{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [],
   "source": [
    "def standard_units(any_numbers):\n",
    "    \"Convert any array of numbers to standard units.\"\n",
    "    return (any_numbers - np.mean(any_numbers))/np.std(any_numbers)  \n",
    "\n",
    "def correlation(t, x, y):\n",
    "    return np.mean(standard_units(t[x])*standard_units(t[y]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have explored ways to use multiple attributes to predict a categorical variable, let us return to predicting a quantitative variable. Predicting a numerical quantity is called regression, and a commonly used method to use multiple attributes for regression is called *multiple linear regression*.\n",
    "\n",
    "## Home Prices\n",
    "\n",
    "The following dataset of house prices and attributes was collected over several years for the city of Ames, Iowa. A [description of the dataset appears online](http://ww2.amstat.org/publications/jse/v19n3/decock.pdf). We will focus only a subset of the columns. We will try to predict the sale price column from the other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2930"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sales = pd.read_csv(path_data + 'house.csv')\n",
    "\n",
    "len(all_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales1 = all_sales[all_sales['Bldg Type'] == '1Fam']\n",
    "sales2 = sales1[all_sales['Sale Condition'] == 'Normal']\n",
    "\n",
    "sales = sales2[['SalePrice', '1st Flr SF', '2nd Flr SF', \n",
    "    'Total Bsmt SF', 'Garage Area', \n",
    "    'Wood Deck SF', 'Open Porch SF', 'Lot Area', \n",
    "    'Year Built', 'Yr Sold']]\n",
    "\n",
    "sales = sales.sort_values(by=['SalePrice'])\n",
    "\n",
    "len(sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A histogram of sale prices shows a large amount of variability and a distribution that is clearly not normal. A long tail to the right contains a few houses that had very high prices. The short left tail does not contain any houses that sold for less than $35,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unit = '$'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "\n",
    "ax.hist(sales['SalePrice'], bins=32, density=True, color='blue', alpha=0.8, ec='white', zorder=5)\n",
    "\n",
    "y_vals = ax.get_yticks()\n",
    "\n",
    "y_label = 'Percent per ' + (unit if unit else 'unit')\n",
    "\n",
    "x_label = 'SalesPrice ($)' \n",
    "\n",
    "ax.set_yticklabels(['{:g}'.format(x * 100) for x in y_vals])\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.title('');\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation\n",
    "\n",
    "No single attribute is sufficient to predict the sale price. For example, the area of first floor, measured in square feet, correlates with sale price but only explains some of its variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "\n",
    "ax.scatter(sales['1st Flr SF'], \n",
    "           sales['SalePrice'],   \n",
    "           color='navy', \n",
    "          alpha=0.5)\n",
    "\n",
    "x_label = '1st Flr SF'\n",
    "\n",
    "y_label = 'SalePrice'\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6424662541030226"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(sales, 'SalePrice', '1st Flr SF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, none of the individual attributes have a correlation with sale price that is above 0.7 (except for the sale price itself)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of SalePrice and SalePrice:\t 1.0000000000000018\n",
      "Correlation of 1st Flr SF and SalePrice:\t 0.6424662541030226\n",
      "Correlation of 2nd Flr SF and SalePrice:\t 0.35752189428008124\n",
      "Correlation of Total Bsmt SF and SalePrice:\t 0.6529786267571697\n",
      "Correlation of Garage Area and SalePrice:\t 0.6385944852520441\n",
      "Correlation of Wood Deck SF and SalePrice:\t 0.352698666195049\n",
      "Correlation of Open Porch SF and SalePrice:\t 0.33690941702637345\n",
      "Correlation of Lot Area and SalePrice:\t 0.29082345511576896\n",
      "Correlation of Year Built and SalePrice:\t 0.565164753713592\n",
      "Correlation of Yr Sold and SalePrice:\t 0.025948579080721384\n"
     ]
    }
   ],
   "source": [
    "for label in sales.columns:\n",
    "    print('Correlation of', label, 'and SalePrice:\\t', correlation(sales, label, 'SalePrice'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, combining attributes can provide higher correlation. In particular, if we sum the first floor and second floor areas, the result has a higher correlation than any single attribute alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7821920556134886"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_copy = sales.copy()\n",
    "\n",
    "both_floors = sales_copy.iloc[:,1] + sales_copy.iloc[:,2]\n",
    "\n",
    "sales_copy['Both Floors'] = both_floors\n",
    "\n",
    "correlation(sales_copy, 'SalePrice', 'Both Floors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This high correlation indicates that we should try to use more than one attribute to predict the sale price. In a dataset with multiple observed attributes and a single numerical value to be predicted (the sale price in this case), multiple linear regression can be an effective technique.\n",
    "\n",
    "## Multiple Linear Regression \n",
    "\n",
    "In multiple linear regression, a numerical output is predicted from numerical input attributes by multiplying each attribute value by a different slope, then summing the results. In this example, the slope for the `1st Flr SF` would represent the dollars per square foot of area on the first floor of the house that should be used in our prediction. \n",
    "\n",
    "Before we begin prediction, we split our data randomly into a training and test set of equal size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train / Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001 training and 1001 test instances.\n"
     ]
    }
   ],
   "source": [
    "sales_copy = sales.copy()\n",
    "train = sales_copy.sample(1001, replace=False)\n",
    "test = sales_copy.drop(train.index)\n",
    "\n",
    "print(len(train), 'training and', len(test), 'test instances.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define function to create train, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001 training and 1001 test instances.\n"
     ]
    }
   ],
   "source": [
    "def split(self, k):\n",
    "    if not 1 <= k <= (len(self) - 1):\n",
    "        raise ValueError(\"Invalid value of k. k must be between 1 and the\"\n",
    "                         \"number of rows - 1\")\n",
    "\n",
    "    rows = np.random.permutation(len(self))\n",
    "\n",
    "    first = self.take(rows[:k])\n",
    "    rest = self.take(rows[k:])\n",
    "\n",
    "    return first, rest\n",
    "\n",
    "train, test = split(sales, 1001)\n",
    "\n",
    "print(len(train), 'training and', len(test), 'test instances.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative (*preferred*) - scikit learn\n",
    "as an aside we could emplot the `scikit learn` function to determine the `train, test split`.\n",
    "\n",
    "[sklearn.model_selection.train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn-model-selection-train-test-split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001 training and 1001 test instances.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(sales_copy, test_size=0.5)\n",
    "\n",
    "print(len(train), 'training and', len(test), 'test instances.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slopes in multiple regression is an array that has one slope value for each attribute in an example. Predicting the sale price involves multiplying each attribute by the slope and summing the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting sale price for:\n",
      "1st Flr SF        952.0\n",
      "2nd Flr SF        684.0\n",
      "Total Bsmt SF     952.0\n",
      "Garage Area       440.0\n",
      "Wood Deck SF        0.0\n",
      "Open Porch SF      84.0\n",
      "Lot Area         7500.0\n",
      "Year Built       1998.0\n",
      "Yr Sold          2009.0\n",
      "Name: 355, dtype: float64\n",
      "\n",
      "Using slopes:\n",
      " [10.78806767 10.35973603  9.98954354 11.48295321 10.14385735  8.64346663\n",
      " 10.92925897  9.80463171  9.88321085]\n",
      "\n",
      "Result: 154059.36299727514\n"
     ]
    }
   ],
   "source": [
    "def predict(slopes, row):\n",
    "    return sum(slopes * np.array(row))\n",
    "\n",
    "example_row1 = test.drop(columns=['SalePrice'])\n",
    "\n",
    "example_row = example_row1.iloc[0]\n",
    "\n",
    "print('Predicting sale price for:')\n",
    "\n",
    "print(example_row)\n",
    "\n",
    "example_slopes = np.random.normal(10, 1, len(example_row))\n",
    "\n",
    "print('\\nUsing slopes:\\n', example_slopes)\n",
    "\n",
    "print('\\nResult:', predict(example_slopes, example_row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is an estimated sale price, which can be compared to the actual sale price to assess whether the slopes provide accurate predictions. Since the `example_slopes` above were chosen at random, we should not expect them to provide accurate predictions at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual sale price: 191000\n",
      "Predicted sale price using random slopes: 154059.36299727514\n"
     ]
    }
   ],
   "source": [
    "print('Actual sale price:', test['SalePrice'].iloc[0])\n",
    "print('Predicted sale price using random slopes:', predict(example_slopes, example_row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Least Squares Regression\n",
    "\n",
    "The next step in performing multiple regression is to define the least squares objective. We perform the prediction for each row in the training set, and then compute the root mean squared error (RMSE) of the predictions from the actual prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of all training examples using random slopes: 87082.30552444536\n"
     ]
    }
   ],
   "source": [
    "train_prices = train.iloc[:,0]\n",
    "train1 = train.copy()\n",
    "train_attributes = train1.drop(train1.columns[0], axis=1)\n",
    "\n",
    "def rmse(slopes, attributes, prices):\n",
    "    errors = []\n",
    "    for i in np.arange(len(prices)):\n",
    "        predicted = predict(slopes, attributes.iloc[i])\n",
    "        actual = prices.iloc[i]\n",
    "        errors.append((predicted - actual) ** 2)\n",
    "    return np.mean(errors) ** 0.5\n",
    "\n",
    "def rmse_train(slopes):\n",
    "    return rmse(slopes, train_attributes, train_prices)\n",
    "\n",
    "print('RMSE of all training examples using random slopes:', rmse_train(example_slopes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use the `minimize` function to find the slopes with the lowest RMSE. Since the function we want to minimize, `rmse_train`, takes an array instead of a number, we must pass the `array=True` argument to `minimize`. When this argument is used, `minimize` also requires an initial guess of the slopes so that it knows the dimension of the input array. Finally, to speed up optimization, we indicate that `rmse_train` is a smooth function using the `smooth=True` attribute. Computation of the best slopes may take several minutes.\n",
    "\n",
    "[scipy optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n",
    "\n",
    "# please don't worry if you do not understand some of the code used to create this fuunction\n",
    "\n",
    "def minimize(f, start=None, smooth=False, log=None, array=False, **vargs):\n",
    "    \"\"\"Minimize a function f of one or more arguments.\n",
    "    Args:\n",
    "        f: A function that takes numbers and returns a number\n",
    "        start: A starting value or list of starting values\n",
    "        smooth: Whether to assume that f is smooth and use first-order info\n",
    "        log: Logging function called on the result of optimization (e.g. print)\n",
    "        vargs: Other named arguments passed to scipy.optimize.minimize\n",
    "    Returns either:\n",
    "        (a) the minimizing argument of a one-argument function\n",
    "        (b) an array of minimizing arguments of a multi-argument function\n",
    "    \"\"\"\n",
    "    if start is None:\n",
    "        assert not array, \"Please pass starting values explicitly when array=True\"\n",
    "        arg_count = f.__code__.co_argcount\n",
    "        assert arg_count > 0, \"Please pass starting values explicitly for variadic functions\"\n",
    "        start = [0] * arg_count\n",
    "    if not hasattr(start, '__len__'):\n",
    "        start = [start]\n",
    "\n",
    "    if array:\n",
    "        objective = f\n",
    "    else:\n",
    "        @functools.wraps(f)\n",
    "        def objective(args):\n",
    "            return f(*args)\n",
    "\n",
    "    if not smooth and 'method' not in vargs:\n",
    "        vargs['method'] = 'Powell'\n",
    "    result = optimize.minimize(objective, start, **vargs)\n",
    "    if log is not None:\n",
    "        log(result)\n",
    "    if len(start) == 1:\n",
    "        return result.x.item(0)\n",
    "    else:\n",
    "        return result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best slopes for the training set:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Yr Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76.759803</td>\n",
       "      <td>75.715687</td>\n",
       "      <td>48.833824</td>\n",
       "      <td>53.868498</td>\n",
       "      <td>43.431381</td>\n",
       "      <td>58.613351</td>\n",
       "      <td>0.547306</td>\n",
       "      <td>444.950456</td>\n",
       "      <td>-446.929025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  1st Flr SF 2nd Flr SF Total Bsmt SF Garage Area Wood Deck SF Open Porch SF  \\\n",
       "0  76.759803  75.715687     48.833824   53.868498    43.431381     58.613351   \n",
       "\n",
       "   Lot Area  Year Built     Yr Sold  \n",
       "0  0.547306  444.950456 -446.929025  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_slopes = minimize(rmse_train, start=example_slopes, smooth=True, array=True)\n",
    " \n",
    "train_df = pd.DataFrame(columns=[train_attributes.columns])\n",
    "\n",
    "train_df.loc[0] = best_slopes\n",
    "\n",
    "print('The best slopes for the training set:')\n",
    "\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of all training examples using the best slopes: 31422.03491697997\n"
     ]
    }
   ],
   "source": [
    "print('RMSE of all training examples using the best slopes:', rmse_train(best_slopes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpreting Multiple Regression \n",
    "\n",
    "Let's interpret these results. The best slopes give us a method for estimating the price of a house from its attributes. A square foot of area on the first floor is worth about \\$75 (the first slope), while one on the second floor is worth about \\\\$70 (the second slope). The final negative value describes the market: prices in later years were lower on average.\n",
    "\n",
    "The RMSE of around \\\\$30,000 means that our best linear prediction of the sale price based on all of the attributes is off by around \\\\$30,000 on the training set, on average.  We find a similar error when predicting prices on the test set, which indicates that our prediction method will generalize to other samples from the same population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Yr Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>952</td>\n",
       "      <td>684</td>\n",
       "      <td>952.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>7500</td>\n",
       "      <td>1998</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>448</td>\n",
       "      <td>272</td>\n",
       "      <td>448.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7008</td>\n",
       "      <td>1900</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1st Flr SF  2nd Flr SF  Total Bsmt SF  Garage Area  Wood Deck SF  \\\n",
       "355          952         684          952.0        440.0             0   \n",
       "2599         448         272          448.0        280.0             0   \n",
       "\n",
       "      Open Porch SF  Lot Area  Year Built  Yr Sold  \n",
       "355              84      7500        1998     2009  \n",
       "2599              0      7008        1900     2006  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_prices = test.iloc[:,0]\n",
    "\n",
    "test_attributes = test.drop(test.columns[0], axis=1)\n",
    "test_attributes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE for multiple linear regression: 31068.13725627553\n"
     ]
    }
   ],
   "source": [
    "test_prices = test.iloc[:,0]\n",
    "test_attributes = test.drop(test.columns[0], axis=1)\n",
    "\n",
    "def rmse_test(slopes):\n",
    "    return rmse(slopes, test_attributes, test_prices)\n",
    "\n",
    "rmse_linear = rmse_test(best_slopes)\n",
    "print('Test set RMSE for multiple linear regression:', rmse_linear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the predictions were perfect, then a scatter plot of the predicted and actual values would be a straight line with slope 1. We see that most dots fall near that line, but there is some error in the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit(row):\n",
    "    return sum(best_slopes * np.array(row))\n",
    "\n",
    "test['Fitted'] = test_attributes.apply(fit, axis=1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "\n",
    "ax.scatter(test['Fitted'], \n",
    "           test['SalePrice'],   \n",
    "           color='navy', \n",
    "          alpha=0.5)\n",
    "\n",
    "x_label = 'Fitted'\n",
    "\n",
    "y_label = 'SalePrice'\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "plt.plot([0, 5e5], [0, 5e5])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A residual plot for multiple regression typically compares the errors (residuals) to the actual values of the predicted variable. We see in the residual plot below that we have systematically underestimated the value of expensive houses, shown by the many positive residual values on the right side of the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Residual'] = test_prices-test_attributes.apply(fit, axis=1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "\n",
    "ax.scatter(test['SalePrice'], \n",
    "           test['Residual'],   \n",
    "           color='navy', \n",
    "          alpha=0.5)\n",
    "\n",
    "x_label = 'SalePrice'\n",
    "\n",
    "y_label = 'SalePrice'\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.plot([0, 7e5], [0, 0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with simple linear regression, interpreting the result of a predictor is at least as important as making predictions. There are many lessons about interpreting multiple regression that are not included in this textbook. A natural next step after completing this text would be to study linear modeling and regression in further depth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nearest Neighbors for Regression\n",
    "\n",
    "Another approach to predicting the sale price of a house is to use the price of similar houses. This *nearest neighbor* approach is very similar to our classifier. To speed up computation, we will only use the attributes that had the highest correlation with the sale price in our original analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Year Built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>103000</td>\n",
       "      <td>848</td>\n",
       "      <td>0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>138000</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>168675</td>\n",
       "      <td>738</td>\n",
       "      <td>753</td>\n",
       "      <td>738.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  1st Flr SF  2nd Flr SF  Total Bsmt SF  Garage Area  \\\n",
       "755      103000         848           0          780.0        539.0   \n",
       "1327     138000        1143           0         1008.0        288.0   \n",
       "2165     168675         738         753          738.0        484.0   \n",
       "\n",
       "      Year Built  \n",
       "755         1925  \n",
       "1327        1928  \n",
       "2165        2006  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_nn = train.iloc[:,[0, 1, 2, 3, 4, 8]]\n",
    "test_nn = test.iloc[:,[0, 1, 2, 3, 4, 8]]\n",
    "train_nn.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The computation of closest neighbors is identical to a nearest-neighbor classifier. In this case, we will exclude the `'SalePrice'` rather than the `'Class'` column from the distance computation. The five nearest neighbors of the first test row are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>185000</td>\n",
       "      <td>961</td>\n",
       "      <td>683</td>\n",
       "      <td>941.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>24.576411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2436</th>\n",
       "      <td>172400</td>\n",
       "      <td>953</td>\n",
       "      <td>711</td>\n",
       "      <td>953.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>33.689761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>192000</td>\n",
       "      <td>943</td>\n",
       "      <td>695</td>\n",
       "      <td>943.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>36.152455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>189500</td>\n",
       "      <td>945</td>\n",
       "      <td>663</td>\n",
       "      <td>945.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>37.947332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>173000</td>\n",
       "      <td>959</td>\n",
       "      <td>712</td>\n",
       "      <td>959.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>43.703547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  1st Flr SF  2nd Flr SF  Total Bsmt SF  Garage Area  \\\n",
       "1759     185000         961         683          941.0        460.0   \n",
       "2436     172400         953         711          953.0        460.0   \n",
       "995      192000         943         695          943.0        472.0   \n",
       "344      189500         945         663          945.0        470.0   \n",
       "2439     173000         959         712          959.0        472.0   \n",
       "\n",
       "      Year Built   Distance  \n",
       "1759        1999  24.576411  \n",
       "2436        2000  33.689761  \n",
       "995         1998  36.152455  \n",
       "344         1997  37.947332  \n",
       "2439        2000  43.703547  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def distance(pt1, pt2):\n",
    "    \"\"\"The distance between two points, represented as arrays.\"\"\"\n",
    "    return np.sqrt(np.sum((pt1 - pt2) ** 2))\n",
    "    \n",
    "\n",
    "def row_distance(row1, row2):\n",
    "    \"\"\"The distance between two rows of a table.\"\"\"\n",
    "    return distance(np.array(row1), np.array(row2))\n",
    "\n",
    "def distances(training, example, output):\n",
    "    \"\"\"Compute the distance from example for each row in training.\"\"\"\n",
    "    dists = []\n",
    "    attributes = training.drop(columns=[output])\n",
    "\n",
    "    for row in range(len(attributes)):\n",
    "        dists.append(row_distance(attributes.iloc[row], example))\n",
    "    \n",
    "    training['Distance'] = dists\n",
    "    #print(training)\n",
    "    return training\n",
    "\n",
    "def closest(training, example, k, output):\n",
    "    \"\"\"Return a table of the k closest neighbors to example.\"\"\"\n",
    "    \n",
    "    distance = distances(training, example, output).sort_values(by=['Distance']).take(np.arange(k))\n",
    "    return distance\n",
    "\n",
    "train_nn_A = train_nn.copy()\n",
    "example_nn_row = test_nn.drop(test_nn.columns[0], axis=1).iloc[0]\n",
    "closest(train_nn_A, example_nn_row, 5, 'SalePrice')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One simple method for predicting the price is to average the prices of the nearest neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1st Flr SF        952.0\n",
       "2nd Flr SF        684.0\n",
       "Total Bsmt SF     952.0\n",
       "Garage Area       440.0\n",
       "Year Built       1998.0\n",
       "Name: 355, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_nn_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182380.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_nn(example):\n",
    "    \"\"\"Return the majority class among the k nearest neighbors.\"\"\"\n",
    "    train_nn_B = train_nn.copy()\n",
    "    \n",
    "    col_sales_price = closest(train_nn_B, example, 5, 'SalePrice')\n",
    "    return np.average(col_sales_price['SalePrice'])\n",
    "\n",
    "predict_nn(example_nn_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can inspect whether our prediction is close to the true sale price for our one test example. Looks reasonable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual sale price: 191000\n",
      "Predicted sale price using nearest neighbors: 182380.0\n"
     ]
    }
   ],
   "source": [
    "print('Actual sale price:', test_nn['SalePrice'].iloc[0])\n",
    "\n",
    "print('Predicted sale price using nearest neighbors:', predict_nn(example_nn_row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation\n",
    "\n",
    "To evaluate the performance of this approach for the whole test set, we apply `predict_nn` to each test example, then compute the root mean squared error of the predictions. Computation of the predictions may take several minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182380.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_nn(example):\n",
    "    \"\"\"Return the majority class among the k nearest neighbors.\"\"\"\n",
    "    train_nn_B = train_nn.copy()\n",
    "    col_sales_price = closest(train_nn_B, example, 5, 'SalePrice')\n",
    "    return np.average(col_sales_price['SalePrice'])\n",
    "\n",
    "predict_nn(example_nn_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE for multiple linear regression:  31068.13725627553\n",
      "Test set RMSE for nearest neighbor regression: 32786.182784735494\n"
     ]
    }
   ],
   "source": [
    "test_nn_C = test_nn.copy()\n",
    "\n",
    "test_nn_drop = test_nn_C.drop(columns=['SalePrice'])\n",
    "\n",
    "nn_test_predictions = test_nn_drop.apply(predict_nn, axis=1)\n",
    "\n",
    "rmse_nn = np.mean((test_prices - nn_test_predictions) ** 2) ** 0.5\n",
    "\n",
    "print('Test set RMSE for multiple linear regression: ', rmse_linear)\n",
    "print('Test set RMSE for nearest neighbor regression:', rmse_nn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For these data, the errors of the two techniques are quite similar! For different data sets, one technique might outperform another. By computing the RMSE of both techniques on the same data, we can compare methods fairly. One note of caution: the difference in performance might not be due to the technique at all; it might be due to the random variation due to sampling the training and test sets in the first place.\n",
    "\n",
    "Finally, we can draw a residual plot for these predictions. We still underestimate the prices of the most expensive houses, but the bias does not appear to be as systematic. However, fewer residuals are very close to zero, indicating that fewer prices were predicted with very high accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Residual'] = test_prices-nn_test_predictions\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,6))\n",
    "\n",
    "ax.scatter(test['SalePrice'], \n",
    "           test['Residual'],   \n",
    "           color='navy', \n",
    "          alpha=0.5)\n",
    "\n",
    "x_label = 'SalePrice'\n",
    "\n",
    "y_label = 'SalePrice'\n",
    "\n",
    "plt.ylabel(y_label)\n",
    "\n",
    "plt.xlabel(x_label)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.plot([0, 7e5], [0, 0])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}