lectures/05_preprocessing-pipelines.ipynb

{
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   "source": [
    "![](img/330-banner.png)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "-"
    }
   },
   "source": [
    "# Lecture 5: Preprocessing and `sklearn` pipelines\n",
    "\n",
    "UBC 2022-23\n",
    "\n",
    "Instructor: Varada Kolhatkar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports, announcement, LOs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "\n",
    "sys.path.append(\"code/.\")\n",
    "\n",
    "import mglearn\n",
    "from IPython.display import display\n",
    "from plotting_functions import *\n",
    "\n",
    "# Classifiers and regressors\n",
    "from sklearn.dummy import DummyClassifier, DummyRegressor\n",
    "\n",
    "# Preprocessing and pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# train test split and cross validation\n",
    "from sklearn.model_selection import cross_val_score, cross_validate, train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    MinMaxScaler,\n",
    "    OneHotEncoder,\n",
    "    OrdinalEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from utils import *\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Announcements\n",
    "- Homework 3 is out. Please start early. \n",
    "- We're mostly done with Homework 1 grading. I will release the grades sometime today. \n",
    "- Homework 1 and 2 solutions are posted on Canvas. Please do not share them with anyone or do not post them anywhere. \n",
    "- No homework pull requests, please. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Learning outcomes\n",
    "\n",
    "From this lecture, you will be able to \n",
    "\n",
    "- explain motivation for preprocessing in supervised machine learning;\n",
    "- identify when to implement feature transformations such as imputation, scaling, and one-hot encoding in a machine learning model development pipeline; \n",
    "- use `sklearn` transformers for applying feature transformations on your dataset;\n",
    "- discuss golden rule in the context of feature transformations;\n",
    "- use `sklearn.pipeline.Pipeline` and `sklearn.pipeline.make_pipeline` to build a preliminary machine learning pipeline. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivation and big picture [[video](https://youtu.be/xx9HlmzORRk)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- So far we have seen\n",
    "    - Three ML models (decision trees, $k$-NNs, SVMs with RBF kernel)\n",
    "    - ML fundamentals (train-validation-test split, cross-validation, the fundamental tradeoff, the golden rule)\n",
    "- Are we ready to do machine learning on real-world datasets?\n",
    "    - Very often real-world datasets need preprocessing before we use them to build ML models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Example: $k$-nearest neighbours on the Spotify dataset\n",
    "\n",
    "- In lab1 you used `DecisionTreeClassifier` to predict whether the user would like a particular song or not. \n",
    "- Can we use $k$-NN classifier for this task? \n",
    "- Intuition: To predict whether the user likes a particular song or not (query point) \n",
    "   - find the songs that are closest to the query point\n",
    "   - let them vote on the target\n",
    "   - take the majority vote as the target for the query point   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to run the code below, you need to download the dataset from [Kaggle](https://www.kaggle.com/geomack/spotifyclassification/download)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "spotify_df = pd.read_csv(\"data/spotify.csv\", index_col=0)\n",
    "train_df, test_df = train_test_split(spotify_df, test_size=0.20, random_state=123)\n",
    "X_train, y_train = (\n",
    "    train_df.drop(columns=[\"song_title\", \"artist\", \"target\"]),\n",
    "    train_df[\"target\"],\n",
    ")\n",
    "X_test, y_test = (\n",
    "    test_df.drop(columns=[\"song_title\", \"artist\", \"target\"]),\n",
    "    test_df[\"target\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean validation score 0.508\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.507740</td>\n",
       "      <td>0.507752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.507740</td>\n",
       "      <td>0.507752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.507740</td>\n",
       "      <td>0.507752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.506211</td>\n",
       "      <td>0.508133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.509317</td>\n",
       "      <td>0.507359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.000321    0.000129    0.507740     0.507752\n",
       "1  0.000205    0.000089    0.507740     0.507752\n",
       "2  0.000193    0.000086    0.507740     0.507752\n",
       "3  0.000187    0.000132    0.506211     0.508133\n",
       "4  0.000184    0.000084    0.509317     0.507359"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = DummyClassifier(strategy=\"most_frequent\")\n",
    "scores = cross_validate(dummy, X_train, y_train, return_train_score=True)\n",
    "print(\"Mean validation score %0.3f\" % (np.mean(scores[\"test_score\"])))\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean validation score 0.546\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001323</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>0.563467</td>\n",
       "      <td>0.717829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000989</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.535604</td>\n",
       "      <td>0.721705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000918</td>\n",
       "      <td>0.003324</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.708527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000789</td>\n",
       "      <td>0.003219</td>\n",
       "      <td>0.537267</td>\n",
       "      <td>0.721921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.003184</td>\n",
       "      <td>0.562112</td>\n",
       "      <td>0.711077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.001323    0.003593    0.563467     0.717829\n",
       "1  0.000989    0.003460    0.535604     0.721705\n",
       "2  0.000918    0.003324    0.529412     0.708527\n",
       "3  0.000789    0.003219    0.537267     0.721921\n",
       "4  0.000775    0.003184    0.562112     0.711077"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "scores = cross_validate(knn, X_train, y_train, return_train_score=True)\n",
    "print(\"Mean validation score %0.3f\" % (np.mean(scores[\"test_score\"])))\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>0.229000</td>\n",
       "      <td>0.494</td>\n",
       "      <td>147893</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0469</td>\n",
       "      <td>-9.743</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>140.832</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.771</td>\n",
       "      <td>227143</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5950</td>\n",
       "      <td>-4.712</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>111.959</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "842      0.229000         0.494       147893   0.666          0.000057    9   \n",
       "654      0.000289         0.771       227143   0.949          0.602000    8   \n",
       "\n",
       "     liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "842    0.0469    -9.743     0       0.0351  140.832             4.0    0.704  \n",
       "654    0.5950    -4.712     1       0.1750  111.959             4.0    0.372  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_songs = X_train.sample(2, random_state=42)\n",
    "two_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.        , 79250.00543825],\n",
       "       [79250.00543825,     0.        ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euclidean_distances(two_songs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider only two features: `duration_ms` and `tempo`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>147893</td>\n",
       "      <td>140.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>227143</td>\n",
       "      <td>111.959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration_ms    tempo\n",
       "842       147893  140.832\n",
       "654       227143  111.959"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_songs_subset = two_songs[[\"duration_ms\", \"tempo\"]]\n",
    "two_songs_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.        , 79250.00525962],\n",
       "       [79250.00525962,     0.        ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euclidean_distances(two_songs_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you see any problem? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The distance is completely dominated by the the features with larger values\n",
    "- The features with smaller values are being ignored. \n",
    "- Does it matter? \n",
    "    - Yes! Scale is based on how data was collected. \n",
    "    - Features on a smaller scale can be highly informative and there is no good reason to ignore them.\n",
    "    - We want our model to be robust and not sensitive to the scale. \n",
    "- Was this a problem for decision trees?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Scaling using `scikit-learn`'s [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
    "\n",
    "- We'll use `scikit-learn`'s [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html), which is a `transformer`.   \n",
    "- Only focus on the syntax for now. We'll talk about scaling in a bit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()  # create feature trasformer object\n",
    "scaler.fit(X_train)  # fitting the transformer on the train split\n",
    "X_train_scaled = scaler.transform(X_train)  # transforming the train split\n",
    "X_test_scaled = scaler.transform(X_test)  # transforming the test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>0.004770</td>\n",
       "      <td>0.585</td>\n",
       "      <td>214740</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0762</td>\n",
       "      <td>-5.594</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>114.059</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>0.114000</td>\n",
       "      <td>0.665</td>\n",
       "      <td>216728</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.303000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-7.314</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3310</td>\n",
       "      <td>100.344</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>0.030200</td>\n",
       "      <td>0.798</td>\n",
       "      <td>216585</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>-10.488</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>127.136</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.912</td>\n",
       "      <td>194040</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0723</td>\n",
       "      <td>-12.719</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>99.346</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.021100</td>\n",
       "      <td>0.697</td>\n",
       "      <td>236456</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.893000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-7.787</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>119.977</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.584</td>\n",
       "      <td>274404</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.002690</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>-3.501</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>74.976</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.535</td>\n",
       "      <td>203500</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>10</td>\n",
       "      <td>0.2630</td>\n",
       "      <td>-3.566</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>116.956</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>256333</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2190</td>\n",
       "      <td>-4.469</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>60.391</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>0.705000</td>\n",
       "      <td>0.894</td>\n",
       "      <td>222307</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3120</td>\n",
       "      <td>-14.311</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0880</td>\n",
       "      <td>104.968</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.8180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>0.623000</td>\n",
       "      <td>0.470</td>\n",
       "      <td>394920</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.187000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>-17.036</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0399</td>\n",
       "      <td>118.176</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1613 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      acousticness  danceability  duration_ms  energy  instrumentalness  key  \\\n",
       "1505      0.004770         0.585       214740   0.614          0.000155   10   \n",
       "813       0.114000         0.665       216728   0.513          0.303000    0   \n",
       "615       0.030200         0.798       216585   0.481          0.000000    7   \n",
       "319       0.106000         0.912       194040   0.317          0.000208    6   \n",
       "320       0.021100         0.697       236456   0.905          0.893000    6   \n",
       "...            ...           ...          ...     ...               ...  ...   \n",
       "2012      0.001060         0.584       274404   0.932          0.002690    1   \n",
       "1346      0.000021         0.535       203500   0.974          0.000149   10   \n",
       "1406      0.503000         0.410       256333   0.648          0.000000    7   \n",
       "1389      0.705000         0.894       222307   0.161          0.003300    4   \n",
       "1534      0.623000         0.470       394920   0.156          0.187000    2   \n",
       "\n",
       "      liveness  loudness  mode  speechiness    tempo  time_signature  valence  \n",
       "1505    0.0762    -5.594     0       0.0370  114.059             4.0   0.2730  \n",
       "813     0.1220    -7.314     1       0.3310  100.344             3.0   0.0373  \n",
       "615     0.1280   -10.488     1       0.3140  127.136             4.0   0.6400  \n",
       "319     0.0723   -12.719     0       0.0378   99.346             4.0   0.9490  \n",
       "320     0.1190    -7.787     0       0.0339  119.977             4.0   0.3110  \n",
       "...        ...       ...   ...          ...      ...             ...      ...  \n",
       "2012    0.1290    -3.501     1       0.3330   74.976             4.0   0.2110  \n",
       "1346    0.2630    -3.566     0       0.1720  116.956             4.0   0.4310  \n",
       "1406    0.2190    -4.469     1       0.0362   60.391             4.0   0.3420  \n",
       "1389    0.3120   -14.311     1       0.0880  104.968             4.0   0.8180  \n",
       "1534    0.1040   -17.036     1       0.0399  118.176             4.0   0.0591  \n",
       "\n",
       "[1613 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train # original X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine transformed value of the energy feature in the first row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.614"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['energy'].iloc[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3180174485124284"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train['energy'].iloc[0] - np.mean(X_train['energy']))/ X_train['energy'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>-0.698</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.399</td>\n",
       "      <td>-0.318</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>1.276</td>\n",
       "      <td>-0.738</td>\n",
       "      <td>0.396</td>\n",
       "      <td>-1.281</td>\n",
       "      <td>-0.618</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>0.139</td>\n",
       "      <td>-0.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>-0.276</td>\n",
       "      <td>0.296</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>-0.796</td>\n",
       "      <td>0.598</td>\n",
       "      <td>-1.487</td>\n",
       "      <td>-0.439</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.781</td>\n",
       "      <td>2.728</td>\n",
       "      <td>-0.803</td>\n",
       "      <td>-3.781</td>\n",
       "      <td>-1.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>-0.600</td>\n",
       "      <td>1.111</td>\n",
       "      <td>-0.376</td>\n",
       "      <td>-0.947</td>\n",
       "      <td>-0.493</td>\n",
       "      <td>0.447</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-0.879</td>\n",
       "      <td>0.781</td>\n",
       "      <td>2.535</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.809</td>\n",
       "      <td>-0.654</td>\n",
       "      <td>-1.722</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-0.763</td>\n",
       "      <td>-1.461</td>\n",
       "      <td>-1.281</td>\n",
       "      <td>-0.609</td>\n",
       "      <td>-0.840</td>\n",
       "      <td>0.139</td>\n",
       "      <td>1.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>-0.635</td>\n",
       "      <td>0.492</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>1.057</td>\n",
       "      <td>2.723</td>\n",
       "      <td>0.170</td>\n",
       "      <td>-0.458</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>-1.281</td>\n",
       "      <td>-0.653</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.139</td>\n",
       "      <td>-0.754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      acousticness  danceability  duration_ms  energy  instrumentalness  \\\n",
       "1505        -0.698        -0.195       -0.399  -0.318            -0.492   \n",
       "813         -0.276         0.296       -0.374  -0.796             0.598   \n",
       "615         -0.600         1.111       -0.376  -0.947            -0.493   \n",
       "319         -0.307         1.809       -0.654  -1.722            -0.492   \n",
       "320         -0.635         0.492       -0.131   1.057             2.723   \n",
       "\n",
       "        key  liveness  loudness   mode  speechiness  tempo  time_signature  \\\n",
       "1505  1.276    -0.738     0.396 -1.281       -0.618 -0.294           0.139   \n",
       "813  -1.487    -0.439    -0.052  0.781        2.728 -0.803          -3.781   \n",
       "615   0.447    -0.400    -0.879  0.781        2.535  0.191           0.139   \n",
       "319   0.170    -0.763    -1.461 -1.281       -0.609 -0.840           0.139   \n",
       "320   0.170    -0.458    -0.176 -1.281       -0.653 -0.074           0.139   \n",
       "\n",
       "      valence  \n",
       "1505   -0.908  \n",
       "813    -1.861  \n",
       "615     0.576  \n",
       "319     1.825  \n",
       "320    -0.754  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_train_scaled, columns=X_train.columns, index=X_train.index).head().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `fit` and `transform` paradigm for transformers\n",
    "- `sklearn` uses `fit` and `transform` paradigms for feature transformations. \n",
    "- We `fit` the transformer on the train split and then transform the train split as well as the test split. \n",
    "- We apply the same transformations on the test split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `sklearn` API summary: estimators\n",
    "\n",
    "Suppose `model` is a classification or regression model. \n",
    "\n",
    "```\n",
    "model.fit(X_train, y_train)\n",
    "X_train_predictions = model.predict(X_train)\n",
    "X_test_predictions = model.predict(X_test)\n",
    "```    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `sklearn` API summary: transformers\n",
    "\n",
    "Suppose `transformer` is a transformer used to change the input representation, for example, to tackle missing values or to scales numeric features.\n",
    "\n",
    "```\n",
    "transformer.fit(X_train, [y_train])\n",
    "X_train_transformed = transformer.transform(X_train)\n",
    "X_test_transformed = transformer.transform(X_test)\n",
    "```  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- You can pass `y_train` in `fit` but it's usually ignored. It allows you to pass it just to be consistent with usual usage of `sklearn`'s `fit` method.   \n",
    "- You can also carry out fitting and transforming in one call using `fit_transform`. But be mindful to use it only on the train split and **not** on the test split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Do you expect `DummyClassifier` results to change after scaling the data? \n",
    "- Let's check whether scaling makes any difference for $k$-NNs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.726\n",
      "Test score: 0.552\n"
     ]
    }
   ],
   "source": [
    "knn_unscaled = KNeighborsClassifier()\n",
    "knn_unscaled.fit(X_train, y_train)\n",
    "print(\"Train score: %0.3f\" % (knn_unscaled.score(X_train, y_train)))\n",
    "print(\"Test score: %0.3f\" % (knn_unscaled.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.798\n",
      "Test score: 0.686\n"
     ]
    }
   ],
   "source": [
    "knn_scaled = KNeighborsClassifier()\n",
    "knn_scaled.fit(X_train_scaled, y_train)\n",
    "print(\"Train score: %0.3f\" % (knn_scaled.score(X_train_scaled, y_train)))\n",
    "print(\"Test score: %0.3f\" % (knn_scaled.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The scores with scaled data are better compared to the unscaled data in case of $k$-NNs.\n",
    "- I am not carrying out cross-validation here for a reason that we'll look into soon. \n",
    "- Note that I am a bit sloppy here and using the test set several times for teaching purposes. But when you build an ML pipeline, please do assessment on the test set only once.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Common preprocessing techniques\n",
    "\n",
    "Some commonly performed feature transformation include:  \n",
    "- Imputation: Tackling missing values\n",
    "- Scaling: Scaling of numeric features\n",
    "- One-hot encoding: Tackling categorical variables      \n",
    "    \n",
    "\n",
    "We can have one lecture on each of them! In this lesson our goal is to getting familiar with them so that we can use them to build ML pipelines. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In the next part of this lecture, we'll build an ML pipeline using [California housing prices regression dataset](https://www.kaggle.com/harrywang/housing). In the process, we will talk about different feature transformations and how can we apply them so that we do not violate the golden rule. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Imputation and scaling [[video](https://youtu.be/G2IXbVzKlt8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dataset, splitting, and baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "We'll be working on [California housing prices regression dataset](https://www.kaggle.com/harrywang/housing) to demonstrate these feature transformation techniques. The task is to predict median house values in Californian districts, given a number of features from these districts. If you are running the notebook on your own, you'll have to download the data and put it in the data directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2948.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
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       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>170100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>129300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5566.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>6303.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>205000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "6051     -117.75     34.04                22.0       2948.0           636.0   \n",
       "20113    -119.57     37.94                17.0        346.0           130.0   \n",
       "14289    -117.13     32.74                46.0       3355.0           768.0   \n",
       "13665    -117.31     34.02                18.0       1634.0           274.0   \n",
       "14471    -117.23     32.88                18.0       5566.0          1465.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "6051       2600.0       602.0         3.1250            113600.0   \n",
       "20113        51.0        20.0         3.4861            137500.0   \n",
       "14289      1457.0       708.0         2.6604            170100.0   \n",
       "13665       899.0       285.0         5.2139            129300.0   \n",
       "14471      6303.0      1458.0         1.8580            205000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "6051           INLAND  \n",
       "20113          INLAND  \n",
       "14289      NEAR OCEAN  \n",
       "13665          INLAND  \n",
       "14471      NEAR OCEAN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df = pd.read_csv(\"data/housing.csv\")\n",
    "train_df, test_df = train_test_split(housing_df, test_size=0.1, random_state=123)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Some column values are mean/median but some are not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's add some new features to the dataset which could help predicting the target: `median_house_value`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "train_df = train_df.assign(\n",
    "    rooms_per_household=train_df[\"total_rooms\"] / train_df[\"households\"]\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    rooms_per_household=test_df[\"total_rooms\"] / test_df[\"households\"]\n",
    ")\n",
    "\n",
    "train_df = train_df.assign(\n",
    "    bedrooms_per_household=train_df[\"total_bedrooms\"] / train_df[\"households\"]\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    bedrooms_per_household=test_df[\"total_bedrooms\"] / test_df[\"households\"]\n",
    ")\n",
    "\n",
    "train_df = train_df.assign(\n",
    "    population_per_household=train_df[\"population\"] / train_df[\"households\"]\n",
    ")\n",
    "test_df = test_df.assign(\n",
    "    population_per_household=test_df[\"population\"] / test_df[\"households\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2948.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4.897010</td>\n",
       "      <td>1.056478</td>\n",
       "      <td>4.318937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.550000</td>\n",
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       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3355.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>170100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>129300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>0.961404</td>\n",
       "      <td>3.154386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5566.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>6303.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>205000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.817558</td>\n",
       "      <td>1.004801</td>\n",
       "      <td>4.323045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "6051     -117.75     34.04                22.0       2948.0           636.0   \n",
       "20113    -119.57     37.94                17.0        346.0           130.0   \n",
       "14289    -117.13     32.74                46.0       3355.0           768.0   \n",
       "13665    -117.31     34.02                18.0       1634.0           274.0   \n",
       "14471    -117.23     32.88                18.0       5566.0          1465.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "6051       2600.0       602.0         3.1250            113600.0   \n",
       "20113        51.0        20.0         3.4861            137500.0   \n",
       "14289      1457.0       708.0         2.6604            170100.0   \n",
       "13665       899.0       285.0         5.2139            129300.0   \n",
       "14471      6303.0      1458.0         1.8580            205000.0   \n",
       "\n",
       "      ocean_proximity  rooms_per_household  bedrooms_per_household  \\\n",
       "6051           INLAND             4.897010                1.056478   \n",
       "20113          INLAND            17.300000                6.500000   \n",
       "14289      NEAR OCEAN             4.738701                1.084746   \n",
       "13665          INLAND             5.733333                0.961404   \n",
       "14471      NEAR OCEAN             3.817558                1.004801   \n",
       "\n",
       "       population_per_household  \n",
       "6051                   4.318937  \n",
       "20113                  2.550000  \n",
       "14289                  2.057910  \n",
       "13665                  3.154386  \n",
       "14471                  4.323045  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(columns = ['population', 'total_rooms', 'total_bedrooms'])\n",
    "test_df =  test_df.drop(columns = ['population', 'total_rooms', 'total_bedrooms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### When is it OK to do things before splitting? \n",
    "\n",
    "- Here it would have been OK to add new features before splitting because we are not using any global information in the data but only looking at one row at a time. \n",
    "- But just to be safe and to avoid accidentally breaking the golden rule, it's better to do it after splitting. \n",
    "\n",
    "- Question: Should we remove `total_rooms`, `total_bedrooms`, and `population` columns? \n",
    "    - Probably. But I am keeping them in this lecture. You could experiment with removing them and examine whether results change. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>113600.0</td>\n",
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       "      <td>1.056478</td>\n",
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       "      <th>20113</th>\n",
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       "      <td>46.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>170100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
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       "      <th>13665</th>\n",
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       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "6051     -117.75     34.04                22.0       602.0         3.1250   \n",
       "20113    -119.57     37.94                17.0        20.0         3.4861   \n",
       "14289    -117.13     32.74                46.0       708.0         2.6604   \n",
       "13665    -117.31     34.02                18.0       285.0         5.2139   \n",
       "14471    -117.23     32.88                18.0      1458.0         1.8580   \n",
       "\n",
       "       median_house_value ocean_proximity  rooms_per_household  \\\n",
       "6051             113600.0          INLAND             4.897010   \n",
       "20113            137500.0          INLAND            17.300000   \n",
       "14289            170100.0      NEAR OCEAN             4.738701   \n",
       "13665            129300.0          INLAND             5.733333   \n",
       "14471            205000.0      NEAR OCEAN             3.817558   \n",
       "\n",
       "       bedrooms_per_household  population_per_household  \n",
       "6051                 1.056478                  4.318937  \n",
       "20113                6.500000                  2.550000  \n",
       "14289                1.084746                  2.057910  \n",
       "13665                0.961404                  3.154386  \n",
       "14471                1.004801                  4.323045  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature scales are quite different. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18576 entries, 6051 to 19966\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   longitude                 18576 non-null  float64\n",
      " 1   latitude                  18576 non-null  float64\n",
      " 2   housing_median_age        18576 non-null  float64\n",
      " 3   households                18576 non-null  float64\n",
      " 4   median_income             18576 non-null  float64\n",
      " 5   median_house_value        18576 non-null  float64\n",
      " 6   ocean_proximity           18576 non-null  object \n",
      " 7   rooms_per_household       18576 non-null  float64\n",
      " 8   bedrooms_per_household    18391 non-null  float64\n",
      " 9   population_per_household  18576 non-null  float64\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have one categorical feature and all other features are numeric features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "      <td>18391.000000</td>\n",
       "      <td>18576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.565888</td>\n",
       "      <td>35.627966</td>\n",
       "      <td>28.622255</td>\n",
       "      <td>500.061100</td>\n",
       "      <td>3.862552</td>\n",
       "      <td>206292.067991</td>\n",
       "      <td>5.426067</td>\n",
       "      <td>1.097516</td>\n",
       "      <td>3.052349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.999622</td>\n",
       "      <td>2.134658</td>\n",
       "      <td>12.588307</td>\n",
       "      <td>383.044313</td>\n",
       "      <td>1.892491</td>\n",
       "      <td>115083.856175</td>\n",
       "      <td>2.512319</td>\n",
       "      <td>0.486266</td>\n",
       "      <td>10.020873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.790000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.560225</td>\n",
       "      <td>119400.000000</td>\n",
       "      <td>4.439360</td>\n",
       "      <td>1.005888</td>\n",
       "      <td>2.430323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>3.527500</td>\n",
       "      <td>179300.000000</td>\n",
       "      <td>5.226415</td>\n",
       "      <td>1.048860</td>\n",
       "      <td>2.818868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>4.736900</td>\n",
       "      <td>263600.000000</td>\n",
       "      <td>6.051620</td>\n",
       "      <td>1.099723</td>\n",
       "      <td>3.283921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>1243.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age    households  \\\n",
       "count  18576.000000  18576.000000        18576.000000  18576.000000   \n",
       "mean    -119.565888     35.627966           28.622255    500.061100   \n",
       "std        1.999622      2.134658           12.588307    383.044313   \n",
       "min     -124.350000     32.540000            1.000000      1.000000   \n",
       "25%     -121.790000     33.930000           18.000000    280.000000   \n",
       "50%     -118.490000     34.250000           29.000000    410.000000   \n",
       "75%     -118.010000     37.710000           37.000000    606.000000   \n",
       "max     -114.310000     41.950000           52.000000   6082.000000   \n",
       "\n",
       "       median_income  median_house_value  rooms_per_household  \\\n",
       "count   18576.000000        18576.000000         18576.000000   \n",
       "mean        3.862552       206292.067991             5.426067   \n",
       "std         1.892491       115083.856175             2.512319   \n",
       "min         0.499900        14999.000000             0.846154   \n",
       "25%         2.560225       119400.000000             4.439360   \n",
       "50%         3.527500       179300.000000             5.226415   \n",
       "75%         4.736900       263600.000000             6.051620   \n",
       "max        15.000100       500001.000000           141.909091   \n",
       "\n",
       "       bedrooms_per_household  population_per_household  \n",
       "count            18391.000000              18576.000000  \n",
       "mean                 1.097516                  3.052349  \n",
       "std                  0.486266                 10.020873  \n",
       "min                  0.333333                  0.692308  \n",
       "25%                  1.005888                  2.430323  \n",
       "50%                  1.048860                  2.818868  \n",
       "75%                  1.099723                  3.283921  \n",
       "max                 34.066667               1243.333333  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Seems like total_bedrooms column has some missing values. \n",
    "- This must have affected our new feature `bedrooms_per_household` as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df[\"total_bedrooms\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## (optional)\n",
    "train_df.hist(bins=50, figsize=(20, 15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## (optional)\n",
    "train_df.plot(\n",
    "    kind=\"scatter\",\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    alpha=0.4,\n",
    "    s=train_df[\"population_per_household\"],\n",
    "    figsize=(10, 7),\n",
    "    c=\"median_house_value\",\n",
    "    cmap=plt.get_cmap(\"jet\"),\n",
    "    colorbar=True,\n",
    "    sharex=False,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What all transformations we need to apply on the dataset? \n",
    "\n",
    "Here is what we see from the EDA. \n",
    "\n",
    "- Some missing values in `total_bedrooms` column\n",
    "- Scales are quite different across columns. \n",
    "- Categorical variable `ocean_proximity`\n",
    "\n",
    "Read about [preprocessing techniques implemented in `scikit-learn`](https://scikit-learn.org/stable/modules/preprocessing.html).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are droping the categorical variable ocean_proximity for now. We'll come back to it in a bit.\n",
    "X_train = train_df.drop(columns=[\"median_house_value\", \"ocean_proximity\"])\n",
    "y_train = train_df[\"median_house_value\"]\n",
    "\n",
    "X_test = test_df.drop(columns=[\"median_house_value\", \"ocean_proximity\"])\n",
    "y_test = test_df[\"median_house_value\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's first run our baseline model `DummyRegressor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict = {}  # dictionary to store our results for different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def mean_std_cross_val_scores(model, X_train, y_train, **kwargs):\n",
    "    \"\"\"\n",
    "    Returns mean and std of cross validation\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model :\n",
    "        scikit-learn model\n",
    "    X_train : numpy array or pandas DataFrame\n",
    "        X in the training data\n",
    "    y_train :\n",
    "        y in the training data\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "        pandas Series with mean scores from cross_validation\n",
    "    \"\"\"\n",
    "\n",
    "    scores = cross_validate(model, X_train, y_train, **kwargs)\n",
    "\n",
    "    mean_scores = pd.DataFrame(scores).mean()\n",
    "    std_scores = pd.DataFrame(scores).std()\n",
    "    out_col = []\n",
    "\n",
    "    for i in range(len(mean_scores)):\n",
    "        out_col.append((f\"%0.3f (+/- %0.3f)\" % (mean_scores[i], std_scores[i])))\n",
    "\n",
    "    return pd.Series(data=out_col, index=mean_scores.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dummy = DummyRegressor(strategy=\"median\")\n",
    "results_dict[\"dummy\"] = mean_std_cross_val_scores(\n",
    "    dummy, X_train, y_train, return_train_score=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>dummy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fit_time</th>\n",
       "      <td>0.001 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_time</th>\n",
       "      <td>0.000 (+/- 0.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_score</th>\n",
       "      <td>-0.055 (+/- 0.012)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_score</th>\n",
       "      <td>-0.055 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          dummy\n",
       "fit_time      0.001 (+/- 0.000)\n",
       "score_time    0.000 (+/- 0.000)\n",
       "test_score   -0.055 (+/- 0.012)\n",
       "train_score  -0.055 (+/- 0.001)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Imputation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>4.897010</td>\n",
       "      <td>1.056478</td>\n",
       "      <td>4.318937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>0.961404</td>\n",
       "      <td>3.154386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>3.817558</td>\n",
       "      <td>1.004801</td>\n",
       "      <td>4.323045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.6389</td>\n",
       "      <td>5.584615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15377</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>33.37</td>\n",
       "      <td>14.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>4.5391</td>\n",
       "      <td>6.016688</td>\n",
       "      <td>1.017972</td>\n",
       "      <td>3.127086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17730</th>\n",
       "      <td>-121.76</td>\n",
       "      <td>37.33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>5.6306</td>\n",
       "      <td>5.958393</td>\n",
       "      <td>1.031564</td>\n",
       "      <td>3.493544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.78</td>\n",
       "      <td>44.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>4.739264</td>\n",
       "      <td>1.024540</td>\n",
       "      <td>1.720859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19966</th>\n",
       "      <td>-119.08</td>\n",
       "      <td>36.21</td>\n",
       "      <td>20.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.5156</td>\n",
       "      <td>5.491379</td>\n",
       "      <td>1.117816</td>\n",
       "      <td>3.566092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18576 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "6051     -117.75     34.04                22.0       602.0         3.1250   \n",
       "20113    -119.57     37.94                17.0        20.0         3.4861   \n",
       "14289    -117.13     32.74                46.0       708.0         2.6604   \n",
       "13665    -117.31     34.02                18.0       285.0         5.2139   \n",
       "14471    -117.23     32.88                18.0      1458.0         1.8580   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "7763     -118.10     33.91                36.0       130.0         3.6389   \n",
       "15377    -117.24     33.37                14.0       779.0         4.5391   \n",
       "17730    -121.76     37.33                 5.0       697.0         5.6306   \n",
       "15725    -122.44     37.78                44.0       326.0         3.8750   \n",
       "19966    -119.08     36.21                20.0       348.0         2.5156   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "6051              4.897010                1.056478                  4.318937  \n",
       "20113            17.300000                6.500000                  2.550000  \n",
       "14289             4.738701                1.084746                  2.057910  \n",
       "13665             5.733333                0.961404                  3.154386  \n",
       "14471             3.817558                1.004801                  4.323045  \n",
       "...                    ...                     ...                       ...  \n",
       "7763              5.584615                     NaN                  3.769231  \n",
       "15377             6.016688                1.017972                  3.127086  \n",
       "17730             5.958393                1.031564                  3.493544  \n",
       "15725             4.739264                1.024540                  1.720859  \n",
       "19966             5.491379                1.117816                  3.566092  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "# knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What's the problem? \n",
    "\n",
    "```\n",
    "ValueError: Input contains NaN, infinity or a value too large for dtype('float64').\n",
    "```\n",
    "\n",
    "- The classifier is not able to deal with missing values (NaNs).\n",
    "- What are possible ways to deal with the problem? \n",
    "    - Delete the rows? \n",
    "    - Replace them with some reasonable values?    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- `SimpleImputer` is a transformer in `sklearn` to deal with this problem. For example, \n",
    "    - You can impute missing values in categorical columns with the most frequent value.\n",
    "    - You can impute the missing values in numeric columns with the mean or median of the column.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20248</th>\n",
       "      <td>-119.23</td>\n",
       "      <td>34.25</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12649</th>\n",
       "      <td>-121.47</td>\n",
       "      <td>38.51</td>\n",
       "      <td>52.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>8.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>-117.76</td>\n",
       "      <td>35.22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.6250</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12138</th>\n",
       "      <td>-117.22</td>\n",
       "      <td>33.87</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8219</th>\n",
       "      <td>-118.21</td>\n",
       "      <td>33.79</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.5938</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4591</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.06</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>1.2254</td>\n",
       "      <td>2.096692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.218830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19485</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.9336</td>\n",
       "      <td>3.662745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.572549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6962</th>\n",
       "      <td>-118.05</td>\n",
       "      <td>33.99</td>\n",
       "      <td>38.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>3.7328</td>\n",
       "      <td>4.535014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.481793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>-117.01</td>\n",
       "      <td>32.74</td>\n",
       "      <td>31.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>2.6973</td>\n",
       "      <td>5.129985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.098966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.6389</td>\n",
       "      <td>5.584615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.769231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18576 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "20248    -119.23     34.25                28.0         9.0         8.0000   \n",
       "12649    -121.47     38.51                52.0         9.0         3.6250   \n",
       "3125     -117.76     35.22                 4.0         6.0         1.6250   \n",
       "12138    -117.22     33.87                16.0        14.0         2.6250   \n",
       "8219     -118.21     33.79                33.0        36.0         4.5938   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "4591     -118.28     34.06                42.0      1179.0         1.2254   \n",
       "19485    -120.98     37.66                10.0       255.0         0.9336   \n",
       "6962     -118.05     33.99                38.0       357.0         3.7328   \n",
       "14970    -117.01     32.74                31.0       677.0         2.6973   \n",
       "7763     -118.10     33.91                36.0       130.0         3.6389   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "20248             2.888889                0.333333                  3.222222  \n",
       "12649             2.222222                0.444444                  8.222222  \n",
       "3125              3.000000                0.500000                  1.333333  \n",
       "12138             4.000000                0.500000                  2.785714  \n",
       "8219              0.888889                0.500000                  2.666667  \n",
       "...                    ...                     ...                       ...  \n",
       "4591              2.096692                     NaN                  3.218830  \n",
       "19485             3.662745                     NaN                  1.572549  \n",
       "6962              4.535014                     NaN                  2.481793  \n",
       "14970             5.129985                     NaN                  3.098966  \n",
       "7763              5.584615                     NaN                  3.769231  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.sort_values(\"bedrooms_per_household\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2064, 8)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(X_train)\n",
    "X_train_imp = imputer.transform(X_train)\n",
    "X_test_imp = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's check whether the NaN values have been replaced or not\n",
    "- Note that `imputer.transform` returns an `numpy` array and not a dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- This problem affects a large number of ML methods.\n",
    "- A number of approaches to this problem. We are going to look into two most popular ones.  \n",
    "\n",
    "| Approach | What it does | How to update $X$ (but see below!) | sklearn implementation | \n",
    "|---------|------------|-----------------------|----------------|\n",
    "| standardization | sets sample mean to $0$, s.d. to $1$   | `X -= np.mean(X,axis=0)`<br>`X /=  np.std(X,axis=0)` | [`StandardScaler()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "There are all sorts of articles on this; see, e.g. [here](http://www.dataminingblog.com/standardization-vs-normalization/) and [here](https://medium.com/@rrfd/standardize-or-normalize-examples-in-python-e3f174b65dfc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [source](https://amueller.github.io/COMS4995-s19/slides/aml-05-preprocessing/#8)\n",
    "mglearn.plots.plot_scaling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.908140</td>\n",
       "      <td>-0.743917</td>\n",
       "      <td>-0.526078</td>\n",
       "      <td>0.266135</td>\n",
       "      <td>-0.389736</td>\n",
       "      <td>-0.210591</td>\n",
       "      <td>-0.083813</td>\n",
       "      <td>0.126398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.002057</td>\n",
       "      <td>1.083123</td>\n",
       "      <td>-0.923283</td>\n",
       "      <td>-1.253312</td>\n",
       "      <td>-0.198924</td>\n",
       "      <td>4.726412</td>\n",
       "      <td>11.166631</td>\n",
       "      <td>-0.050132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.218207</td>\n",
       "      <td>-1.352930</td>\n",
       "      <td>1.380504</td>\n",
       "      <td>0.542873</td>\n",
       "      <td>-0.635239</td>\n",
       "      <td>-0.273606</td>\n",
       "      <td>-0.025391</td>\n",
       "      <td>-0.099240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.128188</td>\n",
       "      <td>-0.753286</td>\n",
       "      <td>-0.843842</td>\n",
       "      <td>-0.561467</td>\n",
       "      <td>0.714077</td>\n",
       "      <td>0.122307</td>\n",
       "      <td>-0.280310</td>\n",
       "      <td>0.010183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.168196</td>\n",
       "      <td>-1.287344</td>\n",
       "      <td>-0.843842</td>\n",
       "      <td>2.500924</td>\n",
       "      <td>-1.059242</td>\n",
       "      <td>-0.640266</td>\n",
       "      <td>-0.190617</td>\n",
       "      <td>0.126808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18571</th>\n",
       "      <td>0.733102</td>\n",
       "      <td>-0.804818</td>\n",
       "      <td>0.586095</td>\n",
       "      <td>-0.966131</td>\n",
       "      <td>-0.118182</td>\n",
       "      <td>0.063110</td>\n",
       "      <td>-0.099558</td>\n",
       "      <td>0.071541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18572</th>\n",
       "      <td>1.163195</td>\n",
       "      <td>-1.057793</td>\n",
       "      <td>-1.161606</td>\n",
       "      <td>0.728235</td>\n",
       "      <td>0.357500</td>\n",
       "      <td>0.235096</td>\n",
       "      <td>-0.163397</td>\n",
       "      <td>0.007458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18573</th>\n",
       "      <td>-1.097293</td>\n",
       "      <td>0.797355</td>\n",
       "      <td>-1.876574</td>\n",
       "      <td>0.514155</td>\n",
       "      <td>0.934269</td>\n",
       "      <td>0.211892</td>\n",
       "      <td>-0.135305</td>\n",
       "      <td>0.044029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18574</th>\n",
       "      <td>-1.437367</td>\n",
       "      <td>1.008167</td>\n",
       "      <td>1.221622</td>\n",
       "      <td>-0.454427</td>\n",
       "      <td>0.006578</td>\n",
       "      <td>-0.273382</td>\n",
       "      <td>-0.149822</td>\n",
       "      <td>-0.132875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18575</th>\n",
       "      <td>0.242996</td>\n",
       "      <td>0.272667</td>\n",
       "      <td>-0.684960</td>\n",
       "      <td>-0.396991</td>\n",
       "      <td>-0.711754</td>\n",
       "      <td>0.025998</td>\n",
       "      <td>0.042957</td>\n",
       "      <td>0.051269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18576 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "0       0.908140 -0.743917           -0.526078    0.266135      -0.389736   \n",
       "1      -0.002057  1.083123           -0.923283   -1.253312      -0.198924   \n",
       "2       1.218207 -1.352930            1.380504    0.542873      -0.635239   \n",
       "3       1.128188 -0.753286           -0.843842   -0.561467       0.714077   \n",
       "4       1.168196 -1.287344           -0.843842    2.500924      -1.059242   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "18571   0.733102 -0.804818            0.586095   -0.966131      -0.118182   \n",
       "18572   1.163195 -1.057793           -1.161606    0.728235       0.357500   \n",
       "18573  -1.097293  0.797355           -1.876574    0.514155       0.934269   \n",
       "18574  -1.437367  1.008167            1.221622   -0.454427       0.006578   \n",
       "18575   0.242996  0.272667           -0.684960   -0.396991      -0.711754   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "0                -0.210591               -0.083813                  0.126398  \n",
       "1                 4.726412               11.166631                 -0.050132  \n",
       "2                -0.273606               -0.025391                 -0.099240  \n",
       "3                 0.122307               -0.280310                  0.010183  \n",
       "4                -0.640266               -0.190617                  0.126808  \n",
       "...                    ...                     ...                       ...  \n",
       "18571             0.063110               -0.099558                  0.071541  \n",
       "18572             0.235096               -0.163397                  0.007458  \n",
       "18573             0.211892               -0.135305                  0.044029  \n",
       "18574            -0.273382               -0.149822                 -0.132875  \n",
       "18575             0.025998                0.042957                  0.051269  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train_imp)\n",
    "X_test_scaled = scaler.transform(X_test_imp)\n",
    "pd.DataFrame(X_train_scaled, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7978563117812038"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "knn.score(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Big difference in the KNN training performance after scaling the data. \n",
    "- But we saw last week that training score doesn't tell us much. We should look at the cross-validation score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ❓❓ Questions for you "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "source": [
    "### (iClicker) Exercise 5.1 \n",
    "\n",
    "**iClicker cloud join link: https://join.iclicker.com/3DP5H**\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "1. `StandardScaler` ensures a fixed range (i.e., minimum and maximum values) for the features. \n",
    "2. `StandardScaler` calculates mean and standard deviation for each feature separately. \n",
    "3. In general, it's a good idea to apply scaling on numeric features before training $k$-NN or SVM RBF models. \n",
    "4. The transformed feature values might be hard to interpret for humans.\n",
    "5. After applying `SimpleImputer` The transformed data has a different shape than the original data.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Questions for class discussion \n",
    "\n",
    "Let's create some synthetic data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs, make_classification\n",
    "\n",
    "# make synthetic data\n",
    "X, y = make_blobs(n_samples=100, centers=3, random_state=12, cluster_std=5)\n",
    "# split it into training and test sets\n",
    "X_train_toy, X_test_toy, y_train_toy, y_test_toy = train_test_split(\n",
    "    X, y, random_state=5, test_size=0.4)\n",
    "plt.scatter(X_train_toy[:, 0], X_train_toy[:, 1], label=\"Training set\", s=60)\n",
    "plt.scatter(\n",
    "    X_test_toy[:, 0], X_test_toy[:, 1], color=mglearn.cm2(1), label=\"Test set\", s=60\n",
    ")\n",
    "plt.legend(loc=\"upper right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Let's transform the data using `StandardScaler` and examine how the data looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "train_transformed = scaler.fit_transform(X_train_toy)\n",
    "test_transformed = scaler.transform(X_test_toy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.600287211119548, 6.270246946519936)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy[:, 0].mean(), X_train_toy[:, 0].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.124686209266641, 6.2749592279985595)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy[:, 1].mean(), X_train_toy[:, 1].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_original_scaled(X_train_toy, X_test_toy, train_transformed, test_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "#### Bad methodology 1: Scaling the data separately (for class discussion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score: 0.63\n",
      "Test score: 0.60\n"
     ]
    }
   ],
   "source": [
    "# DO NOT DO THIS! For illustration purposes only.\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train_toy)\n",
    "train_scaled = scaler.transform(X_train_toy)\n",
    "\n",
    "scaler = StandardScaler()  # Creating a separate object for scaling test data\n",
    "scaler.fit(X_test_toy)  # Calling fit on the test data\n",
    "test_scaled = scaler.transform(\n",
    "    X_test_toy\n",
    ")  # Transforming the test data using the scaler fit on test data\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(train_scaled, y_train_toy)\n",
    "print(f\"Training score: {knn.score(train_scaled, y_train_toy):.2f}\")\n",
    "print(f\"Test score: {knn.score(test_scaled, y_test_toy):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Is anything wrong in methodology 1? If yes, what is it?  \n",
    "- What are the mean and standard deviation of columns in `X_train_toy` and `X_test_toy`?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.600287211119548, 6.270246946519936)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy[:, 0].mean(), X_train_toy[:, 0].std() # mean and std of column 1 in X_train_toy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.124686209266641, 6.2749592279985595)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy[:, 1].mean(), X_train_toy[:, 1].std() # mean and std of column 2 in X_train_toy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the mean and standard deviation of columns in `X_test_toy`?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-7.4360796317881865, 5.415791218175097)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_toy[:, 0].mean(), X_test_toy[:, 0].std() # mean and std of column 1 in X_test_toy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.584450392629585, 5.5947435906433025)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_toy[:, 1].mean(), X_test_toy[:, 1].std() # mean and std of column 2 in X_train_toy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_original_scaled(\n",
    "    X_train_toy,\n",
    "    X_test_toy,\n",
    "    train_scaled,\n",
    "    test_scaled,\n",
    "    title_transformed=\"Improperly transformed\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Bad methodology 2: Scaling the data together (for class discussion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60, 2), (40, 2))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy.shape, X_test_toy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join the train and test sets back together\n",
    "XX = np.vstack((X_train_toy, X_test_toy))\n",
    "XX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(XX)\n",
    "XX_scaled = scaler.transform(XX)\n",
    "XX_train = XX_scaled[:X_train_toy.shape[0]]\n",
    "XX_test = XX_scaled[X_train_toy.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score: 0.63\n",
      "Test score: 0.55\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(XX_train, y_train_toy)\n",
    "print(f\"Training score: {knn.score(XX_train, y_train_toy):.2f}\")  # Misleading score\n",
    "print(f\"Test score: {knn.score(XX_test, y_test_toy):.2f}\")  # Misleading score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Is anything wrong in methodology 2? If yes, what is it? \n",
    "- What's are the mean and std of `X_train_toy` vs. `XX`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.600287211119548, 6.270246946519936)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy[:, 0].mean(), X_train_toy[:, 0].std() # mean and std of column 1 in X_train_toy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.124686209266641, 6.2749592279985595)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_toy[:, 1].mean(), X_train_toy[:, 1].std() # mean and std of column 2 in X_train_toy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the mean and standard deviation of columns in `XX`?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.134604179387004, 5.970528192615454)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX[:, 0].mean(), XX[:, 0].std() # mean and std of column 1 in XX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.9085918826118187, 6.017937808158447)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX[:, 1].mean(), XX[:, 1].std() # mean and std of column 2 in XX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no big difference but they are not the same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_original_scaled(\n",
    "    X_train_toy,\n",
    "    X_test_toy,\n",
    "    XX_train,\n",
    "    XX_test,\n",
    "    title_transformed=\"Improperly transformed\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not a noticeable difference in the transformed data but if the test set is large it might influence the mean and standard deviation significantly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Methodology 3: Cross validation with already preprocessed data (for class discussion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000308</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.604167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.000308    0.000794    0.250000     0.687500\n",
       "1  0.000143    0.000348    0.500000     0.625000\n",
       "2  0.000118    0.000309    0.583333     0.541667\n",
       "3  0.000117    0.000323    0.583333     0.604167\n",
       "4  0.000121    0.000310    0.416667     0.604167"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train_toy)\n",
    "X_train_scaled = scaler.transform(X_train_toy)\n",
    "X_test_scaled = scaler.transform(X_test_toy)\n",
    "scores = cross_validate(knn, X_train_scaled, y_train_toy, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Is there anything wrong in methodology 3? Are we breaking the golden rule here?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_improper_processing(\"kNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_proper_processing(\"kNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformations and the golden rule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### How to carry out cross-validation? \n",
    "\n",
    "- Last week we saw that cross validation is a better way to get a realistic assessment of the model. \n",
    "- Let's try cross-validation with transformed data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003304</td>\n",
       "      <td>0.093237</td>\n",
       "      <td>0.696373</td>\n",
       "      <td>0.794236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002990</td>\n",
       "      <td>0.079818</td>\n",
       "      <td>0.684447</td>\n",
       "      <td>0.791467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002943</td>\n",
       "      <td>0.087731</td>\n",
       "      <td>0.695532</td>\n",
       "      <td>0.789436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003205</td>\n",
       "      <td>0.087728</td>\n",
       "      <td>0.679478</td>\n",
       "      <td>0.793243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.053448</td>\n",
       "      <td>0.680657</td>\n",
       "      <td>0.794820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.003304    0.093237    0.696373     0.794236\n",
       "1  0.002990    0.079818    0.684447     0.791467\n",
       "2  0.002943    0.087731    0.695532     0.789436\n",
       "3  0.003205    0.087728    0.679478     0.793243\n",
       "4  0.002999    0.053448    0.680657     0.794820"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train_imp)\n",
    "X_train_scaled = scaler.transform(X_train_imp)\n",
    "X_test_scaled = scaler.transform(X_test_imp)\n",
    "scores = cross_validate(knn, X_train_scaled, y_train, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Do you see any problem here? \n",
    "- Are we applying `fit_transform` on train portion and `transform` on validation portion in each fold?  \n",
    "    - Here you might be allowing information from the validation set to **leak** into the training step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- You need to apply the **SAME** preprocessing steps to train/validation.\n",
    "- With many different transformations and cross validation the code gets unwieldy very quickly. \n",
    "- Likely to make mistakes and \"leak\" information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- In these examples our test accuracies look fine, but our methodology is flawed.\n",
    "- Implications can be significant in practice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pipelines\n",
    "\n",
    "Can we do this in a more elegant and organized way?\n",
    "\n",
    "- YES!! Using [`scikit-learn Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html).\n",
    "- [`scikit-learn Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) allows you to define a \"pipeline\" of transformers with a final estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's combine the preprocessing and model with pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "### Simple example of a pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipe = Pipeline(\n",
    "    steps=[\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"regressor\", KNeighborsRegressor()),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Syntax: pass in a list of steps.\n",
    "- The last step should be a **model/classifier/regressor**.\n",
    "- All the earlier steps should be **transformers**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Alternative and more compact syntax: `make_pipeline`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Shorthand for `Pipeline` constructor\n",
    "- Does not permit naming steps\n",
    "- Instead the names of steps are set to lowercase of their types automatically; `StandardScaler()` would be named as `standardscaler`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "pipe = make_pipeline(\n",
    "    SimpleImputer(strategy=\"median\"), StandardScaler(), KNeighborsRegressor()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;, KNeighborsRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;, KNeighborsRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
       "                ('standardscaler', StandardScaler()),\n",
       "                ('kneighborsregressor', KNeighborsRegressor())])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that we are passing `X_train` and **not** the imputed or scaled data here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "When you call `fit` on the pipeline, it carries out the following steps:\n",
    "\n",
    "- Fit `SimpleImputer` on `X_train`\n",
    "- Transform `X_train` using the fit `SimpleImputer` to create `X_train_imp`\n",
    "- Fit `StandardScaler` on `X_train_imp`\n",
    "- Transform `X_train_imp` using the fit `StandardScaler` to create `X_train_imp_scaled`\n",
    "- Fit the model (`KNeighborsRegressor` in our case) on `X_train_imp_scaled`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126500., 117380., 187700., ..., 259500., 308120.,  60860.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are passing original data to `predict` as well. This time the pipeline is carrying out following steps:\n",
    "- Transform `X_train` using the fit `SimpleImputer` to create `X_train_imp`\n",
    "- Transform `X_train_imp` using the fit `StandardScaler` to create `X_train_imp_scaled`\n",
    "- Predict using the fit model (`KNeighborsRegressor` in our case) on `X_train_imp_scaled`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](img/pipeline.png)\n",
    "<!-- <img src='./img/pipeline.png' width=\"800\"> -->\n",
    "    \n",
    "[Source](https://amueller.github.io/COMS4995-s20/slides/aml-04-preprocessing/#18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's try cross-validation with our pipeline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>0.001 (+/- 0.000)</td>\n",
       "      <td>0.000 (+/- 0.000)</td>\n",
       "      <td>-0.055 (+/- 0.012)</td>\n",
       "      <td>-0.055 (+/- 0.001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imp + scaling + knn</th>\n",
       "      <td>0.013 (+/- 0.001)</td>\n",
       "      <td>0.087 (+/- 0.007)</td>\n",
       "      <td>0.693 (+/- 0.014)</td>\n",
       "      <td>0.797 (+/- 0.015)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              fit_time         score_time          test_score  \\\n",
       "dummy                0.001 (+/- 0.000)  0.000 (+/- 0.000)  -0.055 (+/- 0.012)   \n",
       "imp + scaling + knn  0.013 (+/- 0.001)  0.087 (+/- 0.007)   0.693 (+/- 0.014)   \n",
       "\n",
       "                            train_score  \n",
       "dummy                -0.055 (+/- 0.001)  \n",
       "imp + scaling + knn   0.797 (+/- 0.015)  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict[\"imp + scaling + knn\"] = mean_std_cross_val_scores(\n",
    "    pipe, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results_dict).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Using a `Pipeline` takes care of applying the `fit_transform` on the train portion and only `transform` on the validation portion in each fold.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Categorical features [[video](https://youtu.be/2mJ9rAhMMl0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Recall that we had dropped the categorical feature `ocean_proximity` feature from the dataframe. But it could potentially be a useful feature in this task. \n",
    "\n",
    "- Let's create our `X_train` and and `X_test` again by keeping the feature in the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>-117.75</td>\n",
       "      <td>34.04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>4.897010</td>\n",
       "      <td>1.056478</td>\n",
       "      <td>4.318937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20113</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>37.94</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14289</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.74</td>\n",
       "      <td>46.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2.6604</td>\n",
       "      <td>4.738701</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>2.057910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13665</th>\n",
       "      <td>-117.31</td>\n",
       "      <td>34.02</td>\n",
       "      <td>18.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>5.2139</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>0.961404</td>\n",
       "      <td>3.154386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14471</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>1.8580</td>\n",
       "      <td>3.817558</td>\n",
       "      <td>1.004801</td>\n",
       "      <td>4.323045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3.6389</td>\n",
       "      <td>5.584615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15377</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>33.37</td>\n",
       "      <td>14.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>4.5391</td>\n",
       "      <td>6.016688</td>\n",
       "      <td>1.017972</td>\n",
       "      <td>3.127086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17730</th>\n",
       "      <td>-121.76</td>\n",
       "      <td>37.33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>5.6306</td>\n",
       "      <td>5.958393</td>\n",
       "      <td>1.031564</td>\n",
       "      <td>3.493544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.78</td>\n",
       "      <td>44.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>3.8750</td>\n",
       "      <td>4.739264</td>\n",
       "      <td>1.024540</td>\n",
       "      <td>1.720859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19966</th>\n",
       "      <td>-119.08</td>\n",
       "      <td>36.21</td>\n",
       "      <td>20.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.5156</td>\n",
       "      <td>5.491379</td>\n",
       "      <td>1.117816</td>\n",
       "      <td>3.566092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18576 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  households  median_income  \\\n",
       "6051     -117.75     34.04                22.0       602.0         3.1250   \n",
       "20113    -119.57     37.94                17.0        20.0         3.4861   \n",
       "14289    -117.13     32.74                46.0       708.0         2.6604   \n",
       "13665    -117.31     34.02                18.0       285.0         5.2139   \n",
       "14471    -117.23     32.88                18.0      1458.0         1.8580   \n",
       "...          ...       ...                 ...         ...            ...   \n",
       "7763     -118.10     33.91                36.0       130.0         3.6389   \n",
       "15377    -117.24     33.37                14.0       779.0         4.5391   \n",
       "17730    -121.76     37.33                 5.0       697.0         5.6306   \n",
       "15725    -122.44     37.78                44.0       326.0         3.8750   \n",
       "19966    -119.08     36.21                20.0       348.0         2.5156   \n",
       "\n",
       "       rooms_per_household  bedrooms_per_household  population_per_household  \n",
       "6051              4.897010                1.056478                  4.318937  \n",
       "20113            17.300000                6.500000                  2.550000  \n",
       "14289             4.738701                1.084746                  2.057910  \n",
       "13665             5.733333                0.961404                  3.154386  \n",
       "14471             3.817558                1.004801                  4.323045  \n",
       "...                    ...                     ...                       ...  \n",
       "7763              5.584615                     NaN                  3.769231  \n",
       "15377             6.016688                1.017972                  3.127086  \n",
       "17730             5.958393                1.031564                  3.493544  \n",
       "15725             4.739264                1.024540                  1.720859  \n",
       "19966             5.491379                1.117816                  3.566092  \n",
       "\n",
       "[18576 rows x 8 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_df.drop(columns=[\"median_house_value\"])\n",
    "y_train = train_df[\"median_house_value\"]\n",
    "\n",
    "X_test = test_df.drop(columns=[\"median_house_value\"])\n",
    "y_test = test_df[\"median_house_value\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's try to build a `KNeighborRegressor` on this data using our pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pipe.fit(X_train, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- This failed because we have non-numeric data. \n",
    "- Imagine how $k$-NN would calculate distances when you have non-numeric features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Can we use this feature in the model? \n",
    "- In `scikit-learn`, most algorithms require numeric inputs.\n",
    "- Decision trees could theoretically work with categorical features.  \n",
    "    - However, the sklearn implementation does not support this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### What are the options? \n",
    "\n",
    "- Drop the column (not recommended)\n",
    "    - If you know that the column is not relevant to the target in any way you may drop it. \n",
    "- We can transform categorical features to numeric ones so that we can use them in the model.     \n",
    "    - [Ordinal encoding](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html) (occasionally recommended)\n",
    "    - One-hot encoding (recommended in most cases) (this lecture)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Mandarin</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>French</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spanish</td>\n",
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       "      <th>12</th>\n",
       "      <td>Mandarin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hindi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      language\n",
       "0      English\n",
       "1   Vietnamese\n",
       "2      English\n",
       "3     Mandarin\n",
       "4      English\n",
       "5      English\n",
       "6     Mandarin\n",
       "7      English\n",
       "8   Vietnamese\n",
       "9     Mandarin\n",
       "10      French\n",
       "11     Spanish\n",
       "12    Mandarin\n",
       "13       Hindi"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_toy = pd.DataFrame(\n",
    "    {\n",
    "        \"language\": [\n",
    "            \"English\",\n",
    "            \"Vietnamese\",\n",
    "            \"English\",\n",
    "            \"Mandarin\",\n",
    "            \"English\",\n",
    "            \"English\",\n",
    "            \"Mandarin\",\n",
    "            \"English\",\n",
    "            \"Vietnamese\",\n",
    "            \"Mandarin\",\n",
    "            \"French\",\n",
    "            \"Spanish\",\n",
    "            \"Mandarin\",\n",
    "            \"Hindi\",\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "X_toy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ordinal encoding (occasionally recommended)\n",
    "\n",
    "- Here we simply assign an integer to each of our unique categorical labels. \n",
    "- We can use sklearn's [`OrdinalEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>language_enc</th>\n",
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       "      <th>8</th>\n",
       "      <td>Vietnamese</td>\n",
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       "      <th>9</th>\n",
       "      <td>Mandarin</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>French</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mandarin</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hindi</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      language  language_enc\n",
       "0      English           0.0\n",
       "1   Vietnamese           5.0\n",
       "2      English           0.0\n",
       "3     Mandarin           3.0\n",
       "4      English           0.0\n",
       "5      English           0.0\n",
       "6     Mandarin           3.0\n",
       "7      English           0.0\n",
       "8   Vietnamese           5.0\n",
       "9     Mandarin           3.0\n",
       "10      French           1.0\n",
       "11     Spanish           4.0\n",
       "12    Mandarin           3.0\n",
       "13       Hindi           2.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "enc = OrdinalEncoder()\n",
    "enc.fit(X_toy)\n",
    "X_toy_ord = enc.transform(X_toy)\n",
    "df = pd.DataFrame(\n",
    "    data=X_toy_ord,\n",
    "    columns=[\"language_enc\"],\n",
    "    index=X_toy.index,\n",
    ")\n",
    "pd.concat([X_toy, df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "What's the problem with this approach? \n",
    "- We have imposed ordinality on the categorical data.\n",
    "- For example, imagine when you are calculating distances. Is it fair to say that French and Hindi are closer than French and Spanish? \n",
    "- In general, label encoding is useful if there is ordinality in your data and capturing it is important for your problem, e.g., `[cold, warm, hot]`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### One-hot encoding (OHE)\n",
    "- Create new binary columns to represent our categories.\n",
    "- If we have $c$ categories in our column.\n",
    "    - We create $c$ new binary columns to represent those categories.\n",
    "- Example: Imagine a language column which has the information on whether you \n",
    "\n",
    "- We can use sklearn's [`OneHotEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "One-hot encoding is called one-hot because only one of the newly created features is 1 for each data point. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kvarada/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
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      ],
      "text/plain": [
       "    language_English  language_French  language_Hindi  language_Mandarin  \\\n",
       "0                1.0              0.0             0.0                0.0   \n",
       "1                0.0              0.0             0.0                0.0   \n",
       "2                1.0              0.0             0.0                0.0   \n",
       "3                0.0              0.0             0.0                1.0   \n",
       "4                1.0              0.0             0.0                0.0   \n",
       "5                1.0              0.0             0.0                0.0   \n",
       "6                0.0              0.0             0.0                1.0   \n",
       "7                1.0              0.0             0.0                0.0   \n",
       "8                0.0              0.0             0.0                0.0   \n",
       "9                0.0              0.0             0.0                1.0   \n",
       "10               0.0              1.0             0.0                0.0   \n",
       "11               0.0              0.0             0.0                0.0   \n",
       "12               0.0              0.0             0.0                1.0   \n",
       "13               0.0              0.0             1.0                0.0   \n",
       "\n",
       "    language_Spanish  language_Vietnamese  \n",
       "0                0.0                  0.0  \n",
       "1                0.0                  1.0  \n",
       "2                0.0                  0.0  \n",
       "3                0.0                  0.0  \n",
       "4                0.0                  0.0  \n",
       "5                0.0                  0.0  \n",
       "6                0.0                  0.0  \n",
       "7                0.0                  0.0  \n",
       "8                0.0                  1.0  \n",
       "9                0.0                  0.0  \n",
       "10               0.0                  0.0  \n",
       "11               1.0                  0.0  \n",
       "12               0.0                  0.0  \n",
       "13               0.0                  0.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "enc = OneHotEncoder(handle_unknown=\"ignore\", sparse=False)\n",
    "enc.fit(X_toy)\n",
    "X_toy_ohe = enc.transform(X_toy)\n",
    "pd.DataFrame(\n",
    "    data=X_toy_ohe,\n",
    "    columns=enc.get_feature_names([\"language\"]),\n",
    "    index=X_toy.index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Let's do it on our housing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder(sparse=False, dtype=\"int\")\n",
    "ohe.fit(X_train[[\"ocean_proximity\"]])\n",
    "X_imp_ohe_train = ohe.transform(X_train[[\"ocean_proximity\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can look at the new features created using `categories_` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kvarada/opt/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
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       "       ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  \\\n",
       "6051                           0                       1   \n",
       "20113                          0                       1   \n",
       "14289                          0                       0   \n",
       "13665                          0                       1   \n",
       "14471                          0                       0   \n",
       "...                          ...                     ...   \n",
       "7763                           1                       0   \n",
       "15377                          1                       0   \n",
       "17730                          1                       0   \n",
       "15725                          0                       0   \n",
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       "\n",
       "       ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n",
       "6051                        0                         0   \n",
       "20113                       0                         0   \n",
       "14289                       0                         0   \n",
       "13665                       0                         0   \n",
       "14471                       0                         0   \n",
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       "13665                           0  \n",
       "14471                           1  \n",
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       "17730                           0  \n",
       "15725                           0  \n",
       "19966                           0  \n",
       "\n",
       "[18576 rows x 5 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_ohe = pd.DataFrame(\n",
    "    data=X_imp_ohe_train,\n",
    "    columns=ohe.get_feature_names([\"ocean_proximity\"]),\n",
    "    index=X_train.index,\n",
    ")\n",
    "transformed_ohe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{seealso} \n",
    "One-hot encoded variables are also referred to as **dummy variables**. \n",
    "You will often see people using [`get_dummies` method of pandas](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html) to convert categorical variables into dummy variables. That said, using `sklearn`'s `OneHotEncoder` has the advantage of making it easy to treat training and test set in a consistent way.  \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### (iClicker) Exercise 5.2\n",
    "\n",
    "**iClicker cloud join link: https://join.iclicker.com/3DP5H**\n",
    "\n",
    "**Select all of the following statements which are TRUE.**\n",
    "\n",
    "1. You can have scaling of numeric features, one-hot encoding of categorical features, and `scikit-learn` estimator within a single pipeline.  \n",
    "2. Once you have a `scikit-learn` pipeline object with an estimator as the last step, you can call `fit`, `predict`, and `score` on it.\n",
    "3. You can carry out data splitting within `scikit-learn` pipeline. \n",
    "4. We have to be careful of the order we put each transformation and model in a pipeline.\n",
    "5. If you call `cross_validate` with a pipeline object, it will call `fit` and `transform` on the training fold and only `transform` on the validation fold. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What did we learn today? \n",
    "\n",
    "- Motivation for preprocessing\n",
    "- Common preprocessing steps\n",
    "    - Imputation \n",
    "    - Scaling\n",
    "    - One-hot encoding\n",
    "- Golden rule in the context of preprocessing\n",
    "- Building simple supervised machine learning pipelines using `sklearn.pipeline.make_pipeline`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Problem: Different transformations on different columns\n",
    "- How do we put this together with other columns in the data before fitting the regressor? \n",
    "- Before we fit our regressor, we want to apply different transformations on different columns \n",
    "    - Numeric columns\n",
    "        - imputation \n",
    "        - scaling         \n",
    "    - Categorical columns \n",
    "        - imputation \n",
    "        - one-hot encoding        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Coming up: sklearn's [`ColumnTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)!!** "
   ]
  }
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