{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n", "/home/karel/anaconda3/lib/python3.6/site-packages/theano/gpuarray/dnn.py:135: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to version 5.1.\n", " warnings.warn(\"Your cuDNN version is more recent than \"\n", "Using cuDNN version 6020 on context None\n", "Mapped name None to device cuda: Graphics Device (0000:02:00.0)\n" ] } ], "source": [ "%matplotlib inline\n", "import utils3\n", "from utils3 import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#path = \"data/ml-20m/\"\n", "path = \"data/ml-small/\"\n", "model_path = path + 'models/'\n", "if not os.path.exists(model_path): os.mkdir(model_path)\n", "batch_size=64" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Set up data" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We're working with the movielens data, which contains one rating per row, like this:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings = pd.read_csv(path+'ratings.csv')\n", "ratings.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "100004" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ratings)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Just for display purposes, let's read in the movie names too." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "users = ratings.userId.unique()\n", "movies = ratings.movieId.unique()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "userid2idx = {o:i for i,o in enumerate(users)}\n", "movieid2idx = {o:i for i,o in enumerate(movies)}" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We update the movie and user ids so that they are contiguous integers, which we want when using embeddings." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "ratings.movieId = ratings.movieId.apply(lambda x: movieid2idx[x])\n", "ratings.userId = ratings.userId.apply(lambda x: userid2idx[x])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(0, 670, 0, 9065)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_min, user_max, movie_min, movie_max = (ratings.userId.min(), \n", " ratings.userId.max(), ratings.movieId.min(), ratings.movieId.max())\n", "user_min, user_max, movie_min, movie_max" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(671, 9066)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_users = ratings.userId.nunique()\n", "n_movies = ratings.movieId.nunique()\n", "n_users, n_movies" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "This is the number of latent factors in each embedding." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "n_factors = 50" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "np.random.seed = 42" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Randomly split into training and validation." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "msk = np.random.rand(len(ratings)) < 0.8\n", "trn = ratings[msk]\n", "val = ratings[~msk]" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Create subset for Excel" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We create a crosstab of the most popular movies and most movie-addicted users which we'll copy into Excel for creating a simple example. This isn't necessary for any of the modeling below however." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "g=ratings.groupby('userId')['rating'].count()\n", "topUsers=g.sort_values(ascending=False)[:20]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "g=ratings.groupby('movieId')['rating'].count()\n", "topMovies=g.sort_values(ascending=False)[:20]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "top_r = ratings.join(topUsers, rsuffix='_r', how='inner', on='userId')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "top_r = top_r.join(topMovies, rsuffix='_r', how='inner', on='movieId')" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "movieId 27 49 57 69 72 79 88 89 92 99 106 121 143 179 \\\n", "userId \n", "14 3.0 5.0 1.0 5.0 3.0 4.0 0.5 4.0 5.0 2.0 5.0 4.0 5.0 5.0 \n", "22 3.5 4.5 4.5 3.5 3.5 3.5 4.0 3.5 4.5 5.0 4.0 3.5 4.5 4.5 \n", "29 5.0 5.0 5.0 5.0 4.0 5.0 5.0 4.0 4.0 5.0 4.0 5.0 4.0 4.0 \n", "72 4.0 5.0 5.0 4.0 4.0 5.0 5.0 3.0 4.5 5.0 5.0 4.5 4.5 5.0 \n", "211 5.0 4.0 4.0 3.0 3.0 5.0 3.0 3.0 4.0 4.5 NaN 4.0 4.0 NaN \n", "212 2.5 NaN 2.0 4.0 5.0 NaN 3.0 4.0 2.5 NaN 5.0 NaN 5.0 5.0 \n", "293 3.0 NaN 4.0 3.5 4.0 4.0 5.0 3.0 NaN 3.0 4.0 NaN 4.0 4.0 \n", "310 3.0 3.0 5.0 4.5 4.5 5.0 3.0 4.5 2.0 4.5 3.5 NaN 4.0 3.0 \n", "379 5.0 5.0 5.0 NaN 4.0 NaN 4.0 4.0 5.0 4.0 4.0 5.0 4.0 4.0 \n", "387 5.0 5.0 4.0 5.0 4.0 5.0 NaN 4.0 4.0 5.0 5.0 4.0 4.5 4.0 \n", "451 4.0 5.0 4.0 4.0 5.0 4.0 NaN 4.0 5.0 5.0 NaN 5.0 4.0 4.0 \n", "456 3.0 5.0 3.5 3.0 2.5 NaN NaN 3.5 3.0 5.0 0.5 3.5 0.5 0.5 \n", "467 3.0 3.5 3.0 3.5 2.5 NaN 2.5 NaN 3.0 3.5 3.5 3.5 3.5 3.0 \n", "471 NaN 5.0 4.0 4.0 4.0 3.0 3.0 2.0 5.0 5.0 3.0 2.0 4.0 4.5 \n", "508 5.0 5.0 4.0 4.0 3.0 5.0 3.5 2.0 4.0 4.0 4.0 4.5 5.0 5.0 \n", "517 NaN 5.0 5.0 5.0 5.0 NaN 4.0 4.0 5.0 5.0 5.0 NaN 5.0 5.0 \n", "546 NaN 5.0 2.0 4.0 3.0 5.0 NaN NaN 5.0 5.0 2.5 5.0 NaN 2.5 \n", "563 1.0 5.0 3.0 4.0 5.0 4.0 4.0 5.0 5.0 NaN 3.0 5.0 2.0 5.0 \n", "579 4.5 4.5 3.5 NaN 3.0 4.0 2.5 4.5 4.0 4.0 3.5 4.5 4.0 4.0 \n", "623 NaN 5.0 3.0 4.0 3.0 NaN NaN 3.0 5.0 NaN 5.0 4.0 5.0 5.0 \n", "\n", "movieId 180 197 402 417 432 505 \n", "userId \n", "14 4.0 5.0 5.0 2.0 2.5 5.0 \n", "22 4.5 4.5 4.0 3.0 3.5 4.5 \n", "29 5.0 5.0 3.0 4.0 4.0 5.0 \n", "72 5.0 5.0 4.5 5.0 3.5 4.0 \n", "211 3.0 3.0 5.0 3.0 3.0 NaN \n", "212 3.0 3.0 4.0 3.0 5.0 2.0 \n", "293 4.5 4.0 4.5 4.0 4.5 NaN \n", "310 4.5 4.5 4.0 3.0 4.5 4.0 \n", "379 NaN 3.0 5.0 4.0 4.0 4.0 \n", "387 5.0 4.0 5.0 NaN 1.0 5.0 \n", "451 4.0 4.0 2.0 3.5 4.0 5.0 \n", "456 2.0 4.0 3.0 1.5 1.5 5.0 \n", "467 3.5 3.0 3.0 4.0 2.0 4.0 \n", "471 5.0 3.0 5.0 5.0 NaN 3.0 \n", "508 5.0 3.0 4.5 3.0 2.0 4.5 \n", "517 5.0 4.0 NaN 5.0 5.0 5.0 \n", "546 2.0 3.5 3.5 3.5 3.5 5.0 \n", "563 5.0 3.0 3.0 4.0 2.0 5.0 \n", "579 3.5 3.0 4.5 4.0 3.0 4.5 \n", "623 5.0 5.0 2.0 5.0 3.0 4.0 " ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(top_r.userId, top_r.movieId, top_r.rating, aggfunc=np.sum)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "heading_collapsed": true }, "source": [ "## Dot product" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The most basic model is a dot product of a movie embedding and a user embedding. Let's see how well that works:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "user_in = Input(shape=(1,), dtype='int64', name='user_in')\n", "u = Embedding(n_users, n_factors, input_length=1, embeddings_regularizer=l2(1e-4))(user_in)\n", "movie_in = Input(shape=(1,), dtype='int64', name='movie_in')\n", "m = Embedding(n_movies, n_factors, input_length=1, embeddings_regularizer=l2(1e-4))(movie_in)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hidden": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/karel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " \"\"\"Entry point for launching an IPython kernel.\n", "/home/karel/anaconda3/lib/python3.6/site-packages/keras/legacy/layers.py:456: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " name=name)\n" ] } ], "source": [ "x = merge([u, m], mode='dot')\n", "x = Flatten()(x)\n", "model = Model([user_in, movie_in], x)\n", "model.compile(Adam(0.001), loss='mse')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/1\n", "80151/80151 [==============================] - 2s - loss: 9.9570 - val_loss: 4.3077\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=1, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.01" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/3\n", "80151/80151 [==============================] - 1s - loss: 3.1407 - val_loss: 2.8060\n", "Epoch 2/3\n", "80151/80151 [==============================] - 1s - loss: 2.4053 - val_loss: 2.5939\n", "Epoch 3/3\n", "80151/80151 [==============================] - 1s - loss: 2.2334 - val_loss: 2.5499\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=3, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.001" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/6\n", "80151/80151 [==============================] - 1s - loss: 2.1683 - val_loss: 2.5380\n", "Epoch 2/6\n", "80151/80151 [==============================] - 1s - loss: 2.1326 - val_loss: 2.5396\n", "Epoch 3/6\n", "80151/80151 [==============================] - 1s - loss: 2.1078 - val_loss: 2.5447\n", "Epoch 4/6\n", "80151/80151 [==============================] - 1s - loss: 2.0879 - val_loss: 2.5420\n", "Epoch 5/6\n", "80151/80151 [==============================] - 1s - loss: 2.0690 - val_loss: 2.5513\n", "Epoch 6/6\n", "80151/80151 [==============================] - 1s - loss: 2.0498 - val_loss: 2.5598\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=6, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The [best benchmarks](http://www.librec.net/example.html) are a bit over 0.9, so this model doesn't seem to be working that well..." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Bias" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The problem is likely to be that we don't have bias terms - that is, a single bias for each user and each movie representing how positive or negative each user is, and how good each movie is. We can add that easily by simply creating an embedding with one output for each movie and each user, and adding it to our output." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "def embedding_input(name, n_in, n_out, reg):\n", " inp = Input(shape=(1,), dtype='int64', name=name)\n", " return inp, Embedding(n_in, n_out, input_length=1, embeddings_regularizer=l2(reg))(inp)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4)\n", "movie_in, m = embedding_input('movie_in', n_movies, n_factors, 1e-4)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "def create_bias(inp, n_in):\n", " x = Embedding(n_in, 1, input_length=1)(inp)\n", " return Flatten()(x)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "ub = create_bias(user_in, n_users)\n", "mb = create_bias(movie_in, n_movies)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "module 'keras.layers' has no attribute 'sum'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: module 'keras.layers' has no attribute 'sum'" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 94, "metadata": { "hidden": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/karel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " \n", "/home/karel/anaconda3/lib/python3.6/site-packages/keras/legacy/layers.py:456: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " name=name)\n", "/home/karel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " after removing the cwd from sys.path.\n", "/home/karel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " \"\"\"\n" ] } ], "source": [ "#x = keras.layers.dot([u,m],axes=1)\n", "x = merge([u, m], mode='dot')\n", "x = Flatten()(x)\n", "x = merge([x, ub], mode='sum')\n", "x = merge([x, mb], mode='sum')\n", "model = Model([user_in, movie_in], x)\n", "model.compile(Adam(0.001), loss='mse')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/1\n", "80151/80151 [==============================] - 2s - loss: 9.1427 - val_loss: 3.8520\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=1, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.01" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/6\n", "80151/80151 [==============================] - 2s - loss: 2.5889 - val_loss: 2.3191\n", "Epoch 2/6\n", "80151/80151 [==============================] - 2s - loss: 2.0127 - val_loss: 2.1242\n", "Epoch 3/6\n", "80151/80151 [==============================] - 2s - loss: 1.8591 - val_loss: 2.0234\n", "Epoch 4/6\n", "80151/80151 [==============================] - 2s - loss: 1.7653 - val_loss: 1.9487\n", "Epoch 5/6\n", "80151/80151 [==============================] - 2s - loss: 1.6849 - val_loss: 1.8686\n", "Epoch 6/6\n", "80151/80151 [==============================] - 2s - loss: 1.6066 - val_loss: 1.8069\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=6, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.optimizer.lr=0.001" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/10\n", "80151/80151 [==============================] - 2s - loss: 1.5306 - val_loss: 1.7436\n", "Epoch 2/10\n", "80151/80151 [==============================] - 2s - loss: 1.4585 - val_loss: 1.6756\n", "Epoch 3/10\n", "80151/80151 [==============================] - 2s - loss: 1.3877 - val_loss: 1.6202\n", "Epoch 4/10\n", "80151/80151 [==============================] - 2s - loss: 1.3198 - val_loss: 1.5638\n", "Epoch 5/10\n", "80151/80151 [==============================] - 2s - loss: 1.2540 - val_loss: 1.5100\n", "Epoch 6/10\n", "80151/80151 [==============================] - 2s - loss: 1.1922 - val_loss: 1.4579\n", "Epoch 7/10\n", "80151/80151 [==============================] - 2s - loss: 1.1333 - val_loss: 1.4138\n", "Epoch 8/10\n", "80151/80151 [==============================] - 2s - loss: 1.0762 - val_loss: 1.3747\n", "Epoch 9/10\n", "80151/80151 [==============================] - 2s - loss: 1.0243 - val_loss: 1.3336\n", "Epoch 10/10\n", "80151/80151 [==============================] - 2s - loss: 0.9741 - val_loss: 1.2971\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=10, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/5\n", "80151/80151 [==============================] - 2s - loss: 0.5352 - val_loss: 1.0270\n", "Epoch 2/5\n", "80151/80151 [==============================] - 2s - loss: 0.5337 - val_loss: 1.0280\n", "Epoch 3/5\n", "80151/80151 [==============================] - 2s - loss: 0.5327 - val_loss: 1.0283\n", "Epoch 4/5\n", "80151/80151 [==============================] - 2s - loss: 0.5314 - val_loss: 1.0292\n", "Epoch 5/5\n", "80151/80151 [==============================] - 2s - loss: 0.5306 - val_loss: 1.0300\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.optimizer.lr=0.0001\n", "model.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=5, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "This result is quite a bit better than the best benchmarks that we could find with a quick google search - so looks like a great approach!" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.save_weights(model_path+'bias.h5')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "model.load_weights(model_path+'bias.h5')" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can use the model to generate predictions by passing a pair of ints - a user id and a movie id. For instance, this predicts that user #3 would really enjoy movie #6." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 4.8826]], dtype=float32)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([np.array([3]), np.array([6])])" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Analyze results" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "To make the analysis of the factors more interesting, we'll restrict it to the top 2000 most popular movies." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "g=ratings.groupby('movieId')['rating'].count()\n", "topMovies=g.sort_values(ascending=False)[:2000]\n", "topMovies = np.array(topMovies.index)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "First, we'll look at the movie bias term. We create a 'model' - which in keras is simply a way of associating one or more inputs with one more more outputs, using the functional API. Here, our input is the movie id (a single id), and the output is the movie bias (a single float)." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "get_movie_bias = Model(movie_in, mb)\n", "movie_bias = get_movie_bias.predict(topMovies)\n", "movie_ratings = [(b[0], movie_names[movies[i]]) for i,b in zip(topMovies,movie_bias)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Now we can look at the top and bottom rated movies. These ratings are corrected for different levels of reviewer sentiment, as well as different types of movies that different reviewers watch." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(-1.1550405, 'Battlefield Earth (2000)'),\n", " (-0.75790542, 'Anaconda (1997)'),\n", " (-0.75535893, 'Jaws 3-D (1983)'),\n", " (-0.75181359, 'Super Mario Bros. (1993)'),\n", " (-0.67879432, 'Speed 2: Cruise Control (1997)'),\n", " (-0.66687667, 'Mighty Morphin Power Rangers: The Movie (1995)'),\n", " (-0.64726079, 'Blade: Trinity (2004)'),\n", " (-0.63065523, 'Police Academy 6: City Under Siege (1989)'),\n", " (-0.58898491, 'Police Academy 5: Assignment: Miami Beach (1988)'),\n", " (-0.55840474, '2 Fast 2 Furious (Fast and the Furious 2, The) (2003)'),\n", " (-0.54347724, 'Howard the Duck (1986)'),\n", " (-0.50879806, 'Police Academy 4: Citizens on Patrol (1987)'),\n", " (-0.50778717, 'Police Academy 3: Back in Training (1986)'),\n", " (-0.48135275, 'Spice World (1997)'),\n", " (-0.45842293, 'Avengers, The (1998)')]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_ratings, key=itemgetter(0))[:15]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(1.8479358, 'Tom Jones (1963)'),\n", " (1.8209023, 'Cinema Paradiso (Nuovo cinema Paradiso) (1989)'),\n", " (1.8173544, 'Band of Brothers (2001)'),\n", " (1.8163081, 'Shawshank Redemption, The (1994)'),\n", " (1.8054329, 'African Queen, The (1951)'),\n", " (1.8042767, 'Midnight in Paris (2011)'),\n", " (1.7968636, 'Treasure of the Sierra Madre, The (1948)'),\n", " (1.7939831, 'Raging Bull (1980)'),\n", " (1.7808105, 'It Happened One Night (1934)'),\n", " (1.7792168, 'Ran (1985)'),\n", " (1.7638524, 'Shadowlands (1993)'),\n", " (1.7613804, 'Lifeboat (1944)'),\n", " (1.7563008, 'About Time (2013)'),\n", " (1.754107, 'When We Were Kings (1996)'),\n", " (1.7394357, 'Modern Times (1936)')]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_ratings, key=itemgetter(0), reverse=True)[:15]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We can now do the same thing for the embeddings." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(2000, 50)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_movie_emb = Model(movie_in, m)\n", "movie_emb = np.squeeze(get_movie_emb.predict([topMovies]))\n", "movie_emb.shape" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Because it's hard to interpret 50 embeddings, we use [PCA](https://plot.ly/ipython-notebooks/principal-component-analysis/) to simplify them down to just 3 vectors. " ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=3)\n", "movie_pca = pca.fit(movie_emb.T).components_" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "fac0 = movie_pca[0]" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac0, topMovies)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Here's the 1st component. It seems to be 'critically acclaimed' or 'classic'." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(0.12125975305290004, 'Independence Day (a.k.a. ID4) (1996)'),\n", " (0.1045719103914792, 'Armageddon (1998)'),\n", " (0.074960192808366286, 'Twister (1996)'),\n", " (0.073455142883208563, 'Pearl Harbor (2001)'),\n", " (0.071090404781506009, 'Con Air (1997)'),\n", " (0.06483078449814686, 'Titanic (1997)'),\n", " (0.063798957800609402, 'Star Trek: First Contact (1996)'),\n", " (0.061372875191478195, 'Rock, The (1996)'),\n", " (0.060593700393838677, 'Stargate (1994)'),\n", " (0.060586123000246538, 'Waterworld (1995)')]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.098936420904429889, 'Fargo (1996)'),\n", " (-0.096851357761291723, 'Pulp Fiction (1994)'),\n", " (-0.087650326309201054, 'Apocalypse Now (1979)'),\n", " (-0.078819178198182294, 'Taxi Driver (1976)'),\n", " (-0.077868641691344817, '2001: A Space Odyssey (1968)'),\n", " (-0.077820108432495691,\n", " \"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\"),\n", " (-0.076728748299739719, 'Clockwork Orange, A (1971)'),\n", " (-0.074547109334331077, 'Willy Wonka & the Chocolate Factory (1971)'),\n", " (-0.07379978740262412, 'Seven (a.k.a. Se7en) (1995)'),\n", " (-0.073367121742733701, 'Big Lebowski, The (1998)')]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0))[:10]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "fac1 = movie_pca[1]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac1, topMovies)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The 2nd is 'hollywood blockbuster'." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(0.061082029679676679, 'Room with a View, A (1986)'),\n", " (0.057337463764076974, 'Truth About Cats & Dogs, The (1996)'),\n", " (0.056208583861747131, 'Charlie and the Chocolate Factory (2005)'),\n", " (0.056108580628010545, 'Alien: Resurrection (1997)'),\n", " (0.048609208733978324, 'Cars (2006)'),\n", " (0.044338850664015904,\n", " 'League of Extraordinary Gentlemen, The (a.k.a. LXG) (2003)'),\n", " (0.04417950593892548, 'Beverly Hills Cop III (1994)'),\n", " (0.043824316162501306, 'Leaving Las Vegas (1995)'),\n", " (0.042688082533826111, 'Showgirls (1995)'),\n", " (0.041980651409766589, 'Bend It Like Beckham (2002)')]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.18456948619807909,\n", " 'Lord of the Rings: The Fellowship of the Ring, The (2001)'),\n", " (-0.17099862162799004, 'Lord of the Rings: The Two Towers, The (2002)'),\n", " (-0.14834337746797663, 'Star Wars: Episode VI - Return of the Jedi (1983)'),\n", " (-0.14802479427086071,\n", " 'Lord of the Rings: The Return of the King, The (2003)'),\n", " (-0.14300009517005471, 'Star Wars: Episode IV - A New Hope (1977)'),\n", " (-0.13618788291585271, 'Matrix, The (1999)'),\n", " (-0.12533769647957538, 'Jurassic Park (1993)'),\n", " (-0.12102383851234931,\n", " 'Star Wars: Episode V - The Empire Strikes Back (1980)'),\n", " (-0.11216020601644694, 'Dark Knight, The (2008)'),\n", " (-0.11105621739146265, 'Saving Private Ryan (1998)')]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0))[:10]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "fac2 = movie_pca[2]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true, "hidden": true }, "outputs": [], "source": [ "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac2, topMovies)]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The 3rd is 'violent vs happy'." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(0.090865679462855009, 'Fight Club (1999)'),\n", " (0.081537356996925034, 'Dumb & Dumber (Dumb and Dumber) (1994)'),\n", " (0.079656748242321246, 'Royal Tenenbaums, The (2001)'),\n", " (0.0759551321231553, 'Clockwork Orange, A (1971)'),\n", " (0.074258254915148772, 'Office Space (1999)'),\n", " (0.073510892872797176, 'Scary Movie (2000)'),\n", " (0.073264050851240214,\n", " 'Léon: The Professional (a.k.a. The Professional) (Léon) (1994)'),\n", " (0.072770257261700069, 'Stargate (1994)'),\n", " (0.070856068183543952, 'Eyes Wide Shut (1999)'),\n", " (0.070166951923358187, 'Seven (a.k.a. Se7en) (1995)')]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "[(-0.11304680591225004, 'Beauty and the Beast (1991)'),\n", " (-0.11188556304168956, 'Finding Nemo (2003)'),\n", " (-0.10714782467022005,\n", " 'Lord of the Rings: The Return of the King, The (2003)'),\n", " (-0.10058998997147339, 'Babe (1995)'),\n", " (-0.099463013587901508, 'Aladdin (1992)'),\n", " (-0.095624337455683578, 'Chicken Run (2000)'),\n", " (-0.085702734714850251, 'Toy Story 2 (1999)'),\n", " (-0.082504572501298848, 'Shrek (2001)'),\n", " (-0.081955469305752551, 'Toy Story (1995)'),\n", " (-0.081275667334143653, 'Sabrina (1995)')]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(movie_comp, key=itemgetter(0))[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 58, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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BE2Ywcf5q8hYpyeCu7blz83qE9zBN+kycPX4kwjLnTh4jlWeGCMt8au6UsWxd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Pme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kMQhJoRM2UEQRCekx1H3+HqzR9pUG8IL7ZdrnXu8NlPOX11GZ51ulT4Qj65qxdT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\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OzmLvb8uIjnpFXGw4AFXc6qOpqcPm9dNIT08lPT2VzeunkpebS+ybMRKJhOVrzhL83Icu\nbfVp30KDXdvms2TVnxibWCic09jEkoiIkHeapyAIgiAIf69Pc3G3IAiCIBTRtEVP6jfsxKOH13j6\n5BZ375zh4N7VDB+9kAFDZsnHOThWU9jP2MSShEJfyB/cu8ieXUt5GeJLakoieXm5ZGdnERcbgYmp\n7It5TnYWk8Y2oUnzHkz6eoPC8QL87vPI+2qJHZpehwXhWqUuvfpNYdXSUZw9tYtaHi1p0rwntnau\nCmOrFArSaGrpYO9QjZAXTwHwf3aP12GBXL5woGCHNxkor8OCMDQ04/WrIHZsnYOvzx0SE6LJk+aR\nl5dHVORLhfMUzQApz7HLy8GxuvzP+UGN+PgoTM2sy32MfCEvfLF3rI6WdsHr6latIUpKSoQEP8XK\n2onW7QZy+MCPREaEYF7Bjgtnd1PdvZn8fO9zbZlZ6YBixkt5qKioMn/x76xcMpLPOpigpKxMbY/W\n1K3fASmycxoYmjLn+wOsWTmO40d+QqKkRMvWn1PJpRZKEqU305OyduV49PSNWbPxKmrqmpw6sZX5\ns3qxYZsnpqZW8nOqq2uSmZn+TvMUBEEQBOHvJYIygiAIwn+GmroGHnXb4FG3DYOHz2HlkpHs2j6f\nPv2nyjNZVFRUFfaRSCTyLJjIiBBmTe1Mp64jGTpqPnp6xgT432fR3P7k5BQs+1FRUcWjbls8b52S\nBwDy5UnzqNewE2MmKNaqATA0NAdgyIh5tGo7AM/bp/C6c5Zd2xcwedpPdOhcvpbLUmkeHbqMoFff\nKcWeM3nzJf3b6V0wMbVmyvRNmJhaoayswvABbsWWL2loaL/zscur8GstkUhkxy+ScfSXeHNsZ5da\n2Ni5cuHsHvr0n8qVS4cYPW6ZfNj7XJu+vgkAycnxxTJT3sbZtTZbdj4gJSWRnOwsDAxNGT+qPs6u\nteVjPOq15beDgSQmxKCsrIKOrgG9ulhg0aovIAsS3rpxgiN/xqCrZ/jmOjdy7+55zvyxg4FDCzpC\nJSXH/Y+9+w6PolofOP7dTe+d9EJJaAECJLSEElroVcWGqOjVqNjxGtvlXktUvNduVFAsgL3RIxh6\nD4SSQEJCEiC9F9Kz2d8fu1mypFOM+Hs/z8MDO3PmzDszG3ReznkPLk2+i0IIIYToepKUEUII8bf1\na1wmy6OTyCqpws3WjKVhvZkz+NLLtXf3fqhU9dTWVuuSMm1JOh1LfX0t4Y++jYGBAQAH9m1o3lCh\n4JnnV/HGK3fz1JLx/Pf97Ti7eAHg6zeYnTE/4Ozi3SwB1JSHpy8enr7Mu/lR3lkezqb1n+klZU4n\nHNCt9lNVVUF6WjyTpy7UnmOIbnRIS0pLCzl/LpFHn/qQwUNDATiTdBSVqv2Vedrru6t4+/Rly8ZV\nVFaU60bLJJzcR0NDA94+fXXtJk6+gz9+X4tPD3+qqyoYE3qTbt+VXJube08sLKw5l34Kn+79rih2\nS0sbQFP890xiLPfc959mbWxsNcmfuCMxlBTnMSpkFoCujpCBgf7/0ikUymZT6tJT4xk9du4VxSiE\nEEKI60NqygghhPhb+jUuk4ifT5JZUoWirgz1rif5z0fvsnLDNrKz0tgZ8wPfrVnO4KETsLCw7lCf\n7p6+NDQ08NP375CdlUbM1m/4+ft3W2yrVCr55wtf0M9/FE8tCSU3RzMtaPa8h6m4WMrLL97K6YSD\nZGWmcuTwNv73xj+orCinpqaKd//7MMeO7iAnO53TCQeJP7FXL7EAsPrLV4k9tJX01ATeem0xhobG\njNfWRllw5zMknjrE228+SPKZODIzUti/dwP/e/MBAKys7LCxdWTT+pVkZqRwPG4n7ywPb/Zi35L2\n+r7eSksLSTlzTO9XQX4WEybfgamJOa+/sojUsyc5cWwXb7/5IKPHztNLskwMu4Nz6af4YsVLjAyZ\nqffsr+TalEolQwInEn98j972qsqLuvjU2mlhKWeO6b4HADtjfiDuyHayMlPZu/s3nnl8MsGj5xA4\nfLKuzZaNq0iI309Wxlm2Rq/m3y/cwvwFj+Pp3RuA/v4jsbK2581X7+Fs8nEunD/DJx8sG/7gwwAA\nIABJREFUJTsrVW9FqJzsdAryMwkcdqlvIYQQQnQ9GSkjhBDib2l5dBJVdSoAGgzMqLbujdn5X1n7\n1sf8rFTh6OTO+Em36U3vaE/PXgN5+PF3+Hb1m6z69EX6DxjFAw8v5+WXbm2xfWNi5tKImRicXbx4\n9+M9rPz4OZ59aiq1NdV0c/YicNgkjIxNAM0S3m++eg9FhdlYWzswIng6Dzzyll7f9z8YyccfPE3G\n+SS8u/fn1eXrMTOz0MX59oc7WbXiRZ58eBwNDSpc3XoQPGaOLq4X//MtH7z9GIsXDsDdvRcPLnmL\nZc/fRHva6/t62/HHd+z44zu9bTff9iQPPvIWr7+9hY/efYKH7xuOsYkpo0Jm8/Dj7+i1dXbxxn9g\nCCeP7+bu+/6tt+9Kr2367Pt589V7eHDJf3UjqJISY3lqyXhdmy8/W8aXny1j8tRF/POFVQAUFmYT\n9f5TFBflYu/gyuQpC7nznhf1+r5wPomVHz9HeVkRzq4+3LHoOb3pVTa2jrz+3818/ukLPPXoBFT1\ndXj59OU/r/+CX+8hunYxW78hcNhkval0QgghhOh6iitdevKvKDAwUB0bG9vVYQghhPgL6P7sRlr6\nL5wCSHt9+p8dzjVz7OgOnloynp835ummtIiu98g/RjF7XjiTpizs6lCaqa2tYdGtfjy/bC3+A4Pb\nP0AIcUNTKBRH1Gp1YPsthRB/BTJ9SQghxN+Sm61Zp7YLcTWeeObjZjVc/ipyc85x+13PSUJGCCGE\n+AuS6UtCCCH+lpaG9Sbi55O6KUwAZkYGLA3r3YVRib+rnr0G0rPXwPYbdgFPLz88vfy6OgwhhBBC\ntEBGygghRCvWHv2a+atm6m377MAnzFgxmeD3Atl4av0V9/3ZgU+4c/UtVxuiaMOcwe5EzhuAu30W\n7m7/wt22gch5A/RWX7oWPj/4Ka9t+3f7Da+RgCHj+GNvQ4emLp0tSGH2Z1Opqqv6EyITQgghhBCd\nJSNlhBA3tKLKQr6O/YJ9aXvIu5iLubEFHjaeTOw9mel9Z2FubH7NzpVamMLnh1bw2vTl+LsMwNLE\nskPHBb8XyCtTXyfUd+I1i+VamL9qJjnl2QCYGJrgZu3OzQG3Mtt/3lX1m12WxU1fzGLlgq/o69z2\nEsGX35v5q2Yyf9At3D6k/bocjedpy73D7uf924ey5GfY+NhobM1sO34hHVBcWcQ3R9fwxe1rdNuO\nZR5l7dGvScpLpKAin+cm/ovp/fSTe0WVhXy0930OnT/AxZpyAtyG8MS4pXjaerV7bQ8FP8odQ+/S\n21ZTX8M/vr+blIJkvfve07EX/V38+TZuDfcMu+9aXroQQgghhLgGJCkjhLhhZZdl8eAPi7EwtuD+\nkQ/S08EXE0MT0opSWZ/wKzamtkzuPeWanS+jJAOAMT3GoVAorlm/11Kdqg4jA6MOt79n2P3MHTCf\nqroqNp1ez5sxr2FpbMUEv0lXfP4/SzdLZ9Yt3qL7/MvJn9h4ah0rF3yp22ZmZE5i3qnrFsO6hF/p\n59IfdxsP3bbKukp6OPRiap/pvLz1X82OUavVPLvhaZQKBa9PfwsLE0u+i1vDY788xJo7f8DMyKzZ\ntQHsOruD/+54g9BeE5r1+cGed3Cy7EZKQXKzfdP7zeKNmFdZGHg3hkr5z74QQgghxF+J/N+ZEOKG\n9db211EqlHx269eYGV0q3upm405w99E0XV0upzyHd3e+xeELhwAI8hrOE2OeppuVs67NmiNf8m3c\nWqrqKhnbMxQ360vTXD478AmfH1oBQMj7QQDsfTSW07kJfLLvI87kJ1KnqqeXYy8eDnkMf1dNbYnG\n6U8vbH4WNoOLlSs/3XNp2tO2M9F8su8jiquKCfQM4tkJL+qN5th4ah1rj3xNVlkmzlYuzBkwn1sC\nbkOp0Mw+DX4vkCfHPkNsxmEOndvP3AE38cjoxzt8D82NzXGw0EyD+cfIh4hJ3sbu1B1M8JtERkkG\n7+/+H6dyE6isrcDLzpv7RjxIcPfRuuPnr5rJtL4zyL2Yw86U7QR5DWd7yh8A3PedZjTHYPchfDD/\n0w7H1FEGSgNd7I3XolQo9bY1lVJwhk/2fcjZwhR87Hvwz/HP07tbH93+k9nH+Xjvh5zOS8DKxJqQ\nHmN4aNQSLNoYEbU1aQuz/OfqbRvlE8IonxAAXm1hWtOFkvMk5Jzki9vW4uukqfPxdGgEM1eGsTUp\nmln+c5pdG8COszEEeg7DzUZ/+tXuszs4mnGEV6e9wf70vc3ON8xrBOXVZcRlHCHIa3ir1yKEEEII\nIf58UlNGCHFDKq0q4eC5/cwbeLNeQqapxtEsDeoGnl3/JEWVRbw/72Pen/cxBRfzeXbj07rEzR9n\ntvLp/igWD/8Hn9+6Gi87b76NW6vr67YhC/nn+BcAWLd4i24UQ2VtJVP6TOOjm1aycsGX9HLy4+l1\nj1FaVQLAygVfAfDP8S+wbvEW3WeAnPJs/jizlcgZb/HOnA84k5/Ep/s/1O1fF/8Ln+z7kPtGPMCa\nO3/gkZDHWXPkK34+8YPedX5+aAUjvYP56o5vmTfwZkCTLHll67JO31djA2NqVbUAVNVVMsJnFO/M\n+ZAvb/+Gcb3G89zGpZwrStc75tu4tXjb+fDZrV/zwKiHdSNV/jf7fdYt3sJr05d3Oo7r4eN9H/Dg\nqCWsunUNNqY2/Dv6Bd3zP1uQwhO/PkJIjzF8efs3vDZ9Ocn5Z3ht239a7a+supT0ojT6dOvbqTga\nRxMZGxrrtikVSowNjDmRfazFYzJLMzhy4TCzL0sA5ZXnsnzH6ywLewUTQ5MWjzUyMKKXox9xmUc7\nFacQQgghhLj+JCkjhLghZZRmoEaNl5233vY5n01jYtRoJkaN5s2Y1wCIvXCIs4UpLJvyCn2d+9HX\nuR/LprzCmbxEYrUjZ74/9g1T+85gzoD5eNl5syhoMf1c+uv6NTc2x0o7YsLBwlE3imGoZxBT+k7H\nx7473vY+PDn2GYwNjDlwbh8AduZ2AFiZWOJg4aj7DKBqUPH8pGX0cvTF33Ugs/3nEnvhsG7/F4dW\n8lDwo4T6TsTNxp2QHmO4c+gifjnxo941T/CdxCz/ObjbeOhGUbjbeOBg3n4h2Eb1DfVsPLWes4Up\nBHlqRlP4Ovkxd8BN9HTshYetJ4uCFuPn1IftKdv0jh3sPoQ7hi7Cw9YTT1svbM0012htaoODhSPW\npjYdjuN6un9EOEM9A/G29+GeYfdxrjid/It5AKw9+hUTfCdx25A78bT1or+LP0+HPsuOszEUVxa1\n2F9OeQ5q1DhaOHUqDm87H5ytXPhk34eUVZdSp6pjdewX5F3MpbCioMVj1if8hq2ZHaN7jNNtUzWo\n+Hf0i9w2+E7diJvWOFo6kVOW1ak4bzQ5OXv56adAGhpU7Tf+k6lUNXz/vT8FBXFdHYoQQggh/mJk\n+pIQ4m/lo5tW0KBu4I2YV3UjPs4VpeNo4YSrtZuunbuNB44WTqQXpRHkNZxzxWnM7D9br6/+LgPI\nKLnQ5vmKK4tYcSCKoxlHKKospEHdQE19DTnlOe3G6mzlolcs2NHCieLKYm2/xeRezOXN7a/x1o7X\ndW1UDSq9aVkAfVoopvvevKh2zw/w6b6P+Pzgp9SqajFSGnH7kIXMHqAp9FtVV8XnBz9lX9oeCisL\nqG+op7a+ll6Ovpedv3MjRbpKzyZxNyZSiquK6WblTGJeIpklF/gjeauuTeN9zizNwM7cnt27HyIl\n5Rt8fe8kJOR9autrADA2NOHw4X8RH/8eHh6TmTTpuzbjMDQw5LXpy4nc9jJTP52AgcKAQM9hjPAe\n1WL7+oZ6Np1ax9S+0zE0uPSf7a9iVzHw4ib8jfzbvXYTQxNqtPFeT3Fxr5Oe/htz5+6/7ue63OHD\nLzFw4FMolQYApKevJylpFYWFJ1CparC17c2gQU/i5TVN77j09HUcPfoa5eVpWFl1Z+jQF/D2nqHX\n5vTplcTHv09VVS62tn0YNuw1XFxGNeljPUlJX1BYeJyamkKmTFmPq2uIbr+BgQn+/o8SG7uMKVN+\nu453QQghhBA3GknKCCFuSB42HihQaKbS9Ly0vXGkiKmhacc6usp6va9sXUZRZSGPjn4SF2tXjA2M\nefSXcOo7UPC2edFVBWoaAHS/Lw2NYIDroDb7MevotbZgweA7mNl/NiaGpjhaOOoVMP5gzzscPLef\nh0Mew9PWC1NDU17+/V/UNehfm2kr08f+apre76ZT2wDU6gZm9p/DgsG3NzvOyfLSSBgLC3fS0n5l\n+PDXsdHW/imtLOLs2e+wsPBodmxr+nTry5e3r+VizUXqVHXYmdtx/3eL6NOteYJtb9puCisLmdl/\njt72IxcO4aeqIWrv+8TvuLT60wPf38N4v0ksC3tFt628ugwXa9dW41GpajEwMG51/19dbu5BSkuT\n6d790j3KydmLq+tohgx5HhMTO86e/YGYmIVMmbJel1DJyzvEjh33Mnjws3h7z+TcufVs334306dv\nwckpEIDU1J85eDCCkSPfwtl5BImJn7F16y3MnbsfS0tPAOrrK+jWbRg9e97M7t3hLcbYs+fNHD78\nIsXFp7GzuzESmUIIIYS4/iQpI4S4ofwal8ny6CSySqpwc/ZlzZFvuGnQgjaXvva296GgIp/ssizd\naJnM0gwKKvLpbt9D08auOwk58cxoMlomIedku/EczzrGE2OfZlR3zb+KF1UWNpuCYqg0RKV9+e8o\ne3MHHC2cyCzNZGrfGe0fcIVszGzwsPVscd+JrGNM6TNdt9pPTX0NmaUZeNp5tdmnoVKz+lNDJ6+5\nK/l160Na0dlW70UjO7v+VFbmkJ7+Kz173YaFsQWJZ3/CwMAEZ+dR1NRcmuqkVjfgb5BHQdzjfHn4\nPqytezJkyAt4e2tGapSXn+fHHwcRGvolu+KjGFZxEPO8ODIzh+HuHgpAQ0Mdhw9GcJd1JtvXDcPU\n1JGePW8mMHAZE43TKFbWE2RSQJCJ5ju3srwnL4W9jIeRik2bplNQEIeJiS2WFQ34+ryki23z5hnY\n2PhhaGhBSso3WFl5MXNmDImJq0hI+JCKigwMDS1wcAhg0qTvUF7hqk2NI2cGDXqao0dfoaqqADe3\nMQQHv4epqYOuXXLyN8THv09Z2VmMjW1wd5/AmDEdG+0FkJr6I66uYzA0vJQgHDHidb02gwf/k4yM\n3zl/fpMuKZOQ8DGurqMZNOhpAGxte5OdvYeEhCjGjftM2+YjfH1vp3fvRdp+3yQj4w8SEz8nMFCz\nulavXrcCUF1d2GqMJiZ2dOs2nNTUnxg69IUOX5sQQggh/t6kpowQ4obxa1wmET+fJLOkCjWQUzCV\nsqpabvnyVrYmbSGtMJXzxefYmrSFlIJkDLQrFAV5DqenQy/+Hf0Cp3NPcTr3FP+OfhG/bn0Y6qFZ\nSenmgFvZfHoD6+J/4ULJeb46vIpTOQntxuRl60V04mbSClM5nZvAS5ufa7Yktau1G0cuHKawooCy\n6rIOX+/i4Q+w5shXfBu3hnPF6aQWprD59Aa+Oryq3WMf/TmcqL0fdPhcLfG09WLX2e0k5SVytiCF\n/0S/SK2q/SkwduZ2mBiacPDcfooqC7lYc/Gq4vgz3Dl0EadyE3gz5jXO5CWSUXKBvWm7eTPm1WZt\n/fzuJDl5NUqFkkDPYWSe+xVf3zt0o28qays5k5/EjkP/oa9hASqnMALGrMbBdRzbty+ksPAkMcnb\niM8+AcCeA8+xJb+UDIeb8HQNZufOxdTVae7Zwbi3MKk6i2u/pcyfH8u4cZ9jY6OZhjV18veYm7sR\nEPAMCxYkEjp9BwB2ihoO77kfL6+pzJ69m4HD/4d5QwnGufrTZs6e/QFQM23aJkaPjqKgII4DB5YS\nEPAM8+YdYsqUX/HwaL78dmddvHiBtLRfGD/+a8LCfqKw8ARHjrys25+YuIr9+5/A1/cO5szZy+TJ\nP2Jv37+NHpvLzd2Po2NAu+3q6i5ibHxpdbP8/EO4uYXqtXF3H09enqbWlEpVS2HhsRbahOradIaT\n0xByc5uvkCWEEEKI/79kpIwQ4oaxPDqJqrpLRTxVKnty8x9E2bCfFQc+Jq88F0MDQ7ztujN3wE3M\nH3QLoJmq8vrM//HOzuUs+flBAII8h/HE2KW6F+mJfpPJKs3kk/0fUVNfTUj3Mdw6+HY2nd7QZkwR\nE1/izZhXuffbhThaOLJ4+D8oqSrWa/NIyOO8v/tt5q5ah5NFN70lsdsyy38OZkamrD36NZ/s+xAT\nQxO62/fQXVdbMksz9Jb7vhKPjn6SyD9e5qEf78PKxJpbBt+mq9PTFkOlIY+PWcoXh1aw6tAKBrkF\nXPGS2I1Lke99NPaKju+oXo6+fDh/BSv2R/HwTw/QoFbhZuPOmJ6hzdr26HEzhw+/RGnpWab7juP0\nns/o3mMBJ46/AUBi3imW/Pwgt1mkk1hnQ1ziIT5LPMTUvjMY6jyS+Pj3KbQZzy9HVzBJAUeqTBnc\nZyH3DLuP2up8zp79lqKikzg7j+TUhT1UYMrkwQ9hamSKpaUnzs6aQswmJnYolQYYGlpibu6MSb3m\nZyMrbS3du8/F3/8RAH47s4tS2wnkZm6iqiofMzPNdCwrKy+GDbs0xSk9fT2GhuZ4eU3FyMgKAHv7\nAVd9b9XqekaP/hBjY03B5969F5GcfGlls+PH36Jfvwfx939Yt83BYWCnzlFRcQFz89anZwGcPr2C\nioosevW69PNTVZWHmVk3vXZmZt2oqtIUgK6pKUStVunuWdM2WVk7OxUjgLm5K+Xl5zt9nBBCCCH+\nviQpI4S4YWSVVDXb1tBgRVb+ZPY+Nb3NY12sXHh9xn/bbHNX0D3cFXSP3rbFIx7Q/TnUdyJ7ffWT\nA75OfqzQLgHdaEpf/VhCeowhpMeYZv027Rtger+ZTO83U2/bpN5TmNR7Sqsxt5as6Ejip702Ltau\nvDv3I71ttw9Z2KE+ZvnPYZb/nBb3NXV5/Jf3l1WWxTCvEe320xjb5fEBDPEIbHYeV2u3Ztv6Ovfj\nf3Peb/c8Jia2eHtPJzl5NVbGNpQbOrE/Mx6LJufb/mAMa9Z489ysVbi5jdUde+TIK2RkbOXmsZ8y\npecofvxxEBHTP6Jbt2EAGGoTC1VVmulIs0Ne4/ff57Lht2Dc3UPx8JiEh8ckFIrmA10br+mXX0Zy\nNjuGtLRfADU1ddX0NjSmASgvT9MlGBwc9EeWuLuPw9LSkx9+CMDdfTxubuPx8ZmhS9BcKQsLT11C\nBjSJierqAu115lNZmYWr69jWDu+Q+vpqDAxaXhIcNMV8Dx/+F+PGfYalZdvT764nAwNTVKrqLju/\nEEIIIf56JCkjhLhhuNmakdlCYsbN9sYoNCs6R61WczQjtlli6M/UtIbRrV7Z9HfW1Mnx9b2T3bsf\nwtDQghGDHqMYdTs9XdK0mDKAUmnUwj7NeRwdB3HzzcfJzIwhK2snu3c/hL29P2Fhv7SYmAFNLRs/\nv4X07/8QWaWZJOSc1CX2mo4mMTTUr8NkZGTFrFk7ycnZR1bWdk6efJujR19m5sw/2h2F0pbm9WgU\nqK9xvSFTUwdqakpa3Jee/hu7doUzZkwUXl5T9fY1HRXTqOnoGRMTBxQKA6qq8ltt0xk1NcWYmnZ8\nqXohhBBC/P1JTRkhxA1jaVhvzIwM9LaZGRmwNKx3F0UkrieFQsEv927Ey867S85/eQ2jyloVidnl\n/BqXiavrWJRKI2pqChnmv5hplxVjNja2xtzclby8g3rb8/IOYGvbue+rkZEVPj6zGTXqf0ya9B3Z\n2bsoK0sFQKk0Rq1W6bV3cBhISUki1tY96OM5mvlBD2Ft3QNr6x56hXBbolQa4uY2hsDAfzF79h7q\n6yu4cCG6U/F2hpmZE+bmbmRnd34qUFP29gMoKUlqtj0t7Rd27XqQ0aM/xMdndrP9Tk7DyMraobct\nK2uHbuSSgYExDg4BbbbpjJKS052emiWEEEKIvzcZKSOEuGHMGaxZ7lq3+pKtGUvDeuu2C3EtXV7D\nCEClVrM8Ook5g92ZM2cParW61Wkz/v5LiIuLxNq6Bw4OAZw9+z25ufuZNWtHh2OIj/8Qc3Nn7O0H\noFQacfbsjxgZWWFhoVlFzNLSi9zc/VRU3IKBgQmmpg4MGPAYGzZMZt++J+jd+x6MjCwpLT3D+fNb\nCA5+p9VzXbiwhbKydFxcRmFiYkt29h7q6i5iY+PX4XivxKBBT3Lo0POYmXXDw2My9fVVZGfv1NXE\n2bVLUwdqzJiPW+3D3X0Cyclf621LTf2JXbseJCjoZZydR1FZmQtoEi0mJnYA9Ov3AJs3T+fEibfx\n8prO+fMbyM7ezfTpm3X99O//ELt3P4iT0xC6dRtOYuIqKitz6NPn0lTHmppiLl7MoLa2FIDy8lSM\njW0wM+uGufml2k65ufsZPPi5q7ldQgghhPibkaSMEOKGMmewuyRhxJ+ipRpGTbe3V2ulX78HqKu7\nyOHD/6K6Oh9r616Ehn7VqeK5RkaWnDz5PmVlqSgUCuztBzBp0g+6qUeDB0ewb98T/PTTEFSqGu65\npxh7e3+mTdvI0aOvsnnzDNRqFZaW3nh7t720urGxDefPb+T48Tepr6/CysqH4OD3dMtHZ2fvYcuW\nmUyZsh5X15AOX0N7+vRZjFJpTHz8h8TGLsPExA4Pj0m6/RUVGe320bPnLcTGLqO4+DR2dn0BSEpa\nhVpdz6FDERw6FKFr6+ISzNSpmgLezs7DGTfuM44efZW4uEisrLozbtznODkF6tr36DGPmpoijh9/\ni8rKXOzs+jJp0nd6tWnOn9/Mnj2XChXv3fsYAAEB/2Tw4GcByMs7RG1tWYsjdoQQQgjx/5dCre74\nPPi/usDAQHVs7PVdoUMIIcT/D8Gvx7RYw8jd1oy9z47vgoi6VnLyGmJj/828eYcxMbFp/4A/WWzs\nMqqrCwkJab9Yc1fYvv1u7O0HMGjQU222K8spYc2iKOa/fzfd/K68ls9fTdq+M+xfEUNZTgl+E/wZ\n/3TbScJGib+fYPeHv3P/b09f5wivrcufY+bxc6x7Zi13f/8YZjbmLR5zo15rWw5/vZuzuxO59dP7\nuzqUG0Jl0UX+WL6enIRM6mvqCI+OaP8graiwSCa/MJeeo/ugUCiOqNXqwPaPujaiwiLHAdsBp/Do\niIJr0J8PkAYEhUdH3BAvd1FhkenAB+HREW+10247sCo8OuKrPyOuzogKi3wYmBIeHTGz3cbimpKR\nMkIIIUQLlob1JuLnk3pTmP4/1zDKyNhKYOCyv2RCBmDgwCc5ffpTGhpUKJUG7R/wJ1KparCz60//\n/g8R89YGkraeBEBpoMTCyZoewX4E3TUaI1NjLJ2sWfTNEkxbeXHvjNV3fYT/zKEE3Dz8qvu6Wjve\n3kTfKYPwnx2IkZlRi23+7HjVajWntxwnMfo4RekFoAAbNzt6je1H/+mDMbZofUWv9lzL5/hXlPj7\nCbb/d2ObbWa9eft1jUFVp+LEr4dJ2X6KkoxCDIwMsfGwp8/kgfSZNAAD4xvvNefYjwepKLzIzVH3\nYmxm3GKbPzvRpU02NBZ3awBygPXA0vDoiPI/JYgOiAqLvBtY1WRTDrAb+Gd4dERalwR1maiwyOmA\nJ7BG+9ke+DcwCc09LgA2AC+ER0cUNjnODngPmKXdtA5YEh4dUdKkjRfwITAeqALWAk+HR0fUavf3\n0+7vB9gAWcC3wLLGNsBK4PmosMjR4dERu6/5DRCtuvH+thJCCCH+BFLDSF9o6BddHUKbjI2tGTTo\nrznCwMDAhICApbrPHoN9mPDMTBrqG8iKv8DOdzZRX1PHmCVTUBooMbe3bLM/Vb0KA8O/VuKpLTUX\nq6kuq8JzaA8sHa9uifVr6Y8315O6J4kht44k+MFJmNmaU3yugJPrjmBma06fyVdWlFlVp8LAyKDd\n53gj6zW2L16BPXSf/3hzPSZWZoSET9RtM7EyI+vE+etyflWdig3PfUvB2VyC7hqNq78nJpam5CVl\nc/znQ9h62OM+6MqKxDc+v65QmlWMk68Ltu72XXL+NvwHiAIMgL7A54AaCO/KoFpQCfQEFEAf4BNg\nXVRYZEB4dISqzSOvUFRYpHGTpEZ7HgO+aBKLG+AOPAOc0v75I+AbYHKT49YCXsAU7eeVwNfATG0M\nBsBGoBAYDTgAX6K5D0u0x9Rqt8UBJcAgYAWafMAzAOHRETVRYZFrgUfRJLTEn0SSMkIIIUQrpIaR\nuB6avrD7je9P1onzpO1LZsySKa1Oe5n28s3Efr2HgtRcwl6ch52XI/s++YPcpCzqKmux9bQnaOFo\nfEb4AvDb0jWU55ayf2UM+1fGAOimQuQkZHBg1Q7yz2RjYmmK9whfRi4O1Y0MyTp5nv0rt1OUno9S\nqcTWw55xT03HwcepxeupKa9iz8fbSD+Qgqq2Hpd+HoSET8Tex0kXP8C6f2p+n/Xm7c1emNuKFyAj\nLp09UVspzymlW29XQp+ajrWLrW5/+oFkDn+9h+Jz+ZjbW+Ib2p/AO0NafblO2Xma5JgEwl6cR4+Q\nS6PfrF1s8R7ei5qL1QDkJWVx8IudFKTkoqpX4dC9GyPvC8Wln4fumKiwSEY/PJmMY+lciE2j/4zB\n+M8a2uI0tNzTmRz6chclFwqx83Zi3ONTcPLVn6aWfiCZfZ/+wcW8Mpz7uhP65DSsXe06fK1n/ojn\nxK+HKblQhIGxIW4DvQh+cKIuIdb4TGa+fhsHV+2gKD0fOy9Hxj42FSdflxbv1+UMTYwwNLk04snA\nyABDE8NWE1HJO05x6IudVJVU4h7gzbgnpulN40qMPsGxHw9Qll2CZTdr+s8YwsA5QSiUihb7O/HL\nYbJOnmf+e/r319rFlp6j+1BXpXlH/m3pGuy9HRn9SJiuTcxbG6gurWTay7fo2th6OmBkakTStpNY\nOdtw0/v3kLAxjuM/HeRiXhlGZsY4+bow7eVbUBooryhmgISNcRz74QAX88uwdLIJs9tmAAAgAElE\nQVRm8C0j6TctANCMFCvP1RQLP7Mtnt6TBjSb5pf4+wliV+8BNN87gNCnpusSiDXlVUS/8gvvTPj3\n4KiwyFTgpfDoiNWNx0eFRboD/wUab8g+4PHw6IjkVoPWKA+PjsjR/jkzKizyezQv/y2KCot0AD7g\nUoIgFXgrPDpiVZM2CuBJ4EE0CYd84Ovw6Ihmc7aiwiKVwPvANGByG/Gqm8SZHRUWuQzNqJSewJmo\nsEgbYDkwBzADjgJPNU6R6mDcO4DTQAWwCEgHglq7F02OcwImAroMfXh0RDwwr0mzlKiwyKXAhqiw\nSOvw6IiyqLDIvmiSMSHh0RH7tX09AOyOCovsHR4dkYQmgdMf8A6PjrigbfMMsDIqLPL58OiIsvDo\niBQgpcm5zmmnnV3+HNcBW6PCIs3DoyMq27sucW3IkthCCCGEEF3I0NgAVV19m20OfLaDYXeP4baV\n/8C5jzt1VbV4BfVgZuSt3BJ1Lz2CexP98s8Un9eMeA97cR4WjlYMvSOYRd8sYdE3mn8sLUzLY/1z\n3+IzwpeboxYT9tI8ClNz2f4/zVSUBlUDm5f9hGt/D26JWsy8dxcxYG4QyjZeNGPe2kheYhZTl81n\n/nuLMDQ1ZMPz31FfU4dLPw8WaKdZhL04j0XfLNFLaDRqLV6AhjoVR7/dT+iT05n3zl3UVtSw670t\nuv3nY1PZ9sY6BswayoJP7yf0yemc3ZPIwVU7Wo05OSYBGw97vYRMUyaWpgDUVtXiN8GfOf+9k/nv\n3o1jj25sfPEHqsv031UOr96DV1BPFnyyGP9ZQ1s97/4VMYxYHMr8D+7B2tWWTS/9QF11nd61xq7e\nQ+hTM5j7zl2oG9Rs+c/PNNaA7Mi1qupVBC0czS1R9zLtPzdTXVrJtsjfmsVy8PMdjLg3lJs+uBdT\nazO2vbGOprUmo8IiOfz11f9jeXluKWd3nmbKS/OZ8doCCs7mcuiLnbr9pzYd4+AXOwi6awy3rvwH\no/4xgbjvDxC//kirfSbHJOAx2KfFuksKpaLTU8+SYxJADXPeWsiEpTPJO5PN7g+iCbwzhNs+e4CZ\nr9+GZ5ORQVcSc+reJPZ8+DsD5wax4JP7GDg3iN0fRJN+QJNfmP/e3XgM9qHnmL4s+mYJwU1GHTXq\nNbYvg+YPw9bDXvdz0mtsX93+2DV76T7SlzcOfHgK+A74XDuthaiwSHM0dV+qgbHASCAb2Kbd1yHa\n/sKAg200M0WT8JiBJlnwLvBJVFjkhCZtXgNeBCLRTKmZBzQbWhUVFmmEJrEyFgjuQAKpqWrt7yba\nJNBGNKNRZgCDgV1ATFRYZOMXqSNxA9yJZhTKaOCuDsYSAtQA8e20s9a2a/xLZiRwEU0CrdFeNEmh\nUU3anG5MyGhFAyZAi38hRYVF9kKT7Nl52a5YNAM3RrYTp7iGZKSMEEIIIUQXyU3M4kxMAh6Du7fZ\nLvDOEDyHXnopNLM1x7HnpeW2h94eTPrBFFL3JDL09mBMrc1QGigxNjPRG71w7IeD9Brbl4CbLtVt\nGbNkCj889DmVJRUolUpqL1bjM8IXGzfN6Aw7L4dW4yrJLCL9QDKz37oDtwGaFakmPDOT1Xd+xJmY\nBPpNDcDMVvO+Z2Jl2upIitbiBU2iaPQjk7Hz1MQx6KZhbP/fJtRqNQqFgqPf7CPgpuH0CdOMFrBx\ns2Pk4lC2vbGOkfePR6FonlAqzSrCzqP162rkEeCj9znk4cmk7k3i/OFU/Cb467b3GtuXflMDdJ/L\nckpoydA7gnXTfsY/NZ2v7viA5O0JumMbVA0Eh0/Ctb8mcTXhmZmsuTuKzLh0PIZ079C19g0bpDuf\ntasdY5ZM4dv7P9WNzmgUtGgM7gHe2rhC+PXJr6koKNe1sfWwx9TarN171B61qoHQp6djYqFJdPWb\nGkDi7yd0+4+s3cuIxaH0HN1HE7OLLYNvKSZhw1EGzG65Vm1pVhFug7xa3HclrFxsGPXApffu1D1J\nGJka4zPCF2NzE6ycbfR+3q4k5uM/HsRvgr9uv62HA/nJOcR9fwCfEb6Y2Zq3O+LI0MQIIzNjFK1M\nc/Sb4I/fBH+yK/Jq0CQ8HgPGAKuBW9EkEu4Jj45Qg27ERR6aJMT3bdyiV7WjTgzQJC52A8+11jg8\nOiITzYiURp9GhUWOB24D/ogKi7QEnkAzSudzbZuzwKHLurJAU7/GFhgTHh1R1EaMeqLCIj3QjErJ\nAJKAUCAATTHixir+L0aFRc4EFgJvthd3k+1p4dERbVdtb84byGtrGlVUWKQt8DKwIjw6ojFT7wLk\nNz4zgPDoCHVUWGSedl9jm9zLuisAVE3aNJ5jHzAETcJmBZc9x/DoiMqosMhSwKdTVyeuiiRlhBBC\nCCH+ROdjU1kx+y3UqgYaVA34jPQl5OFJbR5z+YiAuupaYlfv4dzBFCqKKmioV6Gqrcehe7c2+8lP\nzqE0u5iUnacvbdT+r35ZVjEu/TzoPWkAG577FvfBPngE+NBjdG+surVc4Ln4fCEKpQLnvpem+ZlY\nmGLf3Yni81e9CAugmRrTmJABsLC3oqFORU15NabWZuQn55CXlEXc9weaXJOa+pp6KosqsHBo/vLa\n0cVHK0sqOPzlLjKPn6OquJKGhgZUtfWU55XptXPy69i0H5cm98nIzBh7H/37pFAq6Nb70rO2crbB\n3N6SovMFeAzp3qFrzU/OIXa1ZqpbTXm1bvTLxTz9pEzT70rjPaoqqdS1ue2zBzp0Te2xdLbRJWQ0\n57KiqrRSd76L+WXsem8Lu96PvnRJqgbaekTXevFYp176z89jiA+WztasWRSF59DueAztTo/g3hib\nm1xxzMXnC+nTJGEG4NLfg/T9nRn40TaH7pemGIZHR9RHhUXmA40PeijQHShvnPqkZY5mek9b/gd8\nhiap44lmlMvGqLDI0PDoiIbLG2trnDwLLEAzMsUEMAZ2aJv002774/JjL7MazWie0PDoiIp22gJY\nRIVFXtTGaY5m1Mu88OiI2qiwyKHabfmXXb8p2uvvQNyNWh8S1TozLo3caUabqFoPZKKt8XKdLACs\n0NSUWQ78E81opaaq0MQr/iSSlBFCCCGEuI5+jcvUFYyen51NXx9nbnphNkpDA8wdLDtUtNfQVH/F\non2fxnAhNpWR94/H1t0eQxND/li+gYb6tmtZqtVq+k4ZxKC5zUsgWGhrjox/egYD5wZxITaV9APJ\nHPxiJ1P+NV+vsGtHKGh9ylOn+jG4bLa9ttvGZINarSbwzhDdqIWmGkfpXM7W3Z7iC+0njWKWb6Cq\npILgByZi5WyDgZEB6579ptl9NjJteaWcK9HSyJ5G7V1rXXUtG57/VldM2szGguqySn59ajWqy2JW\nGjavYqC+1tkO0NVg0VGAuuHSswPNaC2Xfh2v32Xrbt+xpJ9C0SxR0tLPiNFlP1/G5ibc/OG9ZJ08\nT8bRdOK+3c+hVTuZ//7dKJTKK4q59RivvotGyuZ/l6i5VK5CCRxDM2Lmcu2NQCnU1iQBSI4Ki3wM\nOIBm9ElLiZWngafQjNQ5iWb6zWtcShB11EY004OCgd870L4SzWiYBiD3skSOEs1okpZq4TRmWTsa\nd0cSRJcrAOxa2qFNyGzSfpwRHh3RNHmTAzhFhUUqmoxwUmhjymnSJviybh3RjGzKabqxyRSnU9ok\n1MqosMjlTUbmANijqfEj/iSSlBFCCCGEuE5+jcvUW1q9slZFUlUt2/OqrqqIdE5CBn4T/XUv5/W1\n9ZRlF2PrcWnVFqWhEnWD/j9iO/VypvhcATbtrO7i2NMZx57ODF4wkg3Pf0fS1pMtJmXsvBxQN6jJ\nPZ2pm75UW1FDUVp+p1cvainejnDq5UzxhcJ2r6kp39B+bI38jdQ9SS3Wlam5WI2JpSk5CRmEhE/C\ne3gvACqLK6gsutjpGBvlnM7SFe2tq66l6FwBvScO0O1XN6jJS8zCRTt9qTyvlMqii9h5OnboWgtT\nC6kurWL4PeN0hZBT91ybEUvXg7mdBRYOlpRmF9N70oD2D9DyDe3HgVU7yDuT3WwUmbpBTV1VLcYW\nJpjZmDd7XgWpeVg7tzzyqymlgRKPAM1osaCFo/liwbucO5hCv2mDryhmOy8HchIy6Dvl0miZnIQM\n7LwcO9wHaBIvjUmtTjqKZhpOQdOllK9Q4w9qa7VoQoD14dERX4MuieCHZtUf0BTKrQEmAG0NFVqp\njfvXqLDI2eHREVvbiUvdJHl0uaOAM9AQHh2ReoVxX404NMkVx/DoCN0PZVRYpBWwGU16bkp4dMTl\nf8HsByzR1HhprCszEs3Urn1N2rwQFRbpER4dkaHdNgnNPW5rVI8STT7AAKjXxtOTS7V1xJ9EkjJC\nCCGEENfJ8ugkXUKmkUqtZnl00lUlZWzc7Unbd4buI/1QGiqJXb0HVa3+eaycbciOv4DvBH8MjAww\nszFn8C0j+fnxL9n57hb6TQ/A2MyY4guFnDuYwtjHplKWU8KpjXH4jPDFwtGKsuwSitLy6D9jSItx\n2Lrb4zPSl53vbmHsY1MxsTTh4Bc7MTI3xje0X6euqaV4O2LoHSFsfukHrLrZ0HNsX5RKJUXn8slL\nymLkfeNbPKbn2L6k7TvDtjfWMeTcSLwCe2Bma0Hxec2S2D1H96HP5IHYuNtzJiaebn3cqK+uZf/K\n7S2NRuiwo9/sxczGHAsHS2LX7MXAUInv+Ev3SWmgZO/H2wgOn4ihiRF7P96GnbcjHkN8OnStlk6a\n0Tzxvx3Bf9YQis8XcujLXVcU6zeLP8F/1tBWa6RcK4ELR7Pno62YWJriFdSTBlUDBck5VBSWM+TW\nUS0eM3BuEOcOnWV9xLcELQzBbYAXxhYmFKTkcuzHgwy/Zyzug7xxD/Bm78fbSNufjK2HPac2xVGR\nX95uUib9QDJl2SW4DvDE1MqMzOPnqKuqxVabHLuSmANuHsHvr/yCk68LHkO7cyE2VbMC2EvzWmzf\nGitnGy7mlZKfnINlN2uMzYwxMO7QK90aNCNBfosKi3wJTVFdT2A28HE7BXStosIiXbg0felNNCMp\n9rXS/gywICosMgTNCJElaKZOxQGER0eUR4VFvgtERoVF1qApuOsADA2Pjohq2lF4dMSn2uTIr1Fh\nkXM6kJhpzTY0BXJ/065MlIim3soUYFt4dMTu9uK+SnFo6veEAL+CLiHzO5rivnPQTL+y0LYvCo+O\nqA2PjjgdFRa5BU3B4X9o930CbNCuvIS2jwTgq6iwyKfQ3MvlaGrTlGnPtRDN9KmTaJbHDkQzbenH\n8OiImiZxjgZSO1lQWVwlScoIIYQQQlwnWSVVndreUcEPTGD7/zbx61OrMbEyZeCcIFS1+is4Bd01\nhl3vbmHt3VGo6lSER0fg0KMbs9+6k0Nf7OS3pWtQq9RYu9rSfZQfoCkkWpJZxO+v/kJVWRXmthb4\nju9PwC0jWo1l/FPT2fPxNjYv+1G3JPaMVxfoLZncES3F2xFegT00S4av2cvxnw6iMFBi627f5igG\nhULBxIjZnN58jNNbjhP3/QEUSgU2rnZ4j+hFj2DN6JnQJ6ex893N/PjIKiwcLAm8M0RXD+VKDL83\nlH0r/qAkowh7b0em/edmvalPSiMDhtw2ipjlGyjPL8O5jxtTXpynm9LU3rWa2Zoz/ukZHPxiJ/Hr\nj+DQvRujHpjAxue/63SsJRlFVJdd3fe0I/pNDcDI1IhjPx7k4Oc7MDAxwt7bsc1VrAyMDZkZeSsn\nfjlM4pYTHPhsBwbGhti629FrbD9d7Z4+YQMpTMtjh3Z1sf4zh9A92I/qdp6hiaUpafvOELtmD/U1\n9Vi72jLu8Wm4DfC84pi7j/Ij5KFJHPvxIHs/3oZlN2tGPxKmW8a+o3qG9CZtbxLrnv2G2ovVekti\nt0VbwHUM8DrwA2ADZKFZkam4ncNf0v4CTTLmMJqlqQtbaf8KmmTGZjT1Sb5AkxRqmqmN0J73RcAD\nzdSir1qJ/ZOrTcxoi+NO08a2As30n1w0iZrG83Yk7isSHh2higqL/By4A21SBk2dn8a/XM9cdkgo\nl2rZ3I5mSfDGIkbrgEcu63s68JH2eqq0ceuW30YzEiYC8EWTXDsHfAi8fdl5b0Nzf8SfSHE95o52\nlcDAQHVsbGxXhyGEEEIIAUDw6zFktpCAcbc1Y++zLY/iEEKIq6FQKI6o1errO8RJdFpUWGQ34BQQ\nFB4dkdbV8VwuKizSH02NIL/w6IjSro7n/5PmFb6EEEIIIcQ1sTSsN2ZG+tNdzIwMWBrWvI6JEEKI\nv6/w6Ig84F7g2q3nfm25AXdJQubPJyNlhBBCCCGuo6arL7nZmrE0rPdV1ZMRQoi2yEgZIW4sUlNG\nCCGEEOI6mjPYXZIwQgghhGiRTF8SQgghhBBCCCGE6AKSlBFCCCGEEEIIIYToApKUEUIIIYQQQggh\nhOgCkpQRQgghhBBCCCGE6AKSlBFCCCGEEEIIIYToApKUEUIIIYQQQgghhOgCkpQRQgghhBBCCCGE\n6AKSlBFCCCGEEEIIIYToApKUEUIIIYQQQgghhOgCkpQRQgghhBBCCCGE6AKSlBFCCCGEEEIIIYTo\nApKUEUIIIYQQQgghhOgCkpQRQgghhBBCCCGE6AJXlZRRKBT2CoViq0KhSNb+btdKuykKhSJJoVCk\nKBSKZ5tsX6ZQKDIVCsUx7a9pTfZFaNsnKRSKsKuJUwghhBBCCCGEEOKv5mpHyjwL/KFWq32BP7Sf\n9SgUCgPgQ2Aq0A+4TaFQ9GvS5G21Wh2g/bVJe0w/4FagPzAF+EjbjxBCCCGEEEIIIcTfwtUmZWYD\nX2r//CUwp4U2w4AUtVqdqlara4Fvtce11++3arW6Rq1WpwEp2n6EEEIIIYQQQggh/hauNinjrFar\ns7V/zgGcW2jjDlxo8jlDu63REoVCcUKhUHzeZPpTe8cIIYQQQgghhBBC3NDaTcooFIptCoUivoVf\neqNd1Gq1GlB38vxRQA8gAMgG/tvJ41EoFP9QKBSxCoUiNj8/v7OHCyGEEEIIIYQQQnQJw/YaqNXq\nia3tUygUuQqFwlWtVmcrFApXIK+FZpmAZ5PPHtptqNXq3CZ9rQA2tHdMC/F9CnwKEBgY2NmkkBBC\nCCGEEEIIIUSXuNrpS+uARdo/LwJ+a6HNYcBXoVB0VygUxmgK+K4D0CZyGs0F4pv0e6tCoTBRKBTd\nAV/g0FXGKoQQrTp3OJms+HNdHYYQQgghhBDi/5GrTcq8DkxSKBTJwETtZxQKhZtCodgEoFar64FH\ngGjgNPC9Wq1O0B7/pkKhOKlQKE4AocAT2mMSgO+BU8AW4GG1Wq26yljFNbDzgw3E/bSv1c9Xqray\nhg0vruZiQdlV99Xo0Ort7Pts61+mn+vhwtGz/PzUZ9f9PJuWrSV5Z3z7DTvp6Pd72PXRpmveb2dl\nnkwncesxHHz0y2Jdft3X6z5cDzveW8/xX/Z3uP3F/FJ+emIlJZmF1zGqzlPV1rNp2doOx5V2IInd\nUZuvc1RXprqskvUvrKaqtKKrQxFCCCGEEH8R7U5faotarS4EJrSwPQuY1uTzJqDZm5darV7YRt+v\nAq9eTXx/Z8UXCoh5+zccvJ0Y99isLotj5D0TURhcbW4PErcdw6WfJ5aO1gBUFJWz5eXvmrVz9fdm\n1OJJV30+0bKETbFkJ5xn4tJ5etsnPD0XA2Oj637+Q6u3Y9XNlr6TB+ttz4hL5eDX2/Ea2ougO8Ze\n03NeLCgjfsNhQh6YgomlaZttr8V92PHeegrTNDM3lQZKzO2t8Bnmh9/4gSiUiqvq+2pYOFgx/d+3\nY2zR9j3orNykTPZ8vJmZry3E2Myk08cbGBviO24AJ9cfYvSDU9tsq6qr59TmIwxfNF63LXXfac7H\nplCWUwyArbsD/acF4tBdPwGXsiuB5J0nqS6rwsbVjoFzRuDYw0W3X92g5lT0UdIPJFFbVYODdzcC\nbhqFtbOdrs3ZvafIiEulJLOQ+uo6pv7rNsxtLXT7Ta3N8RzSk9PRcQy5JaTT90IIIYQQQvz9XFVS\nRnSd9INJ9Azuy7nYZMpyi/VeDP5M1+IFrr62nvQDSYy6b3KzfcEPTMHWzV73WWlkcNXnE51nYmnW\npedPO5BE7wkDSdmVQMC8kRiZGV+zvi0drQmLuLlDba/VffAZ0Zv+U4eiqlORFX+OE78dQGGoxG/c\ngGvS/5VQKJWYWpt32fnb4hXYi5MbDlOeV4JVN9tW22UcS8PQ1EgvmZKfko3nkJ44dHdGaWhA8o6T\n7P54MxOXztMlgc8fSeHEuoMMvikYB59unN1zir2fRjPp2Zt0SZWkP46RsiuewNvGYOlow6noo+z5\neAuTn70JQxNNok5Vp8Kljyeu/t6c/O1gizH6DPdjx7vr8J8ZdEVJKiGEEEII8fciSZkbkKq2ngtH\nzjJ2yQxUdfWkHzjDwNnDdfsbR5kMWxhK6r7TFJ3Px6qbLUG3jwGFgqPf76E0qwhbdweC7hiHhYOV\n7tis+HOcjj5KWU4JptZmeA7pSb+wISgNW06G7PxgA9au9gyePwqAhnoVCZuPcOFICrWVNVi72NFv\nWiAufTxavZ6cUxdAoWj2L9cAJuYmLb4oNqgaOPr9HvJTsqgur8Lc1oLuI/vgO24ACoX+aINTW46S\nuvcUqjoVHgHdCZg/CgMjzVd/x3vrsfN0ZNDckbr2h1Zvp76mvtUROeoGNUkxx0nbn0R1eSWWjtb0\nmRiA55CegGYaSPRrPzDh6bnYujvo4v3l6c8Zce9E3Af4kH7wDMd+3seEp+Zi1c0GgBPrDpJ5LI2J\nS+e1mnRIP3iGU1uOUFtRTTc/d7r5uTVrk3VS+wxzNc/Qa2gv+k4erHuGGcfSOP37US4WlGFgZIiN\nqx0j7p5AVvw5ErceA+CnJ1YCEHTHWLwCfdm0bC2+oQPxHeuvu5YhC0LIOZ1BbmIGplZm9J8WqLsH\nLWlQNXBy3SHOHToDSgU+w3zRLNrWtoqicgrTchh+93gK03K5cPQsPYL76vY3jsQIfmAK8doXd3sv\nJ4YtDKU8r5Tjv+ynorCcbr5uBN42Ri+RmHYgkeTtJ6kouoi5nSU9Q/rSc3T/Zt+hRk3vA0BtVQ0n\nfztIdsJ5VHUqbD0cGDh7BHaejm1ek6Gxoe577TvWn+z4c2SfPKdLymQcS+N09FEu5pdiYmVGz+B+\n+E0YqIvr8jig5e9yU+cOnSFl9yku5pdiYGSAUy9XBs4ZiZmNJo7Lv7eN93X0Q9OIX3+IspxirF3s\nGLJgtO57fa209xxMLM1w8O7GhaNn6TdlaKv9XDh6Ftf+Xnrbht81Xu/zkJtDyDqZTm5SBpaO/QBI\n3nESn+F+dB/RG4CA+aPIOX2BtH2n6T8tEHWDmpRdCfSZGID7wO4ABN0+lg0vreFCXKruuMbnV5je\nUs17DVt3B4wtTMk6eQ6fYX6duU1CCCGEEOJvSJIyN6CM42mY21ti42aPV2AvDn4Zg/+MIJSXTSM6\nteUoA+eMwMLBimM/7uXgV9t1L8+mlmYcXruTYz/vI/j+MAByEjM4vHoHg+aOxLGnC5XFF4n7YS8N\n9Q16SZ+2xH6zi4rCMoIWhmJmY0HO6QvsW/k745+Y3eqLXEFqDnYeDq2+CLdE3dCAuZ0FwxdNwMTS\nlKL0PI5+vwcTC1O8m7zo5CdnYWBkwJiHplNZcpEj3+4mfmMsg+aM6PC5Lhe/8TDZ8ecYfHMwVk42\nFKblcOTbXRiZm7SZfGrKZ7gfOacvcHj1dsY9Nov8lGxSdsYz5qHprSZkCtNyOfLdLvpPC8R9YHfy\nkjM5temIXpvsUxc4vFb7DHtonuHR7/fQoGpgwMxhVJVUcOjrGAbMGo7bAG/qa+ooTNO8QHoN6UVZ\ndgl5ZzIZHa6ZJtLWiJTT0XH4zwhiwIwgUvcnErt2Jw49XPSmazR1JuYE5w6dYciC0Vi72nF2dwIZ\ncanYeTm1ea/OHTyDc19PjM1M8ArsRereRL2kjC6eLUcImDcSQxMjDn29nYNfxqA0NGDorWMAOLBq\nG6d/P8aguZpnf3bvKRJ/P0bAvJHYejhSmlXE0e93ozQwaLH/y6kb1Oz9JBoTS1OC7w/D0MyYc4fO\nsPujTf/H3n3HV1ne/x9/39l7kj1IgEDYK4BMAUEQreDeo9bV2uG3raPLfn9d0lpbv21t1apVq+JE\nQJChqOy9RwgEkpAN2TvnnOT+/ZFwyCGDsLxRXs/Hg4c597mu677OfQ7IeXNdn1tX/vxG+QR2f9WJ\nm6e7bLWNkqSynGPa9MZKDZgxQvHDe6ss55h2fLBOHr6e6j1+QLfHPFVzc7MGzhqpgIhg2WobtOfj\nzdry5hea9MjVXfbbt2SLBl87Rt6Bvtr54XptefNLTXv8+jP6/dqV7r4PoYkRKjlc1Ok4ZrOp0qxi\n9Ryd0uX5mh1NanY0O1epNNkcqsgvU+r0k1vmDMNQVL94Z7hSU1KlxpoGRfWLc7bx8PZUeHKUyrKL\nnaFMd4UlRqjkcCGhDAAAAM650C8skL0pQ4lpfSRJPXrHyN3TQwV72t81JmXyIMUMSFBQVIhSJg9W\ndXGFek8coMiUWAXFhKr3xAE6nlnobJ/x6U71nTpESWP6KqBHkCJTYjXoW6N0ZH16t1Y01JRUKXfH\nYY255wpF9I5RQI8g9Zk4UNH9E5S14UCn/erKa+QT1PEX+VX/WKwFT7zm/HXiS5m7p4cGzBypsMQI\n+YcFKmFEbyWPTVXujiMu/d3c3TTy1kkKiglVdP8EDbw6TUfWp6vJ5jjt6+mIvcGmzNX7NPK2SYpO\njZd/eKAS01LUc0w/HVm7/4zGGnHzBDVU12vn/PXaOm+V+k4doh69ozttf2jVXkX1i1fqtGEKjAxW\n7/ED2q0KOPDpDvW7YqiSRrd5D69ueQ8lqb6yVmazqfihyfIPC1RwTJh6jUuVd4Cv3L085OHtIcPN\nkE+Qn3yC/JwrijrSc1SKEkf2UUBEsAbNSpMklR7p/Etz5uq96jdtqOKHJZT63xQAACAASURBVCso\nKkTDrh/XrobL6DunuNSTMZtN5Ww55Py8xw/tpari8g6Lvg6claYevaIVEheu5LGpKs0q1tA5YxSW\nGKGwxAj1HJWi45kFba7VTg2ZPUZxQ5PlHx6o2ME91XfqEB1e17338djBfFUVl2vMvVcoNDFCgRHB\nGnT1KPmG+OvotsPdGsNsNlW476iOHSxwrno69OUeRabEqf+MEQqMDFbPUSlKuXyQDq7c3a0xO5N8\nWaqi+7fUbQrrGalhN4zT8cxCNVTVddlv4Kw0RfSJUVBUS62fqqJyNVbXn9Nc2uru++AT7KfasupO\nx7HVNcrRaJdvJ3+WnLB3yVZ5+no5f+801jZIpimfQNetad6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start=50; end=100\n", "X = fac0[start:end]\n", "Y = fac2[start:end]\n", "plt.figure(figsize=(15,15))\n", "plt.scatter(X, Y)\n", "for i, x, y in zip(topMovies[start:end], X, Y):\n", " plt.text(x,y,movie_names[movies[i]], color=np.random.rand(3)*0.7, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than creating a special purpose architecture (like our dot-product with bias earlier), it's often both easier and more accurate to use a standard neural network. Let's try it! Here, we simply concatenate the user and movie embeddings into a single vector, which we feed into the neural net." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4)\n", "movie_in, m = embedding_input('movie_in', n_movies, n_factors, 1e-4)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/karel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " \"\"\"Entry point for launching an IPython kernel.\n", "/home/karel/anaconda3/lib/python3.6/site-packages/keras/legacy/layers.py:456: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", " name=name)\n" ] } ], "source": [ "x = merge([u, m], mode='concat')\n", "x = Flatten()(x)\n", "x = Dropout(0.3)(x)\n", "x = Dense(80, activation='relu')(x)\n", "x = Dropout(0.75)(x)\n", "x = Dense(1)(x)\n", "nn = Model([user_in, movie_in], x)\n", "nn.compile(Adam(0.001), loss='mse')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/8\n", "80151/80151 [==============================] - 2s - loss: 2.4185 - val_loss: 0.9250\n", "Epoch 2/8\n", "80151/80151 [==============================] - 2s - loss: 1.4447 - val_loss: 0.8808\n", "Epoch 3/8\n", "80151/80151 [==============================] - 2s - loss: 1.2212 - val_loss: 0.8739\n", "Epoch 4/8\n", "80151/80151 [==============================] - 2s - loss: 1.0343 - val_loss: 0.8554\n", "Epoch 5/8\n", "80151/80151 [==============================] - 2s - loss: 0.9081 - val_loss: 0.8446\n", "Epoch 6/8\n", "80151/80151 [==============================] - 2s - loss: 0.8469 - val_loss: 0.8379\n", "Epoch 7/8\n", "80151/80151 [==============================] - 2s - loss: 0.8186 - val_loss: 0.8406\n", "Epoch 8/8\n", "80151/80151 [==============================] - 2s - loss: 0.8087 - val_loss: 0.8426\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nn.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=8, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 80151 samples, validate on 19853 samples\n", "Epoch 1/10\n", "80151/80151 [==============================] - 2s - loss: 0.7943 - val_loss: 0.8394\n", "Epoch 2/10\n", "80151/80151 [==============================] - 2s - loss: 0.7929 - val_loss: 0.8439\n", "Epoch 3/10\n", "80151/80151 [==============================] - 2s - loss: 0.7892 - val_loss: 0.8426\n", "Epoch 4/10\n", "80151/80151 [==============================] - 2s - loss: 0.7946 - val_loss: 0.8409\n", "Epoch 5/10\n", "80151/80151 [==============================] - 2s - loss: 0.7909 - val_loss: 0.8401\n", "Epoch 6/10\n", "80151/80151 [==============================] - 2s - loss: 0.7907 - val_loss: 0.8359\n", "Epoch 7/10\n", "80151/80151 [==============================] - 2s - loss: 0.7888 - val_loss: 0.8395\n", "Epoch 8/10\n", "80151/80151 [==============================] - 2s - loss: 0.7898 - val_loss: 0.8378\n", "Epoch 9/10\n", "80151/80151 [==============================] - 2s - loss: 0.7873 - val_loss: 0.8406\n", "Epoch 10/10\n", "80151/80151 [==============================] - 2s - loss: 0.7853 - val_loss: 0.8406\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.optimizer.lr=0.0001\n", "nn.fit([trn.userId, trn.movieId], trn.rating, batch_size=64, epochs=10, \n", " validation_data=([val.userId, val.movieId], val.rating))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This improves on our already impressive accuracy even further!" ] } ], "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }