example/recommenders/demo1-MF.ipynb (252 lines of code) (raw):
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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
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"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
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"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)]"
]
},
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"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"
]
},
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"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,
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"collapsed": false,
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"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,
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"collapsed": false,
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"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
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"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
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"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."
]
},
{
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"execution_count": null,
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