{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***\n", "***\n", "# 使用GraphLab进行音乐推荐\n", "***\n", "***\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import graphlab as gl\n", "# set canvas to show sframes and sgraphs in ipython notebook\n", "gl.canvas.set_target('ipynb')\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "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": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------\n", "Inferred types from first line of file as \n", "column_type_hints=[str,str,int]\n", "If parsing fails due to incorrect types, you can correct\n", "the inferred type list above and pass it to read_csv in\n", "the column_type_hints argument\n", "------------------------------------------------------\n", "PROGRESS: Read 844838 lines. Lines per second: 810295\n", "PROGRESS: Finished parsing file /Users/chengjun/bigdata/millionsong/song_usage_10000.txt\n", "PROGRESS: Parsing completed. Parsed 2000000 lines in 1.59616 secs.\n" ] }, { "data": { "application/javascript": [ "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", "$(\"head\").append($(\"\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//dato.com/files/canvas/1.5.2/css/canvas.css\"\n", "}));\n", "\n", " (function(){\n", "\n", " var e = null;\n", " if (typeof element == 'undefined') {\n", " var scripts = document.getElementsByTagName('script');\n", " var thisScriptTag = scripts[scripts.length-1];\n", " var parentDiv = thisScriptTag.parentNode;\n", " e = document.createElement('div');\n", " parentDiv.appendChild(e);\n", " } else {\n", " e = element[0];\n", " }\n", "\n", " require(['//dato.com/files/canvas/1.5.2/js/ipython_app.js'], function(IPythonApp){\n", " var app = new IPythonApp();\n", " app.attachView('sframe','Summary', {\"ipython\": 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\"f3af4a8667a742d16d69d95ae4dbfd5163562e1a\": {\"frequency\": 267, \"value\": \"f3af4a8667a742d16d ...\"}}, \"size\": 2000000}}, \"selected_variable\": {\"name\": [\"sf\"], \"descriptives\": {\"rows\": 2000000, \"columns\": 3}, \"view_component\": \"Summary\", \"view_file\": \"sframe\", \"view_params\": {\"y\": null, \"x\": null, \"columns\": [\"user_id\", \"music_id\", \"rating\"], \"view\": null}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"str\", \"name\": \"user_id\"}, {\"dtype\": \"str\", \"name\": \"music_id\"}, {\"dtype\": \"int\", \"name\": \"rating\"}], \"column_identifiers\": [\"rating\", \"music_id\", \"user_id\"]}, \"columns\": [{\"dtype\": \"str\", \"name\": \"user_id\"}, {\"dtype\": \"str\", \"name\": \"music_id\"}, {\"dtype\": \"int\", \"name\": \"rating\"}]}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#train_file = 'http://s3.amazonaws.com/dato-datasets/millionsong/10000.txt'\n", "train_file = '/Users/chengjun/bigdata/millionsong/song_usage_10000.txt'\n", "sf = gl.SFrame.read_csv(train_file, header=False, delimiter='\\t', verbose=False)\n", "sf.rename({'X1':'user_id', 'X2':'music_id', 'X3':'rating'}).show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "(train_set, test_set) = sf.random_split(0.8, seed=1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Recsys training: model = popularity\n", "PROGRESS: Preparing data set.\n", "PROGRESS: Data has 1599753 observations with 76085 users and 10000 items.\n", "PROGRESS: Data prepared in: 1.23558s\n", "PROGRESS: 1599753 observations to process; with 10000 unique items.\n" ] } ], "source": [ "popularity_model = gl.popularity_recommender.create(train_set, 'user_id', 'music_id', target = 'rating')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Recsys training: model = item_similarity\n", "PROGRESS: Preparing data set.\n", "PROGRESS: Data has 1599753 observations with 76085 users and 10000 items.\n", "PROGRESS: Data prepared in: 1.34152s\n", "PROGRESS: Computing item similarity statistics:\n", "PROGRESS: Computing most similar items for 10000 items:\n", "PROGRESS: +-----------------+-----------------+\n", "PROGRESS: | Number of items | Elapsed Time |\n", "PROGRESS: +-----------------+-----------------+\n", "PROGRESS: | 1000 | 1.67234 |\n", "PROGRESS: | 2000 | 1.70878 |\n", "PROGRESS: | 3000 | 1.74289 |\n", "PROGRESS: | 4000 | 1.77751 |\n", "PROGRESS: | 5000 | 1.81794 |\n", "PROGRESS: | 6000 | 1.85361 |\n", "PROGRESS: | 7000 | 1.88976 |\n", "PROGRESS: | 8000 | 1.92744 |\n", "PROGRESS: | 9000 | 1.96709 |\n", "PROGRESS: | 10000 | 2.08439 |\n", "PROGRESS: +-----------------+-----------------+\n", "PROGRESS: Finished training in 2.50669s\n", "PROGRESS: Finished prediction in 0.734376s\n" ] } ], "source": [ "item_sim_model = gl.item_similarity_recommender.create(train_set, 'user_id', 'music_id', target = 'rating', \n", " similarity_type='cosine')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: Recsys training: model = factorization_recommender\n", "PROGRESS: Preparing data set.\n", "PROGRESS: Data has 1599753 observations with 76085 users and 10000 items.\n", "PROGRESS: Data prepared in: 1.31298s\n", "PROGRESS: Training factorization_recommender for recommendations.\n", "PROGRESS: +--------------------------------+--------------------------------------------------+----------+\n", "PROGRESS: | Parameter | Description | Value |\n", "PROGRESS: +--------------------------------+--------------------------------------------------+----------+\n", "PROGRESS: | num_factors | Factor Dimension | 8 |\n", "PROGRESS: | regularization | L2 Regularization on Factors | 1e-08 |\n", "PROGRESS: | solver | Solver used for training | sgd |\n", "PROGRESS: | linear_regularization | L2 Regularization on Linear Coefficients | 1e-10 |\n", "PROGRESS: | max_iterations | Maximum Number of Iterations | 50 |\n", "PROGRESS: +--------------------------------+--------------------------------------------------+----------+\n", "PROGRESS: Optimizing model using SGD; tuning step size.\n", "PROGRESS: Using 199969 / 1599753 points for tuning the step size.\n", "PROGRESS: +---------+-------------------+------------------------------------------+\n", "PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value |\n", "PROGRESS: +---------+-------------------+------------------------------------------+\n", "PROGRESS: | 0 | 25 | No Decrease (234.956 >= 45.6461) |\n", "PROGRESS: | 1 | 6.25 | No Decrease (222.818 >= 45.6461) |\n", "PROGRESS: | 2 | 1.5625 | No Decrease (193.879 >= 45.6461) |\n", "PROGRESS: | 3 | 0.390625 | No Decrease (93.6001 >= 45.6461) |\n", "PROGRESS: | 4 | 0.0976562 | 18.1929 |\n", "PROGRESS: | 5 | 0.0488281 | 12.7349 |\n", "PROGRESS: | 6 | 0.0244141 | 27.6064 |\n", "PROGRESS: +---------+-------------------+------------------------------------------+\n", "PROGRESS: | Final | 0.0488281 | 12.7349 |\n", "PROGRESS: +---------+-------------------+------------------------------------------+\n", "PROGRESS: Starting Optimization.\n", "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+\n", "PROGRESS: | Iter. | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size |\n", "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+\n", "PROGRESS: | Initial | 388us | 43.795 | 6.61778 | |\n", "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+\n", "PROGRESS: | 1 | 242.781ms | 43.525 | 6.59695 | 0.0488281 |\n", "PROGRESS: | 2 | 369.391ms | 40.9211 | 6.3966 | 0.0290334 |\n", "PROGRESS: | 3 | 491.657ms | 37.9834 | 6.1627 | 0.0214205 |\n", "PROGRESS: | 4 | 603.858ms | 35.2255 | 5.93471 | 0.0172633 |\n", "PROGRESS: | 5 | 743.824ms | 32.7566 | 5.7229 | 0.014603 |\n", "PROGRESS: | 6 | 861.2ms | 30.8412 | 5.553 | 0.0127367 |\n", "PROGRESS: | 10 | 1.39s | 24.7548 | 4.97477 | 0.008683 |\n", "PROGRESS: | 11 | 1.53s | 23.5887 | 4.85613 | 0.00808399 |\n", "PROGRESS: | 20 | 2.76s | 17.6337 | 4.19832 | 0.00516295 |\n", "PROGRESS: | 30 | 3.96s | 14.4135 | 3.79539 | 0.00380916 |\n", "PROGRESS: | 40 | 5.17s | 12.5212 | 3.53725 | 0.00306991 |\n", "PROGRESS: | 50 | 6.39s | 9.83216 | 3.13412 | 0.00154408 |\n", "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+\n", "PROGRESS: Optimization Complete: Maximum number of passes through the data reached.\n", "PROGRESS: Computing final objective value and training RMSE.\n", "PROGRESS: Final objective value: 8.86198\n", "PROGRESS: Final training RMSE: 2.97532\n" ] } ], "source": [ "factorization_machine_model = gl.recommender.factorization_recommender.create(train_set, 'user_id', 'music_id',\n", " target='rating')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compare_models: using 6871 users to estimate model performance\n", "PROGRESS: Evaluate model M0\n", "PROGRESS: recommendations finished on 1000/6871 queries. users per second: 12410\n", "PROGRESS: recommendations finished on 2000/6871 queries. users per second: 14958.4\n", "PROGRESS: recommendations finished on 3000/6871 queries. users per second: 15825.3\n", "PROGRESS: recommendations finished on 4000/6871 queries. users per second: 16808.7\n", "PROGRESS: recommendations finished on 5000/6871 queries. users per second: 17280.5\n", "PROGRESS: recommendations finished on 6000/6871 queries. users per second: 17228\n", "\n", "Precision and recall summary statistics by cutoff\n", "+--------+-------------------+-------------------+\n", "| cutoff | mean_precision | mean_recall |\n", "+--------+-------------------+-------------------+\n", "| 2 | 0.000363848057051 | 0.000222530101733 |\n", "| 4 | 0.000509387279872 | 0.000644168294629 |\n", "| 6 | 0.000460874205598 | 0.000838220591723 |\n", "| 8 | 0.000418425265609 | 0.000983759814544 |\n", "| 10 | 0.000465725513026 | 0.00128720279373 |\n", "| 12 | 0.000412361131325 | 0.00132237477257 |\n", "| 14 | 0.000457408986007 | 0.00161781917277 |\n", "| 16 | 0.000491194877019 | 0.00189451695711 |\n", "| 18 | 0.000468959717977 | 0.00196078928179 |\n", "| 20 | 0.000480279435308 | 0.00211815339109 |\n", "+--------+-------------------+-------------------+\n", "[10 rows x 3 columns]\n", "\n", "\n", "Overall RMSE: 5.79840126177\n", "\n", "Per User RMSE (best)\n", "+-------------------------------+-------+-----------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+-----------------+\n", "| 907f83008d1b7a7958766544a0... | 1 | 0.0160085378869 |\n", "+-------------------------------+-------+-----------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per User RMSE (worst)\n", "+-------------------------------+-------+---------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+---------------+\n", "| 2c263b458bb317ee91c346ae90... | 4 | 172.795342779 |\n", "+-------------------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (best)\n", "+--------------------+-------+------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+------+\n", "| SOZWCBD12AB01848DD | 1 | 0.0 |\n", "+--------------------+-------+------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (worst)\n", "+--------------------+-------+---------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+---------------+\n", "| SOTGIKV12AB0182176 | 1 | 173.804878049 |\n", "+--------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "PROGRESS: Evaluate model M1\n", "PROGRESS: recommendations finished on 1000/6871 queries. users per second: 1291.91\n", "PROGRESS: recommendations finished on 2000/6871 queries. users per second: 1303.24\n", "PROGRESS: recommendations finished on 3000/6871 queries. users per second: 1303.16\n", "PROGRESS: recommendations finished on 4000/6871 queries. users per second: 1309.34\n", "PROGRESS: recommendations finished on 5000/6871 queries. users per second: 1309.57\n", "PROGRESS: recommendations finished on 6000/6871 queries. users per second: 1318.84\n", "\n", "Precision and recall summary statistics by cutoff\n", "+--------+-------------------+-------------------+\n", "| cutoff | mean_precision | mean_recall |\n", "+--------+-------------------+-------------------+\n", "| 2 | 0.000509387279872 | 0.000106894612147 |\n", "| 4 | 0.000509387279872 | 0.00041709974353 |\n", "| 6 | 0.000557900354145 | 0.000741928943341 |\n", "| 8 | 0.000491194877019 | 0.000896131215139 |\n", "| 10 | 0.000509387279872 | 0.0010508900222 |\n", "| 12 | 0.000533643817009 | 0.00124220214402 |\n", "| 14 | 0.000509387279872 | 0.00144984014027 |\n", "| 16 | 0.000491194877019 | 0.00164649135206 |\n", "| 18 | 0.000517472792251 | 0.00213570216873 |\n", "| 20 | 0.000509387279872 | 0.00236702556808 |\n", "+--------+-------------------+-------------------+\n", "[10 rows x 3 columns]\n", "\n", "PROGRESS: Finished prediction in 0.226961s\n", "\n", "Overall RMSE: 6.09897586494\n", "\n", "Per User RMSE (best)\n", "+-------------------------------+-------+------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+------+\n", "| 91e5266cafbdd11964d70fb1d8... | 1 | 0.0 |\n", "+-------------------------------+-------+------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per User RMSE (worst)\n", "+-------------------------------+-------+---------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+---------------+\n", "| 2c263b458bb317ee91c346ae90... | 4 | 161.518485703 |\n", "+-------------------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (best)\n", "+--------------------+-------+------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+------+\n", "| SOOIQZC12A6701FEA1 | 2 | 0.0 |\n", "+--------------------+-------+------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (worst)\n", "+--------------------+-------+-------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+-------+\n", "| SOTGIKV12AB0182176 | 1 | 172.0 |\n", "+--------------------+-------+-------+\n", "[1 rows x 3 columns]\n", "\n", "PROGRESS: Evaluate model M2\n", "PROGRESS: recommendations finished on 1000/6871 queries. users per second: 10178.8\n", "PROGRESS: recommendations finished on 2000/6871 queries. users per second: 11326.8\n", "PROGRESS: recommendations finished on 3000/6871 queries. users per second: 12072.2\n", "PROGRESS: recommendations finished on 4000/6871 queries. users per second: 12724.1\n", "PROGRESS: recommendations finished on 5000/6871 queries. users per second: 12371.5\n", "PROGRESS: recommendations finished on 6000/6871 queries. users per second: 12328.1\n", "\n", "Precision and recall summary statistics by cutoff\n", "+--------+-------------------+-------------------+\n", "| cutoff | mean_precision | mean_recall |\n", "+--------+-------------------+-------------------+\n", "| 2 | 0.000291078445641 | 0.000204967738806 |\n", "| 4 | 0.000291078445641 | 0.000321852261162 |\n", "| 6 | 0.000315334982778 | 0.000400252854408 |\n", "| 8 | 0.000382040459904 | 0.000704556888155 |\n", "| 10 | 0.000480279435308 | 0.00107200975662 |\n", "| 12 | 0.000533643817009 | 0.00138531272247 |\n", "| 14 | 0.000550969914964 | 0.0016547624225 |\n", "| 16 | 0.000591253092708 | 0.00185731432805 |\n", "| 18 | 0.000582156891282 | 0.00207921438986 |\n", "| 20 | 0.000574879930141 | 0.00223034316254 |\n", "+--------+-------------------+-------------------+\n", "[10 rows x 3 columns]\n", "\n", "\n", "Overall RMSE: 7.66449264849\n", "\n", "Per User RMSE (best)\n", "+-------------------------------+-------+-------------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+-------------------+\n", "| ac810151e32857e9f4200e8fa7... | 1 | 0.000812624627255 |\n", "+-------------------------------+-------+-------------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per User RMSE (worst)\n", "+-------------------------------+-------+---------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+---------------+\n", "| 2c263b458bb317ee91c346ae90... | 4 | 182.725743431 |\n", "+-------------------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (best)\n", "+--------------------+-------+-------------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+-------------------+\n", "| SOJWIJT12A8C136100 | 1 | 0.000881766015195 |\n", "+--------------------+-------+-------------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (worst)\n", "+--------------------+-------+--------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+--------------+\n", "| SOTGIKV12AB0182176 | 1 | 236.91250578 |\n", "+--------------------+-------+--------------+\n", "[1 rows x 3 columns]\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/chengjun/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", " \"matplotlib is currently using a non-GUI backend, \"\n" ] }, { "data": { "image/png": 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qJW+/RW9+NUmuP1VVEXGVHwIqq2qkiDQGfhKRuqp6FnhYVQ+JSGHgRxHpoarT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\n/NenxZLTSdH7TkRuV9VFIvIAznCdSML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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result = gl.recommender.util.compare_models(test_set, [popularity_model, item_sim_model, factorization_machine_model],\n", " user_sample=.1, skip_set=train_set)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "K = 10\n", "users = gl.SArray(sf['user_id'].unique().head(100))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
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\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... | SOFCGSE12AF72A674F | 20.686440678 | 1 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOQYHJW12AB0182AA6 | 20.0 | 2 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOELDGL12A8C135ED7 | 20.0 | 3 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOUWZPO12A6D4F83E3 | 20.0 | 4 |\n", "| 279292bb36dbfc7f505e36ebf0... | SONAQRQ12AB017FD0B | 20.0 | 5 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOMVIOV12A6D4F719A | 20.0 | 6 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOHJWLZ12A6D4F7756 | 20.0 | 7 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOIRUXQ12A8C133060 | 20.0 | 8 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOLVHIW12A8C13BA03 | 20.0 | 9 |\n", "| 279292bb36dbfc7f505e36ebf0... | SOPWZGK12A67020744 | 20.0 | 10 |\n", "+-------------------------------+--------------------+--------------+------+\n", "[10 rows x 4 columns]" ] }, "execution_count": 11, "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 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }