{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# 使用Turicreate进行音乐推荐\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-06-13T07:57:48.013117Z", "start_time": "2020-06-13T05:56:30.069715Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting turicreate\n", " Downloading turicreate-6.3-cp37-cp37m-macosx_10_12_intel.macosx_10_12_x86_64.macosx_10_13_intel.macosx_10_13_x86_64.macosx_10_14_intel.macosx_10_14_x86_64.whl (33.1 MB)\n", "\u001b[K |████████████████████████████████| 33.1 MB 13 kB/s eta 0:00:0197 |████████▍ | 8.6 MB 19 kB/s eta 0:20:35 |███████████████████▌ | 20.2 MB 24 kB/s eta 0:08:42 |███████████████████▊ | 20.4 MB 12 kB/s eta 0:16:49 |██████████████████████████████▉ | 31.8 MB 39 kB/s eta 0:00:32\n", "\u001b[?25hCollecting coremltools==3.3\n", " Downloading coremltools-3.3-cp37-none-macosx_10_14_intel.whl (3.5 MB)\n", "\u001b[K |████████████████████████████████| 3.5 MB 18 kB/s eta 0:00:0115 |███████▋ | 829 kB 12 kB/s eta 0:03:30\n", "\u001b[?25hRequirement already satisfied: requests>=2.9.1 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (2.22.0)\n", "Requirement already satisfied: pandas>=0.23.2 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (1.0.1)\n", "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (1.18.1)\n", "Requirement already satisfied: decorator>=4.0.9 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (4.4.1)\n", "Requirement already satisfied: pillow>=5.2.0 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (7.0.0)\n", "Requirement already satisfied: prettytable==0.7.2 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (0.7.2)\n", "Requirement already satisfied: six>=1.10.0 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (1.14.0)\n", "Collecting resampy==0.2.1\n", " Using cached resampy-0.2.1.tar.gz (322 kB)\n", "Collecting tensorflow>=2.0.0\n", " Downloading tensorflow-2.2.0-cp37-cp37m-macosx_10_11_x86_64.whl (175.3 MB)\n", "\u001b[K |████████████████████████████████| 175.3 MB 8.8 kB/s ta 0:00:018 |██▏ | 12.0 MB 62 kB/s eta 0:43:16 |████████▎ | 45.2 MB 21 kB/s eta 1:39:12 |████████▎ | 45.5 MB 17 kB/s eta 2:07:02 |█████████ | 48.7 MB 36 kB/s eta 0:58:32 |█████████▍ | 51.2 MB 40 kB/s eta 0:50:59 |██████████▍ | 56.8 MB 14 kB/s eta 2:11:51 |██████████████▊ | 81.0 MB 30 kB/s eta 0:51:49 |███████████████▉ | 86.8 MB 36 kB/s eta 0:40:44 |████████████████▍ | 90.1 MB 34 kB/s eta 0:40:50 |█████████████████ | 92.9 MB 35 kB/s eta 0:38:29 |██████████████████ | 99.0 MB 60 kB/s eta 0:20:57 |██████████████████▋ | 102.1 MB 76 kB/s eta 0:16:01 |██████████████████▉ | 103.4 MB 35 kB/s eta 0:33:33 |████████████████████▏ | 110.3 MB 23 kB/s eta 0:46:13 |████████████████████▏ | 110.5 MB 23 kB/s eta 0:45:17 |████████████████████▍ | 111.7 MB 53 kB/s eta 0:19:58 |██████████████████████▍ | 122.5 MB 91 kB/s eta 0:09:39 |███████████████████████ | 125.9 MB 119 kB/s eta 0:06:54 |██████████████████████████▋ | 145.9 MB 91 kB/s eta 0:05:22 |███████████████████████████▍ | 149.7 MB 22 kB/s eta 0:19:01 |████████████████████████████████| 175.3 MB 54 kB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: scipy>=1.1.0 in /opt/anaconda3/lib/python3.7/site-packages (from turicreate) (1.4.1)\n", "Collecting protobuf>=3.1.0\n", " Downloading protobuf-3.12.2-cp37-cp37m-macosx_10_9_x86_64.whl (1.3 MB)\n", "\u001b[K |████████████████████████████████| 1.3 MB 38 kB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.9.1->turicreate) (3.0.4)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.9.1->turicreate) (1.25.8)\n", "Requirement already satisfied: idna<2.9,>=2.5 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.9.1->turicreate) (2.8)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.9.1->turicreate) (2019.11.28)\n", "Requirement already satisfied: pytz>=2017.2 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.2->turicreate) (2019.3)\n", "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.2->turicreate) (2.8.1)\n", "Requirement already satisfied: numba>=0.32 in /opt/anaconda3/lib/python3.7/site-packages (from resampy==0.2.1->turicreate) (0.48.0)\n", "Collecting termcolor>=1.1.0\n", " Downloading termcolor-1.1.0.tar.gz (3.9 kB)\n", "Collecting tensorflow-estimator<2.3.0,>=2.2.0\n", " Downloading tensorflow_estimator-2.2.0-py2.py3-none-any.whl (454 kB)\n", "\u001b[K |████████████████████████████████| 454 kB 49 kB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: wheel>=0.26; 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Preparing data set.
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    Data has 1599753 observations with 76085 users and 10000 items.
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    Data prepared in: 4.15079s
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1599753 observations to process; with 10000 unique items.
" ], "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": "2020-06-13T08:02:17.217932Z", "start_time": "2020-06-13T08:02:08.102763Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
Preparing data set.
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    Data has 1599753 observations with 76085 users and 10000 items.
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    Data prepared in: 3.7942s
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Training model from provided data.
" ], "text/plain": [ "Training model from provided data." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Gathering per-item and per-user statistics.
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+--------------------------------+------------+
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| Elapsed Time (Item Statistics) | % Complete |
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+--------------------------------+------------+
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Setting up lookup tables.
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Processing data in one pass using dense lookup tables.
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| 4.01s                               | 100              | 10000           |
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+-------------------------------------+------------------+-----------------+
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Finalizing lookup tables.
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Generating candidate set for working with new users.
" ], "text/plain": [ "Generating candidate set for working with new users." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Finished training in 5.31028s
" ], "text/plain": [ "Finished training in 5.31028s" ] }, "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": "2020-06-13T08:03:05.251295Z", "start_time": "2020-06-13T08:02:23.686739Z" }, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
Preparing data set.
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    Data has 1599753 observations with 76085 users and 10000 items.
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    Data prepared in: 4.32575s
" ], "text/plain": [ " Data prepared in: 4.32575s" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Training factorization_recommender for recommendations.
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+--------------------------------+--------------------------------------------------+----------+
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| Parameter                      | Description                                      | Value    |
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+--------------------------------+--------------------------------------------------+----------+
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| num_factors                    | Factor Dimension                                 | 8        |
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| regularization                 | L2 Regularization on Factors                     | 1e-08    |
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| solver                         | Solver used for training                         | sgd      |
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| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |
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| max_iterations                 | Maximum Number of Iterations                     | 50       |
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+--------------------------------+--------------------------------------------------+----------+
" ], "text/plain": [ "+--------------------------------+--------------------------------------------------+----------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
  Optimizing model using SGD; tuning step size.
" ], "text/plain": [ " Optimizing model using SGD; tuning step size." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
  Using 199969 / 1599753 points for tuning the step size.
" ], "text/plain": [ " Using 199969 / 1599753 points for tuning the step size." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
+---------+-------------------+------------------------------------------+
" ], "text/plain": [ "+---------+-------------------+------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
| Attempt | Initial Step Size | Estimated Objective Value                |
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+---------+-------------------+------------------------------------------+
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+---------+-------------------+------------------------------------------+
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| Final   | 0.0488281         | 11.4371                                  |
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+---------+-------------------+------------------------------------------+
" ], "text/plain": [ "+---------+-------------------+------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Starting Optimization.
" ], "text/plain": [ "Starting Optimization." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
+---------+--------------+-------------------+-----------------------+-------------+
" ], "text/plain": [ "+---------+--------------+-------------------+-----------------------+-------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
| Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |
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+---------+--------------+-------------------+-----------------------+-------------+
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| Initial | 387us        | 43.795            | 6.61778               |             |
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+---------+--------------+-------------------+-----------------------+-------------+
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Optimization Complete: Maximum number of passes through the data reached.
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Computing final objective value and training RMSE.
" ], "text/plain": [ "Computing final objective value and training RMSE." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
       Final objective value: 8.8282
" ], "text/plain": [ " Final objective value: 8.8282" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
       Final training RMSE: 2.96963
" ], "text/plain": [ " Final training RMSE: 2.96963" ] }, "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": "2020-06-13T08:03:18.663826Z", "start_time": "2020-06-13T08:03:18.636917Z" } }, "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": "2020-06-13T08:04:48.668769Z", "start_time": "2020-06-13T08:03:22.701004Z" }, "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" ] }, { "data": { "text/html": [ "
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user_idmusic_idscorerank
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOXUQNR12AF72A69D63.0224226514498391
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOUFAZA12AC3DFAB201.33684277534484862
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOSFSTC12A8C1412191.0919821262359623
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOVIWFP12A58A7D1BD1.0451638698577884
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOBMTQD12AB01833D01.02945168813069655
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOCMNRG12AB0189D3F0.97564379374186196
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOXOHUM12A67ADC8260.95068732897440597
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038c81eb1d1d63e ...		SOWBFVW12A6D4F612B0.90923706690470378
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOXFYTY127E9433E7D0.89772780736287449
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		SOYBLYP12A58A79D320.897092819213867210
\n", "[10 rows x 4 columns]
\n", "
" ], "text/plain": [ "Columns:\n", "\tuser_id\tstr\n", "\tmusic_id\tstr\n", "\tscore\tfloat\n", "\trank\tint\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-------------------------------+--------------------+--------------------+------+\n", "| user_id | music_id | score | rank |\n", "+-------------------------------+--------------------+--------------------+------+\n", "| 279292bb36dbfc7f505e36ebf0... | SOXUQNR12AF72A69D6 | 3.022422651449839 | 1 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOUFAZA12AC3DFAB20 | 1.3368427753448486 | 2 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOSFSTC12A8C141219 | 1.091982126235962 | 3 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOVIWFP12A58A7D1BD | 1.045163869857788 | 4 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOBMTQD12AB01833D0 | 1.0294516881306965 | 5 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOCMNRG12AB0189D3F | 0.9756437937418619 | 6 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOXOHUM12A67ADC826 | 0.9506873289744059 | 7 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOWBFVW12A6D4F612B | 0.9092370669047037 | 8 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOXFYTY127E9433E7D | 0.8977278073628744 | 9 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOYBLYP12A58A79D32 | 0.8970928192138672 | 10 |\n", "+-------------------------------+--------------------+--------------------+------+\n", "[10 rows x 4 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recs = item_sim_model.recommend(users=users, k=K)\n", "recs.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.7.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "48px", "left": "930px", "top": "110.398px", "width": "159px" }, "toc_section_display": false, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }