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     "source": [
      "# Matrix Factorization (MF) Example\n",
      "Demonstrates matrix factorization with MXNet on the [MovieLens 100k](http://grouplens.org/datasets/movielens/100k/) dataset. \n",
      "\n",
      "You need to have python package pandas and bokeh installed (pip install pandas bokeh)."
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "import mxnet as mx\n",
      "from movielens_data import get_data_iter, max_id\n",
      "from matrix_fact import train"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "# If MXNet is not compiled with GPU support (e.g. on OSX), set to [mx.cpu(0)]\n",
      "# Can be changed to [mx.gpu(0), mx.gpu(1), ..., mx.gpu(N-1)] if there are N GPUs\n",
      "ctx = [mx.gpu(0)]"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "train_test_data = get_data_iter(batch_size=50)\n",
      "max_user, max_item = max_id('./ml-100k/u.data')\n",
      "(max_user, max_item)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Linear MF"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "def plain_net(k):\n",
      "    # input\n",
      "    user = mx.symbol.Variable('user')\n",
      "    item = mx.symbol.Variable('item')\n",
      "    score = mx.symbol.Variable('score')\n",
      "    # user feature lookup\n",
      "    user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k) \n",
      "    # item feature lookup\n",
      "    item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)\n",
      "    # predict by the inner product, which is elementwise product and then sum\n",
      "    pred = user * item\n",
      "    pred = mx.symbol.sum(data = pred, axis = 1)\n",
      "    pred = mx.symbol.Flatten(data = pred)\n",
      "    # loss layer\n",
      "    pred = mx.symbol.LinearRegressionOutput(data = pred, label = score)\n",
      "    return pred\n",
      "\n",
      "net1 = plain_net(64)\n",
      "mx.viz.plot_network(net1)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false,
      "scrolled": false
     },
     "outputs": [],
     "source": [
      "results1 = train(net1, train_test_data, num_epoch=15, learning_rate=0.02, ctx=ctx)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Neural Network (non-linear) MF"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false,
      "scrolled": false
     },
     "outputs": [],
     "source": [
      "def get_one_layer_mlp(hidden, k):\n",
      "    # input\n",
      "    user = mx.symbol.Variable('user')\n",
      "    item = mx.symbol.Variable('item')\n",
      "    score = mx.symbol.Variable('score')\n",
      "    # user latent features\n",
      "    user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)\n",
      "    user = mx.symbol.Activation(data = user, act_type='relu')\n",
      "    user = mx.symbol.FullyConnected(data = user, num_hidden = hidden)\n",
      "    # item latent features\n",
      "    item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)\n",
      "    item = mx.symbol.Activation(data = item, act_type='relu')\n",
      "    item = mx.symbol.FullyConnected(data = item, num_hidden = hidden)\n",
      "    # predict by the inner product\n",
      "    pred = user * item\n",
      "    pred = mx.symbol.sum(data = pred, axis = 1)\n",
      "    pred = mx.symbol.Flatten(data = pred)\n",
      "    # loss layer\n",
      "    pred = mx.symbol.LinearRegressionOutput(data = pred, label = score)\n",
      "    return pred\n",
      "\n",
      "net2 = get_one_layer_mlp(64, 64)\n",
      "mx.viz.plot_network(net2)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false,
      "scrolled": false
     },
     "outputs": [],
     "source": [
      "results2 = train(net2, train_test_data, num_epoch=15, learning_rate=0.02, ctx=ctx)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Visualizing results\n",
      "Now let's draw a single chart that compares the learning curves of the three different models.\n",
      "We'll use the bokeh library since it gives nice interactive charting."
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "import bokeh\n",
      "import bokeh.io\n",
      "import bokeh.plotting\n",
      "bokeh.io.output_notebook()\n",
      "import pandas as pd\n",
      "\n",
      "def viz_lines(fig, results, legend, color):\n",
      "    df = pd.DataFrame(results._data['eval'])\n",
      "    fig.line(df.elapsed,df.RMSE, color=color, legend=legend, line_width=2)\n",
      "    df = pd.DataFrame(results._data['train'])\n",
      "    fig.line(df.elapsed,df.RMSE, color=color, line_dash='dotted', alpha=0.1)\n",
      "\n",
      "fig = bokeh.plotting.Figure(x_axis_type='datetime', x_axis_label='Training time', y_axis_label='RMSE')\n",
      "viz_lines(fig, results1, \"Linear MF\", \"orange\")\n",
      "viz_lines(fig, results2, \"MLP\", \"blue\")\n",
      "\n",
      "bokeh.io.show(fig)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "collapsed": true
     },
     "source": [
      "## Acknowledgement\n",
      "\n",
      "This tutorial is based on examples from [xlvector/github](https://github.com/xlvector/)."
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 10,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "# What if we let the linear model train for a longer time?\n",
      "results1 = train(net1, train_test_data, num_epoch=30, learning_rate=0.02, ctx=ctx)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Next steps\n",
      "See [this notebook](demo1-MF2-fancy.ipynb) to try using fancier network structures and optimizers on this same problem."
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
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      "collapsed": true
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     "outputs": [],
     "source": []
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