{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ACTION REQUIRED: Dependencies libstdc++-6.dll and libgcc_s_seh-1.dll not found.\n", "\n", "1. Ensure user account has write permission to C:\\Anaconda3\\envs\\gl-env\\lib\\site-packages\\graphlab\n", "2. Run graphlab.get_dependencies() to download and install them.\n", "3. Restart Python and import graphlab again.\n", "\n", "By running the above function, you agree to the following licenses.\n", "\n", "* libstdc++: https://gcc.gnu.org/onlinedocs/libstdc++/manual/license.html\n", "* xz: http://git.tukaani.org/?p=xz.git;a=blob;f=COPYING\n", " \n", "\n", "By running this function, you agree to the following licenses.\n", "\n", "* libstdc++: https://gcc.gnu.org/onlinedocs/libstdc++/manual/license.html\n", "* xz: http://git.tukaani.org/?p=xz.git;a=blob;f=COPYING\n", " \n", "Downloading xz.\n", "Extracting xz.\n", "Downloading gcc-libs.\n", "Extracting gcc-libs.\n" ] }, { "ename": "CalledProcessError", "evalue": "Command '['c:\\\\users\\\\atul~1.sin\\\\appdata\\\\local\\\\temp\\\\tmphcai4r\\\\bin_x86-64\\\\xz.exe', '-d', 'c:\\\\users\\\\atul~1.sin\\\\appdata\\\\local\\\\temp\\\\tmpbozmyr.xz']' returned non-zero exit status 1", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mCalledProcessError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgraphlab\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mgraphlab\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dependencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mC:\\Anaconda3\\envs\\gl-env\\lib\\site-packages\\graphlab\\dependencies.pyc\u001b[0m in \u001b[0;36mget_dependencies\u001b[1;34m()\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[0mprev_cwd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdllarchive_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m \u001b[0msubprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'-d'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdllarchive_file\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 48\u001b[0m \u001b[0mdllarchive_tar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtarfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdllarchive_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[0mdllarchive_tar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Anaconda3\\envs\\gl-env\\lib\\subprocess.pyc\u001b[0m in \u001b[0;36mcheck_call\u001b[1;34m(*popenargs, **kwargs)\u001b[0m\n\u001b[0;32m 184\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcmd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[0mcmd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpopenargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 187\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mCalledProcessError\u001b[0m: Command '['c:\\\\users\\\\atul~1.sin\\\\appdata\\\\local\\\\temp\\\\tmphcai4r\\\\bin_x86-64\\\\xz.exe', '-d', 'c:\\\\users\\\\atul~1.sin\\\\appdata\\\\local\\\\temp\\\\tmpbozmyr.xz']' returned non-zero exit status 1" ] } ], "source": [ "import graphlab \n", "graphlab.get_dependencies()" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ACTION REQUIRED: Dependencies libstdc++-6.dll and libgcc_s_seh-1.dll not found.\n", "\n", "1. Ensure user account has write permission to C:\\Anaconda3\\envs\\gl-env\\lib\\site-packages\\graphlab\n", "2. Run graphlab.get_dependencies() to download and install them.\n", "3. Restart Python and import graphlab again.\n", "\n", "By running the above function, you agree to the following licenses.\n", "\n", "* libstdc++: https://gcc.gnu.org/onlinedocs/libstdc++/manual/license.html\n", "* xz: http://git.tukaani.org/?p=xz.git;a=blob;f=COPYING\n", " \n" ] }, { "ename": "ImportError", "evalue": "No module named matplotlib.pyplot", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgraphlab\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: No module named matplotlib.pyplot" ] } ], "source": [ "# import\n", "import graphlab as gl\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gl.canvas.set_target('ipynb')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfront
39803003712014-09-26 00:00:00+00:00142000.00.00.0290.02087510
28561014792014-07-01 00:00:00+00:00276000.01.00.75370.0180110
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viewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelat
0112900196309802447.53077245
0553700192309811747.67782145
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longsqft_living15sqft_lot15
-121.888423271620.022850.0
-122.389112081340.05000.0
\n", "[2 rows x 21 columns]
\n", "
" ], "text/plain": [ "Columns:\n", "\tid\tstr\n", "\tdate\tdatetime\n", "\tprice\tfloat\n", "\tbedrooms\tfloat\n", "\tbathrooms\tfloat\n", "\tsqft_living\tfloat\n", "\tsqft_lot\tint\n", "\tfloors\tstr\n", "\twaterfront\tint\n", "\tview\tint\n", "\tcondition\tint\n", "\tgrade\tint\n", "\tsqft_above\tint\n", "\tsqft_basement\tint\n", "\tyr_built\tint\n", "\tyr_renovated\tint\n", "\tzipcode\tstr\n", "\tlat\tfloat\n", "\tlong\tfloat\n", "\tsqft_living15\tfloat\n", "\tsqft_lot15\tfloat\n", "\n", "Rows: 2\n", "\n", "Data:\n", "+------------+---------------------------+----------+----------+-----------+\n", "| id | date | price | bedrooms | bathrooms |\n", "+------------+---------------------------+----------+----------+-----------+\n", "| 3980300371 | 2014-09-26 00:00:00+00:00 | 142000.0 | 0.0 | 0.0 |\n", "| 2856101479 | 2014-07-01 00:00:00+00:00 | 276000.0 | 1.0 | 0.75 |\n", "+------------+---------------------------+----------+----------+-----------+\n", "+-------------+----------+--------+------------+------+-----------+-------+------------+\n", "| sqft_living | sqft_lot | floors | waterfront | view | condition | grade | sqft_above |\n", "+-------------+----------+--------+------------+------+-----------+-------+------------+\n", "| 290.0 | 20875 | 1 | 0 | 0 | 1 | 1 | 290 |\n", "| 370.0 | 1801 | 1 | 0 | 0 | 5 | 5 | 370 |\n", "+-------------+----------+--------+------------+------+-----------+-------+------------+\n", "+---------------+----------+--------------+---------+-------------+\n", "| sqft_basement | yr_built | yr_renovated | zipcode | lat |\n", "+---------------+----------+--------------+---------+-------------+\n", "| 0 | 1963 | 0 | 98024 | 47.53077245 |\n", "| 0 | 1923 | 0 | 98117 | 47.67782145 |\n", "+---------------+----------+--------------+---------+-------------+\n", "+---------------+---------------+-----+\n", "| long | sqft_living15 | ... |\n", "+---------------+---------------+-----+\n", "| -121.88842327 | 1620.0 | ... |\n", "| -122.38911208 | 1340.0 | ... |\n", "+---------------+---------------+-----+\n", "[2 rows x 21 columns]" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading data\n", "sales = gl.SFrame('data/kc_house_data.gl/')\n", "sales = sales.sort(['sqft_living','price'])\n", "sales.head(2)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def polynomial_sframe(feature, degree):\n", " # assume that degree >= 1\n", " # initialize the SFrame:\n", " poly_sframe = gl.SFrame()\n", " # and set poly_sframe['power_1'] equal to the passed feature\n", " poly_sframe['power_1'] = feature\n", " \n", " # first check if degree > 1\n", " if degree > 1:\n", " # then loop over the remaining degrees:\n", " for power in range(2, degree+1):\n", " # first we'll give the column a name:\n", " name = 'power_' + str(power)\n", " # assign poly_sframe[name] to be feature^power\n", " #poly_sframe[name]= feature.apply(lambda x: x**power)\n", " poly_sframe[name]= feature**power # can use this as well\n", " return poly_sframe" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": true }, "outputs": [], "source": [ "poly1_data = polynomial_sframe(sales['sqft_living'], 1)\n", "poly1_data['price'] = sales['price']\n", "poly1_data_names = poly1_data.column_names()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Linear regression:
" ], "text/plain": [ "Linear regression:" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
--------------------------------------------------------
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Number of examples          : 21613
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Number of features          : 1
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Number of unpacked features : 1
" ], "text/plain": [ "Number of unpacked features : 1" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Number of coefficients    : 2
" ], "text/plain": [ "Number of coefficients : 2" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Starting Newton Method
" ], "text/plain": [ "Starting Newton Method" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
--------------------------------------------------------
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+-----------+----------+--------------+--------------------+---------------+
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| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |
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+-----------+----------+--------------+--------------------+---------------+
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| 1         | 2        | 0.011007     | 4362074.696077     | 261440.790724 |
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+-----------+----------+--------------+--------------------+---------------+
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SUCCESS: Optimal solution found.
" ], "text/plain": [ "SUCCESS: Optimal solution found." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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      ],
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     "metadata": {},
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    }
   ],
   "source": [
    "model1 = gl.linear_regression.create(poly1_data, target = 'price', features = ['power_1'], validation_set = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2/KqeDQ3uOLJGTrQgA7/swfr14dZRrPEdIRgZ1xot3du39nyracIE87l0Bh99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\n",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly1_data['power_1'], poly1_data['price'], '.',\n",
    "        poly1_data['power_1'], model1.predict(poly1_data), '-', linewidth=2)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rnv30WXI0h/va38e6QevoktjFdnYZFQ7zqRhGCSkon8zOjJ0MnjOYt9a+BUCb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\nZpYbFxUzM8uNi4qZmeXGRcXMzHLzP+Agvl+tws3SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly2_data = polynomial_sframe(sales['sqft_living'], 2)\n",
    "poly2_data_names = poly2_data.column_names()\n",
    "poly2_data['price'] = sales['price']\n",
    "\n",
    "\n",
    "model2 = gl.linear_regression.create(poly2_data, target = 'price', features = poly2_data_names, \n",
    "                                     validation_set = None, verbose=False)\n",
    "\n",
    "plt.plot(poly2_data['power_2'], poly2_data['price'], '.',\n",
    "        poly2_data['power_2'], model2.predict(poly2_data), '-', linewidth=2)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bAKSlpDFuwDh+fOmP6d22d4JHZxhGbWExlXIIxTTWrYO0NOeG6tMHlixx9845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\n70W3nwSci+zRR+Huu8Pl993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      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly3_data = polynomial_sframe(sales['sqft_living'], 3)\n",
    "poly3_data_names = poly3_data.column_names()\n",
    "poly3_data['price'] = sales['price']\n",
    "\n",
    "model3 = gl.linear_regression.create(poly3_data, target = 'price', features = poly3_data_names, validation_set = None, verbose=False)\n",
    "\n",
    "plt.plot(poly3_data['power_3'], poly3_data['price'], '.',\n",
    "        poly3_data['power_3'], model3.predict(poly3_data), '-', linewidth=2)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n9D379f+haKGiWb1NY/KdQkGF6F6/O8k9k+l+XXcA3l3xLpHvRPLu8nc5nXra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\nptNXnbhh3A0s37nc7fD8jk9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      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly15_data = polynomial_sframe(sales['sqft_living'], 15)\n",
    "poly15_data_names = poly15_data.column_names()\n",
    "poly15_data['price'] = sales['price']\n",
    "\n",
    "model15 = gl.linear_regression.create(poly15_data, target = 'price', features = poly15_data_names, validation_set = None, verbose=False)\n",
    "\n",
    "plt.plot(poly15_data['power_15'], poly15_data['price'], '.',\n",
    "        poly15_data['power_15'], model15.predict(poly15_data), '-', linewidth=2)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "first split sales into 2 subsets with .random_split(.5) use seed = 0!\n",
    "next split these into 2 more subsets (4 total) using random_split(0.5) again set seed = 0!\n",
    "you should have 4 subsets of (approximately) equal size, call them set_1, set_2, set_3, and set_4\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales1, sales2 = sales.random_split(.5, seed = 0)\n",
    "set_1, set_2 =   sales1.random_split(.5, seed = 0)\n",
    "set_3, set_4 =   sales2.random_split(.5, seed = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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nameindexvaluestderr
(intercept)None223312.750249733363.025988
power_1None118.0861275882903.54146793
power_2None-0.04734820113474.46221745406
power_3None3.25310342469e-050.00314111911541
power_4None-3.3237215256e-09nan
power_5None-9.75830457808e-14nan
power_6None1.15440303429e-17nan
power_7None1.05145869404e-21nan
power_8None3.46049616547e-26nan
power_9None-1.0965445418e-306.15656668872e-25
\n",
       "[16 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "
"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 16\n",
       "\n",
       "Data:\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "|     name    | index |       value        |       stderr      |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "| (intercept) |  None |   223312.750249    |   733363.025988   |\n",
       "|   power_1   |  None |   118.086127588    |   2903.54146793   |\n",
       "|   power_2   |  None |  -0.0473482011347  |   4.46221745406   |\n",
       "|   power_3   |  None | 3.25310342469e-05  |  0.00314111911541 |\n",
       "|   power_4   |  None | -3.3237215256e-09  |        nan        |\n",
       "|   power_5   |  None | -9.75830457808e-14 |        nan        |\n",
       "|   power_6   |  None | 1.15440303429e-17  |        nan        |\n",
       "|   power_7   |  None | 1.05145869404e-21  |        nan        |\n",
       "|   power_8   |  None | 3.46049616547e-26  |        nan        |\n",
       "|   power_9   |  None | -1.0965445418e-30  | 6.15656668872e-25 |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "[16 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KaQNY6PQ99/WzRXJq8l9zKS/c+IIVFON38oXko2ejniT0TqDndT1RlPErx1P9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\nRAWAGlEBoEZUAKgRFQBqHlCvSLCE2ZThAAAAAElFTkSuQmCC\n",
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly15_set_1 = polynomial_sframe(set_1['sqft_living'], 15)\n",
    "poly15_set_1_names = poly15_set_1.column_names()\n",
    "poly15_set_1['price'] = set_1['price']\n",
    "\n",
    "#print(poly15_set_1.head(2))\n",
    "\n",
    "model15_set_1 = gl.linear_regression.create(poly15_set_1, target = 'price', \n",
    "                                            features = poly15_set_1_names, validation_set = None,\n",
    "                                            verbose=False)\n",
    "\n",
    "plt.plot(poly15_set_1['power_15'], poly15_set_1['price'], '.',\n",
    "         poly15_set_1['power_15'], model15_set_1.predict(poly15_set_1), '-', linewidth=2)\n",
    "plt.grid(True)\n",
    "\n",
    "model15_set_1.get('coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "poly15_set_2 = polynomial_sframe(set_2['sqft_living'], 15)\n",
    "poly15_set_2['price'] = set_2['price']\n",
    "\n",
    "model15_set_2 = gl.linear_regression.create(poly15_set_2, target = 'price', features = ['power_15'], validation_set = None, verbose=False)\n",
    "\n",
    "plt.plot(poly15_set_2['power_15'], poly15_set_2['price'], '.',\n",
    "        poly15_set_2['power_15'], model15_set_2.predict(poly15_set_2), '-', linewidth=2)\n",
    "plt.grid(True)\n",
    "\n",
    "model15_set_2.get('coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "poly15_set_3 = polynomial_sframe(set_3['sqft_living'], 15)\n",
    "poly15_set_3['price'] = set_3['price']\n",
    "\n",
    "model15_set_3 = gl.linear_regression.create(poly15_set_3, target = 'price', features = ['power_15'], validation_set = None, verbose=False)\n",
    "\n",
    "plt.plot(poly15_set_3['power_15'], poly15_set_3['price'], '.',\n",
    "        poly15_set_3['power_15'], model15_set_3.predict(poly15_set_3), '-', linewidth=2)\n",
    "plt.grid(True)\n",
    "\n",
    "model15_set_3.get('coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "poly15_set_4 = polynomial_sframe(set_4['sqft_living'], 15)\n",
    "poly15_set_4['price'] = set_4['price']\n",
    "\n",
    "model15_set_4 = gl.linear_regression.create(poly15_set_4, target = 'price', features = ['power_15'], validation_set = None, verbose=False)\n",
    "\n",
    "plt.plot(poly15_set_4['power_15'], poly15_set_4['price'], '.',\n",
    "        poly15_set_4['power_15'], model15_set_4.predict(poly15_set_4), '-', linewidth=2)\n",
    "plt.grid(True)\n",
    "\n",
    "model15_set_4.get('coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_and_validation, testing =  sales.random_split(0.9, seed=1)\n",
    "training, validation =  training_and_validation.random_split(0.5, seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RSS_validation = {}\n",
    "RSS_testing = {}\n",
    "for degree in range(1, 16):\n",
    "    model_name = 'model_'+ str(degree)\n",
    "    dataset_name = 'dataset_'+ str(degree)\n",
    "    feature_name =  'power_'+ str(degree)\n",
    "    \n",
    "    validation_dataset = gl.SFrame()\n",
    "    validation_dataset[feature_name] = validation['sqft_living']\n",
    "    validation_dataset['price'] = validation['price']\n",
    "    \n",
    "    testing_dataset = gl.SFrame()\n",
    "    testing_dataset[feature_name] = testing['sqft_living']\n",
    "    testing_dataset['price'] = testing['price']\n",
    "    \n",
    "    dataset_name = polynomial_sframe(training['sqft_living'], degree)\n",
    "    dataset_name['price'] = training['price']\n",
    "    \n",
    "    model_name = gl.linear_regression.create(dataset_name, \n",
    "                                             target = 'price', \n",
    "                                             features = [feature_name], \n",
    "                                             validation_set = None, \n",
    "                                             verbose = False)\n",
    "    validation_dataset['prediction'] = model_name.predict(validation_dataset)\n",
    "    #print(validation_dataset['prediction'])\n",
    "    rss = np.sum(np.square(validation_dataset['price'] - validation_dataset['prediction']))\n",
    "    rss2 = np.sum(np.square(testing_dataset['price'] - model_name.predict(testing_dataset)))\n",
    "\n",
    "    RSS_validation[degree] = rss\n",
    "    RSS_testing[degree] = rss2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sorting the dict\n",
    "for k in sorted(RSS_validation, key=RSS_validation.get):\n",
    "    print k,'\\t',RSS_validation[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RSS_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = gl.SArray((1,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x.apply(lambda p: p**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gl-env]",
   "language": "python",
   "name": "conda-env-gl-env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}