{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "***\n",
    "***\n",
    "# 使用Turicreate进行音乐推荐\n",
    "***\n",
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:52:36.245161Z",
     "start_time": "2019-06-15T06:52:35.598667Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import turicreate as tc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 下载数据\n",
    "http://s3.amazonaws.com/dato-datasets/millionsong/10000.txt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:52:43.170306Z",
     "start_time": "2019-06-15T06:52:40.686881Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#train_file = 'http://s3.amazonaws.com/dato-datasets/millionsong/10000.txt'\n",
    "train_file = '/Users/datalab/bigdata/cjc/millionsong/song_usage_10000.txt'\n",
    "sf = tc.SFrame.read_csv(train_file, header=False, delimiter='\\t', verbose=False)\n",
    "sf = sf.rename({'X1':'user_id', 'X2':'music_id', 'X3':'rating'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:53:08.135628Z",
     "start_time": "2019-06-15T06:53:08.131503Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "train_set, test_set = sf.random_split(0.8, seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:53:19.922705Z",
     "start_time": "2019-06-15T06:53:18.188121Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 1599753 observations with 76085 users and 10000 items.</pre>"
      ],
      "text/plain": [
       "    Data has 1599753 observations with 76085 users and 10000 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.907738s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.907738s"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>1599753 observations to process; with 10000 unique items.</pre>"
      ],
      "text/plain": [
       "1599753 observations to process; with 10000 unique items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popularity_model = tc.popularity_recommender.create(train_set, \n",
    "                                                    'user_id', 'music_id', \n",
    "                                                    target = 'rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:53:45.311295Z",
     "start_time": "2019-06-15T06:53:42.362326Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 1599753 observations with 76085 users and 10000 items.</pre>"
      ],
      "text/plain": [
       "    Data has 1599753 observations with 76085 users and 10000 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.939059s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.939059s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training model from provided data.</pre>"
      ],
      "text/plain": [
       "Training model from provided data."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Gathering per-item and per-user statistics.</pre>"
      ],
      "text/plain": [
       "Gathering per-item and per-user statistics."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Item Statistics) | % Complete |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Item Statistics) | % Complete |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1.878ms                        | 1.25       |</pre>"
      ],
      "text/plain": [
       "| 1.878ms                        | 1.25       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 29.154ms                       | 100        |</pre>"
      ],
      "text/plain": [
       "| 29.154ms                       | 100        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+------------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Setting up lookup tables.</pre>"
      ],
      "text/plain": [
       "Setting up lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Processing data in one pass using dense lookup tables.</pre>"
      ],
      "text/plain": [
       "Processing data in one pass using dense lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |</pre>"
      ],
      "text/plain": [
       "| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 199.684ms                           | 0                | 0               |</pre>"
      ],
      "text/plain": [
       "| 199.684ms                           | 0                | 0               |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 957.28ms                            | 100              | 10000           |</pre>"
      ],
      "text/plain": [
       "| 957.28ms                            | 100              | 10000           |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-------------------------------------+------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-------------------------------------+------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finalizing lookup tables.</pre>"
      ],
      "text/plain": [
       "Finalizing lookup tables."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Generating candidate set for working with new users.</pre>"
      ],
      "text/plain": [
       "Generating candidate set for working with new users."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished training in 2.00431s</pre>"
      ],
      "text/plain": [
       "Finished training in 2.00431s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "item_sim_model = tc.item_similarity_recommender.create(train_set, \n",
    "                                                       'user_id', 'music_id', \n",
    "                                                       target = 'rating', \n",
    "                                                       similarity_type='cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:53:59.590335Z",
     "start_time": "2019-06-15T06:53:51.789055Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Preparing data set.</pre>"
      ],
      "text/plain": [
       "Preparing data set."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data has 1599753 observations with 76085 users and 10000 items.</pre>"
      ],
      "text/plain": [
       "    Data has 1599753 observations with 76085 users and 10000 items."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>    Data prepared in: 0.930001s</pre>"
      ],
      "text/plain": [
       "    Data prepared in: 0.930001s"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training factorization_recommender for recommendations.</pre>"
      ],
      "text/plain": [
       "Training factorization_recommender for recommendations."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Parameter                      | Description                                      | Value    |</pre>"
      ],
      "text/plain": [
       "| Parameter                      | Description                                      | Value    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| num_factors                    | Factor Dimension                                 | 8        |</pre>"
      ],
      "text/plain": [
       "| num_factors                    | Factor Dimension                                 | 8        |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| regularization                 | L2 Regularization on Factors                     | 1e-08    |</pre>"
      ],
      "text/plain": [
       "| regularization                 | L2 Regularization on Factors                     | 1e-08    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| solver                         | Solver used for training                         | sgd      |</pre>"
      ],
      "text/plain": [
       "| solver                         | Solver used for training                         | sgd      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |</pre>"
      ],
      "text/plain": [
       "| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
      ],
      "text/plain": [
       "| max_iterations                 | Maximum Number of Iterations                     | 50       |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>"
      ],
      "text/plain": [
       "+--------------------------------+--------------------------------------------------+----------+"
      ]
     },
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    },
    {
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      "text/html": [
       "<pre>  Optimizing model using SGD; tuning step size.</pre>"
      ],
      "text/plain": [
       "  Optimizing model using SGD; tuning step size."
      ]
     },
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    },
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     "data": {
      "text/html": [
       "<pre>  Using 199969 / 1599753 points for tuning the step size.</pre>"
      ],
      "text/plain": [
       "  Using 199969 / 1599753 points for tuning the step size."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
      ],
      "text/plain": [
       "| Attempt | Initial Step Size | Estimated Objective Value                |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0       | 25                | No Decrease (224.847 >= 36.2873)         |</pre>"
      ],
      "text/plain": [
       "| 0       | 25                | No Decrease (224.847 >= 36.2873)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1       | 6.25              | No Decrease (211.831 >= 36.2873)         |</pre>"
      ],
      "text/plain": [
       "| 1       | 6.25              | No Decrease (211.831 >= 36.2873)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2       | 1.5625            | No Decrease (184.589 >= 36.2873)         |</pre>"
      ],
      "text/plain": [
       "| 2       | 1.5625            | No Decrease (184.589 >= 36.2873)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 0.390625          | No Decrease (83.9764 >= 36.2873)         |</pre>"
      ],
      "text/plain": [
       "| 3       | 0.390625          | No Decrease (83.9764 >= 36.2873)         |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4       | 0.0976562         | 11.3523                                  |</pre>"
      ],
      "text/plain": [
       "| 4       | 0.0976562         | 11.3523                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5       | 0.0488281         | 7.5686                                   |</pre>"
      ],
      "text/plain": [
       "| 5       | 0.0488281         | 7.5686                                   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6       | 0.0244141         | 21.6581                                  |</pre>"
      ],
      "text/plain": [
       "| 6       | 0.0244141         | 21.6581                                  |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Final   | 0.0488281         | 7.5686                                   |</pre>"
      ],
      "text/plain": [
       "| Final   | 0.0488281         | 7.5686                                   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+-------------------+------------------------------------------+</pre>"
      ],
      "text/plain": [
       "+---------+-------------------+------------------------------------------+"
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    {
     "data": {
      "text/html": [
       "<pre>Starting Optimization.</pre>"
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       "Starting Optimization."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
      ],
      "text/plain": [
       "| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
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      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre>| Initial | 99us         | 43.795            | 6.61778               |             |</pre>"
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      "text/plain": [
       "| Initial | 99us         | 43.795            | 6.61778               |             |"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
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       "+---------+--------------+-------------------+-----------------------+-------------+"
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    {
     "data": {
      "text/html": [
       "<pre>| 1       | 99.622ms     | 43.5086           | 6.59571               | 0.0488281   |</pre>"
      ],
      "text/plain": [
       "| 1       | 99.622ms     | 43.5086           | 6.59571               | 0.0488281   |"
      ]
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    {
     "data": {
      "text/html": [
       "<pre>| 2       | 191.248ms    | 40.9101           | 6.39574               | 0.0290334   |</pre>"
      ],
      "text/plain": [
       "| 2       | 191.248ms    | 40.9101           | 6.39574               | 0.0290334   |"
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre>| 3       | 280.477ms    | 37.8972           | 6.15571               | 0.0214205   |</pre>"
      ],
      "text/plain": [
       "| 3       | 280.477ms    | 37.8972           | 6.15571               | 0.0214205   |"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>| 4       | 378.603ms    | 35.2936           | 5.94045               | 0.0172633   |</pre>"
      ],
      "text/plain": [
       "| 4       | 378.603ms    | 35.2936           | 5.94045               | 0.0172633   |"
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    {
     "data": {
      "text/html": [
       "<pre>| 5       | 474.372ms    | 32.7773           | 5.72471               | 0.014603    |</pre>"
      ],
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       "| 5       | 474.372ms    | 32.7773           | 5.72471               | 0.014603    |"
      ]
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       "<pre>| 10      | 959.686ms    | 24.5984           | 4.95903               | 0.008683    |</pre>"
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       "| 10      | 959.686ms    | 24.5984           | 4.95903               | 0.008683    |"
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       "<pre>| 50      | 5.02s        | 9.19885           | 3.0314                | 0.00154408  |</pre>"
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       "| 50      | 5.02s        | 9.19885           | 3.0314                | 0.00154408  |"
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     "data": {
      "text/html": [
       "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>"
      ],
      "text/plain": [
       "+---------+--------------+-------------------+-----------------------+-------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>"
      ],
      "text/plain": [
       "Optimization Complete: Maximum number of passes through the data reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Computing final objective value and training RMSE.</pre>"
      ],
      "text/plain": [
       "Computing final objective value and training RMSE."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final objective value: 8.16243</pre>"
      ],
      "text/plain": [
       "       Final objective value: 8.16243"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Final training RMSE: 2.85534</pre>"
      ],
      "text/plain": [
       "       Final training RMSE: 2.85534"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factorization_machine_model = tc.recommender.factorization_recommender.create(train_set, \n",
    "                                                                              'user_id', 'music_id',\n",
    "                                                                              target='rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:54:08.224344Z",
     "start_time": "2019-06-15T06:54:08.211939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1599753"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-15T06:54:48.295571Z",
     "start_time": "2019-06-15T06:54:24.849739Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compare_models: using 34354 users to estimate model performance\n",
      "PROGRESS: Evaluate model M0\n"
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     "text": [
      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------------+------------------------+\n",
      "| cutoff |      mean_recall       |     mean_precision     |\n",
      "+--------+------------------------+------------------------+\n",
      "|   1    | 4.3383582709425655e-05 | 0.00032019561040927034 |\n",
      "|   2    | 9.351370595195784e-05  | 0.0003493043022646556  |\n",
      "|   3    | 0.00013332475867271565 | 0.00032989850769439354 |\n",
      "|   4    | 0.00025157207340778813 | 0.0003638586481923528  |\n",
      "|   5    | 0.0003743018484379922  | 0.00043663037783082004 |\n",
      "|   6    | 0.00044921257061878193 | 0.00042207603190312675 |\n",
      "|   7    | 0.0005172658393736786  | 0.00041583845507697374 |\n",
      "|   8    |  0.000573334927644493  | 0.0004038830994935098  |\n",
      "|   9    | 0.0008801308762376106  | 0.0005304250515870728  |\n",
      "|   10   | 0.0009133251742113642  | 0.0005035803690982172  |\n",
      "+--------+------------------------+------------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "\n",
      "Overall RMSE: 6.339345574168611\n",
      "\n",
      "Per User RMSE (best)\n",
      "+-------------------------------+------+-------+\n",
      "|            user_id            | rmse | count |\n",
      "+-------------------------------+------+-------+\n",
      "| 6d61c9b3678aa6c015ea9fd502... | 0.0  |   1   |\n",
      "+-------------------------------+------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+-------------------------------+-------------------+-------+\n",
      "|            user_id            |        rmse       | count |\n",
      "+-------------------------------+-------------------+-------+\n",
      "| 38767872c514c1b43bab5c7b21... | 341.2071760874715 |   2   |\n",
      "+-------------------------------+-------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+--------------------+---------------------+-------+\n",
      "|      music_id      |         rmse        | count |\n",
      "+--------------------+---------------------+-------+\n",
      "| SOXDPFW12A81C2319B | 0.07352941176470584 |   6   |\n",
      "+--------------------+---------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+--------------------+--------------------+-------+\n",
      "|      music_id      |        rmse        | count |\n",
      "+--------------------+--------------------+-------+\n",
      "| SOLGIWB12A58A77A05 | 109.15045476689721 |   35  |\n",
      "+--------------------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M1\n"
     ]
    },
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      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+----------------------+----------------------+\n",
      "| cutoff |     mean_recall      |    mean_precision    |\n",
      "+--------+----------------------+----------------------+\n",
      "|   1    | 0.014688842060795695 | 0.050445362985387564 |\n",
      "|   2    | 0.03291712354999962  | 0.06248180706759065  |\n",
      "|   3    | 0.05399142375515901  | 0.07436300479323134  |\n",
      "|   4    | 0.06963924374087777  | 0.07609012050998376  |\n",
      "|   5    | 0.08384375244888208  | 0.07552541188798938  |\n",
      "|   6    |  0.0959100718474003  | 0.07364499039413211  |\n",
      "|   7    | 0.10643883706242792  | 0.07136619566031029  |\n",
      "|   8    | 0.11604275927620446  | 0.06902398556208861  |\n",
      "|   9    |  0.1246532903037569  | 0.06660392126422279  |\n",
      "|   10   | 0.13280279585995652  | 0.06440006986086082  |\n",
      "+--------+----------------------+----------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "\n",
      "Overall RMSE: 7.041096333660663\n",
      "\n",
      "Per User RMSE (best)\n",
      "+-------------------------------+----------------------+-------+\n",
      "|            user_id            |         rmse         | count |\n",
      "+-------------------------------+----------------------+-------+\n",
      "| 714c577dfeaa6ffaf778286302... | 0.022276699542999268 |   1   |\n",
      "+-------------------------------+----------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+-------------------------------+--------------------+-------+\n",
      "|            user_id            |        rmse        | count |\n",
      "+-------------------------------+--------------------+-------+\n",
      "| 38767872c514c1b43bab5c7b21... | 343.85311015265717 |   2   |\n",
      "+-------------------------------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+--------------------+--------------------+-------+\n",
      "|      music_id      |        rmse        | count |\n",
      "+--------------------+--------------------+-------+\n",
      "| SOBHIJM12AB018194F | 0.7032955339939242 |   6   |\n",
      "+--------------------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+--------------------+--------------------+-------+\n",
      "|      music_id      |        rmse        | count |\n",
      "+--------------------+--------------------+-------+\n",
      "| SOLGIWB12A58A77A05 | 109.83254923449184 |   35  |\n",
      "+--------------------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "PROGRESS: Evaluate model M2\n"
     ]
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      "\n",
      "Precision and recall summary statistics by cutoff\n",
      "+--------+------------------------+------------------------+\n",
      "| cutoff |      mean_recall       |     mean_precision     |\n",
      "+--------+------------------------+------------------------+\n",
      "|   1    |  7.0023278723497e-05   | 0.0004075216859754317  |\n",
      "|   2    | 0.00015701647350617692 | 0.0004220760319031288  |\n",
      "|   3    | 0.00025150526145133087 | 0.00041722458326056295 |\n",
      "|   4    | 0.00034565751961335053 | 0.00043663037783081874 |\n",
      "|   5    | 0.00046127750964837765 | 0.0004424521162018989  |\n",
      "|   6    | 0.0006061722786786824  | 0.0004899963128990292  |\n",
      "|   7    |  0.00070561257944388   | 0.0005197980688462193  |\n",
      "|   8    | 0.0008535975421092137  | 0.0005457879722885229  |\n",
      "|   9    | 0.0009773205690967743  | 0.0005433622479672466  |\n",
      "|   10   | 0.0011201566127395915  | 0.0005647086219945271  |\n",
      "+--------+------------------------+------------------------+\n",
      "[10 rows x 3 columns]\n",
      "\n",
      "\n",
      "Overall RMSE: 7.780049286375036\n",
      "\n",
      "Per User RMSE (best)\n",
      "+-------------------------------+------------------------+-------+\n",
      "|            user_id            |          rmse          | count |\n",
      "+-------------------------------+------------------------+-------+\n",
      "| 80495441caacdc7e069b441047... | 5.1963096963536515e-05 |   1   |\n",
      "+-------------------------------+------------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per User RMSE (worst)\n",
      "+-------------------------------+--------------------+-------+\n",
      "|            user_id            |        rmse        | count |\n",
      "+-------------------------------+--------------------+-------+\n",
      "| 38767872c514c1b43bab5c7b21... | 338.99132097019395 |   2   |\n",
      "+-------------------------------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (best)\n",
      "+--------------------+---------------------+-------+\n",
      "|      music_id      |         rmse        | count |\n",
      "+--------------------+---------------------+-------+\n",
      "| SOCOZST12A67020452 | 0.06951240175355755 |   1   |\n",
      "+--------------------+---------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n",
      "\n",
      "Per Item RMSE (worst)\n",
      "+--------------------+--------------------+-------+\n",
      "|      music_id      |        rmse        | count |\n",
      "+--------------------+--------------------+-------+\n",
      "| SOVQSQZ12A8C13F960 | 113.92735214599614 |   17  |\n",
      "+--------------------+--------------------+-------+\n",
      "[1 rows x 3 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = tc.recommender.util.compare_models(test_set, \n",
    "                                            [popularity_model, item_sim_model, factorization_machine_model],\n",
    "                                            user_sample=.5, skip_set=train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-14T16:29:22.874873Z",
     "start_time": "2019-06-14T16:29:22.451971Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "K = 10\n",
    "users = gl.SArray(sf['user_id'].unique().head(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2019-06-14T16:29:24.410860Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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       "\tmusic_id\tstr\n",
       "\tscore\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 10\n",
       "\n",
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "recs = item_sim_model.recommend(users=users, k=K)\n",
    "recs.head()"
   ]
  },
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   "execution_count": null,
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