_data_matrix)))\n",
    "print('Item-based CF MSE: ' , str(mse(item_prediction, test_data_matrix)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60392124a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mses = [6.47, .957, .897, .804, .801, .79]\n",
    "algos = ['cosine_memory', 'KNN', \"NMF\", 'SVD', 'PMF', 'DL']\n",
    "plt.plot(algos, mses, 'go',  )\n",
    "plt.xlabel(\"Different algos\")\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='collaborating_filter.ipynb' target='_blank'>collaborating_filter.ipynb</a><br>"
      ],
      "text/plain": [
       "/home/ubuntu/collaborate_filter/collaborating_filter.ipynb"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileLink('collaborating_filter.ipynb')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}