Deep_Matrix_Factorization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will demonstrate how to use the tensorflow low level API to build a deep learning model to predict movie rating from scratch.\n",
    "\n",
    "Our dataset: movielens 1m dataset.\n",
    "Our parameters: movie embeddings, user embeddings, weights and bias in DNN.\n",
    "Our Model: \n",
    "\n",
    "1. 4 layer neural network, with activation function: relu, relu, relu, softmax.\n",
    "2. Input layer is the element wise prodcut of the user embedding and the movie embedding.\n",
    "3. Cost function: a combination of cross entropy and mean squared error.\n",
    "4. User embedding and movie embedding will be initialized with the results from matrix factorization model.\n",
    "5. First 50 epochs only update weights and bias in DNN, from 51th epoch train model end-to-end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### download movie data and create model directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: mf_model: File exists\n",
      "mkdir: cache: File exists\n"
     ]
    }
   ],
   "source": [
    "# download movie data and make directory for the model\n",
    "\n",
    "#! wget -q http://files.grouplens.org/datasets/movielens/ml-1m.zip\n",
    "#! unzip ml-1m.zip\n",
    "! mkdir mf_model\n",
    "!mkdir cache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step 1: load data to pandas dataframes, merge the dataframes to one table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_desc = {\n",
    "    1: \"Under 18\", 18: \"18-24\", 25: \"25-34\", 35: \"35-44\", 45: \"45-49\", 50: \"50-55\", 56: \"56+\"\n",
    "}\n",
    "occupation_desc = { \n",
    "    0: \"other or not specified\", 1: \"academic/educator\", 2: \"artist\", 3: \"clerical/admin\",\n",
    "    4: \"college/grad student\", 5: \"customer service\", 6: \"doctor/health care\",\n",
    "    7: \"executive/managerial\", 8: \"farmer\", 9: \"homemaker\", 10: \"K-12 student\", 11: \"lawyer\",\n",
    "    12: \"programmer\", 13: \"retired\", 14: \"sales/marketing\", 15: \"scientist\", 16: \"self-employed\",\n",
    "    17: \"technician/engineer\", 18: \"tradesman/craftsman\", 19: \"unemployed\", 20: \"writer\"\n",
    "}\n",
    "rating_data = pd.read_csv(\n",
    "    \"ml-1m/ratings.dat\",\n",
    "    sep=\"::\",\n",
    "    engine=\"python\",\n",
    "    encoding=\"latin-1\",\n",
    "    names=['userid', 'movieid', 'rating', 'timestamp'])\n",
    "\n",
    "user_data = pd.read_csv(\n",
    "    \"ml-1m/users.dat\", \n",
    "    sep='::', \n",
    "    engine='python', \n",
    "    encoding='latin-1',\n",
    "    names=['userid', 'gender', 'age', 'occupation', 'zipcode']\n",
    ")\n",
    "user_data['age_desc'] = user_data['age'].apply(lambda x: age_desc[x])\n",
    "user_data['occ_desc'] = user_data['occupation'].apply(lambda x: occupation_desc[x])\n",
    "\n",
    "movie_data = pd.read_csv(\n",
    "    \"ml-1m/movies.dat\",\n",
    "    sep='::', \n",
    "    engine='python', \n",
    "    encoding='latin-1',\n",
    "    names=['movieid', 'title', 'genre']\n",
    ")\n",
    "\n",
    "dataset = pd.merge(pd.merge(rating_data, movie_data, how=\"left\", on=\"movieid\"), user_data, how=\"left\", on=\"userid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: preprocess the movie id and user id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_cols(df, cols):\n",
    "    \"\"\"\n",
    "    checks if there are gaps of index, and if the index starts from 0\n",
    "    \n",
    "    Arguments:\n",
    "    df -- dataframe of the dataset\n",
    "    cols -- dataframe columns that needs to be checked, in our case user id and movie id\n",
    "    \n",
    "    Returns:\n",
    "    a list of tuple [('COLUMN_NAME', boolean)], if True, the column needs to be fixed, if False, the column is ok.\n",
    "    \"\"\"\n",
    "    return [(col, False) if len(dataset[col].unique())-1 == dataset[col].max() else (col, True) for col in cols]\n",
    "\n",
    "def remove_gaps(df, col):\n",
    "    \"\"\"\n",
    "    preprocess the index of user id and movie id to start from 0 and reduce the gap of index\n",
    "    \n",
    "    Arguments:\n",
    "    df -- dataframe of the dataset\n",
    "    col -- dataframe columns that needs to be adjusted, in our case both user id and movie id\n",
    "    \n",
    "    Returns:\n",
    "    a dataframe with adjusted columns.\n",
    "    \"\"\"\n",
    "    adj_col_uni = df[col].sort_values().unique()\n",
    "    adj_df = pd.DataFrame(adj_col_uni).reset_index().rename(columns = {0: col, 'index': \"adj_%s\"%(col,)})\n",
    "    return pd.merge(adj_df, df, how=\"right\", on=col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before fix:\n",
      "userid needs fix!\n",
      "movieid needs fix!\n",
      "\n",
      "after fix\n",
      "adj_userid ok.\n",
      "adj_movieid ok.\n"
     ]
    }
   ],
   "source": [
    "index_cols = [\"userid\", \"movieid\"]\n",
    "cols_check = check_cols(dataset, index_cols)\n",
    "print_check = lambda check: print(*[\"%s needs fix!\"%(c,) if f else \"%s ok.\"%(c,) for c, f in check], sep=\"\\n\")\n",
    "print(\"before fix:\")\n",
    "print_check(cols_check)\n",
    "for col, needs_fix in cols_check:\n",
    "    if needs_fix:\n",
    "        dataset = remove_gaps(dataset, col)\n",
    "\n",
    "print(\"\\nafter fix\")\n",
    "print_check(check_cols(dataset, [\"adj_userid\", \"adj_movieid\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step 3: shuffle data and split the data to train and validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.sample(frac=1, replace=False)\n",
    "n_split = int(len(dataset)*.7)\n",
    "trainset = dataset[:n_split]\n",
    "validset = dataset[n_split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Matrix Factorization Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize_features(num_users, num_movies, dim):\n",
    "    \"\"\"\n",
    "    Initialize features. User_features and movie_features need to be trained by the matrix factorization model.\n",
    "    \n",
    "    Arguments:\n",
    "    num_users -- number of users\n",
    "    num_movies -- number of movies\n",
    "    dim -- dimension of learned user and movie features, it's a hyper-parameter\n",
    "    \n",
    "    Returns:\n",
    "    user_features -- a matrix (variable) of shape [number of users, dim]\n",
    "    movie_features -- a matrix (variable) of shape [number of movies, dim]\n",
    "    \"\"\" \n",
    "    user_features = tf.get_variable(\n",
    "        \"theta\",\n",
    "        shape = [num_users, dim],\n",
    "        dtype = tf.float32,\n",
    "        initializer = tf.truncated_normal_initializer(mean=0, stddev=.05)\n",
    "    )\n",
    "    movie_features = tf.get_variable(\n",
    "        \"phi\",\n",
    "        shape = [num_movies, dim],\n",
    "        dtype = tf.float32,\n",
    "        initializer = tf.truncated_normal_initializer(mean=0, stddev=.05)\n",
    "    )\n",
    "    return user_features, movie_features\n",
    "\n",
    "def create_dataset(user_ids, movie_ids, ratings):\n",
    "    \"\"\"\n",
    "    Load user id, movie id and rating values. Turn numpy array to tensors.\n",
    "    \n",
    "    Arguments:\n",
    "    user_ids -- user index\n",
    "    movie_ids -- movies index\n",
    "    ratings -- true rating value\n",
    "    \n",
    "    Returns:\n",
    "    user_id_var -- a constant of shape [number of training examples, 1]\n",
    "    movie_id_var -- a constant of shape [number of training examples, 1]\n",
    "    ratings_var -- a constant of shape [number of training examples, 1]\n",
    "    \"\"\" \n",
    "    user_id_var = tf.constant(name=\"userid\", value=user_ids)\n",
    "    movie_id_var = tf.constant(name=\"movieid\", value=movie_ids)\n",
    "    ratings_var = tf.constant(name=\"ratings\", value=np.asarray(ratings, dtype=np.float32))\n",
    "    return user_id_var, movie_id_var, ratings_var\n",
    "   \n",
    "def lookup_features(user_features, movie_features, user_ids, movie_ids): \n",
    "    \"\"\"\n",
    "    Retrieve embeddings based on user ids and movie ids respectively.\n",
    "    We use tf.gather function for this. tf.gather gathers slices from params according to indices.\n",
    "    \n",
    "    Arguments:\n",
    "    user_features -- shape [number of user ids, dim]\n",
    "    movie_features -- shape [number of movie ids, dim]\n",
    "    user_ids -- user id tensor (in our case loaded user ids from dataset)\n",
    "    movie_ids -- movie id tensor (in our case loaded movie ids from dataset)\n",
    "    \n",
    "    Returns:\n",
    "    selected_user_features -- a tensor of shape [number of examples, dim]\n",
    "    selected_movie_features -- a tensor of shape [number of examples, dim]\n",
    "    \"\"\" \n",
    "    selected_user_features = tf.gather(user_features, user_ids)\n",
    "    selected_movie_features = tf.gather(movie_features, movie_ids)\n",
    "    return selected_user_features, selected_movie_features\n",
    "\n",
    "def predict(selected_user_features, selected_movie_features):\n",
    "    \"\"\"\n",
    "    Calculate predictions. This is the dot product of user features and movie features. \n",
    "    For each training example, this corresponds to a single number.\n",
    "    \n",
    "    Arguments:\n",
    "    selected_user_features -- matrix of user features for each example -- shape [number of examples, dim]\n",
    "    selected_movie_features -- matrix of movies features value for each example -- shape [number of examples, dim]\n",
    "    \n",
    "    Returns:\n",
    "    selected_predictions -- a tensor of shape [number of examples, 1]\n",
    "    \"\"\" \n",
    "    selected_predictions = tf.reduce_sum(\n",
    "        selected_user_features * selected_movie_features,\n",
    "        axis = 1\n",
    "    )\n",
    "    ##alternatively, using tf.reduce_sum(tf.multiply(selected_user_embeddings,selected_movie_embeddings), axis=1)\n",
    "    return selected_predictions\n",
    "\n",
    "def mean_squared_difference(predictions, ratings):\n",
    "    \"\"\"\n",
    "    Calculate cost.\n",
    "    \n",
    "    Arguments:\n",
    "    predictions -- predicted ratings.\n",
    "    ratings -- true ratings.\n",
    "    \n",
    "    Returns:\n",
    "    difference -- mean squared error. It's a real number. \n",
    "    \"\"\" \n",
    "    difference = tf.reduce_mean(tf.squared_difference(predictions, ratings))\n",
    "    return difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Matrix Factorization Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train the matrix factorization model and save the final user features and movie features value for DNN. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid loss at step 1: 14.087109\n",
      "valid loss at step 51: 2.435236\n",
      "valid loss at step 101: 1.049312\n",
      "valid loss at step 151: 0.908301\n",
      "valid loss at step 201: 0.866292\n",
      "valid loss at step 251: 0.844459\n",
      "valid loss at step 301: 0.827398\n",
      "valid loss at step 351: 0.812384\n",
      "valid loss at step 401: 0.799647\n",
      "valid loss at step 451: 0.789436\n",
      "valid loss at step 501: 0.781518\n",
      "valid loss at step 551: 0.775590\n",
      "valid loss at step 601: 0.771296\n",
      "valid loss at step 651: 0.768209\n",
      "valid loss at step 701: 0.765954\n",
      "valid loss at step 751: 0.764278\n",
      "valid loss at step 801: 0.763009\n",
      "valid loss at step 851: 0.762045\n",
      "valid loss at step 901: 0.761302\n",
      "valid loss at step 951: 0.760731\n",
      "valid loss at step 1001: 0.760300\n",
      "valid loss at step 1051: 0.759977\n",
      "valid loss at step 1101: 0.759739\n",
      "valid loss at step 1151: 0.759582\n",
      "valid loss at step 1201: 0.759484\n",
      "valid loss at step 1251: 0.759441\n",
      "valid loss at step 1301: 0.759444\n",
      "valid loss at step 1351: 0.759480\n",
      "features saved\n"
     ]
    }
   ],
   "source": [
    "emb_dim = 8\n",
    "mf_learning_rate = 50\n",
    "mf_epochs = 1400\n",
    "\n",
    "n_users = len(dataset.adj_userid.unique())\n",
    "n_movies = len(dataset.adj_movieid.unique())\n",
    "\n",
    "def mf_train(emb_dim, n_users, n_movies, trainset, validset, learning_rate, epochs):\n",
    "    with tf.Graph().as_default():\n",
    "        with tf.variable_scope(\"features\"):\n",
    "            usr_embs, mov_embs = initialize_features(len(dataset.adj_userid.unique()), len(dataset.adj_movieid.unique()), emb_dim)\n",
    "        with tf.variable_scope(\"train_set\"):\n",
    "            train_data = trainset[[\"adj_userid\", \"adj_movieid\", \"rating\"]].values.T #shape(3, 700146)\n",
    "            train_usr_ids, train_mov_ids, train_ratings = create_dataset(*train_data)# expend to 3 lists\n",
    "        with tf.variable_scope(\"valid_set\"):\n",
    "            valid_data = validset[[\"adj_userid\", \"adj_movieid\", \"rating\"]].values.T\n",
    "            valid_usr_ids, valid_mov_ids, valid_ratings = create_dataset(*valid_data)\n",
    "        with tf.variable_scope(\"training\"):\n",
    "            train_sel_usr_emb, train_sel_mov_emb = lookup_features(usr_embs, mov_embs, train_usr_ids, train_mov_ids)\n",
    "            train_preds = predict(train_sel_usr_emb, train_sel_mov_emb)\n",
    "            train_loss = mean_squared_difference(train_preds, train_ratings)\n",
    "            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "            train_usr_embs = optimizer.minimize(train_loss, var_list=[usr_embs])\n",
    "            train_mov_embs = optimizer.minimize(train_loss, var_list=[mov_embs])   \n",
    "        with tf.variable_scope(\"validation\"):\n",
    "            valid_sel_usr_emb, valid_sel_mov_emb = lookup_features(usr_embs, mov_embs, valid_usr_ids, valid_mov_ids)\n",
    "            valid_preds = predict(valid_sel_usr_emb, valid_sel_mov_emb)\n",
    "            valid_loss = mean_squared_difference(valid_preds, valid_ratings)\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            train_loss_history = []\n",
    "            valid_loss_history = []\n",
    "            for i in range(epochs):\n",
    "                current_train_loss, _ = sess.run([train_loss, train_usr_embs])\n",
    "                current_train_loss, _ = sess.run([train_loss, train_mov_embs])\n",
    "                current_valid_loss = sess.run(valid_loss)\n",
    "                if i%50 == 0:\n",
    "                    print(\"valid loss at step %i: %f\"%(i+1, current_valid_loss))\n",
    "                train_loss_history.append(current_train_loss)\n",
    "                valid_loss_history.append(current_valid_loss)\n",
    "            final_user_embeddings, final_movie_embeddings = sess.run([usr_embs, mov_embs])\n",
    "            final_valid_predictions = sess.run(valid_preds)\n",
    "    return (final_user_embeddings, final_movie_embeddings, train_loss_history, valid_loss_history, final_valid_predictions)\n",
    "\n",
    "mf_result = mf_train(emb_dim, n_users, n_movies, trainset, validset, mf_learning_rate, mf_epochs)\n",
    "mf_user_embs, mf_movie_embs, mf_tlh, mf_vlh, mf_fvp = mf_result\n",
    "\n",
    "np.save(\"mf_model/user_features\", mf_user_embs)\n",
    "np.save(\"mf_model/movie_features\", mf_movie_embs)\n",
    "\n",
    "print(\"features saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot the traing loss and valid loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vCNExIkQDAAAcGQjRMSJEAwAAHBkI0TEiRAMAABwZCNExIkQDAAAcGQjRMTKTsiqpWCZE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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6781d8c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(np.array(mf_tlh), color=\"red\", label=\"train loss\")\n",
    "plt.plot(np.array(mf_vlh), color=\"blue\", label=\"valid loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### build DNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some notation first:\n",
    " - We have layers $1,...,n$ of our neural network\n",
    " - $W_i$ and $b_i$ are the weights and biases for the $i^{th}$ layer of our DNN. $W_1$ is the weight matrix of first layer. $W_n$ is the weight matrix of last layer.\n",
    " - for the $i^{th}$ layer of neural network, if $d_{in}$ is the dimension of input layer, $d_{out}$ is the dimension of output layer, then the dimension of $W_i$ is [$d_{in}$, $d_{out}$] and the dimension of $b_i$ is [$1$, $d_{out}$]\n",
    " - $z_i$ is the linear unit of the output from the previous layer: $z_i$ = $a_{i-1}\\cdot W_i + b_i$\n",
    " - $a_i$ is the activation unit of $z_i$: if $f_i$ is the action function of ith layer: $a_i = f_i(z_i)$\n",
    " \n",
    "\n",
    "In order to build the DNN, the steps below are followed:\n",
    "1. load the movie and user embeddings from the results learned in matrix factorization model\n",
    "2. initialize the DNN parameters\n",
    "3. calculate the logits $z_n$ (linear output of last hidden layer)\n",
    "4. calculate the predictions $softmax(z_n)$\n",
    "5. calculate the loss\n",
    "6. train and validate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. load the movie and user embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_embeddings(user_embs_file, movie_embs_file):\n",
    "    \"\"\"\n",
    "    In the deep model we initialize the user embedding and movie embedding with value that learned from the matrix \n",
    "    factorization model. So here load already saved values. \n",
    "    \n",
    "    Arguments:\n",
    "    user_embs_file -- saved user embedding file name\n",
    "    movie_embs_file -- saved movie embedding file name\n",
    "    \n",
    "    Returns:\n",
    "    usr_embs -- initialized user embeddings array\n",
    "    mov_embs -- initialized user embeddings array\n",
    "    \"\"\"       \n",
    "\n",
    "    usr_embs_array = np.load(user_embs_file)\n",
    "    mov_embs_array = np.load(movie_embs_file)\n",
    "    usr_embs = tf.get_variable(\n",
    "        \"usr_embs\",\n",
    "        initializer = tf.constant_initializer(usr_embs_array),\n",
    "        shape = usr_embs_array.shape\n",
    "    )\n",
    "    mov_embs = tf.get_variable(\n",
    "        \"mov_embs\",\n",
    "        initializer = tf.constant_initializer(mov_embs_array),\n",
    "        shape = mov_embs_array.shape\n",
    "    )\n",
    "    return (usr_embs, mov_embs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. initialize the DNN parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "before start explain the forward propagation, w and b dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize_nn_params(net_dims):\n",
    "    \"\"\"\n",
    "    initializes parameter Ws and bs for the whole NN. \n",
    "\n",
    "    net_dims -- layer dimensions, it's a list looks like [dim_input, d_1, ..., d_n]\n",
    "\n",
    "    Returns:\n",
    "    params -- a dictionary of variables containing W1, b1, ..., Wn, bn\n",
    "    \"\"\"\n",
    "    \n",
    "    #We first zip the two lists of net_dims to get the list of tuples (d_in, d_out) for each layer.\n",
    "    #enumerate allows us to loop over something and have an automatic counter, in this case we use the counter as the \n",
    "    #index of W and b and we use the tuple (d_in, d_out) for the shape of our W and b.\n",
    "    layer_dims = list(zip(net_dims[:-1], net_dims[1:]))\n",
    "    params = {}\n",
    "    for i, (d_in, d_out) in enumerate(layer_dims):\n",
    "        params[\"W\"+str(i+1)] = tf.get_variable(\n",
    "            \"W\"+str(i+1), \n",
    "            [d_in, d_out], \n",
    "            initializer = tf.contrib.layers.xavier_initializer(seed =1)\n",
    "        )\n",
    "        params[\"b\"+str(i+1)] = tf.get_variable(\n",
    "            \"b\"+str(i+1), \n",
    "            [1,d_out], \n",
    "            initializer = tf.zeros_initializer()\n",
    "        )\n",
    "    return params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. calculate the linear ouput of the last linear unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_inputs(user_embeddings, movie_embeddings, user_ids, movie_ids):\n",
    "    \"\"\"\n",
    "    use the lookup_embeddings function defined above to look up the embeddings based on the ids for the whole dataset.\n",
    "    \n",
    "    Arguments:\n",
    "    user_embeddings -- shape [number of users, dim]\n",
    "    movie_embeddings -- shape [number of movie, dim]\n",
    "    user_ids -- user id variable, loaded user id from dataset\n",
    "    movie_ids -- movie id variable, loaded movie id from dataset\n",
    "    \n",
    "    Returns:\n",
    "    inputs --  input layer of the NN, shape [number of examples, dimension of the embeddings] \n",
    "    \"\"\"\n",
    "    sel_usr_embs, sel_mov_embs = lookup_embeddings(user_embeddings, movie_embeddings, user_ids, movie_ids)\n",
    "    inputs = sel_usr_embs * sel_mov_embs\n",
    "    return inputs\n",
    "\n",
    "def forward_propagate(inputs, params, acts, net_dims):\n",
    "    \"\"\"\n",
    "    propagate the inputs through the network: INPUTS -> LINEAR_1 -> ACT_1 -> ... -> LINEAR_N\n",
    "    \n",
    "    Arguments:\n",
    "    inputs -- the user and movie embedding\n",
    "    params -- python dictionary containing parameters W1, b1, ..., Wn, bn as returned by initialize_nn_params\n",
    "    acts -- list of activation functions, e.g.['tf.nn.relu', 'tf.nn.relu', 'None'] (None means linear activation)\n",
    "    net_dims -- layer dimensions, it's a list looks like [dim_input, d_1, ..., d_n]\n",
    "\n",
    "    Returns:\n",
    "    a -- the output of the last hidden layer\n",
    "    \"\"\"\n",
    "    a = inputs\n",
    "    n_layers = len(net_dims)-1\n",
    "    for i in range(n_layers):\n",
    "        W = params['W'+str(i+1)]\n",
    "        b = params['b'+str(i+1)]\n",
    "        z = tf.matmul(a, W) + b\n",
    "        a = acts[i](z) if acts[i] is not None else z\n",
    "    return a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. calculate the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_predict(logits):\n",
    "    \"\"\"\n",
    "    calculates predictions, wraps tf.nn.softmax for documentation purposes.\n",
    "    \n",
    "    Arguments:\n",
    "    logits -- tensor of logits, for example the (linear) output of the last hidden layer of a NN\n",
    "\n",
    "    Returns:\n",
    "    prediction -- softmax of logits, represents probability of each category, shape (number of examples, 5)\n",
    "    \"\"\"\n",
    "    return tf.nn.softmax(logits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. calculate the loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss is a combination of cross entropy and mean squared error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cross_entropy(logits, ratings):\n",
    "    \"\"\"\n",
    "    computes the cross entropy.\n",
    "    \n",
    "    Arguments:\n",
    "    logits -- tensor of logits, for example the (linear) output of the last hidden layer of a NN of\n",
    "              shape (number of examples, 5)\n",
    "    y -- \"true\" labels, densely encoded starting from 1, shape of (number of examples, 1). This will be our true\n",
    "              ratings, i.e. [4,5,3,5,1,...]\n",
    "    \n",
    "    Returns:\n",
    "    cross_entropy - scalar, mean cross entropy across examples\n",
    "    \"\"\"\n",
    "    labels = ratings-1\n",
    "    \n",
    "    cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n",
    "    \n",
    "    return cross_entropy\n",
    "\n",
    "def mean_squared_error(predictions, ratings):\n",
    "    \"\"\"\n",
    "    computes the mean squared error.\n",
    "     \n",
    "     \n",
    "    Arguments:\n",
    "    prediction -- predicted value, shape (number of examples, 5)\n",
    "    rating -- \"true\" labels, shape (number of examples, 5)\n",
    "    \n",
    "    Returns:\n",
    "    mean_distance -- mean squared error \n",
    "    \"\"\"\n",
    "    \n",
    "    #tf.expand_dims returns a tensor with the same data as input, but its shape has an additional \n",
    "    #dimension of size 1 added. e.g ratings=[1,1] (rank 1), tf.expand_dims(ratings,-1)=[[1][1]] (rank 2).\n",
    "    #tf.tile creates a new tensor by replicating input multiples times. e.g. tf.tile([[1][1]], [1,5]) allows\n",
    "    #us to replicate the value 5 times on the 2nd axis. \n",
    "    #[[1 1 1 1 1]\n",
    "    # [1 1 1 1 1]]\n",
    "    #distance: the further away it is, it will be assigned a big number and it's a non-linear increase, we used square.\n",
    "    #[[ 0  1  4  9 16]\n",
    "    #[ 0  1  4  9 16]]\n",
    "    #\n",
    "    #predictions: \n",
    "    #[[0.,  0.,  0.,  0.4, 0.6]\n",
    "    #[ 0.2, 0.7, 0.1, 0,   0  ]] \n",
    "    #this means the model predicts 5, 2\n",
    "    #\n",
    "    #let's multiply predictions and distance:\n",
    "    #\n",
    "    #[[ 0.          0.          0.          3.60000014  9.60000038]\n",
    "    #[ 0.          0.69999999  0.40000001  0.          0.        ]]\n",
    "    stars = tf.range(1,6)\n",
    "    distances = (stars - tf.tile(tf.expand_dims(ratings, -1), [1, 5]))**2 \n",
    "    mean_distance = tf.reduce_mean(tf.reduce_sum(predictions * tf.cast(distances, tf.float32), axis=-1))\n",
    "    return mean_distance\n",
    "\n",
    "def mean_squared_error2(predictions, ratings):\n",
    "    \"\"\"\n",
    "    computes the mean squared error.\n",
    "    \n",
    "    Arguments:\n",
    "    prediction -- predicted value, shape (number of examples, 5)\n",
    "    rating -- \"true\" labels, shape (number of examples, 5)\n",
    "    \n",
    "    Returns:\n",
    "    mean_squared_error - mean squared error\n",
    "    \"\"\"\n",
    "    mean_squared_error=tf.reduce_mean(tf.squared_difference(tf.cast(tf.argmax(predictions, axis = 1),tf.float32), \n",
    "                                                            tf.cast(ratings-1, tf.float32)))\n",
    "    return mean_squared_error\n",
    "\n",
    "def dfm_loss(logits, ratings, params, alpha, beta):\n",
    "    \"\"\"\n",
    "    combines the mean squared error, cross entropy and regularization term to compute the total loss. \n",
    "    \n",
    "    Arguments:\n",
    "    logits -- output of forward propagation (output of the last LINEAR unit), of shape (number of examples, 5)\n",
    "    rating -- \"true\" labels, shape (number of examples, 1)\n",
    "    params -- a dictionary of variables containing W1, b1,...Wn, bn\n",
    "    alpha -- hyper-parameter, controls the contribution of two loss functions\n",
    "    beta --  hyper-parameter for the regularization term\n",
    "    \n",
    "    Returns:\n",
    "    cost - total loss\n",
    "    \"\"\"\n",
    "    predictions = calc_predict(logits)\n",
    "    loss = alpha * mean_squared_error(predictions, ratings) + (1-alpha) * cross_entropy(logits, ratings)\n",
    "    \n",
    "    reg = 0\n",
    "    for n, p in params.items():\n",
    "        reg = reg + tf.reduce_mean(p**2)\n",
    "    return loss + beta*reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. train and validate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: 1.877511/1.619234; 2: 1.428925/1.458286; 3: 1.349173/1.424797; 4: 1.324164/1.408225; 5: 1.311318/1.398968; 6: 1.303878/1.393317; 7: 1.299159/1.389538; 8: 1.295935/1.386855; 9: 1.293601/1.384878; 10: 1.291840/1.383357; 11: 1.290469/1.382157; 12: 1.289379/1.381186; 13: 1.288496/1.380387; 14: 1.287755/1.379701; 15: 1.287116/1.379100; 16: 1.286603/1.378624; 17: 1.286204/1.378261; 18: 1.285897/1.377973; 19: 1.285661/1.377757; 20: 1.285471/1.377588; 21: 1.285321/1.377441; 22: 1.285204/1.377330; 23: 1.285101/1.377238; 24: 1.285016/1.377164; 25: 1.284943/1.377100; 26: 1.284883/1.377043; 27: 1.284832/1.377000; 28: 1.284788/1.376953; 29: 1.284750/1.376920; 30: 1.284716/1.376890; 31: 1.284686/1.376867; 32: 1.284661/1.376843; 33: 1.284638/1.376823; 34: 1.284619/1.376802; 35: 1.284603/1.376788; 36: 1.284587/1.376770; 37: 1.284575/1.376755; 38: 1.284561/1.376746; 39: 1.284550/1.376737; 40: 1.284540/1.376728; 41: 1.284531/1.376721; 42: 1.284522/1.376714; 43: 1.284514/1.376708; 44: 1.284507/1.376703; 45: 1.284501/1.376701; 46: 1.284495/1.376694; 47: 1.284491/1.376693; 48: 1.284486/1.376689; 49: 1.284481/1.376687; 50: 1.284477/1.376682; 51: 1.283772/1.374165; 52: 1.282068/1.371945; 53: 1.280584/1.369890; 54: 1.279219/1.367975; 55: 1.277956/1.366181; 56: 1.276779/1.364502; 57: 1.275676/1.362911; 58: 1.274637/1.361403; 59: 1.273651/1.359963; 60: 1.272714/1.358587; 61: 1.271818/1.357270; 62: 1.270959/1.356009; 63: 1.270132/1.354796; 64: 1.269335/1.353624; 65: 1.268564/1.352493; 66: 1.267817/1.351400; 67: 1.267092/1.350340; 68: 1.266386/1.349311; 69: 1.265698/1.348311; 70: 1.265028/1.347337; 71: 1.264372/1.346385; 72: 1.263731/1.345458; 73: 1.263104/1.344553; 74: 1.262489/1.343672; 75: 1.261886/1.342807; 76: 1.261293/1.341961; 77: 1.260713/1.341131; 78: 1.260142/1.340320; 79: 1.259580/1.339523; 80: 1.259027/1.338741; 81: 1.258483/1.337971; 82: 1.257948/1.337215; 83: 1.257419/1.336473; 84: 1.256899/1.335745; 85: 1.256386/1.335026; 86: 1.255880/1.334318; 87: 1.255380/1.333623; 88: 1.254887/1.332939; 89: 1.254401/1.332263; 90: 1.253920/1.331598; 91: 1.253445/1.330940; 92: 1.252976/1.330293; 93: 1.252512/1.329655; 94: 1.252053/1.329025; 95: 1.251599/1.328403; 96: 1.251150/1.327788; 97: 1.250706/1.327183; 98: 1.250267/1.326585; 99: 1.249833/1.325991; 100: 1.249403/1.325405; 101: 1.248978/1.324827; 102: 1.248556/1.324255; 103: 1.248139/1.323689; 104: 1.247726/1.323130; 105: 1.247317/1.322577; 106: 1.246912/1.322027; 107: 1.246511/1.321483; 108: 1.246113/1.320947; 109: 1.245720/1.320415; 110: 1.245330/1.319890; 111: 1.244943/1.319370; 112: 1.244560/1.318857; 113: 1.244181/1.318349; 114: 1.243805/1.317844; 115: 1.243432/1.317345; 116: 1.243062/1.316850; 117: 1.242695/1.316360; 118: 1.242332/1.315874; 119: 1.241972/1.315394; 120: 1.241614/1.314917; 121: 1.241260/1.314445; 122: 1.240909/1.313976; 123: 1.240560/1.313512; 124: 1.240215/1.313053; 125: 1.239872/1.312598; 126: 1.239532/1.312148; 127: 1.239195/1.311700; 128: 1.238860/1.311257; 129: 1.238528/1.310817; 130: 1.238199/1.310381; 131: 1.237872/1.309947; 132: 1.237548/1.309518; 133: 1.237226/1.309090; 134: 1.236907/1.308668; 135: 1.236590/1.308249; 136: 1.236276/1.307835; 137: 1.235964/1.307420; 138: 1.235654/1.307012; 139: 1.235347/1.306606; 140: 1.235041/1.306204; 141: 1.234739/1.305805; 142: 1.234438/1.305408; 143: 1.234139/1.305016; 144: 1.233843/1.304627; 145: 1.233549/1.304239; 146: 1.233257/1.303856; 147: 1.232967/1.303475; 148: 1.232679/1.303096; 149: 1.232393/1.302721; 150: 1.232108/1.302349; 151: 1.231827/1.301979; 152: 1.231546/1.301610; 153: 1.231267/1.301246; 154: 1.230991/1.300884; 155: 1.230716/1.300525; 156: 1.230443/1.300167; 157: 1.230173/1.299814; 158: 1.229903/1.299462; 159: 1.229636/1.299113; 160: 1.229371/1.298766; 161: 1.229107/1.298421; 162: 1.228845/1.298078; 163: 1.228585/1.297739; 164: 1.228326/1.297402; 165: 1.228070/1.297070; 166: 1.227814/1.296738; 167: 1.227561/1.296408; 168: 1.227308/1.296080; 169: 1.227058/1.295754; 170: 1.226809/1.295431; 171: 1.226561/1.295109; 172: 1.226315/1.294791; 173: 1.226071/1.294474; 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      "689: 1.173948/1.234519; 690: 1.173901/1.234478; 691: 1.173856/1.234435; 692: 1.173810/1.234391; 693: 1.173765/1.234348; 694: 1.173720/1.234308; 695: 1.173675/1.234268; 696: 1.173630/1.234228; 697: 1.173585/1.234186; 698: 1.173540/1.234145; 699: 1.173495/1.234102; 700: 1.173451/1.234060; 701: 1.173406/1.234021; 702: 1.173362/1.233981; 703: 1.173317/1.233942; 704: 1.173272/1.233902; 705: 1.173227/1.233859; 706: 1.173184/1.233817; 707: 1.173140/1.233776; 708: 1.173096/1.233736; 709: 1.173052/1.233697; 710: 1.173008/1.233659; 711: 1.172964/1.233620; 712: 1.172920/1.233580; 713: 1.172876/1.233539; 714: 1.172833/1.233497; 715: 1.172790/1.233458; 716: 1.172746/1.233419; 717: 1.172703/1.233383; 718: 1.172660/1.233345; 719: 1.172616/1.233305; 720: 1.172573/1.233264; 721: 1.172530/1.233224; 722: 1.172488/1.233185; 723: 1.172445/1.233147; 724: 1.172403/1.233112; 725: 1.172360/1.233075; 726: 1.172317/1.233037; 727: 1.172275/1.232998; 728: 1.172232/1.232957; 729: 1.172190/1.232919; 730: 1.172148/1.232881; 731: 1.172106/1.232844; 732: 1.172064/1.232809; 733: 1.172021/1.232772; 734: 1.171979/1.232734; 735: 1.171937/1.232693; 736: 1.171895/1.232655; 737: 1.171854/1.232616; 738: 1.171812/1.232581; 739: 1.171771/1.232546; 740: 1.171729/1.232511; 741: 1.171687/1.232473; 742: 1.171646/1.232433; 743: 1.171605/1.232395; 744: 1.171564/1.232358; 745: 1.171523/1.232322; 746: 1.171482/1.232288; 747: 1.171441/1.232252; 748: 1.171399/1.232216; 749: 1.171358/1.232178; 750: 1.171317/1.232140; 751: 1.171277/1.232102; 752: 1.171236/1.232065; 753: 1.171195/1.232032; 754: 1.171155/1.231996; 755: 1.171114/1.231964; 756: 1.171074/1.231926; 757: 1.171033/1.231890; 758: 1.170993/1.231851; 759: 1.170953/1.231815; 760: 1.170913/1.231781; 761: 1.170873/1.231748; 762: 1.170833/1.231713; 763: 1.170793/1.231678; 764: 1.170753/1.231641; 765: 1.170714/1.231604; 766: 1.170675/1.231568; 767: 1.170635/1.231536; 768: 1.170596/1.231503; 769: 1.170556/1.231470; 770: 1.170516/1.231434; 771: 1.170476/1.231398; 772: 1.170437/1.231363; 773: 1.170398/1.231326; 774: 1.170359/1.231293; 775: 1.170320/1.231261; 776: 1.170280/1.231229; 777: 1.170241/1.231195; 778: 1.170203/1.231160; 779: 1.170164/1.231122; 780: 1.170126/1.231087; 781: 1.170087/1.231055; 782: 1.170049/1.231025; 783: 1.170010/1.230993; 784: 1.169972/1.230957; 785: 1.169933/1.230924; 786: 1.169894/1.230888; 787: 1.169856/1.230853; 788: 1.169818/1.230819; 789: 1.169780/1.230789; 790: 1.169742/1.230756; 791: 1.169704/1.230724; 792: 1.169665/1.230691; 793: 1.169627/1.230656; 794: 1.169590/1.230621; 795: 1.169552/1.230588; 796: 1.169515/1.230556; 797: 1.169477/1.230527; 798: 1.169440/1.230495; 799: 1.169402/1.230463; 800: 1.169364/1.230428; 801: 1.169327/1.230395; 802: 1.169290/1.230360; 803: 1.169252/1.230327; 804: 1.169215/1.230297; 805: 1.169178/1.230265; 806: 1.169140/1.230237; 807: 1.169103/1.230202; 808: 1.169066/1.230168; 809: 1.169029/1.230134; 810: 1.168993/1.230101; 811: 1.168956/1.230070; 812: 1.168920/1.230044; 813: 1.168883/1.230014; 814: 1.168846/1.229981; 815: 1.168809/1.229948; 816: 1.168773/1.229917; 817: 1.168737/1.229885; 818: 1.168701/1.229853; 819: 1.168665/1.229824; 820: 1.168628/1.229795; 821: 1.168592/1.229765; 822: 1.168556/1.229732; 823: 1.168519/1.229701; 824: 1.168483/1.229669; 825: 1.168448/1.229637; 826: 1.168412/1.229604; 827: 1.168377/1.229576; 828: 1.168341/1.229549; 829: 1.168305/1.229519; 830: 1.168269/1.229488; 831: 1.168233/1.229456; 832: 1.168198/1.229422; 833: 1.168163/1.229391; 834: 1.168127/1.229360; 835: 1.168092/1.229334; 836: 1.168056/1.229305; 837: 1.168021/1.229276; 838: 1.167985/1.229244; 839: 1.167950/1.229213; 840: 1.167915/1.229178; 841: 1.167881/1.229148; 842: 1.167846/1.229121; 843: 1.167811/1.229095; 844: 1.167776/1.229066; 845: 1.167741/1.229038; 846: 1.167706/1.229007; 847: 1.167671/1.228976; 848: 1.167636/1.228943; 849: 1.167602/1.228914; 850: 1.167568/1.228884; 851: 1.167533/1.228859; 852: 1.167498/1.228832; 853: 1.167464/1.228804; 854: 1.167429/1.228771; 855: 1.167395/1.228739; 856: 1.167361/1.228709; 857: 1.167327/1.228678; 858: 1.167293/1.228653; 859: 1.167259/1.228627; 860: 1.167224/1.228600; 861: 1.167190/1.228570; 862: 1.167156/1.228539; 863: 1.167122/1.228508; 864: 1.167089/1.228477; 865: 1.167056/1.228448; 866: 1.167022/1.228422; 867: 1.166988/1.228397; 868: 1.166954/1.228371; 869: 1.166921/1.228341; 870: 1.166886/1.228310; 871: 1.166853/1.228283; 872: 1.166820/1.228254; 873: 1.166787/1.228223; 874: 1.166753/1.228197; 875: 1.166720/1.228172; 876: 1.166686/1.228147; 877: 1.166653/1.228118; 878: 1.166619/1.228088; 879: 1.166585/1.228059; 880: 1.166553/1.228030; 881: 1.166521/1.228001; 882: 1.166488/1.227975; 883: 1.166454/1.227950; 884: 1.166421/1.227926; 885: 1.166388/1.227898; 886: 1.166355/1.227868; 887: 1.166321/1.227840; 888: 1.166289/1.227810; 889: 1.166256/1.227782; 890: 1.166224/1.227756; 891: 1.166192/1.227733; 892: 1.166159/1.227707; 893: 1.166125/1.227681; 894: 1.166092/1.227652; 895: 1.166060/1.227622; 896: 1.166028/1.227592; 897: 1.165996/1.227566; 898: 1.165964/1.227542; 899: 1.165931/1.227518; 900: 1.165899/1.227492; 901: 1.165866/1.227464; 902: 1.165833/1.227434; 903: 1.165802/1.227407; 904: 1.165770/1.227377; 905: 1.165739/1.227354; 906: 1.165707/1.227331; 907: 1.165675/1.227306; 908: 1.165642/1.227282; 909: 1.165610/1.227254; 910: 1.165578/1.227226; 911: 1.165547/1.227197; 912: 1.165515/1.227170; 913: 1.165484/1.227144; 914: 1.165453/1.227121; 915: 1.165421/1.227097; 916: 1.165388/1.227074; 917: 1.165356/1.227046; 918: 1.165325/1.227017; 919: 1.165294/1.226989; 920: 1.165263/1.226965; 921: 1.165233/1.226942; 922: 1.165201/1.226919; 923: 1.165169/1.226897; 924: 1.165138/1.226871; 925: 1.165106/1.226844; 926: 1.165075/1.226815; 927: 1.165045/1.226788; 928: 1.165014/1.226765; 929: 1.164983/1.226743; 930: 1.164952/1.226719; 931: 1.164920/1.226696; 932: 1.164889/1.226669; 933: 1.164858/1.226642; 934: 1.164828/1.226614; 935: 1.164797/1.226588; 936: 1.164767/1.226566; 937: 1.164736/1.226542; 938: 1.164705/1.226520; 939: 1.164674/1.226497; 940: 1.164643/1.226471; 941: 1.164613/1.226443; 942: 1.164582/1.226416; 943: 1.164553/1.226392; 944: 1.164523/1.226368; 945: 1.164492/1.226347; 946: 1.164461/1.226326; 947: 1.164431/1.226300; 948: 1.164401/1.226275; 949: 1.164371/1.226248; 950: 1.164341/1.226222; 951: 1.164312/1.226198; 952: 1.164282/1.226175; 953: 1.164252/1.226156; 954: 1.164221/1.226132; 955: 1.164191/1.226107; 956: 1.164161/1.226082; 957: 1.164131/1.226054; 958: 1.164102/1.226027; 959: 1.164073/1.226004; 960: 1.164043/1.225982; 961: 1.164013/1.225961; 962: 1.163984/1.225939; 963: 1.163954/1.225915; 964: 1.163924/1.225890; 965: 1.163895/1.225862; 966: 1.163866/1.225837; 967: 1.163838/1.225814; 968: 1.163809/1.225793; 969: 1.163779/1.225773; 970: 1.163750/1.225751; 971: 1.163720/1.225728; 972: 1.163691/1.225702; 973: 1.163662/1.225675; 974: 1.163633/1.225651; 975: 1.163605/1.225627; 976: 1.163576/1.225606; 977: 1.163547/1.225588; 978: 1.163518/1.225568; 979: 1.163489/1.225545; 980: 1.163460/1.225518; 981: 1.163431/1.225492; 982: 1.163403/1.225467; 983: 1.163375/1.225443; 984: 1.163346/1.225424; 985: 1.163318/1.225403; 986: 1.163288/1.225384; 987: 1.163260/1.225361; 988: 1.163231/1.225332; 989: 1.163203/1.225307; 990: 1.163175/1.225284; 991: 1.163147/1.225260; 992: 1.163119/1.225242; 993: 1.163091/1.225223; 994: 1.163062/1.225204; 995: 1.163034/1.225178; 996: 1.163005/1.225153; 997: 1.162977/1.225128; 998: 1.162950/1.225104; 999: 1.162922/1.225081; 1000: 1.162894/1.225062; done!\n"
     ]
    }
   ],
   "source": [
    "user_embs_file = \"mf_model/user_features.npy\"\n",
    "movie_embs_file = \"mf_model/movie_features.npy\"\n",
    "\n",
    "bs = 4096\n",
    "epochs = 1000\n",
    "end_to_end_start = 50\n",
    "nn_lr = 0.001\n",
    "eb_lr = 0.0001\n",
    "alpha = 0.5\n",
    "beta = 1.75\n",
    "net_dims = [8, 64, 32, 16, 5]\n",
    "acts = [tf.nn.relu, tf.nn.relu, tf.nn.relu, None]\n",
    "\n",
    "with tf.Graph().as_default():\n",
    "    tf.set_random_seed(seed=123)\n",
    "    with tf.variable_scope(\"embeddings\"):\n",
    "        usr_embs, mov_embs = load_embeddings(user_embs_file, movie_embs_file)\n",
    "    with tf.variable_scope(\"neural_network\"):\n",
    "        params = initialize_nn_params(net_dims)\n",
    "    with tf.variable_scope(\"train_set\"):\n",
    "        train_data = trainset[[\"adj_userid\", \"adj_movieid\", \"rating\"]].values.T\n",
    "        train_examples = create_dataset(*train_data)\n",
    "        train_batches = tf.contrib.data.Dataset.from_tensor_slices(train_examples).batch(bs).cache(\"ds_cache\")\n",
    "        train_iterator = train_batches.make_initializable_iterator()\n",
    "        train_usr_id_bs, train_mov_id_bs, train_rating_bs = train_iterator.get_next()\n",
    "    with tf.variable_scope(\"valid_set\"):\n",
    "        valid_data = validset[[\"adj_userid\", \"adj_movieid\", \"rating\"]].values.T\n",
    "        valid_usr_ids, valid_mov_ids, valid_ratings = create_dataset(*valid_data)\n",
    "    with tf.variable_scope(\"training\"):\n",
    "        train_inputs = create_inputs(usr_embs, mov_embs, train_usr_id_bs, train_mov_id_bs)\n",
    "        train_logits_bs = forward_propagate(train_inputs, params, acts, net_dims)\n",
    "        train_loss = dfm_loss(train_logits_bs, train_rating_bs, params, alpha, beta)\n",
    "        train_step_nn = tf.train.AdamOptimizer(learning_rate=nn_lr).minimize(train_loss, var_list=list(params.values()))\n",
    "        train_step_eb = tf.train.AdamOptimizer(learning_rate=eb_lr).minimize(train_loss, var_list=[list(params.values())+[usr_embs, mov_embs]])\n",
    "    with tf.variable_scope(\"validation\"):\n",
    "        valid_inputs = create_inputs(usr_embs, mov_embs, valid_usr_ids, valid_mov_ids)\n",
    "        valid_logits = forward_propagate(valid_inputs, params, acts, net_dims)\n",
    "        valid_preds = calc_predict(valid_logits)\n",
    "        valid_loss = dfm_loss(valid_logits, valid_ratings, params, alpha, beta)\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        sess.run(train_iterator.initializer)\n",
    "        train_loss_history = []\n",
    "        valid_loss_history = []\n",
    "        for epoch in range(epochs):\n",
    "            try:\n",
    "                train_epoch_loss_history = []\n",
    "                while True:\n",
    "                    if epoch < end_to_end_start:\n",
    "                        current_train_loss = sess.run([train_loss, train_step_nn])[0]\n",
    "                    else:\n",
    "                        current_train_loss = sess.run([train_loss, train_step_eb])[0]\n",
    "                    train_epoch_loss_history.append(current_train_loss)\n",
    "            except tf.errors.OutOfRangeError:\n",
    "                train_epoch_loss = np.mean(train_epoch_loss_history)\n",
    "                train_loss_history.append(train_epoch_loss)\n",
    "                valid_epoch_loss = sess.run(valid_loss)\n",
    "                valid_loss_history.append(valid_epoch_loss)\n",
    "                print(\"%i: %f/%f\"%(epoch+1, train_epoch_loss, valid_epoch_loss), end=\"; \")\n",
    "                sess.run(train_iterator.initializer)\n",
    "        print(\"done!\")\n",
    "        final_valid_predictions = sess.run(valid_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNN Accuracy: 50.740345%\n"
     ]
    }
   ],
   "source": [
    "dnn_accuracy = np.sum(np.argmax(final_valid_predictions, axis=-1)+1 == validset.rating.values) / len(final_valid_predictions)\n",
    "print(\"DNN Accuracy: %f%%\"%(dnn_accuracy*100,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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42cBPSpy9Yqtmr9gabFEAAAD4QqD2yBirPH5cAAAAKISE6JEJoUMNAACA2Eeg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\nUID/A+iPrk/iUiunAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6781da0518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(train_loss_history, color=\"red\", label=\"train loss\")\n",
    "plt.plot(valid_loss_history, color=\"blue\", label=\"valid loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.axvline(end_to_end_start-1, ls = \"--\", label=\"end-to-end start\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eAgMAAAAYvUHvgQFj8p8/+OnsvfnRua/7WPvfedXPzH1ZAACATUWBwZbx8MNH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f671f5ab160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols, n_rows = 5, 6\n",
    "n = n_cols * n_rows\n",
    "ratings = validset.rating.values[:n]\n",
    "probs = final_valid_predictions[:n]\n",
    "\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols*3, n_rows*3), sharex=True, sharey=True)\n",
    "for ax, r, ps  in zip(axs.flatten(), ratings, probs):\n",
    "    for i, p in enumerate(ps):\n",
    "        ax.set_xlabel(\"rating\")\n",
    "        ax.set_ylabel(\"probability\")\n",
    "        ax.bar(r, 1, .9, color=\"white\", edgecolor=\"green\", alpha=.1, zorder=1)\n",
    "        ax.bar(i+1, p, .9, color=\"blue\", alpha=.5, edgecolor=\"black\", zorder=2)\n",
    "        ax.set_xticks(np.arange(1, 6))\n",
    "    #ax.bar(np.argmax(ps, axis=-1)+1, 1, .9, color=\"red\", edgecolor=\"red\", alpha=.1, zorder=1)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>userid</th>\n",
       "      <th>movieid</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>occ_desc</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "      <th>rating</th>\n",
       "      <th>prediction</th>\n",
       "      <th>rating 1</th>\n",
       "      <th>rating 2</th>\n",
       "      <th>rating 3</th>\n",
       "      <th>rating 4</th>\n",
       "      <th>rating 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>607758</th>\n",
       "      <td>M</td>\n",
       "      <td>5256</td>\n",
       "      <td>2245</td>\n",
       "      <td>25-34</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Working Girl (1988)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>0.041364</td>\n",
       "      <td>0.553513</td>\n",
       "      <td>0.384811</td>\n",
       "      <td>0.018776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327696</th>\n",
       "      <td>F</td>\n",
       "      <td>4918</td>\n",
       "      <td>1223</td>\n",
       "      <td>18-24</td>\n",
       "      <td>programmer</td>\n",
       "      <td>Grand Day Out, A (1992)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>0.063851</td>\n",
       "      <td>0.488759</td>\n",
       "      <td>0.443373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928026</th>\n",
       "      <td>M</td>\n",
       "      <td>5964</td>\n",
       "      <td>3544</td>\n",
       "      <td>18-24</td>\n",
       "      <td>customer service</td>\n",
       "      <td>Shakes the Clown (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.011993</td>\n",
       "      <td>0.109099</td>\n",
       "      <td>0.489936</td>\n",
       "      <td>0.387670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91117</th>\n",
       "      <td>M</td>\n",
       "      <td>3111</td>\n",
       "      <td>339</td>\n",
       "      <td>18-24</td>\n",
       "      <td>college/grad student</td>\n",
       "      <td>While You Were Sleeping (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.038992</td>\n",
       "      <td>0.241403</td>\n",
       "      <td>0.599990</td>\n",
       "      <td>0.111933</td>\n",
       "      <td>0.007682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931648</th>\n",
       "      <td>M</td>\n",
       "      <td>3280</td>\n",
       "      <td>3555</td>\n",
       "      <td>25-34</td>\n",
       "      <td>executive/managerial</td>\n",
       "      <td>U-571 (2000)</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.088169</td>\n",
       "      <td>0.333843</td>\n",
       "      <td>0.487353</td>\n",
       "      <td>0.082623</td>\n",
       "      <td>0.008012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320177</th>\n",
       "      <td>M</td>\n",
       "      <td>272</td>\n",
       "      <td>1215</td>\n",
       "      <td>18-24</td>\n",
       "      <td>other or not specified</td>\n",
       "      <td>Army of Darkness (1993)</td>\n",
       "      <td>Action|Adventure|Comedy|Horror|Sci-Fi</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.009777</td>\n",
       "      <td>0.240133</td>\n",
       "      <td>0.749766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265192</th>\n",
       "      <td>M</td>\n",
       "      <td>5977</td>\n",
       "      <td>1082</td>\n",
       "      <td>35-44</td>\n",
       "      <td>academic/educator</td>\n",
       "      <td>Candidate, The (1972)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.150173</td>\n",
       "      <td>0.630912</td>\n",
       "      <td>0.209190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556962</th>\n",
       "      <td>M</td>\n",
       "      <td>4524</td>\n",
       "      <td>2028</td>\n",
       "      <td>25-34</td>\n",
       "      <td>artist</td>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.010349</td>\n",
       "      <td>0.276956</td>\n",
       "      <td>0.712436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63854</th>\n",
       "      <td>M</td>\n",
       "      <td>1737</td>\n",
       "      <td>240</td>\n",
       "      <td>35-44</td>\n",
       "      <td>writer</td>\n",
       "      <td>Hideaway (1995)</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.002330</td>\n",
       "      <td>0.042700</td>\n",
       "      <td>0.463205</td>\n",
       "      <td>0.453283</td>\n",
       "      <td>0.038483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991195</th>\n",
       "      <td>F</td>\n",
       "      <td>1203</td>\n",
       "      <td>3882</td>\n",
       "      <td>25-34</td>\n",
       "      <td>academic/educator</td>\n",
       "      <td>Bring It On (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.008227</td>\n",
       "      <td>0.319658</td>\n",
       "      <td>0.641761</td>\n",
       "      <td>0.030230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender  userid  movieid age_desc                occ_desc  \\\n",
       "607758      M    5256     2245    25-34           self-employed   \n",
       "327696      F    4918     1223    18-24              programmer   \n",
       "928026      M    5964     3544    18-24        customer service   \n",
       "91117       M    3111      339    18-24    college/grad student   \n",
       "931648      M    3280     3555    25-34    executive/managerial   \n",
       "320177      M     272     1215    18-24  other or not specified   \n",
       "265192      M    5977     1082    35-44       academic/educator   \n",
       "556962      M    4524     2028    25-34                  artist   \n",
       "63854       M    1737      240    35-44                  writer   \n",
       "991195      F    1203     3882    25-34       academic/educator   \n",
       "\n",
       "                                 title                                  genre  \\\n",
       "607758             Working Girl (1988)                           Comedy|Drama   \n",
       "327696         Grand Day Out, A (1992)                       Animation|Comedy   \n",
       "928026         Shakes the Clown (1991)                                 Comedy   \n",
       "91117   While You Were Sleeping (1995)                         Comedy|Romance   \n",
       "931648                    U-571 (2000)                        Action|Thriller   \n",
       "320177         Army of Darkness (1993)  Action|Adventure|Comedy|Horror|Sci-Fi   \n",
       "265192           Candidate, The (1972)                                  Drama   \n",
       "556962      Saving Private Ryan (1998)                       Action|Drama|War   \n",
       "63854                  Hideaway (1995)                               Thriller   \n",
       "991195              Bring It On (2000)                                 Comedy   \n",
       "\n",
       "        rating  prediction  rating 1  rating 2  rating 3  rating 4  rating 5  \n",
       "607758       4           3  0.001536  0.041364  0.553513  0.384811  0.018776  \n",
       "327696       5           4  0.000227  0.003790  0.063851  0.488759  0.443373  \n",
       "928026       4           4  0.001301  0.011993  0.109099  0.489936  0.387670  \n",
       "91117        5           3  0.038992  0.241403  0.599990  0.111933  0.007682  \n",
       "931648       3           3  0.088169  0.333843  0.487353  0.082623  0.008012  \n",
       "320177       5           5  0.000012  0.000311  0.009777  0.240133  0.749766  \n",
       "265192       5           4  0.000500  0.009225  0.150173  0.630912  0.209190  \n",
       "556962       4           5  0.000007  0.000253  0.010349  0.276956  0.712436  \n",
       "63854        4           3  0.002330  0.042700  0.463205  0.453283  0.038483  \n",
       "991195       4           4  0.000125  0.008227  0.319658  0.641761  0.030230  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = validset[[\"gender\", \"userid\",\"movieid\",\"age_desc\",\"occ_desc\", \"title\", \"genre\", \"rating\"]].copy()\n",
    "results[\"prediction\"] = final_valid_predictions.argmax(axis=1)+1\n",
    "results[\"rating 1\"] = final_valid_predictions[:,0]\n",
    "results[\"rating 2\"] = final_valid_predictions[:,1]\n",
    "results[\"rating 3\"] = final_valid_predictions[:,2]\n",
    "results[\"rating 4\"] = final_valid_predictions[:,3]\n",
    "results[\"rating 5\"] = final_valid_predictions[:,4]\n",
    "results.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_recall(prediction_col, target_col):\n",
    "    recall=[]\n",
    "    for i in range(5):\n",
    "        rating_df = results[results[target_col]==i+1]\n",
    "        num_true_rating = len(rating_df)+0.0\n",
    "        current_recall = (len(rating_df[rating_df[prediction_col]==i+1]))/num_true_rating\n",
    "        recall.append(current_recall)\n",
    "    return recall\n",
    "\n",
    "def compute_precision(prediction_col, target_col): \n",
    "    precision=[]\n",
    "    for i in range(5):\n",
    "        pred_df = results[results[prediction_col]==i+1]\n",
    "        pred_rating = len(pred_df)+0.0\n",
    "        current_precision = (len(pred_df[pred_df[target_col]==i+1]))/pred_rating\n",
    "        precision.append(current_precision)\n",
    "    return precision    \n",
    "\n",
    "def compute_mae(prediction_col, target_col):\n",
    "    return np.mean(np.abs(results[prediction_col]-results[target_col]))\n",
    "\n",
    "def compute_rmse(prediction_col, target_col):\n",
    "    return np.sqrt(1/len(results)*np.sum((results[prediction_col]- results[target_col])**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.27154839093450717,\n",
       " 0.22613703210625716,\n",
       " 0.5351970575464228,\n",
       " 0.6565219050526637,\n",
       " 0.4372816392717624]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_recall('prediction', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6939799331103679,\n",
       " 0.39378908777226657,\n",
       " 0.44853421241343877,\n",
       " 0.49884737505980603,\n",
       " 0.6809687786960514]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_precision('prediction', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5658711670549185"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_mae('prediction', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8533055876670943"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_rmse('prediction', 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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