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musicinformationretrieval-com
Commit 33c21547 authored by kyungyunlee's avatar kyungyunlee
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updated cross_val_score function

parent c5c37337
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Showing with 134 additions and 66 deletions
cross_validation.ipynb
View file @ 33c21547
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 16,
"metadata": {
"collapsed": true
},
......@@ -36,67 +36,73 @@
"\n",
"1. Divide the data set into 10 random partitions.\n",
"2. Choose one of the partitions as the test set. Train on the other nine partitions.\n",
"3. Repeat for the partitions."
"3. Repeat for the partitions.\n",
"\n",
"Why cross validation is good?\n",
"* In K-fold cross validation, evaluation on models can be done K times, but each time on a different partition of the data. \n",
"* It can be used to tune parameters and to choose the best model and/or features."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load some features from ten kick drums and ten snare drums:"
"### Setup Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load some features from ten kick drums and ten snare drums:\n",
"* training_features is a 2 dimensional vector with zero crossing rate and spectral centroids as features of drum samples."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "[Errno socket error] [Errno 8] nodename nor servname provided, or not known",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-a7910ded3ac4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstanford_mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/steve/stanford-mir/stanford_mir/core.pyc\u001b[0m in \u001b[0;36mget_features\u001b[0;34m(collection, features, scaler)\u001b[0m\n\u001b[1;32m 20\u001b[0m scaler=None):\n\u001b[1;32m 21\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcollection\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'drum_samples_train'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mkick_filepaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnare_filepaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdownload_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'drum_samples_train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m kick_signals = [\n\u001b[1;32m 24\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkick_filepaths\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/steve/stanford-mir/stanford_mir/core.pyc\u001b[0m in \u001b[0;36mdownload_samples\u001b[0;34m(collection)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%s_%02d.wav'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdrum_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m urllib.urlretrieve('http://audio.musicinformationretrieval.com/drum_samples/%s' % filename,\n\u001b[0;32m---> 88\u001b[0;31m filename=os.path.join(collection, filename))\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0mkick_filepaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'kick_%02d.wav'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0msnare_filepaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'snare_%02d.wav'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_urlopener\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0m_urlopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFancyURLopener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_urlopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreporthook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0murlcleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_urlopener\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self, url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 241\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 209\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib.pyc\u001b[0m in \u001b[0;36mopen_http\u001b[0;34m(self, url, data)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrealhost\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mputheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Host'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrealhost\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddheaders\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mputheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetreply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 968\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 971\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m 827\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmessage_body\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 829\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 830\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 831\u001b[0m \u001b[0;31m#message_body was not a string (i.e. it is a file) and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 792\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotConnected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[0;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 771\u001b[0m self.sock = socket.create_connection((self.host,self.port),\n\u001b[0;32m--> 772\u001b[0;31m self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tunnel_host\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/socket.pyc\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maddress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 554\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[0msock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIOError\u001b[0m: [Errno socket error] [Errno 8] nodename nor servname provided, or not known"
]
}
],
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"training_features, training_labels, scaler = stanford_mir.get_features()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.56578947 0.2490469 ]\n",
" [-0.92105263 -0.63084112]\n",
" [-1. -1. ]\n",
" [-1. 0.32776395]\n",
" [-0.77631579 -0.55766302]\n",
" [-0.96052632 -0.69890631]\n",
" [-0.68421053 -0.6353643 ]\n",
" [-0.73684211 -0.10209073]\n",
" [-0.82894737 -0.38692759]\n",
" [-0.69736842 -0.5365142 ]\n",
" [ 0.53947368 0.89837486]\n",
" [ 0.63157895 0.55248334]\n",
" [ 1. 0.99378504]\n",
" [ 0.93421053 1. ]\n",
" [ 0.85526316 0.87391641]\n",
" [ 0.89473684 0.96797404]\n",
" [ 0.23684211 0.86119147]\n",
" [ 0.97368421 0.79526304]\n",
" [ 0.40789474 0.38793404]\n",
" [ 0.88157895 0.88600432]]\n",
"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1.]\n"
]
}
],
"source": [
"print training_features\n",
"print training_labels"
]
},
......@@ -109,21 +115,28 @@
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'training_features' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-f00bdfd12558>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'training_features' is not defined"
]
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x1c121d9b50>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1c11f931d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
......@@ -134,67 +147,122 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize the classifier:"
"### Load model and perform cross-validation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize the K-Nearest Neighbor classifier:\n",
"* Note that K=3 here (3 neighbors), but K is so-called a hyperparameter, which can be tuned to select the best value (We will get to that below!)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=3)\n",
"model = sklearn.linear_model.LogisticRegression()"
"model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform 5-fold cross validation:"
"Perform 5-fold cross validation (cv=5):"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"acc = sklearn.cross_validation.cross_val_score(model, random_features, training_labels, cv=5)"
"acc = sklearn.model_selection.cross_val_score(model, training_features, training_labels, cv=5)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 1. 1. 1. 1.]\n",
"1.0\n"
]
}
],
"source": [
"print acc\n",
"print acc.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
"collapsed": true
},
"source": [
"### Tuning parameter K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since value of K has been arbitrarily chosen, we do not know whether it was the best choice (although here in this example, we have a perfect score anyway...). \n",
"Therefore, testing the result with several other values will help to choose the best parameter."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5\n"
"[K=1] Accuracy=1.000\n",
"[K=2] Accuracy=1.000\n",
"[K=3] Accuracy=1.000\n",
"[K=4] Accuracy=1.000\n",
"[K=5] Accuracy=1.000\n"
]
}
],
"source": [
"print acc.mean()"
"K_choices = [1,2,3,4,5]\n",
"for k in K_choices:\n",
" model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=k)\n",
" mean_score = sklearn.model_selection.cross_val_score(model, training_features, training_labels, cv=5).mean()\n",
" print \"[K=%d] Accuracy=%.3f\"%(k, mean_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bonus : Try out other models and perform cross validations to find the best model!"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k"
"# Logistic regression for binary classification\n",
"model = sklearn.linear_model.LogisticRegression()"
]
},
{
......@@ -221,9 +289,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}
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