{
 "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 - 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        1244.0\n",
      "2nd Flr SF           0.0\n",
      "Total Bsmt SF     1244.0\n",
      "Garage Area        336.0\n",
      "Wood Deck SF         0.0\n",
      "Open Porch SF       40.0\n",
      "Lot Area         11988.0\n",
      "Year Built        1957.0\n",
      "Yr Sold           2007.0\n",
      "Name: 1922, dtype: float64\n",
      "\n",
      "Using slopes:\n",
      " [10.66266607 11.04000738 11.05823199  9.70462703  8.84114377  9.56944347\n",
      " 11.61432997 10.37252802 11.38349509]\n",
      "\n",
      "Result: 213042.62920044773\n"
     ]
    }
   ],
   "source": [
    "def predict(slopes, row):\n",
    "    return sum(slopes * np.array(row))\n",
    "\n",
    "example_row1 = test.drop(columns=['SalePrice'])\n",
    "example_row = example_row1.iloc[0]\n",
    "\n",
    "print('Predicting sale price for:')\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: 150000\n",
      "Predicted sale price using random slopes: 213042.62920044773\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",
    "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: 114914.11566039815\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",
    "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>78.980021</td>\n",
       "      <td>75.902626</td>\n",
       "      <td>48.804937</td>\n",
       "      <td>48.98349</td>\n",
       "      <td>45.186775</td>\n",
       "      <td>7.047521</td>\n",
       "      <td>0.435624</td>\n",
       "      <td>538.362254</td>\n",
       "      <td>-537.790128</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  78.980021  75.902626     48.804937    48.98349    45.186775      7.047521   \n",
       "\n",
       "   Lot Area  Year Built     Yr Sold  \n",
       "0  0.435624  538.362254 -537.790128  "
      ]
     },
     "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: 34594.95413398824\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",
    "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>1922</th>\n",
       "      <td>1244</td>\n",
       "      <td>0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>11988</td>\n",
       "      <td>1957</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>832</td>\n",
       "      <td>629</td>\n",
       "      <td>832.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>0</td>\n",
       "      <td>204</td>\n",
       "      <td>10800</td>\n",
       "      <td>1949</td>\n",
       "      <td>2009</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",
       "1922        1244           0         1244.0        336.0             0   \n",
       "685          832         629          832.0        384.0             0   \n",
       "\n",
       "      Open Porch SF  Lot Area  Year Built  Yr Sold  \n",
       "1922             40     11988        1957     2007  \n",
       "685             204     10800        1949     2009  "
      ]
     },
     "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: 27254.40534406947\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",
    "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>1137</th>\n",
       "      <td>173000</td>\n",
       "      <td>798</td>\n",
       "      <td>842</td>\n",
       "      <td>798.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>255500</td>\n",
       "      <td>1717</td>\n",
       "      <td>0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2598</th>\n",
       "      <td>68000</td>\n",
       "      <td>715</td>\n",
       "      <td>0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>1929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  1st Flr SF  2nd Flr SF  Total Bsmt SF  Garage Area  \\\n",
       "1137     173000         798         842          798.0        520.0   \n",
       "437      255500        1717           0         1709.0        908.0   \n",
       "2598      68000         715           0          715.0        660.0   \n",
       "\n",
       "      Year Built  \n",
       "1137        2004  \n",
       "437         2006  \n",
       "2598        1929  "
      ]
     },
     "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>1929</th>\n",
       "      <td>129500</td>\n",
       "      <td>1216</td>\n",
       "      <td>0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>39.648455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>155000</td>\n",
       "      <td>1269</td>\n",
       "      <td>0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>1960</td>\n",
       "      <td>45.199558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>143000</td>\n",
       "      <td>1214</td>\n",
       "      <td>0</td>\n",
       "      <td>1214.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>1967</td>\n",
       "      <td>47.159304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>156000</td>\n",
       "      <td>1216</td>\n",
       "      <td>0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1953</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>108538</td>\n",
       "      <td>1206</td>\n",
       "      <td>0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>58.889727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  1st Flr SF  2nd Flr SF  Total Bsmt SF  Garage Area  \\\n",
       "1929     129500        1216           0         1216.0        336.0   \n",
       "1272     155000        1269           0         1269.0        308.0   \n",
       "1875     143000        1214           0         1214.0        318.0   \n",
       "1944     156000        1216           0         1216.0        371.0   \n",
       "146      108538        1206           0         1206.0        312.0   \n",
       "\n",
       "      Year Built   Distance  \n",
       "1929        1955  39.648455  \n",
       "1272        1960  45.199558  \n",
       "1875        1967  47.159304  \n",
       "1944        1953  53.000000  \n",
       "146         1959  58.889727  "
      ]
     },
     "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",
    "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",
    "    for row in range(len(attributes)):\n",
    "        dists.append(row_distance(attributes.iloc[row], example))\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",
    "    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')"
   ]
  },
  {
   "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       1244.0\n",
       "2nd Flr SF          0.0\n",
       "Total Bsmt SF    1244.0\n",
       "Garage Area       336.0\n",
       "Year Built       1957.0\n",
       "Name: 1922, 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": [
       "138407.6"
      ]
     },
     "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: 150000\n",
      "Predicted sale price using nearest neighbors: 138407.6\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": [
       "138407.6"
      ]
     },
     "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",
    "    \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:  27254.40534406947\n",
      "Test set RMSE for nearest neighbor regression: 28324.30879280499\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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}