13-recsys-intro-surprise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用Surprise构建推荐系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T14:58:19.010939Z",
     "start_time": "2020-08-31T14:58:17.934111Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from surprise import Dataset\n",
    "from surprise import Reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T14:58:32.236700Z",
     "start_time": "2020-08-31T14:58:32.229934Z"
    }
   },
   "outputs": [],
   "source": [
    "critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,\n",
    "      'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,\n",
    "      'The Night Listener': 3.0},\n",
    "     'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,\n",
    "      'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,\n",
    "      'You, Me and Dupree': 3.5},\n",
    "     'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,\n",
    "      'Superman Returns': 3.5, 'The Night Listener': 4.0},\n",
    "     'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,\n",
    "      'The Night Listener': 4.5, 'Superman Returns': 4.0,\n",
    "      'You, Me and Dupree': 2.5},\n",
    "     'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,\n",
    "      'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,\n",
    "      'You, Me and Dupree': 2.0},\n",
    "     'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,\n",
    "      'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},\n",
    "     'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:01:42.234635Z",
     "start_time": "2020-08-31T15:01:42.231117Z"
    }
   },
   "outputs": [],
   "source": [
    "dat = []\n",
    "for i in critics:\n",
    "    for j in critics[i]: \n",
    "        dat.append([i, j, critics[i][j]])\n",
    "\n",
    "df = pd.DataFrame(dat, columns = ['user', 'item', 'rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:16:05.590202Z",
     "start_time": "2020-08-31T15:16:05.580848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>item</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lisa Rose</td>\n",
       "      <td>Lady in the Water</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lisa Rose</td>\n",
       "      <td>Snakes on a Plane</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lisa Rose</td>\n",
       "      <td>Just My Luck</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lisa Rose</td>\n",
       "      <td>Superman Returns</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lisa Rose</td>\n",
       "      <td>You, Me and Dupree</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user                item  rating\n",
       "0  Lisa Rose   Lady in the Water     2.5\n",
       "1  Lisa Rose   Snakes on a Plane     3.5\n",
       "2  Lisa Rose        Just My Luck     3.0\n",
       "3  Lisa Rose    Superman Returns     3.5\n",
       "4  Lisa Rose  You, Me and Dupree     2.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:02:45.619738Z",
     "start_time": "2020-08-31T15:02:45.612335Z"
    }
   },
   "outputs": [],
   "source": [
    "from surprise import Dataset\n",
    "from surprise import Reader\n",
    "\n",
    "# Loads Pandas dataframe\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df[[\"user\", \"item\", \"rating\"]], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:12:33.549619Z",
     "start_time": "2020-08-31T15:12:33.546004Z"
    }
   },
   "outputs": [],
   "source": [
    "from surprise import KNNBasic\n",
    "\n",
    "sim_options_item = {'name': 'cosine',\n",
    "               'user_based': False  # compute  similarities between items\n",
    "                   }\n",
    "sim_options_user = {'name': 'cosine',\n",
    "               'user_based': True  # compute  similarities between items\n",
    "                   }\n",
    "algo_userCF = KNNBasic(sim_options=sim_options_user)\n",
    "algo_itemCF = KNNBasic(sim_options=sim_options_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:13:27.247055Z",
     "start_time": "2020-08-31T15:13:27.232466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Evaluating RMSE, MAE of algorithm KNNBasic on 5 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
      "RMSE (testset)    0.5594  1.0800  0.7700  0.3077  0.7342  0.6902  0.2544  \n",
      "MAE (testset)     0.4107  0.9477  0.6591  0.2540  0.6568  0.5857  0.2376  \n",
      "Fit time          0.00    0.00    0.00    0.00    0.00    0.00    0.00    \n",
      "Test time         0.00    0.00    0.00    0.00    0.00    0.00    0.00    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([0.55942374, 1.07995147, 0.76995934, 0.30772061, 0.73418814]),\n",
       " 'test_mae': array([0.41069342, 0.94772278, 0.65913578, 0.25399999, 0.65678592]),\n",
       " 'fit_time': (0.001051187515258789,\n",
       "  0.0003440380096435547,\n",
       "  0.0001010894775390625,\n",
       "  9.703636169433594e-05,\n",
       "  9.989738464355469e-05),\n",
       " 'test_time': (0.00010585784912109375,\n",
       "  9.703636169433594e-05,\n",
       "  8.988380432128906e-05,\n",
       "  0.00010228157043457031,\n",
       "  8.320808410644531e-05)}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from surprise.model_selection import cross_validate\n",
    "\n",
    "# Run 5-fold cross-validation and print results.\n",
    "cross_validate(algo_userCF, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:30:24.986962Z",
     "start_time": "2020-08-31T15:30:24.983212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid='Toby', iid='The Night Listener', r_ui=None, est=3.6392076086091056, details={'actual_k': 4, 'was_impossible': False})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_userCF.predict('Toby', 'The Night Listener')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:30:44.213730Z",
     "start_time": "2020-08-31T15:30:44.209962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid='Toby', iid='Lady in the Water', r_ui=None, est=2.7929167902350653, details={'actual_k': 5, 'was_impossible': False})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_userCF.predict('Toby', 'Lady in the Water')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:31:21.661239Z",
     "start_time": "2020-08-31T15:31:21.657178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid='Toby', iid='Just My Luck', r_ui=None, est=2.283675166153972, details={'actual_k': 2, 'was_impossible': False})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_userCF.predict('Toby', 'Just My Luck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T15:13:43.993003Z",
     "start_time": "2020-08-31T15:13:43.984372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Evaluating RMSE, MAE of algorithm KNNBasic on 5 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
      "RMSE (testset)    1.1602  0.7796  0.8432  1.3696  1.1644  1.0634  0.2202  \n",
      "MAE (testset)     0.8664  0.6102  0.7048  0.9182  0.9780  0.8155  0.1371  \n",
      "Fit time          0.00    0.00    0.00    0.00    0.00    0.00    0.00    \n",
      "Test time         0.00    0.00    0.00    0.00    0.00    0.00    0.00    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([1.16017293, 0.77964545, 0.84315228, 1.3695608 , 1.1644471 ]),\n",
       " 'test_mae': array([0.86642863, 0.61020025, 0.70480684, 0.91823044, 0.97800152]),\n",
       " 'fit_time': (0.00036406517028808594,\n",
       "  0.00010514259338378906,\n",
       "  0.00011110305786132812,\n",
       "  0.00010418891906738281,\n",
       "  0.00012087821960449219),\n",
       " 'test_time': (0.0001251697540283203,\n",
       "  9.417533874511719e-05,\n",
       "  8.082389831542969e-05,\n",
       "  7.796287536621094e-05,\n",
       "  8.320808410644531e-05)}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run 5-fold cross-validation and print results.\n",
    "cross_validate(algo_itemCF, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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