SVD_RS-NuevoDataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "from surprise import SVD\n",
    "from surprise import Dataset\n",
    "from surprise import accuracy\n",
    "from surprise import Reader\n",
    "\n",
    "from surprise.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from surprise.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings = pd.read_csv('ml-latest-small/ratings.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>610</td>\n",
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       "      <td>5.0</td>\n",
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       "      <th>100834</th>\n",
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      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargamos el dataset nuestro\n",
    "\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId', 'rating']], reader=reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Asignamos los datos a una lista raw_ratings, sin indices\n",
    "raw_ratings = data.raw_ratings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(raw_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separamos en train y test\n",
    "threshold = int(.8 * len(raw_ratings))\n",
    "train_raw_ratings = raw_ratings[:threshold]\n",
    "test_raw_ratings = raw_ratings[threshold:]\n",
    "\n",
    "data.raw_ratings = train_raw_ratings \n",
    "# Reemplaza en data con los valores de entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid Search...\n"
     ]
    }
   ],
   "source": [
    "# Utilizamos gridsearch para obetener los mejores parametros para el algoritmo\n",
    "print('Grid Search...')\n",
    "param_grid = {'n_factors': [50,100,150],'n_epochs':[25,50,75],'lr_all': [0.005,0.01],'reg_all':[0.1,0.5,1]}\n",
    "grid_search = GridSearchCV(SVD, param_grid, measures=['rmse'], cv=3, n_jobs = -1)\n",
    "grid_search.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8724699875762031\n",
      "{'n_factors': 150, 'n_epochs': 75, 'lr_all': 0.01, 'reg_all': 0.1}\n"
     ]
    }
   ],
   "source": [
    "algo = grid_search.best_estimator['rmse']\n",
    "\n",
    "print(grid_search.best_score['rmse'])\n",
    "print(grid_search.best_params['rmse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<surprise.prediction_algorithms.matrix_factorization.SVD object at 0x7faa52ff9340>\n"
     ]
    }
   ],
   "source": [
    "print(algo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7faa52ff9340>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se reentrena el set de entrenamiento con el mejor conjunto de parametros obtenido\n",
    "\n",
    "trainset = data.build_full_trainset()\n",
    "algo.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Trainset,   RMSE: 0.4985\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4984600557347057"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Con la primer funcion creamos un set de test a partir del set de entrenamiento\n",
    "predictions = algo.test(trainset.build_testset())\n",
    "print('Accuracy on Trainset,', end='   ')\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Testset, RMSE: 0.8368\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8367634899030604"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prueba el modelo con el set de test\n",
    "testset = data.construct_testset(test_raw_ratings)  \n",
    "predictions2 = algo.test(testset)\n",
    "print('Accuracy on Testset,', end=' ')\n",
    "accuracy.rmse(predictions2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "p= predictions+predictions2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "610\n",
      "8989\n",
      "(8989, 150)\n",
      "(610, 150)\n"
     ]
    }
   ],
   "source": [
    "print(trainset.n_users)\n",
    "print(trainset.n_items)\n",
    "print(algo.qi.shape)\n",
    "print(algo.pu.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions (Tiene las predicciones para un usuario y un item, el rating reak y el estimado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import os\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_n(predictions, n = 10):\n",
    "    \n",
    "    top_n = defaultdict(list)\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        top_n[uid].append((iid, est))\n",
    "        \n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "        top_n[uid] = user_ratings[:n]\n",
    "    \n",
    "    return top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n = 10\n",
    "top_pred = get_top_n(p, n = top_n)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies = pd.read_csv('ml-latest-small/movies.csv')\n",
    "\n",
    "#rid_to_name, name_to_rid = read_item_names('ml-latest-small/movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                      Adventure|Children|Fantasy  \n",
       "2                                  Comedy|Romance  \n",
       "3                            Comedy|Drama|Romance  \n",
       "4                                          Comedy  \n",
       "...                                           ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9742 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tituloPelicula(iid):\n",
    "    return df_movies.loc[df_movies.movieId == iid, 'title'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recomendaciones para el usuario 100: \n",
      "Movie: 1958 - Terms of Endearment (1983) , rating: 4.631887341457577\n",
      "Movie: 2423 - Christmas Vacation (National Lampoon's Christmas Vacation) (1989) , rating: 4.4445952413381375\n",
      "Movie: 6183 - Pillow Talk (1959) , rating: 4.346143640794372\n",
      "Movie: 4995 - Beautiful Mind, A (2001) , rating: 4.340347116081119\n",
      "Movie: 838 - Emma (1996) , rating: 4.323807198093856\n",
      "Movie: 932 - Affair to Remember, An (1957) , rating: 4.31134613548217\n",
      "Movie: 356 - Forrest Gump (1994) , rating: 4.3060533725600365\n",
      "Movie: 1246 - Dead Poets Society (1989) , rating: 4.30425924175819\n",
      "Movie: 5380 - Importance of Being Earnest, The (2002) , rating: 4.297965631302695\n",
      "Movie: 899 - Singin' in the Rain (1952) , rating: 4.28862157176267\n"
     ]
    }
   ],
   "source": [
    "# User raw Id\n",
    "uid_list = [100]\n",
    "\n",
    "# Imprime las 10 recomendaciones de peliculas para un determinado usuario\n",
    "for uid, user_ratings in top_pred.items():\n",
    "    if uid in uid_list:\n",
    "        print(\"Recomendaciones para el usuario \"+ str(uid) + ': ')\n",
    "        for (iid, rating) in user_ratings:\n",
    "            movie = tituloPelicula(iid)\n",
    "            print('Movie:', iid, '-', movie, ', rating:', str(rating))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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