01_regression_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression example\n",
    "\n",
    "We are going to work on a classification problem. The code below is taken, and in part modified, from Chapter 2 of [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/). The original code can be downloaded from its [Git repository](https://github.com/ageron/handson-ml2).\n",
    "\n",
    "## The California Housing Prices dataset\n",
    "\n",
    "We will work with a publicly abailable California Hoousing Prices dataset from the StatLib repository. This dataset can be loaded directly within scikit-learn. We will learn how to load datasets from file or URLs in a future lecture.\n",
    "\n",
    "A description of the California Hoousing Prices dataset can be found [here](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset). It contains the following attributes:\n",
    "- *MedInc*: median income in block\n",
    "- *HouseAge*: median house age in block\n",
    "- *AveRooms*: average number of rooms\n",
    "- *AveBedrms*: average number of bedrooms\n",
    "- *Population*: block population\n",
    "- *AveOccup*: average house occupancy\n",
    "- *Latitude*: house block latitude\n",
    "- *Longitude*: house block longitude\n",
    "- ***MedHouseVal***: average house value in units of $100,000 (this is our *Target*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import dataset\n",
    "\n",
    "Because this is one of the example datastes, scikit learn provides a function to load it. The resul is a scikit learn \"Bunch\" object that contains information about the dataset un different fields. To simulate what you will get more realistically when you load your own data, we will convert the Bunch object to a Pandas Data Frame, which you can think of as a spreadsheed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries in Python\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "#dd = fetch_california_housing() # Load the dataset from scikitlearn; dd is a Bunch object\n",
    "#data = pd.DataFrame(dd.data, columns=dd.feature_names) # convert dataset to Pandas DataFrame\n",
    "#data.insert(0,\"median_house_value\", dd.target*100000) # Insert target of regression as first columns\n",
    "\n",
    "dd = fetch_california_housing(as_frame=True) # Load the dataset from scikitlearn; dd is a Bunch object\n",
    "data = dd.frame # extract DataFrame from Bunch object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore dataset\n",
    "\n",
    "You can now start exploring your data by using Pandas function and  functions provided by other Python libraries (e.g. matplotlib)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  MedHouseVal  \n",
       "0    -122.23        4.526  \n",
       "1    -122.22        3.585  \n",
       "2    -122.24        3.521  \n",
       "3    -122.25        3.413  \n",
       "4    -122.25        3.422  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   MedInc       20640 non-null  float64\n",
      " 1   HouseAge     20640 non-null  float64\n",
      " 2   AveRooms     20640 non-null  float64\n",
      " 3   AveBedrms    20640 non-null  float64\n",
      " 4   Population   20640 non-null  float64\n",
      " 5   AveOccup     20640 non-null  float64\n",
      " 6   Latitude     20640 non-null  float64\n",
      " 7   Longitude    20640 non-null  float64\n",
      " 8   MedHouseVal  20640 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.068558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>1.153956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>1.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>1.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>2.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>5.000010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
       "std       10.386050      2.135952      2.003532      1.153956  \n",
       "min        0.692308     32.540000   -124.350000      0.149990  \n",
       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
       "max     1243.333333     41.950000   -114.310000      5.000010  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram of all columns (features) in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt # for \"clean\" inline ploting\n",
    "data.hist(bins=50, figsize=(20,15))\n",
    "plt.show() # for \"clean\" inline ploting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Training/Test dataset split\n",
    "\n",
    "The first thing you need to do to train an ML model is to separate your dataset into a Traning set and a Test set. Scikit-learn provides the `train_test_split` function to do this. You need to decide on the ratio of elements in the Traning and Test sets. In this example we will use an 80:20 split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(data, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now look at the histograms in the Traning and Test sets to make sure they are both representative of the original set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_set.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram look pretty similar, so we should be OK. There are situations though in which you want to make sure that all levels/values are represented in the right proportion as the appear in the original data. Scikit-learn provide the `StratifiedShuffleSplit` function to fdo this (see textbook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data are in a single Pandas table, but most ML algorithms want you to split the independent variables/features, from the dependent variables/labels. We will do it here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_set.drop(\"MedHouseVal\", axis=1)\n",
    "y_train = train_set[\"MedHouseVal\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>3.2596</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.017657</td>\n",
       "      <td>1.006421</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3.691814</td>\n",
       "      <td>32.71</td>\n",
       "      <td>-117.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>3.8125</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.473545</td>\n",
       "      <td>1.041005</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>1.738095</td>\n",
       "      <td>33.77</td>\n",
       "      <td>-118.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>4.1563</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.645833</td>\n",
       "      <td>0.985119</td>\n",
       "      <td>915.0</td>\n",
       "      <td>2.723214</td>\n",
       "      <td>34.66</td>\n",
       "      <td>-120.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>1.9425</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.002817</td>\n",
       "      <td>1.033803</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>3.994366</td>\n",
       "      <td>32.69</td>\n",
       "      <td>-117.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>3.5542</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6.268421</td>\n",
       "      <td>1.134211</td>\n",
       "      <td>874.0</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>36.78</td>\n",
       "      <td>-119.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "14196  3.2596      33.0  5.017657   1.006421      2300.0  3.691814     32.71   \n",
       "8267   3.8125      49.0  4.473545   1.041005      1314.0  1.738095     33.77   \n",
       "17445  4.1563       4.0  5.645833   0.985119       915.0  2.723214     34.66   \n",
       "14265  1.9425      36.0  4.002817   1.033803      1418.0  3.994366     32.69   \n",
       "2271   3.5542      43.0  6.268421   1.134211       874.0  2.300000     36.78   \n",
       "\n",
       "       Longitude  \n",
       "14196    -117.03  \n",
       "8267     -118.16  \n",
       "17445    -120.48  \n",
       "14265    -117.11  \n",
       "2271     -119.80  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14196    1.030\n",
       "8267     3.821\n",
       "17445    1.726\n",
       "14265    0.934\n",
       "2271     0.965\n",
       "Name: MedHouseVal, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear regression\n",
    "\n",
    "We will apply a simple linear regression to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the full preprocessing pipeline on a few training instances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [1.93725845 2.48910616 2.64735483 1.56589492 1.61312839]\n"
     ]
    }
   ],
   "source": [
    "some_X = X_train.iloc[:5]\n",
    "some_y = y_train.iloc[:5]\n",
    "\n",
    "print(\"Predictions:\", lin_reg.predict(some_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare against the actual values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True values: [1.03, 3.821, 1.726, 0.934, 0.965]\n"
     ]
    }
   ],
   "source": [
    "print(\"True values:\", list(some_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating performance of linear regression\n",
    "\n",
    "The next step is to evaluate our model. How well does it perform? We can use the Test set to evaluate its performance. With regression we can use different types of metrics such as the *root mean squared error (RMSE)* or the *mean absolut error (MAE)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_set.drop(\"MedHouseVal\", axis=1)\n",
    "y_test = test_set[\"MedHouseVal\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set RMSE =  0.7196757085831575\n",
      "Test set RMSE =  0.7455813830127763\n",
      " \n",
      "Training set MAE =  0.5286283596581924\n",
      "Test set MAE =  0.5332001304956556\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "predictions_train = lin_reg.predict(X_train)\n",
    "predictions_test = lin_reg.predict(X_test)\n",
    "\n",
    "# Compute RMSE on the training and test set\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, predictions_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, predictions_test))\n",
    "print(\"Training set RMSE = \", rmse_train)\n",
    "print(\"Test set RMSE = \", rmse_test)\n",
    "\n",
    "print(\" \")\n",
    "# Compute MAE on the training and test set\n",
    "mae_train = mean_absolute_error(y_train, predictions_train)\n",
    "mae_test = mean_absolute_error(y_test, predictions_test)\n",
    "\n",
    "print(\"Training set MAE = \", mae_train)\n",
    "print(\"Test set MAE = \", mae_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree\n",
    "\n",
    "Let's try a different model, a Decision Tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating performance of Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set RMSE =  3.0584875752102123e-16\n",
      "Test set RMSE =  0.7056736565432687\n",
      " \n",
      "Training set MAE =  4.35697989315105e-17\n",
      "Test set MAE =  0.45543598837209304\n"
     ]
    }
   ],
   "source": [
    "predictions_train = tree_reg.predict(X_train)\n",
    "predictions_test = tree_reg.predict(X_test)\n",
    "\n",
    "# Compute RMSE on the training and test set\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, predictions_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, predictions_test))\n",
    "print(\"Training set RMSE = \", rmse_train)\n",
    "print(\"Test set RMSE = \", rmse_test)\n",
    "\n",
    "print(\" \")\n",
    "# Compute MAE on the training and test set\n",
    "mae_train = mean_absolute_error(y_train, predictions_train)\n",
    "mae_test = mean_absolute_error(y_test, predictions_test)\n",
    "\n",
    "\n",
    "print(\"Training set MAE = \", mae_train)\n",
    "print(\"Test set MAE = \", mae_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation\n",
    "\n",
    "A better way to evaluate and compare across models is to use cross-validation. With cross-validation we split the traning sets into K folds and generate K training sets where each time one fold is left out and used as the Test set. Training then occurs on each of the K folds and evaluation is done on the corresponind Test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.68492376 0.75513759 0.72296609 0.69512578 0.73691612 0.70625758\n",
      " 0.68887227 0.73677179 0.73573577 0.74201925]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    10.000000\n",
       "mean      0.720473\n",
       "std       0.024828\n",
       "min       0.684924\n",
       "25%       0.697909\n",
       "50%       0.729351\n",
       "75%       0.736880\n",
       "max       0.755138\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = np.sqrt(-cross_val_score(lin_reg, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10))\n",
    "print(\"Scores:\", scores)\n",
    "pd.Series(scores).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.72129894 0.74989194 0.69080813 0.73787062 0.71623233 0.69025932\n",
      " 0.70598123 0.75358186 0.7432831  0.75793138]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    10.000000\n",
       "mean      0.726714\n",
       "std       0.025429\n",
       "min       0.690259\n",
       "25%       0.708544\n",
       "50%       0.729585\n",
       "75%       0.748240\n",
       "max       0.757931\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = np.sqrt(-cross_val_score(tree_reg, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10))\n",
    "print(\"Scores:\", scores)\n",
    "pd.Series(scores).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=100, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.48410549 0.52034486 0.50082901 0.5221447  0.52120136 0.48324897\n",
      " 0.4869273  0.51565877 0.50346522 0.52112475]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    10.000000\n",
       "mean      0.505905\n",
       "std       0.016375\n",
       "min       0.483249\n",
       "25%       0.490403\n",
       "50%       0.509562\n",
       "75%       0.520930\n",
       "max       0.522145\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = np.sqrt(-cross_val_score(forest_reg, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10))\n",
    "print(\"Scores:\", scores)\n",
    "pd.Series(scores).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set RMSE =  0.18798014206480804\n",
      "Test set RMSE =  0.5067477004365527\n"
     ]
    }
   ],
   "source": [
    "forest_reg.fit(X_train, y_train)\n",
    "\n",
    "predictions_train = forest_reg.predict(X_train)\n",
    "predictions_test = forest_reg.predict(X_test)\n",
    "\n",
    "# Compute RMSE on the training and test set\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, predictions_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, predictions_test))\n",
    "print(\"Training set RMSE = \", rmse_train)\n",
    "print(\"Test set RMSE = \", rmse_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the three models' performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "# Linear Regression\n",
    "cv_results = cross_validate(lin_reg, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10, return_train_score=True)\n",
    "d = {'lin_reg_train': np.sqrt(-cv_results['train_score']), 'lin_reg_test': np.sqrt(-cv_results['test_score'])}\n",
    "cv_scores = pd.DataFrame(data=d)\n",
    "\n",
    "# Decision Tree\n",
    "cv_results = cross_validate(tree_reg, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10, return_train_score=True)\n",
    "cv_scores['tree_reg_train'] = np.sqrt(-cv_results['train_score'])\n",
    "cv_scores['tree_reg_test'] = np.sqrt(-cv_results['test_score'])\n",
    "\n",
    "# Random Forest\n",
    "cv_results = cross_validate(forest_reg, X_train, y_train, scoring=\"neg_mean_squared_error\", cv=10, return_train_score=True)\n",
    "cv_scores['forest_reg_train'] = np.sqrt(-cv_results['train_score'])\n",
    "cv_scores['forest_reg_test'] = np.sqrt(-cv_results['test_score'])\n",
    "cv_scores\n",
    "\n",
    "cv_scores.boxplot(figsize=(20,15))"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}