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pedr-wine-rec.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyMkfkdLJ0GEJ/qMgTH0NYHe" | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "id": "3mgsncFvY9ks" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "! [ -e /content ] && pip install -Uqq fastbook\n", | |
| "import fastbook\n", | |
| "fastbook.setup_book()\n", | |
| "\n", | |
| "from fastbook import *\n", | |
| "from fastai.collab import *\n", | |
| "from fastai.tabular.all import *" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "wines = pd.read_csv('/content/XWines_Slim_1K_wines.csv',\n", | |
| " usecols=['WineID', 'WineName', 'Type', 'Grapes',\n", | |
| " 'Body', 'Harmonize', 'Code'],\n", | |
| " delimiter=',', header=None, skiprows=1,\n", | |
| " names=['WineID', 'WineName', 'Type', 'Elaborate', 'Grapes',\n", | |
| " 'Harmonize', 'ABV', 'Body', 'Acidity', 'Code',\n", | |
| " 'Country', 'RegionID', 'RegionName', 'WineryID',\n", | |
| " 'WineryName','Website', 'Vintages'],\n", | |
| " dtype={'WineID': int, 'WineName': str, 'Type': str,\n", | |
| " 'Grapes': str, 'Harmonize': str, 'Body': str,\n", | |
| " 'Code': str})\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "ratings = pd.read_csv('/content/XWines_Slim_150K_ratings.csv', usecols=['UserID', 'WineID', 'Rating'], delimiter=',', header=None, skiprows=1,\n", | |
| " names=['RatingID', 'UserID', 'WineID', 'Vintage', 'Rating', 'Date'],\n", | |
| " dtype={'UserID': int, 'WineID': int, 'Rating': float})" | |
| ], | |
| "metadata": { | |
| "id": "2PeMkTLzazqw" | |
| }, | |
| "execution_count": 84, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "ratings = ratings.merge(wines)\n", | |
| "ratings.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "3KL0e4GWZ2ct", | |
| "outputId": "3bc692a3-4d3e-4450-bc57-cd0071d0bbd5" | |
| }, | |
| "execution_count": 85, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " UserID WineID Rating WineName Type \\\n", | |
| "0 1356810 103471 4.5 Presidential Colheita Port Dessert/Port \n", | |
| "1 1212696 103471 4.5 Presidential Colheita Port Dessert/Port \n", | |
| "2 1653207 103471 4.0 Presidential Colheita Port Dessert/Port \n", | |
| "3 1314309 103471 4.0 Presidential Colheita Port Dessert/Port \n", | |
| "4 1201703 103471 4.0 Presidential Colheita Port Dessert/Port \n", | |
| "\n", | |
| " Grapes \\\n", | |
| "0 ['Touriga Nacional', 'Tinta Roriz'] \n", | |
| "1 ['Touriga Nacional', 'Tinta Roriz'] \n", | |
| "2 ['Touriga Nacional', 'Tinta Roriz'] \n", | |
| "3 ['Touriga Nacional', 'Tinta Roriz'] \n", | |
| "4 ['Touriga Nacional', 'Tinta Roriz'] \n", | |
| "\n", | |
| " Harmonize Body Code \n", | |
| "0 ['Beef', 'Maturated Cheese', 'Hard Cheese'] Very full-bodied PT \n", | |
| "1 ['Beef', 'Maturated Cheese', 'Hard Cheese'] Very full-bodied PT \n", | |
| "2 ['Beef', 'Maturated Cheese', 'Hard Cheese'] Very full-bodied PT \n", | |
| "3 ['Beef', 'Maturated Cheese', 'Hard Cheese'] Very full-bodied PT \n", | |
| "4 ['Beef', 'Maturated Cheese', 'Hard Cheese'] Very full-bodied PT " | |
| ], | |
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| "\n", | |
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| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
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| " <thead>\n", | |
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| " <th></th>\n", | |
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| " <th>0</th>\n", | |
| " <td>1356810</td>\n", | |
| " <td>103471</td>\n", | |
| " <td>4.5</td>\n", | |
| " <td>Presidential Colheita Port</td>\n", | |
| " <td>Dessert/Port</td>\n", | |
| " <td>['Touriga Nacional', 'Tinta Roriz']</td>\n", | |
| " <td>['Beef', 'Maturated Cheese', 'Hard Cheese']</td>\n", | |
| " <td>Very full-bodied</td>\n", | |
| " <td>PT</td>\n", | |
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| " <th>1</th>\n", | |
| " <td>1212696</td>\n", | |
| " <td>103471</td>\n", | |
| " <td>4.5</td>\n", | |
| " <td>Presidential Colheita Port</td>\n", | |
| " <td>Dessert/Port</td>\n", | |
| " <td>['Touriga Nacional', 'Tinta Roriz']</td>\n", | |
| " <td>['Beef', 'Maturated Cheese', 'Hard Cheese']</td>\n", | |
| " <td>Very full-bodied</td>\n", | |
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| " <th>2</th>\n", | |
| " <td>1653207</td>\n", | |
| " <td>103471</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>Presidential Colheita Port</td>\n", | |
| " <td>Dessert/Port</td>\n", | |
| " <td>['Touriga Nacional', 'Tinta Roriz']</td>\n", | |
| " <td>['Beef', 'Maturated Cheese', 'Hard Cheese']</td>\n", | |
| " <td>Very full-bodied</td>\n", | |
| " <td>PT</td>\n", | |
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| " <th>3</th>\n", | |
| " <td>1314309</td>\n", | |
| " <td>103471</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>Presidential Colheita Port</td>\n", | |
| " <td>Dessert/Port</td>\n", | |
| " <td>['Touriga Nacional', 'Tinta Roriz']</td>\n", | |
| " <td>['Beef', 'Maturated Cheese', 'Hard Cheese']</td>\n", | |
| " <td>Very full-bodied</td>\n", | |
| " <td>PT</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1201703</td>\n", | |
| " <td>103471</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>Presidential Colheita Port</td>\n", | |
| " <td>Dessert/Port</td>\n", | |
| " <td>['Touriga Nacional', 'Tinta Roriz']</td>\n", | |
| " <td>['Beef', 'Maturated Cheese', 'Hard Cheese']</td>\n", | |
| " <td>Very full-bodied</td>\n", | |
| " <td>PT</td>\n", | |
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| " const element = document.querySelector('#df-e1519468-01b4-4ee5-8ab9-fd43d42fef20');\n", | |
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| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
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| "metadata": {}, | |
| "execution_count": 85 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dls = CollabDataLoaders.from_df(ratings, item_name='WineName', bs=64)\n", | |
| "dls.show_batch()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 363 | |
| }, | |
| "id": "lnhX4Sa_Z0Kl", | |
| "outputId": "20be8b31-2298-4643-92ed-3da944f19af4" | |
| }, | |
| "execution_count": 86, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>UserID</th>\n", | |
| " <th>WineName</th>\n", | |
| " <th>Rating</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1103573</td>\n", | |
| " <td>Crianza</td>\n", | |
| " <td>3.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1289776</td>\n", | |
| " <td>Saint-Julien (Grand Cru Classé)</td>\n", | |
| " <td>4.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1208908</td>\n", | |
| " <td>Pedro Ximénez Murillo Selección del Centenario</td>\n", | |
| " <td>4.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1156121</td>\n", | |
| " <td>Koonunga Hill Shiraz-Cabernet</td>\n", | |
| " <td>3.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1176768</td>\n", | |
| " <td>Grand Vintage Brut Champagne</td>\n", | |
| " <td>4.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>1360720</td>\n", | |
| " <td>Blu Prosecco Extra Dry</td>\n", | |
| " <td>4.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>1394003</td>\n", | |
| " <td>Red Blend</td>\n", | |
| " <td>3.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>1153307</td>\n", | |
| " <td>Esporão Reserva Tinto</td>\n", | |
| " <td>3.5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>1378235</td>\n", | |
| " <td>Riesling</td>\n", | |
| " <td>4.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>1128562</td>\n", | |
| " <td>Blanc de Blancs Brut Champagne</td>\n", | |
| " <td>5.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "n_users = len(dls.classes['UserID'])\n", | |
| "n_wines = len(dls.classes['WineName'])\n", | |
| "n_factors = 5\n", | |
| "\n", | |
| "user_factors = torch.randn(n_users, n_factors)\n", | |
| "wine_factors = torch.randn(n_wines, n_factors)" | |
| ], | |
| "metadata": { | |
| "id": "kjlNMfvxerkM" | |
| }, | |
| "execution_count": 87, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class DotProduct(Module):\n", | |
| " def __init__(self, n_users, n_wines, n_factors, y_range=(0,5.5)):\n", | |
| " self.user_factors = Embedding(n_users, n_factors)\n", | |
| " self.user_bias = Embedding(n_users, 1)\n", | |
| " self.wine_factors = Embedding(n_wines, n_factors)\n", | |
| " self.wine_bias = Embedding(n_wines, 1)\n", | |
| " self.y_range = y_range\n", | |
| "\n", | |
| " def forward(self, x):\n", | |
| " users = self.user_factors(x[:,0])\n", | |
| " wines = self.wine_factors(x[:,1])\n", | |
| " res = (users * wines).sum(dim=1, keepdim=True)\n", | |
| " res += self.user_bias(x[:,0]) + self.wine_bias(x[:,1])\n", | |
| " return sigmoid_range(res, *self.y_range)" | |
| ], | |
| "metadata": { | |
| "id": "Z48J8LR_h5J6" | |
| }, | |
| "execution_count": 103, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = DotProduct(n_users, n_wines, 50)\n", | |
| "learn = Learner(dls, model, loss_func=MSELossFlat())\n", | |
| "learn.fit_one_cycle(5, 5e-3)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "YGEdQKv7fFTh", | |
| "outputId": "3ee372dc-3a94-4f5c-f5d4-e7ba8e117d8d" | |
| }, | |
| "execution_count": 106, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| "<style>\n", | |
| " /* Turns off some styling */\n", | |
| " progress {\n", | |
| " /* gets rid of default border in Firefox and Opera. */\n", | |
| " border: none;\n", | |
| " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", | |
| " background-size: auto;\n", | |
| " }\n", | |
| " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n", | |
| " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n", | |
| " }\n", | |
| " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", | |
| " background: #F44336;\n", | |
| " }\n", | |
| "</style>\n" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>0.324220</td>\n", | |
| " <td>0.305056</td>\n", | |
| " <td>00:27</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>0.255239</td>\n", | |
| " <td>0.274921</td>\n", | |
| " <td>00:30</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.161736</td>\n", | |
| " <td>0.289275</td>\n", | |
| " <td>00:28</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.086282</td>\n", | |
| " <td>0.291900</td>\n", | |
| " <td>00:28</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.043770</td>\n", | |
| " <td>0.294777</td>\n", | |
| " <td>00:28</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "embs = get_emb_sz(dls)\n", | |
| "embs" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "HL4M47cToTUg", | |
| "outputId": "f4757205-16bd-49a6-d042-3b963dd2e47f" | |
| }, | |
| "execution_count": 108, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[(10507, 286), (804, 68)]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 108 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class CollabNN(Module):\n", | |
| " def __init__(self, user_sz, item_sz, y_range=(0,5.5), n_act=100):\n", | |
| " self.user_factors = Embedding(*user_sz)\n", | |
| " self.item_factors = Embedding(*item_sz)\n", | |
| " self.layers = nn.Sequential(\n", | |
| " nn.Linear(user_sz[1]+item_sz[1], n_act),\n", | |
| " nn.ReLU(),\n", | |
| " nn.Linear(n_act, 1))\n", | |
| " self.y_range = y_range\n", | |
| "\n", | |
| " def forward(self, x):\n", | |
| " embs = self.user_factors(x[:,0]),self.item_factors(x[:,1])\n", | |
| " x = self.layers(torch.cat(embs, dim=1))\n", | |
| " return sigmoid_range(x, *self.y_range)" | |
| ], | |
| "metadata": { | |
| "id": "43TPrOSuoZ8z" | |
| }, | |
| "execution_count": 109, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = CollabNN(*embs)" | |
| ], | |
| "metadata": { | |
| "id": "auFEYOgHocjq" | |
| }, | |
| "execution_count": 110, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "learn = collab_learner(dls, use_nn=True, y_range=(0, 5.5), layers=[100,50])\n", | |
| "learn.fit_one_cycle(5, 5e-3, wd=0.1)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "xTKnuKdMofKE", | |
| "outputId": "f813b759-e079-4063-89c2-020f8c26ef26" | |
| }, | |
| "execution_count": 112, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| "<style>\n", | |
| " /* Turns off some styling */\n", | |
| " progress {\n", | |
| " /* gets rid of default border in Firefox and Opera. */\n", | |
| " border: none;\n", | |
| " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", | |
| " background-size: auto;\n", | |
| " }\n", | |
| " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n", | |
| " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n", | |
| " }\n", | |
| " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", | |
| " background: #F44336;\n", | |
| " }\n", | |
| "</style>\n" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>0.293264</td>\n", | |
| " <td>0.282452</td>\n", | |
| " <td>02:05</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>0.262291</td>\n", | |
| " <td>0.260737</td>\n", | |
| " <td>01:33</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.249280</td>\n", | |
| " <td>0.254707</td>\n", | |
| " <td>01:32</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.228655</td>\n", | |
| " <td>0.253738</td>\n", | |
| " <td>01:28</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.188885</td>\n", | |
| " <td>0.266842</td>\n", | |
| " <td>01:31</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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