Week 4 - Ridge Regression Assignment 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Week 4: Ridge Regression (interpretation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of L2 regularization. In particular, we will:\n",
    "* Use a pre-built implementation of regression (GraphLab Create) to run polynomial regression\n",
    "* Use matplotlib to visualize polynomial regressions\n",
    "* Use a pre-built implementation of regression (GraphLab Create) to run polynomial regression, this time with L2 penalty\n",
    "* Use matplotlib to visualize polynomial regressions under L2 regularization\n",
    "* Choose best L2 penalty using cross-validation.\n",
    "* Assess the final fit using test data.\n",
    "\n",
    "We will continue to use the House data from previous notebooks.  (In the next programming assignment for this module, you will implement your own ridge regression learning algorithm using gradient descent.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fire up graphlab create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import graphlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial regression, revisited"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build on the material from Week 3, where we wrote the function to produce an SFrame with columns containing the powers of a given input. Copy and paste the function `polynomial_sframe` from Week 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def polynomial_sframe(feature, degree):\n",
    "    poly_sframe = graphlab.SFrame()\n",
    "    poly_sframe['power_1'] = feature\n",
    "    if degree > 1:\n",
    "        for power in range(2, degree+1):\n",
    "            name = 'power_' + str(power)\n",
    "            poly_sframe[name] = feature ** power\n",
    "    return poly_sframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use matplotlib to visualize what a polynomial regression looks like on the house data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to amitha353@gmail.com and will expire on May 07, 2019.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\\Users\\Amitha\\AppData\\Local\\Temp\\graphlab_server_1531731220.log.0\n"
     ]
    }
   ],
   "source": [
    "sales = graphlab.SFrame('kc_house_data.gl/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in Week 3, we will use the sqft_living variable. For plotting purposes (connecting the dots), you'll need to sort by the values of sqft_living. For houses with identical square footage, we break the tie by their prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sales = sales.sort(['sqft_living','price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us revisit the 15th-order polynomial model using the 'sqft_living' input. Generate polynomial features up to degree 15 using `polynomial_sframe()` and fit a model with these features. When fitting the model, use an L2 penalty of `1e-5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l2_small_penalty = 1e-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: When we have so many features and so few data points, the solution can become highly numerically unstable, which can sometimes lead to strange unpredictable results.  Thus, rather than using no regularization, we will introduce a tiny amount of regularization (`l2_penalty=1e-5`) to make the solution numerically stable.  (In lecture, we discussed the fact that regularization can also help with numerical stability, and here we are seeing a practical example.)\n",
    "\n",
    "With the L2 penalty specified above, fit the model and print out the learned weights.\n",
    "\n",
    "Hint: make sure to add 'price' column to the new SFrame before calling `graphlab.linear_regression.create()`. Also, make sure GraphLab Create doesn't create its own validation set by using the option `validation_set=None` in this call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly15_data = polynomial_sframe(sales['sqft_living'], 15)\n",
    "poly15_features = poly15_data.column_names()\n",
    "poly15_data['price'] = sales['price']\n",
    "model15_1 = graphlab.linear_regression.create(poly15_data, \n",
    "                target='price', features = poly15_features, \n",
    "             l2_penalty =l2_small_penalty, validation_set = None,\n",
    "                                             verbose = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">167924.864</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">103.09093176</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.134604574378</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.000129071378072</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.18929005637e-08</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-7.77169405666e-12</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.71144993371e-16</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.5117779689e-20</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4.78838623954e-25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.33343500122e-28</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[16 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "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 |     167924.864     |  nan   |\n",
       "|   power_1   |  None |    103.09093176    |  nan   |\n",
       "|   power_2   |  None |   0.134604574378   |  nan   |\n",
       "|   power_3   |  None | -0.000129071378072 |  nan   |\n",
       "|   power_4   |  None | 5.18929005637e-08  |  nan   |\n",
       "|   power_5   |  None | -7.77169405666e-12 |  nan   |\n",
       "|   power_6   |  None | 1.71144993371e-16  |  nan   |\n",
       "|   power_7   |  None |  4.5117779689e-20  |  nan   |\n",
       "|   power_8   |  None | -4.78838623954e-25 |  nan   |\n",
       "|   power_9   |  None | -2.33343500122e-28 |  nan   |\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model15_1.get('coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***QUIZ QUESTION:  What's the learned value for the coefficient of feature `power_1`?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "103.09093176"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observe overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall from Week 3 that the polynomial fit of degree 15 changed wildly whenever the data changed. In particular, when we split the sales data into four subsets and fit the model of degree 15, the result came out to be very different for each subset. The model had a *high variance*. We will see in a moment that ridge regression reduces such variance. But first, we must reproduce the experiment we did in Week 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, split the data into split the sales data into four subsets of roughly equal size and call them `set_1`, `set_2`, `set_3`, and `set_4`. Use `.random_split` function and make sure you set `seed=0`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(semi_split1, semi_split2) = sales.random_split(.5,seed=0)\n",
    "(set_1, set_2) = semi_split1.random_split(0.5, seed=0)\n",
    "(set_3, set_4) = semi_split2.random_split(0.5, seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, fit a 15th degree polynomial on `set_1`, `set_2`, `set_3`, and `set_4`, using 'sqft_living' to predict prices. Print the weights and make a plot of the resulting model.\n",
    "\n",
    "Hint: When calling `graphlab.linear_regression.create()`, use the same L2 penalty as before (i.e. `l2_small_penalty`).  Also, make sure GraphLab Create doesn't create its own validation set by using the option `validation_set = None` in this call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_poly_model(set_data, l2_penalty):\n",
    "    poly15_data = polynomial_sframe(set_data['sqft_living'], 15)\n",
    "    poly15_features = poly15_data.column_names()\n",
    "    poly15_data['price'] = set_data['price']\n",
    "    model15 = graphlab.linear_regression.create(poly15_data, \n",
    "            target='price', features=poly15_features, \n",
    "        l2_penalty = l2_penalty, validation_set=None, verbose = False)\n",
    "    return poly15_data, model15\n",
    "\n",
    "def get_coeff(set_data, l2_penalty):\n",
    "    poly15_data, model15 = get_poly_model(set_data, l2_penalty)\n",
    "    return model15.get('coefficients')\n",
    "\n",
    "def plot_fitted_line(set_data, l2_penalty):\n",
    "    poly15_data, model15 = get_poly_model(set_data, l2_penalty)\n",
    "    return plt.plot(poly15_data['power_1'], poly15_data['price'], '.',\n",
    "         poly15_data['power_1'], model15.predict(poly15_data), '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+---------------+\n",
      "|   name  | index |     value     |     stderr    |\n",
      "+---------+-------+---------------+---------------+\n",
      "| power_1 |  None | 585.865810569 | 3356.00730977 |\n",
      "+---------+-------+---------------+---------------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x230e1470>,\n",
       " <matplotlib.lines.Line2D at 0x230e1518>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEACAYAAABoJ6s/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f83550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_1_coeff = get_coeff(set_1, l2_small_penalty)\n",
    "print set_1_coeff[set_1_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_1, l2_small_penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+---------------+\n",
      "|   name  | index |     value     |     stderr    |\n",
      "+---------+-------+---------------+---------------+\n",
      "| power_1 |  None | 783.493767944 | 8453.77893674 |\n",
      "+---------+-------+---------------+---------------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x232d0b00>,\n",
       " <matplotlib.lines.Line2D at 0x232d0ba8>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEACAYAAABoJ6s/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232298d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_2_coeff = get_coeff(set_2, l2_small_penalty)\n",
    "print set_2_coeff[set_2_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_2, l2_small_penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+----------------+---------------+\n",
      "|   name  | index |     value      |     stderr    |\n",
      "+---------+-------+----------------+---------------+\n",
      "| power_1 |  None | -759.251857376 | 8559.07130311 |\n",
      "+---------+-------+----------------+---------------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x235ca550>,\n",
       " <matplotlib.lines.Line2D at 0x235ca5f8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEACAYAAABoJ6s/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMFoMEx3DMAyjxTDRMQzDMFqM/w+rQsacIiE5WwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23345978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_3_coeff = get_coeff(set_3, l2_small_penalty)\n",
    "print set_3_coeff[set_3_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_3, l2_small_penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+--------+\n",
      "|   name  | index |     value     | stderr |\n",
      "+---------+-------+---------------+--------+\n",
      "| power_1 |  None | 1247.59035474 |  nan   |\n",
      "+---------+-------+---------------+--------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23835c50>,\n",
       " <matplotlib.lines.Line2D at 0x23835cf8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEACAYAAACK+7BGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naAzDMIyMYkJjGIZhZJT/DyDu9peDCLX5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2352b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_4_coeff = get_coeff(set_4, l2_small_penalty)\n",
    "print set_4_coeff[set_4_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_4, l2_small_penalty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The four curves should differ from one another a lot, as should the coefficients you learned.\n",
    "\n",
    "***QUIZ QUESTION:  For the models learned in each of these training sets, what are the smallest and largest values you learned for the coefficient of feature `power_1`?***  (For the purpose of answering this question, negative numbers are considered \"smaller\" than positive numbers. So -5 is smaller than -3, and -3 is smaller than 5 and so forth.)\n",
    "\n",
    "* Set-1 -> 585.865810569\n",
    "* Set-2 -> 783.493767944\n",
    "* Set-3 -> -759.251857376\n",
    "* Set-4 -> 1247.59035474 \n",
    "\n",
    "** Range = -759 to 1248"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge regression comes to rescue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, whenever we see weights change so much in response to change in data, we believe the variance of our estimate to be large. Ridge regression aims to address this issue by penalizing \"large\" weights. (Weights of `model15` looked quite small, but they are not that small because 'sqft_living' input is in the order of thousands.)\n",
    "\n",
    "With the argument `l2_penalty=1e5`, fit a 15th-order polynomial model on `set_1`, `set_2`, `set_3`, and `set_4`. Other than the change in the `l2_penalty` parameter, the code should be the same as the experiment above. Also, make sure GraphLab Create doesn't create its own validation set by using the option `validation_set = None` in this call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "l2_new_penalty = 1e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+---------------+\n",
      "|   name  | index |     value     |     stderr    |\n",
      "+---------+-------+---------------+---------------+\n",
      "| power_1 |  None | 2.58738875673 | 5050.38009321 |\n",
      "+---------+-------+---------------+---------------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x37d905c0>,\n",
       " <matplotlib.lines.Line2D at 0x37d90668>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEACAYAAABoJ6s/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37b99668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_1_coeff = get_coeff(set_1, l2_new_penalty)\n",
    "print set_1_coeff[set_1_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_1, l2_new_penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+---------------+\n",
      "|   name  | index |     value     |     stderr    |\n",
      "+---------+-------+---------------+---------------+\n",
      "| power_1 |  None | 2.04470474182 | 11651.1634853 |\n",
      "+---------+-------+---------------+---------------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x380302e8>,\n",
       " <matplotlib.lines.Line2D at 0x38030390>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEACAYAAABoJ6s/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37a67cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_2_coeff = get_coeff(set_2, l2_new_penalty)\n",
    "print set_2_coeff[set_2_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_2, l2_new_penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+---------------+\n",
      "|   name  | index |     value     |     stderr    |\n",
      "+---------+-------+---------------+---------------+\n",
      "| power_1 |  None | 2.26890421877 | 11931.4574858 |\n",
      "+---------+-------+---------------+---------------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x392f5470>,\n",
       " <matplotlib.lines.Line2D at 0x392f5518>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEACAYAAABoJ6s/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwtfz3LEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37ab6470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_3_coeff = get_coeff(set_3, l2_new_penalty)\n",
    "print set_3_coeff[set_3_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_3, l2_new_penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+---------------+--------+\n",
      "|   name  | index |     value     | stderr |\n",
      "+---------+-------+---------------+--------+\n",
      "| power_1 |  None | 1.91040938244 |  nan   |\n",
      "+---------+-------+---------------+--------+\n",
      "[? rows x 4 columns]\n",
      "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
      "You can use sf.materialize() to force materialization.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x394dba90>,\n",
       " <matplotlib.lines.Line2D at 0x394dbb38>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEACAYAAACK+7BGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOE68rgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3822e2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_4_coeff = get_coeff(set_4, l2_new_penalty)\n",
    "print set_4_coeff[set_4_coeff['name'] == 'power_1']\n",
    "plot_fitted_line(set_4, l2_new_penalty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These curves should vary a lot less, now that you applied a high degree of regularization.\n",
    "\n",
    "***QUIZ QUESTION:  For the models learned with the high level of regularization in each of these training sets, what are the smallest and largest values you learned for the coefficient of feature `power_1`?*** (For the purpose of answering this question, negative numbers are considered \"smaller\" than positive numbers. So -5 is smaller than -3, and -3 is smaller than 5 and so forth.)\n",
    "\n",
    "\n",
    "* Set-1 -> 2.58738875673 \n",
    "* Set-2 -> 2.04470474182\n",
    "* Set-3 -> 2.26890421877\n",
    "* Set-4 -> 1.91040938244 \n",
    "\n",
    "** Range = 1.91 - 2.58"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting an L2 penalty via cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the polynomial degree, the L2 penalty is a \"magic\" parameter we need to select. We could use the validation set approach as we did in the last module, but that approach has a major disadvantage: it leaves fewer observations available for training. **Cross-validation** seeks to overcome this issue by using all of the training set in a smart way.\n",
    "\n",
    "We will implement a kind of cross-validation called **k-fold cross-validation**. The method gets its name because it involves dividing the training set into k segments of roughtly equal size. Similar to the validation set method, we measure the validation error with one of the segments designated as the validation set. The major difference is that we repeat the process k times as follows:\n",
    "\n",
    "Set aside segment 0 as the validation set, and fit a model on rest of data, and evalutate it on this validation set<br>\n",
    "Set aside segment 1 as the validation set, and fit a model on rest of data, and evalutate it on this validation set<br>\n",
    "...<br>\n",
    "Set aside segment k-1 as the validation set, and fit a model on rest of data, and evalutate it on this validation set\n",
    "\n",
    "After this process, we compute the average of the k validation errors, and use it as an estimate of the generalization error. Notice that  all observations are used for both training and validation, as we iterate over segments of data. \n",
    "\n",
    "To estimate the generalization error well, it is crucial to shuffle the training data before dividing them into segments. GraphLab Create has a utility function for shuffling a given SFrame. We reserve 10% of the data as the test set and shuffle the remainder. (Make sure to use `seed=1` to get consistent answer.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(train_valid, test) = sales.random_split(.9, seed=1)\n",
    "train_valid_shuffled = graphlab.toolkits.cross_validation.shuffle(train_valid, random_seed=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the data is shuffled, we divide it into equal segments. Each segment should receive `n/k` elements, where `n` is the number of observations in the training set and `k` is the number of segments. Since the segment 0 starts at index 0 and contains `n/k` elements, it ends at index `(n/k)-1`. The segment 1 starts where the segment 0 left off, at index `(n/k)`. With `n/k` elements, the segment 1 ends at index `(n*2/k)-1`. Continuing in this fashion, we deduce that the segment `i` starts at index `(n*i/k)` and ends at `(n*(i+1)/k)-1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this pattern in mind, we write a short loop that prints the starting and ending indices of each segment, just to make sure you are getting the splits right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (0, 1938)\n",
      "1 (1939, 3878)\n",
      "2 (3879, 5817)\n",
      "3 (5818, 7757)\n",
      "4 (7758, 9697)\n",
      "5 (9698, 11636)\n",
      "6 (11637, 13576)\n",
      "7 (13577, 15515)\n",
      "8 (15516, 17455)\n",
      "9 (17456, 19395)\n"
     ]
    }
   ],
   "source": [
    "n = len(train_valid_shuffled)\n",
    "k = 10 # 10-fold cross-validation\n",
    "\n",
    "for i in xrange(k):\n",
    "    start = (n*i)/k\n",
    "    end = (n*(i+1))/k-1\n",
    "    print i, (start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Let us familiarize ourselves with array slicing with SFrame. To extract a continuous slice from an SFrame, use colon in square brackets. For instance, the following cell extracts rows 0 to 9 of `train_valid_shuffled`. Notice that the first index (0) is included in the slice but the last index (10) is omitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">price</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bedrooms</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bathrooms</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">floors</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">waterfront</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2780400035</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-05-05 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">665000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2800.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5900</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1703050500</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2015-03-21 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">645000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2490.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5978</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5700002325</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-06-05 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">640000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.75</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2340.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4206</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0475000510</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-11-18 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">594000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1320.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5000</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0844001052</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2015-01-28 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">365000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1904.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8200</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2781280290</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2015-04-27 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">305000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1610.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3516</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2214800630</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-11-05 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">239950.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1560.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8280</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2114700540</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-10-21 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">366000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1320.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4320</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2596400050</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-07-30 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">375000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1960.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7955</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4140900050</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2015-01-26 00:00:00+00:00</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">440000.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.75</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2180.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10200</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">view</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">condition</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">grade</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_above</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_basement</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_built</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_renovated</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">zipcode</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">lat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1660</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1140</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1963</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98115</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.68093246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2490</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2003</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98074</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.62984888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1170</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1170</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1917</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98144</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.57587004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1090</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">230</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1920</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98107</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.66737217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1904</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1999</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98010</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.31068733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1610</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2006</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98055</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.44911017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1560</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1979</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98001</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.33933392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">660</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">660</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1918</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98106</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.53271982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1260</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">700</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1963</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98177</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.76407345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2000</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">180</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1966</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98028</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.76382378</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.28583258</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2580.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.02177564</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2710.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6629.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.28796</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1360.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.36472902</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1700.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.0012452</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1560.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.1878086</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1610.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.25864364</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1920.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.34716948</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1190.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.36361517</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1850.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.27022456</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2590.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10445.0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 21 columns]<br/>\n",
       "</div>"
      ],
      "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: 10\n",
       "\n",
       "Data:\n",
       "+------------+---------------------------+----------+----------+-----------+\n",
       "|     id     |            date           |  price   | bedrooms | bathrooms |\n",
       "+------------+---------------------------+----------+----------+-----------+\n",
       "| 2780400035 | 2014-05-05 00:00:00+00:00 | 665000.0 |   4.0    |    2.5    |\n",
       "| 1703050500 | 2015-03-21 00:00:00+00:00 | 645000.0 |   3.0    |    2.5    |\n",
       "| 5700002325 | 2014-06-05 00:00:00+00:00 | 640000.0 |   3.0    |    1.75   |\n",
       "| 0475000510 | 2014-11-18 00:00:00+00:00 | 594000.0 |   3.0    |    1.0    |\n",
       "| 0844001052 | 2015-01-28 00:00:00+00:00 | 365000.0 |   4.0    |    2.5    |\n",
       "| 2781280290 | 2015-04-27 00:00:00+00:00 | 305000.0 |   3.0    |    2.5    |\n",
       "| 2214800630 | 2014-11-05 00:00:00+00:00 | 239950.0 |   3.0    |    2.25   |\n",
       "| 2114700540 | 2014-10-21 00:00:00+00:00 | 366000.0 |   3.0    |    2.5    |\n",
       "| 2596400050 | 2014-07-30 00:00:00+00:00 | 375000.0 |   3.0    |    1.0    |\n",
       "| 4140900050 | 2015-01-26 00:00:00+00:00 | 440000.0 |   4.0    |    1.75   |\n",
       "+------------+---------------------------+----------+----------+-----------+\n",
       "+-------------+----------+--------+------------+------+-----------+-------+------------+\n",
       "| sqft_living | sqft_lot | floors | waterfront | view | condition | grade | sqft_above |\n",
       "+-------------+----------+--------+------------+------+-----------+-------+------------+\n",
       "|    2800.0   |   5900   |   1    |     0      |  0   |     3     |   8   |    1660    |\n",
       "|    2490.0   |   5978   |   2    |     0      |  0   |     3     |   9   |    2490    |\n",
       "|    2340.0   |   4206   |   1    |     0      |  0   |     5     |   7   |    1170    |\n",
       "|    1320.0   |   5000   |   1    |     0      |  0   |     4     |   7   |    1090    |\n",
       "|    1904.0   |   8200   |   2    |     0      |  0   |     5     |   7   |    1904    |\n",
       "|    1610.0   |   3516   |   2    |     0      |  0   |     3     |   8   |    1610    |\n",
       "|    1560.0   |   8280   |   2    |     0      |  0   |     4     |   7   |    1560    |\n",
       "|    1320.0   |   4320   |   1    |     0      |  0   |     3     |   6   |    660     |\n",
       "|    1960.0   |   7955   |   1    |     0      |  0   |     4     |   7   |    1260    |\n",
       "|    2180.0   |  10200   |   1    |     0      |  2   |     3     |   8   |    2000    |\n",
       "+-------------+----------+--------+------------+------+-----------+-------+------------+\n",
       "+---------------+----------+--------------+---------+-------------+\n",
       "| sqft_basement | yr_built | yr_renovated | zipcode |     lat     |\n",
       "+---------------+----------+--------------+---------+-------------+\n",
       "|      1140     |   1963   |      0       |  98115  | 47.68093246 |\n",
       "|       0       |   2003   |      0       |  98074  | 47.62984888 |\n",
       "|      1170     |   1917   |      0       |  98144  | 47.57587004 |\n",
       "|      230      |   1920   |      0       |  98107  | 47.66737217 |\n",
       "|       0       |   1999   |      0       |  98010  | 47.31068733 |\n",
       "|       0       |   2006   |      0       |  98055  | 47.44911017 |\n",
       "|       0       |   1979   |      0       |  98001  | 47.33933392 |\n",
       "|      660      |   1918   |      0       |  98106  | 47.53271982 |\n",
       "|      700      |   1963   |      0       |  98177  | 47.76407345 |\n",
       "|      180      |   1966   |      0       |  98028  | 47.76382378 |\n",
       "+---------------+----------+--------------+---------+-------------+\n",
       "+---------------+---------------+-----+\n",
       "|      long     | sqft_living15 | ... |\n",
       "+---------------+---------------+-----+\n",
       "| -122.28583258 |     2580.0    | ... |\n",
       "| -122.02177564 |     2710.0    | ... |\n",
       "|   -122.28796  |     1360.0    | ... |\n",
       "| -122.36472902 |     1700.0    | ... |\n",
       "|  -122.0012452 |     1560.0    | ... |\n",
       "|  -122.1878086 |     1610.0    | ... |\n",
       "| -122.25864364 |     1920.0    | ... |\n",
       "| -122.34716948 |     1190.0    | ... |\n",
       "| -122.36361517 |     1850.0    | ... |\n",
       "| -122.27022456 |     2590.0    | ... |\n",
       "+---------------+---------------+-----+\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_valid_shuffled[0:10] # rows 0 to 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us extract individual segments with array slicing. Consider the scenario where we group the houses in the `train_valid_shuffled` dataframe into k=10 segments of roughly equal size, with starting and ending indices computed as above.\n",
    "Extract the fourth segment (segment 3) and assign it to a variable called `validation4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation4 = train_valid_shuffled[5818 : 7757]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify that we have the right elements extracted, run the following cell, which computes the average price of the fourth segment. When rounded to nearest whole number, the average should be $536,234."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "536353\n"
     ]
    }
   ],
   "source": [
    "print int(round(validation4['price'].mean(), 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After designating one of the k segments as the validation set, we train a model using the rest of the data. To choose the remainder, we slice (0:start) and (end+1:n) of the data and paste them together. SFrame has `append()` method that pastes together two disjoint sets of rows originating from a common dataset. For instance, the following cell pastes together the first and last two rows of the `train_valid_shuffled` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------------------------+-----------+----------+-----------+\n",
      "|     id     |            date           |   price   | bedrooms | bathrooms |\n",
      "+------------+---------------------------+-----------+----------+-----------+\n",
      "| 2780400035 | 2014-05-05 00:00:00+00:00 |  665000.0 |   4.0    |    2.5    |\n",
      "| 1703050500 | 2015-03-21 00:00:00+00:00 |  645000.0 |   3.0    |    2.5    |\n",
      "| 4139480190 | 2014-09-16 00:00:00+00:00 | 1153000.0 |   3.0    |    3.25   |\n",
      "| 7237300290 | 2015-03-26 00:00:00+00:00 |  338000.0 |   5.0    |    2.5    |\n",
      "+------------+---------------------------+-----------+----------+-----------+\n",
      "+-------------+----------+--------+------------+------+-----------+-------+------------+\n",
      "| sqft_living | sqft_lot | floors | waterfront | view | condition | grade | sqft_above |\n",
      "+-------------+----------+--------+------------+------+-----------+-------+------------+\n",
      "|    2800.0   |   5900   |   1    |     0      |  0   |     3     |   8   |    1660    |\n",
      "|    2490.0   |   5978   |   2    |     0      |  0   |     3     |   9   |    2490    |\n",
      "|    3780.0   |  10623   |   1    |     0      |  1   |     3     |   11  |    2650    |\n",
      "|    2400.0   |   4496   |   2    |     0      |  0   |     3     |   7   |    2400    |\n",
      "+-------------+----------+--------+------------+------+-----------+-------+------------+\n",
      "+---------------+----------+--------------+---------+-------------+\n",
      "| sqft_basement | yr_built | yr_renovated | zipcode |     lat     |\n",
      "+---------------+----------+--------------+---------+-------------+\n",
      "|      1140     |   1963   |      0       |  98115  | 47.68093246 |\n",
      "|       0       |   2003   |      0       |  98074  | 47.62984888 |\n",
      "|      1130     |   1999   |      0       |  98006  | 47.55061236 |\n",
      "|       0       |   2004   |      0       |  98042  | 47.36923712 |\n",
      "+---------------+----------+--------------+---------+-------------+\n",
      "+---------------+---------------+-----+\n",
      "|      long     | sqft_living15 | ... |\n",
      "+---------------+---------------+-----+\n",
      "| -122.28583258 |     2580.0    | ... |\n",
      "| -122.02177564 |     2710.0    | ... |\n",
      "| -122.10144844 |     3850.0    | ... |\n",
      "| -122.12606473 |     1880.0    | ... |\n",
      "+---------------+---------------+-----+\n",
      "[4 rows x 21 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n = len(train_valid_shuffled)\n",
    "first_two = train_valid_shuffled[0:2]\n",
    "last_two = train_valid_shuffled[n-2:n]\n",
    "print first_two.append(last_two)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the remainder of the data after *excluding* fourth segment (segment 3) and assign the subset to `train4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17455\n"
     ]
    }
   ],
   "source": [
    "first_part = train_valid_shuffled[0:5817]\n",
    "last_part = train_valid_shuffled[7758:]\n",
    "train4 = first_part.append(last_part)\n",
    "print len(train4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify that we have the right elements extracted, run the following cell, which computes the average price of the data with fourth segment excluded. When rounded to nearest whole number, the average should be $539,450."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "539458\n"
     ]
    }
   ],
   "source": [
    "print int(round(train4['price'].mean(), 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to implement k-fold cross-validation. Write a function that computes k validation errors by designating each of the k segments as the validation set. It accepts as parameters (i) `k`, (ii) `l2_penalty`, (iii) dataframe, (iv) name of output column (e.g. `price`) and (v) list of feature names. The function returns the average validation error using k segments as validation sets.\n",
    "\n",
    "* For each i in [0, 1, ..., k-1]:\n",
    "  * Compute starting and ending indices of segment i and call 'start' and 'end'\n",
    "  * Form validation set by taking a slice (start:end+1) from the data.\n",
    "  * Form training set by appending slice (end+1:n) to the end of slice (0:start).\n",
    "  * Train a linear model using training set just formed, with a given l2_penalty\n",
    "  * Compute validation error using validation set just formed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def k_fold_cross_validation(k, l2_penalty, data, output_name, features_list):    \n",
    "    rss_sum = 0\n",
    "    n = len(data)\n",
    "    for i in xrange(k):\n",
    "        start = (n*i)/k\n",
    "        end = (n*(i+1))/k-1\n",
    "        validation_set = data[start:end+1]\n",
    "        training_set = data[0:start].append(data[end+1:n])    \n",
    "        model = graphlab.linear_regression.create(training_set, target = output_name, features = features_list, \n",
    "                                                  l2_penalty=l2_penalty,\n",
    "                                                  validation_set=None,verbose=False)\n",
    "        predictions = model.predict(validation_set)\n",
    "        residuals = validation_set['price'] - predictions\n",
    "        rss = sum(residuals * residuals)\n",
    "        rss_sum += rss\n",
    "    validation_error = rss_sum / k # average = sum / size or you can use np.mean(list_of_validation_error)\n",
    "    return validation_error    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have a function to compute the average validation error for a model, we can write a loop to find the model that minimizes the average validation error. Write a loop that does the following:\n",
    "* We will again be aiming to fit a 15th-order polynomial model using the `sqft_living` input\n",
    "* For `l2_penalty` in [10^1, 10^1.5, 10^2, 10^2.5, ..., 10^7] (to get this in Python, you can use this Numpy function: `np.logspace(1, 7, num=13)`.)\n",
    "    * Run 10-fold cross-validation with `l2_penalty`\n",
    "* Report which L2 penalty produced the lowest average validation error.\n",
    "\n",
    "Note: since the degree of the polynomial is now fixed to 15, to make things faster, you should generate polynomial features in advance and re-use them throughout the loop. Make sure to use `train_valid_shuffled` when generating polynomial features!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{10.0: 491826427769358.8}\n",
      "{10.0: 491826427769358.8, 31.622776601683793: 287504229919158.3}\n",
      "{10.0: 491826427769358.8, 100.0: 160908965822180.16, 31.622776601683793: 287504229919158.3}\n",
      "{10.0: 491826427769358.8, 100.0: 160908965822180.16, 31.622776601683793: 287504229919158.3, 316.2277660168379: 122090967326083.33}\n",
      "{1000.0: 121192264451214.97, 10.0: 491826427769358.8, 100.0: 160908965822180.16, 31.622776601683793: 287504229919158.3, 316.2277660168379: 122090967326083.33}\n",
      "{100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 316.2277660168379: 122090967326083.33}\n",
      "{100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 10000.0: 136837175247518.95, 316.2277660168379: 122090967326083.33}\n",
      "{100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 316.2277660168379: 122090967326083.33}\n",
      "{100000.0: 229361431260422.75, 100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 316.2277660168379: 122090967326083.33}\n",
      "{100000.0: 229361431260422.75, 316227.76601683791: 252940568728599.75, 100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 316.2277660168379: 122090967326083.33}\n",
      "{100000.0: 229361431260422.75, 316227.76601683791: 252940568728599.75, 100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 316.2277660168379: 122090967326083.33, 1000000.0: 258682548441132.5}\n",
      "{100000.0: 229361431260422.75, 316227.76601683791: 252940568728599.75, 100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 3162277.6601683791: 262819399742234.16, 316.2277660168379: 122090967326083.33, 1000000.0: 258682548441132.5}\n",
      "{100000.0: 229361431260422.75, 316227.76601683791: 252940568728599.75, 100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 3162277.6601683791: 262819399742234.16, 316.2277660168379: 122090967326083.33, 1000000.0: 258682548441132.5, 10000000.0: 264889015377543.8}\n",
      "{100000.0: 229361431260422.75, 316227.76601683791: 252940568728599.75, 100.0: 160908965822180.16, 3162.2776601683795: 123950009289897.55, 31.622776601683793: 287504229919158.3, 1000.0: 121192264451214.97, 10.0: 491826427769358.8, 31622.776601683792: 171728094842297.3, 10000.0: 136837175247518.95, 3162277.6601683791: 262819399742234.16, 316.2277660168379: 122090967326083.33, 1000000.0: 258682548441132.5, 10000000.0: 264889015377543.8}\n"
     ]
    }
   ],
   "source": [
    "poly_data = polynomial_sframe(train_valid_shuffled['sqft_living'], 15)\n",
    "my_features = poly_data.column_names()\n",
    "poly_data['price'] = train_valid_shuffled['price']\n",
    "\n",
    "val_err_dict = {}\n",
    "for l2_penalty in np.logspace(1, 7, num=13):\n",
    "    val_err = k_fold_cross_validation(10, l2_penalty, poly_data, 'price', my_features)    \n",
    "    val_err_dict[l2_penalty] = val_err\n",
    "    print val_err_dict\n",
    "print val_err_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***QUIZ QUESTIONS:  What is the best value for the L2 penalty according to 10-fold validation?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000.0 1.21192264451e+14\n"
     ]
    }
   ],
   "source": [
    "min_val, l2_penalty_minval = min(val_err_dict.items(), \n",
    "                                 key = lambda x:x[1])\n",
    "print min_val, l2_penalty_minval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may find it useful to plot the k-fold cross-validation errors you have obtained to better understand the behavior of the method.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+-------------------+\n",
      "|   l2_penalty  |  validation_error |\n",
      "+---------------+-------------------+\n",
      "|    100000.0   |  2.2936143126e+14 |\n",
      "| 316227.766017 | 2.52940568729e+14 |\n",
      "|     100.0     | 1.60908965822e+14 |\n",
      "| 3162.27766017 |  1.2395000929e+14 |\n",
      "| 31.6227766017 | 2.87504229919e+14 |\n",
      "|     1000.0    | 1.21192264451e+14 |\n",
      "|      10.0     | 4.91826427769e+14 |\n",
      "| 31622.7766017 | 1.71728094842e+14 |\n",
      "|    10000.0    | 1.36837175248e+14 |\n",
      "| 3162277.66017 | 2.62819399742e+14 |\n",
      "+---------------+-------------------+\n",
      "[13 rows x 2 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.\n"
     ]
    }
   ],
   "source": [
    "# Plot the l2_penalty values in the x axis and the cross-validation error in the y axis.\n",
    "# Using plt.xscale('log') will make your plot more intuitive.\n",
    "\n",
    "l2_penalty = graphlab.SArray(val_err_dict.keys())\n",
    "validation_error = graphlab.SArray(val_err_dict.values())\n",
    "\n",
    "sf = graphlab.SFrame({'l2_penalty':l2_penalty,\n",
    "                      'validation_error':validation_error})\n",
    "print sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3803f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sf['l2_penalty'],sf['validation_error'],'k.')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you found the best value for the L2 penalty using cross-validation, it is important to retrain a final model on all of the training data using this value of `l2_penalty`. This way, your final model will be trained on the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Linear regression:</pre>"
      ],
      "text/plain": [
       "Linear regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 19396</pre>"
      ],
      "text/plain": [
       "Number of examples          : 19396"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of features          : 15</pre>"
      ],
      "text/plain": [
       "Number of features          : 15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 15</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 16</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Newton Method</pre>"
      ],
      "text/plain": [
       "Starting Newton Method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+--------------------+---------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+--------------------+---------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Elapsed Time | Training-max_error | Training-rmse |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+--------------------+---------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+--------------------+---------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 2        | 0.122079     | 2461778.986191     | 248914.007014 |</pre>"
      ],
      "text/plain": [
       "| 1         | 2        | 0.122079     | 2461778.986191     | 248914.007014 |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+--------------------+---------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+--------------------+---------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>SUCCESS: Optimal solution found.</pre>"
      ],
      "text/plain": [
       "SUCCESS: Optimal solution found."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre></pre>"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly_data = polynomial_sframe(train_valid_shuffled['sqft_living'], 15)\n",
    "features_list = poly_data.column_names()\n",
    "poly_data['price'] = train_valid_shuffled['price']\n",
    "l2_penalty_best = 1000.0\n",
    "\n",
    "model = graphlab.linear_regression.create(poly_data, target='price',\n",
    "                                         features=features_list,\n",
    "                                         l2_penalty=l2_penalty_best,\n",
    "                                         validation_set=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***QUIZ QUESTION: Using the best L2 penalty found above, train a model using all training data. What is the RSS on the TEST data of the model you learn with this L2 penalty? ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.28780855058e+14\n"
     ]
    }
   ],
   "source": [
    "poly_test = polynomial_sframe(test['sqft_living'], 15)\n",
    "predictions = model.predict(poly_test)\n",
    "errors = predictions-test['price']\n",
    "rss = (errors*errors).sum()\n",
    "print rss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quiz\n",
    "<img src=\"quiz-w4-A1-1,2.png\">\n",
    "<img src=\"quiz-w4-A1-3,4.png\">\n",
    "<img src=\"quiz-w4-A1-5,6,7.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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
}