lab1/lab_1.ipynb

{
   "cells": [
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "# Regresja liniowa i logistyczna"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Wstęp\n",
            "\n",
            "Celem tego laboratorium będzie stworzenie modelu uczenia maszynowego do estymacji cen nieruchomości na podstawie danych o jej położeniu, ilości sypialń, roku budowy, typie budynku oraz wielu innych parametrów.\n",
            "\n",
            "![house-price-gif](house-price.gif)\n",
            "\n",
            "W trakcie realizacji tego labratorium zapoznamy się z następującymi zagadnieniami:\n",
            "\n",
            "* przygotowaniem danych:\n",
            "    * ładowaniem danych,\n",
            "    * typami danych,\n",
            "    * czyszczeniem danych,\n",
            "    * rozkładami danych,\n",
            "    * obsługą wartości brakujących,\n",
            "    * zmiennymi kategorycznymi uporządkowanymi i nieuporządkowanymi,\n",
            "    * skalowaniem wartości,\n",
            "    * API biblioteki Scikit-Learn dla transformacji danych;\n",
            "* regresją liniową, w szczególności z:\n",
            "    * podziałem zbioru na część treningową i testową,\n",
            "    * oceną jakości modelu,\n",
            "    * walidacją skrośną,\n",
            "    * wyszukiwaniem hiperparametrów,\n",
            "    * problemem przeuczenia, niedouczenia,\n",
            "    * regularyzacją L1 i L2,\n",
            "    * regresją wielomianową;\n",
            "* regresją logistyczną, w szczególności z:\n",
            "    * różnymi rodzajami błędów klasyfikacji,\n",
            "    * metrykami oceniającymi jakość klasyfikatorów.\n",
            "\n",
            "Na pierwszych zajęciach możesz korzystać ze środowiska Google Colab i zdalnego środowiska obliczeniowego. Jeżeli interesuje Cię skonfigurowanie Pythona  na własnym komputerze, to niezbędne informacje są podane w sekcji \"Konfiguracja własnego komputera\".\n",
            "\n",
            "**Uwaga:** niektóre zadania zamiast kodu wymagają podania pisemnej odpowiedzi w miejscu oznaczonym `// skomentuj tutaj`."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Wykorzystywane biblioteki\n",
            "\n",
            "Na zajęciach korzystać będziesz z kilku popularnych bibliotek Pythona, które umożliwiają klasyfikację danych, ich wizualizację czy preprocessing. Są to:\n",
            "\n",
            "* [numpy](https://numpy.org/) - bibliotek do wykonywania obliczeń macierzowych. Pozwala na efektywne przeprowadzanie obliczeń naukowych. Dobrze współgra z biblioteką pandas.\n",
            "* [pandas](https://pandas.pydata.org/) - narzędzie do analizy danych tabelarycznych, ich strukturyzowania oraz manipulacji na nich.\n",
            "* [sklearn](https://scikit-learn.org/stable/) - narzędzie do tworzenia modeli klasyfikacji, regresji, clusteringu itp. Biblioteka ta jest dość rozbudowana i pozwala także na mapowanie danych czy redukcję wymiarów. Więcej informacji znajdziesz w podanym linku.\n",
            "* [missingno](https://pypi.org/project/missingno/) - narzędzie do wizualizacji kompletności danych (brakujących wartości).\n",
            "* [seaborn](https://seaborn.pydata.org/) - kompleksowe narzędzie do wizualizacji danych jako takich. Pozwala na stworzenie bardzo szerokiej gamy wykresów w zależności od potrzeb.\n",
            "\n",
            "Zostały tutaj pominięte pewne standardowe biblioteki jak np. `os` czy `matplotlib`."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Wykorzystanie Google Colab\n",
            "\n",
            "Korzystanie Google Colab nie jest wymagane. W niektórych laboratorich może być jednak przydatny dostęp do środowiska wyposażonego w kartę GPU.\n",
            "\n",
            "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/apohllo/sztuczna-inteligencja/blob/master/lab1/lab_1.ipynb)\n",
            "\n",
            "Jeżeli pracujesz na Google Colab, zacznij od przeniesienia dwóch plików CSV, które zostały dołączone do laboratorium ([ames_data.csv](ames_data.csv) oraz [bank_marketing_data.csv](bank_marketing_data.csv)), do folderu `/content`. Nie musisz ich umieszczać w `/content/sample_data` - ważne, aby znalazły się w `/content`. Jeżeli pracujesz lokalnie, to wystarczy, że pliki te będą obok tego notebooka.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Konfiguracja własnego komputera\n",
            "\n",
            "Jeżeli korzystasz z własnego komputera, to musisz zainstalować trochę więcej bibliotek (Google Colab ma je już zainstalowane). Najlepiej używać Pythona 3.9 lub nowszej wersji. Laboratorium było testowane z wersją 3.9."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Poetry\n",
            "\n",
            "Narzędzie [poetry](https://python-poetry.org/) zyskuje dużą popularność w zakresie zarządzania zależnościami w Pythonie.\n",
            "Projekt posiada pliku `pyproject.toml` oraz `poetry.lock`, które zawierają informacje o zależnościach.\n",
            "\n",
            "Użycie Poetry sprawadza się do następujących poleceń.\n",
            "\n",
            "1. Instalacja Poetry w środowisku:\n",
            "   * `pip install poetry`\n",
            "2. Zainstalowanie zależności (w katalogu głównym projektu):\n",
            "   * `poetry install --no-root`\n",
            "3. Po konfiguracji możemy uruchamiać Jupyter Lab poleceniem:\n",
            "   * `poetry run jupyter lab`\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Anaconda\n",
            "\n",
            "Jeżeli korzystasz z Anacondy (możesz uruchomić w terminalu):"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 1,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T17:35:49.000694662Z",
               "start_time": "2023-09-15T17:35:48.923664169Z"
            }
         },
         "outputs": [],
         "source": [
            "# !conda install -c conda-forge --yes numpy pandas scikit-learn matplotlib missingno"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### venv\n",
            "\n",
            "Jeżeli używasz zwykłego venv'a (**zdecydowanie niezalecane, szczególnie na Windowsie**):"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 2,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T17:35:49.476264618Z",
               "start_time": "2023-09-15T17:35:49.441019250Z"
            }
         },
         "outputs": [],
         "source": [
            "# !pip install --yes numpy pandas scikit-learn matplotlib missingno"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "W przypadku własnego komputera, jeżeli instalowałeś z terminala, pamiętaj, aby zarejestrować aktualne środowisko wirtualne jako kernel (środowisko uruchomieniowe) dla Jupyter Notebooka. Wybierz go jako używany kernel w menu na górze notebooka (nazwa jak w komendzie poniżej)."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 3,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T17:35:50.025546447Z",
               "start_time": "2023-09-15T17:35:49.999161583Z"
            }
         },
         "outputs": [],
         "source": [
            "# !ipython kernel install --user --name \"PSI\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Zbiór danych do regresji"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Wykorzystamy zbiór danych [Ames housing](https://www.openintro.org/book/statdata/?data=ames), w którym zadaniem jest przewidywanie wartości domu na podstawie cech budynku, działki, lokalizacji itp. Jest to więc przewidywanie wartości ciągłej, czyli regresja. Zbiór ten zawiera zmienne numeryczne (floaty i inty), kategoryczne nieuporządkowane (*categorical nominal*) oraz kategoryczne uporządkowane (*categorical ordinal*), więc będzie wymagał wstępnego przetworzenia tak jak większość prawdziwych danych w uczeniu maszynowym.\n",
            "\n",
            "Inne znane, ale gorsze jakościowo zbiory tego typu, to na przykład:\n",
            "- Boston housing - rasistowski, z tego powodu usunięty np. ze Scikit-learn ([wyjaśnienie](https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html), [dyskusja](https://github.com/quantumblacklabs/causalnex/issues/92), [badanie](https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8))\n",
            "- California housing - zbyt prosty (tylko kilka zmiennych numerycznych), użyty np. w książce \"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow\" A. Geron ([opis](https://inria.github.io/scikit-learn-mooc/python_scripts/datasets_california_housing.html))\n",
            "\n",
            "Autor zbioru to Dean De Cock, a zbiór został opisany oryginalnie w [tym artykule](https://jse.amstat.org/v19n3/decock.pdf). "
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 4,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T18:11:58.167689459Z",
               "start_time": "2023-09-15T18:11:57.378074504Z"
            }
         },
         "outputs": [],
         "source": [
            "import matplotlib.pyplot as plt\n",
            "import numpy as np\n",
            "import pandas as pd"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Ładowanie danych tabelarycznych"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "\n",
            "Pliki [ames_data.csv](ames_data.csv) oraz [bank_marketing_data.csv](bank_marketing_data.csv) to dwa zbiory danych, niezależne od siebie. Pierwszy jest wykorzystywany w pierwszej części laboratorium (regresji liniowej), natomiast drugi przyda się przy regresji logistycznej (klasyfikacji). Jego celem jest przewidywanie wartości domu.\n",
            "\n",
            "Wczytajmy dane `ames_data.csv` do zmiennej `df` (takiej nazwy często się używa, żeby oznaczyć obiekt `DataFrame` - zaawansowanej tablicy, dostarczonej nam przez bibliotekę `pandas`)."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 5,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:54.649270399Z",
               "start_time": "2023-09-15T21:17:54.491837233Z"
            }
         },
         "outputs": [],
         "source": [
            "df = pd.read_csv(\"ames_data.csv\")\n",
            "\n",
            "# remove dots from names to match data_description.txt\n",
            "df.columns = [col.replace(\".\", \"\") for col in df.columns]"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Zobaczmy jakie dane znajdują się w naszej tabeli. Wykorzystajmy do tego metodę `info()`."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 6,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:54.959663968Z",
               "start_time": "2023-09-15T21:17:54.871675198Z"
            }
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "<class 'pandas.core.frame.DataFrame'>\n",
                  "RangeIndex: 2930 entries, 0 to 2929\n",
                  "Data columns (total 82 columns):\n",
                  " #   Column         Non-Null Count  Dtype  \n",
                  "---  ------         --------------  -----  \n",
                  " 0   Order          2930 non-null   int64  \n",
                  " 1   PID            2930 non-null   int64  \n",
                  " 2   MSSubClass     2930 non-null   int64  \n",
                  " 3   MSZoning       2930 non-null   object \n",
                  " 4   LotFrontage    2440 non-null   float64\n",
                  " 5   LotArea        2930 non-null   int64  \n",
                  " 6   Street         2930 non-null   object \n",
                  " 7   Alley          198 non-null    object \n",
                  " 8   LotShape       2930 non-null   object \n",
                  " 9   LandContour    2930 non-null   object \n",
                  " 10  Utilities      2930 non-null   object \n",
                  " 11  LotConfig      2930 non-null   object \n",
                  " 12  LandSlope      2930 non-null   object \n",
                  " 13  Neighborhood   2930 non-null   object \n",
                  " 14  Condition1     2930 non-null   object \n",
                  " 15  Condition2     2930 non-null   object \n",
                  " 16  BldgType       2930 non-null   object \n",
                  " 17  HouseStyle     2930 non-null   object \n",
                  " 18  OverallQual    2930 non-null   int64  \n",
                  " 19  OverallCond    2930 non-null   int64  \n",
                  " 20  YearBuilt      2930 non-null   int64  \n",
                  " 21  YearRemodAdd   2930 non-null   int64  \n",
                  " 22  RoofStyle      2930 non-null   object \n",
                  " 23  RoofMatl       2930 non-null   object \n",
                  " 24  Exterior1st    2930 non-null   object \n",
                  " 25  Exterior2nd    2930 non-null   object \n",
                  " 26  MasVnrType     1155 non-null   object \n",
                  " 27  MasVnrArea     2907 non-null   float64\n",
                  " 28  ExterQual      2930 non-null   object \n",
                  " 29  ExterCond      2930 non-null   object \n",
                  " 30  Foundation     2930 non-null   object \n",
                  " 31  BsmtQual       2850 non-null   object \n",
                  " 32  BsmtCond       2850 non-null   object \n",
                  " 33  BsmtExposure   2847 non-null   object \n",
                  " 34  BsmtFinType1   2850 non-null   object \n",
                  " 35  BsmtFinSF1     2929 non-null   float64\n",
                  " 36  BsmtFinType2   2849 non-null   object \n",
                  " 37  BsmtFinSF2     2929 non-null   float64\n",
                  " 38  BsmtUnfSF      2929 non-null   float64\n",
                  " 39  TotalBsmtSF    2929 non-null   float64\n",
                  " 40  Heating        2930 non-null   object \n",
                  " 41  HeatingQC      2930 non-null   object \n",
                  " 42  CentralAir     2930 non-null   object \n",
                  " 43  Electrical     2929 non-null   object \n",
                  " 44  X1stFlrSF      2930 non-null   int64  \n",
                  " 45  X2ndFlrSF      2930 non-null   int64  \n",
                  " 46  LowQualFinSF   2930 non-null   int64  \n",
                  " 47  GrLivArea      2930 non-null   int64  \n",
                  " 48  BsmtFullBath   2928 non-null   float64\n",
                  " 49  BsmtHalfBath   2928 non-null   float64\n",
                  " 50  FullBath       2930 non-null   int64  \n",
                  " 51  HalfBath       2930 non-null   int64  \n",
                  " 52  BedroomAbvGr   2930 non-null   int64  \n",
                  " 53  KitchenAbvGr   2930 non-null   int64  \n",
                  " 54  KitchenQual    2930 non-null   object \n",
                  " 55  TotRmsAbvGrd   2930 non-null   int64  \n",
                  " 56  Functional     2930 non-null   object \n",
                  " 57  Fireplaces     2930 non-null   int64  \n",
                  " 58  FireplaceQu    1508 non-null   object \n",
                  " 59  GarageType     2773 non-null   object \n",
                  " 60  GarageYrBlt    2771 non-null   float64\n",
                  " 61  GarageFinish   2771 non-null   object \n",
                  " 62  GarageCars     2929 non-null   float64\n",
                  " 63  GarageArea     2929 non-null   float64\n",
                  " 64  GarageQual     2771 non-null   object \n",
                  " 65  GarageCond     2771 non-null   object \n",
                  " 66  PavedDrive     2930 non-null   object \n",
                  " 67  WoodDeckSF     2930 non-null   int64  \n",
                  " 68  OpenPorchSF    2930 non-null   int64  \n",
                  " 69  EnclosedPorch  2930 non-null   int64  \n",
                  " 70  X3SsnPorch     2930 non-null   int64  \n",
                  " 71  ScreenPorch    2930 non-null   int64  \n",
                  " 72  PoolArea       2930 non-null   int64  \n",
                  " 73  PoolQC         13 non-null     object \n",
                  " 74  Fence          572 non-null    object \n",
                  " 75  MiscFeature    106 non-null    object \n",
                  " 76  MiscVal        2930 non-null   int64  \n",
                  " 77  MoSold         2930 non-null   int64  \n",
                  " 78  YrSold         2930 non-null   int64  \n",
                  " 79  SaleType       2930 non-null   object \n",
                  " 80  SaleCondition  2930 non-null   object \n",
                  " 81  SalePrice      2930 non-null   int64  \n",
                  "dtypes: float64(11), int64(28), object(43)\n",
                  "memory usage: 1.8+ MB\n"
               ]
            }
         ],
         "source": [
            "df.info()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Mamy naprawdę dużo cech! Ich szczegółowy opis znajdziesz w dołączonym do laboratorium pliku [ames_description.txt](ames_description.txt)."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Wstępna analiza danych"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Zawsze, zanim zaczniesz robić jakąkolwiek predykcję czy analizę danych, dobrze jest zapoznać się z nimi, z ich kodowaniem i znaczeniem. Kolejnym istotnym aspektem jest typ danych. Nie każdy klasyfikator nadaje się do każdego typu.\n",
            "\n",
            "Wyświetlmy teraz kilka przykładowych rekordów z początku pliku, korzystając z metody `head()`."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 7,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:55.507220376Z",
               "start_time": "2023-09-15T21:17:55.401007891Z"
            }
         },
         "outputs": [
            {
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                     "      <th>PID</th>\n",
                     "      <th>MSSubClass</th>\n",
                     "      <th>MSZoning</th>\n",
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                     "      <th>LotArea</th>\n",
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                     "      <th>Alley</th>\n",
                     "      <th>LotShape</th>\n",
                     "      <th>LandContour</th>\n",
                     "      <th>...</th>\n",
                     "      <th>PoolArea</th>\n",
                     "      <th>PoolQC</th>\n",
                     "      <th>Fence</th>\n",
                     "      <th>MiscFeature</th>\n",
                     "      <th>MiscVal</th>\n",
                     "      <th>MoSold</th>\n",
                     "      <th>YrSold</th>\n",
                     "      <th>SaleType</th>\n",
                     "      <th>SaleCondition</th>\n",
                     "      <th>SalePrice</th>\n",
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                     "      <td>5</td>\n",
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                     "      <td>80.0</td>\n",
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                     "      <td>Pave</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>Reg</td>\n",
                     "      <td>Lvl</td>\n",
                     "      <td>...</td>\n",
                     "      <td>0</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>MnPrv</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>0</td>\n",
                     "      <td>6</td>\n",
                     "      <td>2010</td>\n",
                     "      <td>WD</td>\n",
                     "      <td>Normal</td>\n",
                     "      <td>105000</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>2</th>\n",
                     "      <td>3</td>\n",
                     "      <td>526351010</td>\n",
                     "      <td>20</td>\n",
                     "      <td>RL</td>\n",
                     "      <td>81.0</td>\n",
                     "      <td>14267</td>\n",
                     "      <td>Pave</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>IR1</td>\n",
                     "      <td>Lvl</td>\n",
                     "      <td>...</td>\n",
                     "      <td>0</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>Gar2</td>\n",
                     "      <td>12500</td>\n",
                     "      <td>6</td>\n",
                     "      <td>2010</td>\n",
                     "      <td>WD</td>\n",
                     "      <td>Normal</td>\n",
                     "      <td>172000</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>3</th>\n",
                     "      <td>4</td>\n",
                     "      <td>526353030</td>\n",
                     "      <td>20</td>\n",
                     "      <td>RL</td>\n",
                     "      <td>93.0</td>\n",
                     "      <td>11160</td>\n",
                     "      <td>Pave</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>Reg</td>\n",
                     "      <td>Lvl</td>\n",
                     "      <td>...</td>\n",
                     "      <td>0</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>0</td>\n",
                     "      <td>4</td>\n",
                     "      <td>2010</td>\n",
                     "      <td>WD</td>\n",
                     "      <td>Normal</td>\n",
                     "      <td>244000</td>\n",
                     "    </tr>\n",
                     "    <tr>\n",
                     "      <th>4</th>\n",
                     "      <td>5</td>\n",
                     "      <td>527105010</td>\n",
                     "      <td>60</td>\n",
                     "      <td>RL</td>\n",
                     "      <td>74.0</td>\n",
                     "      <td>13830</td>\n",
                     "      <td>Pave</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>IR1</td>\n",
                     "      <td>Lvl</td>\n",
                     "      <td>...</td>\n",
                     "      <td>0</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>MnPrv</td>\n",
                     "      <td>NaN</td>\n",
                     "      <td>0</td>\n",
                     "      <td>3</td>\n",
                     "      <td>2010</td>\n",
                     "      <td>WD</td>\n",
                     "      <td>Normal</td>\n",
                     "      <td>189900</td>\n",
                     "    </tr>\n",
                     "  </tbody>\n",
                     "</table>\n",
                     "<p>5 rows × 82 columns</p>\n",
                     "</div>"
                  ],
                  "text/plain": [
                     "   Order        PID  MSSubClass MSZoning  LotFrontage  LotArea Street Alley  \\\n",
                     "0      1  526301100          20       RL        141.0    31770   Pave   NaN   \n",
                     "1      2  526350040          20       RH         80.0    11622   Pave   NaN   \n",
                     "2      3  526351010          20       RL         81.0    14267   Pave   NaN   \n",
                     "3      4  526353030          20       RL         93.0    11160   Pave   NaN   \n",
                     "4      5  527105010          60       RL         74.0    13830   Pave   NaN   \n",
                     "\n",
                     "  LotShape LandContour  ... PoolArea PoolQC  Fence MiscFeature MiscVal MoSold  \\\n",
                     "0      IR1         Lvl  ...        0    NaN    NaN         NaN       0      5   \n",
                     "1      Reg         Lvl  ...        0    NaN  MnPrv         NaN       0      6   \n",
                     "2      IR1         Lvl  ...        0    NaN    NaN        Gar2   12500      6   \n",
                     "3      Reg         Lvl  ...        0    NaN    NaN         NaN       0      4   \n",
                     "4      IR1         Lvl  ...        0    NaN  MnPrv         NaN       0      3   \n",
                     "\n",
                     "  YrSold SaleType  SaleCondition  SalePrice  \n",
                     "0   2010      WD          Normal     215000  \n",
                     "1   2010      WD          Normal     105000  \n",
                     "2   2010      WD          Normal     172000  \n",
                     "3   2010      WD          Normal     244000  \n",
                     "4   2010      WD          Normal     189900  \n",
                     "\n",
                     "[5 rows x 82 columns]"
                  ]
               },
               "execution_count": 7,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "df.head()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Jeżeli potrzebujesz szybko stwierdzić, ile dane zawierają rekordów i kolumn, pomocna jest opcja `shape`:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 8,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:55.871716894Z",
               "start_time": "2023-09-15T21:17:55.857639555Z"
            }
         },
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "(2930, 82)"
                  ]
               },
               "execution_count": 8,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "df.shape"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Eksploracja danych, czyszczenie danych i inżynieria cech"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Usunięcie niepotrzebnych kolumn"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Niektóre kolumny są **nieinformatywne (uninformative)**, czyli nie niosą żadnej informacji dla zadania, czyli przewidywania wartości domu. Są pewnym rodzajem metadanych. Przykładowo mamy tutaj kolumny **Order** oraz **PID**.\n",
            "\n",
            "**Order** jest po prostu numerem rekordu w zbiorze danych, moglibyśmy przetasować cały zbiór i to nie powinno w żaden sposób wpłynąć na cokolwiek, a więc możemy spokojnie tę kolumnę usunąć.\n",
            "\n",
            "Formalnie czynimy założenie, że rekordy w naszych danych (próbki / wiersze, poszczególne domy w przypadku tego zbioru) są **niezależne i równomiernie rozłożone** (ang. **independent and identically distributed - i.i.d.**). Innymi słowy, kolejność w danych nie ma znaczenia, bo zbieraliśmy dane taką samą metodą i w identycznych warunkach. Jest to bardzo typowe w ML.\n",
            "\n",
            "**PID** jest po prostu numerem identyfikacyjnym danej nieruchomości w systemie informatycznym, a więc też możemy to usunąć."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 9,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:56.307582736Z",
               "start_time": "2023-09-15T21:17:56.154655657Z"
            }
         },
         "outputs": [],
         "source": [
            "df = df.drop(['Order', \"PID\"], axis=\"columns\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Usunięcie słabo reprezentowanych dzielnic"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Dzielnice *GrnHill* oraz *Landmrk* obejmują w sumie zaledwie 3 domy."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 10,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:56.788664312Z",
               "start_time": "2023-09-15T21:17:56.700703449Z"
            }
         },
         "outputs": [],
         "source": [
            "df = df.loc[~df[\"Neighborhood\"].isin([\"GrnHill\", \"Landmrk\"]), :]"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Usunięcie obserwacji odstających (outliers)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Usuniemy budynki, które mają powyżej 4000 stóp kwadratowych (ok. 370 metrów kwadratowych) powierzchni. Możemy zobaczyć je na wykresie poniżej. "
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 11,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:58.554777139Z",
               "start_time": "2023-09-15T21:17:58.245198347Z"
            }
         },
         "outputs": [
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plt.scatter(df[\"GrLivArea\"], df[\"SalePrice\"])\n",
            "plt.title(\"House area vs price\")\n",
            "plt.xlabel(\"GrLivArea\")\n",
            "plt.ylabel(\"SalePrice\")\n",
            "plt.show()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Jak widać na wykresie, jest dosłownie kilka domów o tej powierzhcni. Takie skrajne przypadki raczej nas nie interesują - a na pewno stanowią problem dla tak prostego modelu jak regresja liniowa. Nie chcemy też, żeby nasz model uczył się takich anomalii, więc lepiej je usunąć.\n",
            "\n",
            "Tutaj robimy to ręcznie, ale istnieją też algorytmy do detekcji i usuwania obserwacji odstających."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 1 (0.25 punktu)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Usuń rekordy nieruchomości o powierzchni (**GrLivArea**) ponad (ostra nierówność) 4 tys. stóp kwadratowych.\n",
            "\n",
            "Podpowiedź: w Pandas korzysta się z `.loc[]` do filtrowania wierszy i kolumn. Pierwszy indeks oznacza, które wiersze zostawić, a drugi indeks, które kolumny wybrać. Jeżeli chcemy zostawić wszystko (np. nie usuwać żadnych kolumn), to zadziała standardowy Pythonowy `:`, jak przy indeksowaniu list."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 12,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:17:59.547691643Z",
               "start_time": "2023-09-15T21:17:59.468151807Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "df = df.loc[df[\"GrLivArea\"] < 4000, :]\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 13,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Solution is correct!\n"
               ]
            }
         ],
         "source": [
            "for e in df.itertuples():\n",
            "    assert 0 < e.GrLivArea < 4000\n",
            "\n",
            "print(\"Solution is correct!\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Zobaczmy jak teraz wygląda ten sam wykres."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 14,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:18:00.523344768Z",
               "start_time": "2023-09-15T21:18:00.374304098Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plt.scatter(df[\"GrLivArea\"], df[\"SalePrice\"])\n",
            "plt.title(\"House area vs price, outliers removed\")\n",
            "plt.xlabel(\"GrLivArea\")\n",
            "plt.ylabel(\"SalePrice\")\n",
            "plt.show()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Transformacja logarytmiczna zmiennej zależnej"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Zawsze warto też przyjrzeć się rozkładowi zmiennej docelowej, żeby poznać jej typ i skalę. Jak widać poniżej, rozkład jest dość skośny, co ma sens - mało jest bardzo drogich domów."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 15,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:18:01.827131630Z",
               "start_time": "2023-09-15T21:18:01.782774803Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "count      2922.000000\n",
                     "mean     180358.266940\n",
                     "std       78536.952287\n",
                     "min       12789.000000\n",
                     "25%      129425.000000\n",
                     "50%      160000.000000\n",
                     "75%      213430.000000\n",
                     "max      625000.000000\n",
                     "Name: SalePrice, dtype: float64"
                  ]
               },
               "execution_count": 15,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "df[\"SalePrice\"].describe()"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 16,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:18:03.121984012Z",
               "start_time": "2023-09-15T21:18:02.976688658Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "df[\"SalePrice\"].hist()\n",
            "plt.title(\"Original sale price\")\n",
            "plt.show()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Rozkład normalny jest zwykle korzystniejszy dla tworzenia modeli, bo daje sensowną \"wartość środkową\" do przewidywania, a także penalizuje tak samo błędy niezależnie od ich znaku (zaniżona i zawyżona predykcja). Dokonamy dlatego **transformacji logarytmicznej (log transform)**, czyli zlogarytmujemy zmienną docelową (zależną). Dla stabilności numerycznej używa się zwykle `np.log1p`, a nie `np.log` (tutaj [wyjaśnienie](https://stackoverflow.com/questions/49538185/purpose-of-numpy-log1p)).\n",
            "\n",
            "Dodatkowa korzyść z takiej transformacji jest taka, że regresja liniowa przewiduje dowolne wartości rzeczywiste. Po przekształceniu logarytmicznym jest to całkowicie ok, natomiast w oryginalnej przestrzeni trzeba by wymusić przewidywanie tylko wartości pozytywnych (negatywne ceny są bez sensu). Da się to zrobić, ale zwiększa to koszt obliczeniowy. Operowanie na tzw. log-price jest bardzo częste w finansach."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 2 (0.25 punktu) "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Przekształć zmienną **SalePrice** za pomocą funkcji logarytmicznej `np.log1p`."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 17,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:29:33.982163197Z",
               "start_time": "2023-09-15T21:29:33.815793944Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "df[\"SalePrice\"] = np.log1p(df[\"SalePrice\"])\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 18,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Solution is correct!\n"
               ]
            }
         ],
         "source": [
            "assert 9.0 <= df.loc[:, \"SalePrice\"].max() <= 14.0\n",
            "\n",
            "print(\"Solution is correct!\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Sprawdźmy teraz jak rozkład **SalePrice** wygląda po transformacji:"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 19,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:29:35.216598401Z",
               "start_time": "2023-09-15T21:29:35.051482952Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "pd.Series(np.log(df[\"SalePrice\"])).hist()\n",
            "plt.title(\"Log sale price\")\n",
            "plt.show()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Uzupełnianie wartości brakujących"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Sprawdźmy też wartości brakujące. Są zmienne, które mają poniżej 10% wartości - takie zmienne dla modeli regresji liniowej są po prostu bezużyteczne, ponieważ brakujących wartości nie można wprost zamodelować. Znacząca liczba cech ma jednak co najmniej 10% braków. Z nich będziemy jednak starali się zrobić użytek."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 20,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T21:37:04.538950013Z",
               "start_time": "2023-09-15T21:37:01.638196543Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "<Axes: >"
                  ]
               },
               "execution_count": 20,
               "metadata": {},
               "output_type": "execute_result"
            },
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 2500x2750 with 3 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "import missingno as msno\n",
            "\n",
            "msno.bar(df)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "W ramach dalszego czyszczenia danych automatycznie uzupełnimy wartości brakujące. Trzeba tu jednak wziąć pod uwagę:\n",
            "- zmienne kategoryczne - nie można w nich dokonać zastąpienia wartości brakującej średnią, medianą itp.\n",
            "- wiele brakujących wartości - estymacja modą czy medianą byłaby niedokładna,\n",
            "- możliwość wykorzystania wiedzy o innych zmiennych na podstawie opisu cech.\n",
            "\n",
            "Można więc zastosować odpowiednią wiedzę i przyjąć wartości domyślne. Przykładowo, brak informacji o powierzchni piwnicy możemy uznać po prostu za brak piwnicy i wpisać tam odpowiednią wartość. W przypadku niektórych zmiennych może doprowadzić to do stworzenia nowej wartości, która implicite będzie reprezentować wartość brakującą.\n",
            "\n",
            "Znaczna część poniższej analizy została zainspirowana [tym notebookiem na Kaggle](https://www.kaggle.com/code/juliencs/a-study-on-regression-applied-to-the-ames-dataset)."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 21,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T23:24:27.212091582Z",
               "start_time": "2023-09-15T23:24:27.122178104Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [],
         "source": [
            "def replace_na(df: pd.DataFrame, col: str, value) -> None:\n",
            "    df.loc[:, col] = df.loc[:, col].fillna(value)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 22,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T23:24:27.599968103Z",
               "start_time": "2023-09-15T23:24:27.445745089Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [],
         "source": [
            "# Alley : data description says NA means \"no alley access\"\n",
            "replace_na(df, \"Alley\", value=\"None\")\n",
            "\n",
            "# BedroomAbvGr : NA most likely means 0\n",
            "replace_na(df, \"BedroomAbvGr\", value=0)\n",
            "\n",
            "# BsmtQual etc : data description says NA for basement features is \"no basement\"\n",
            "replace_na(df, \"BsmtQual\", value=\"No\")\n",
            "replace_na(df, \"BsmtCond\", value=\"No\")\n",
            "replace_na(df, \"BsmtExposure\", value=\"No\")\n",
            "replace_na(df, \"BsmtFinType1\", value=\"No\")\n",
            "replace_na(df, \"BsmtFinType2\", value=\"No\")\n",
            "replace_na(df, \"BsmtFullBath\", value=0)\n",
            "replace_na(df, \"BsmtHalfBath\", value=0)\n",
            "replace_na(df, \"BsmtUnfSF\", value=0)\n",
            "\n",
            "# Condition : NA most likely means Normal\n",
            "replace_na(df, \"Condition1\", value=\"Norm\")\n",
            "replace_na(df, \"Condition2\", value=\"Norm\")\n",
            "\n",
            "# External stuff : NA most likely means average\n",
            "replace_na(df, \"ExterCond\", value=\"TA\")\n",
            "replace_na(df, \"ExterQual\", value=\"TA\")\n",
            "\n",
            "# Fence : data description says NA means \"no fence\"\n",
            "replace_na(df, \"Fence\", value=\"No\")\n",
            "\n",
            "# Functional : data description says NA means typical\n",
            "replace_na(df, \"Functional\", value=\"Typ\")\n",
            "\n",
            "# GarageType etc : data description says NA for garage features is \"no garage\"\n",
            "replace_na(df, \"GarageType\", value=\"No\")\n",
            "replace_na(df, \"GarageFinish\", value=\"No\")\n",
            "replace_na(df, \"GarageQual\", value=\"No\")\n",
            "replace_na(df, \"GarageCond\", value=\"No\")\n",
            "replace_na(df, \"GarageArea\", value=0)\n",
            "replace_na(df, \"GarageCars\", value=0)\n",
            "\n",
            "# HalfBath : NA most likely means no half baths above grade\n",
            "replace_na(df, \"HalfBath\", value=0)\n",
            "\n",
            "# HeatingQC : NA most likely means typical\n",
            "replace_na(df, \"HeatingQC\", value=\"Ta\")\n",
            "\n",
            "# KitchenAbvGr : NA most likely means 0\n",
            "replace_na(df, \"KitchenAbvGr\", value=0)\n",
            "\n",
            "# KitchenQual : NA most likely means typical\n",
            "replace_na(df, \"KitchenQual\", value=\"TA\")\n",
            "\n",
            "# LotFrontage : NA most likely means no lot frontage\n",
            "replace_na(df, \"LotFrontage\", value=0)\n",
            "\n",
            "# LotShape : NA most likely means regular\n",
            "replace_na(df, \"LotShape\", value=\"Reg\")\n",
            "\n",
            "# MasVnrType : NA most likely means no veneer\n",
            "replace_na(df, \"MasVnrType\", value=\"None\")\n",
            "replace_na(df, \"MasVnrArea\", value=0)\n",
            "\n",
            "# MiscFeature : data description says NA means \"no misc feature\"\n",
            "replace_na(df, \"MiscFeature\", value=\"No\")\n",
            "replace_na(df, \"MiscVal\", value=0)\n",
            "\n",
            "# OpenPorchSF : NA most likely means no open porch\n",
            "replace_na(df, \"OpenPorchSF\", value=0)\n",
            "\n",
            "# PavedDrive : NA most likely means not paved\n",
            "replace_na(df, \"PavedDrive\", value=\"N\")\n",
            "\n",
            "# PoolQC : data description says NA means \"no pool\"\n",
            "replace_na(df, \"PoolQC\", value=\"No\")\n",
            "replace_na(df, \"PoolArea\", value=0)\n",
            "\n",
            "# SaleCondition : NA most likely means normal sale\n",
            "replace_na(df, \"SaleCondition\", value=\"Normal\")\n",
            "\n",
            "# ScreenPorch : NA most likely means no screen porch\n",
            "replace_na(df, \"ScreenPorch\", value=0)\n",
            "\n",
            "# TotRmsAbvGrd : NA most likely means 0\n",
            "replace_na(df, \"TotRmsAbvGrd\", value=0)\n",
            "\n",
            "# Utilities : NA most likely means all public utilities\n",
            "replace_na(df, \"Utilities\", value=\"AllPub\")\n",
            "\n",
            "# WoodDeckSF : NA most likely means no wood deck\n",
            "replace_na(df, \"WoodDeckSF\", value=0)\n",
            "\n",
            "# FireplaceQu : data description says NA means \"no fireplace\"\n",
            "replace_na(df, \"FireplaceQu\", value=\"No\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "W przypadku wykonywania tego typu zmian - o ile istnieje taka możliwość - warto rozważyć różne interpretacje brakujących wartości. Może okazać się, że przyjęte przez nas założenia są błędne i prowadzą do pogorszenia działania modelu. Dlatego warto porównać jakoś predykcji z danymi uzupełnionymi oraz z danymi, w których kolumna z brakującymi wartościami jest po prostu usuwana."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 3 (0.5 punktu)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Z pomocą dokumentacji zmiennych w pliku [ames_description.txt](ames_description.txt) zdecyduj, jakie wartości domyślne przypisać zmiennym:\n",
            "- `CentralAir`\n",
            "- `EnclosedPorch`\n",
            "- `Fireplaces`\n",
            "- `SaleCondition`\n",
            "\n",
            "W praktyce niestety zwykle nie jest tak łatwo, że mamy dokumentację i ten krok zajmuje kilka godzin (lub dni) konsultacji z różnymi osobami w firmie :) \n",
            "Czasami w ogóle nie da się ustalić jaka wartość byłaby sensowna, ponieważ nie mamy żadnego dostępu do osób odpowiedzialnych za przygotowanie wykorzystywanego zbioru danych."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 23,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T23:24:29.533701007Z",
               "start_time": "2023-09-15T23:24:29.415321162Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "# CentralAir : NA most likely means no central air conditioning\n",
            "replace_na(df, \"CentralAir\", value=\"N\")\n",
            "\n",
            "#EnclosedPorch : NA most likely means no enclosed porch\n",
            "replace_na(df, \"EnclosedPorch\", value=0)\n",
            "\n",
            "#Fireplaces : NA most likely means no fireplaces\n",
            "replace_na(df, \"Fireplaces\", value=0)\n",
            "\n",
            "#SaleCondition : NA most likely means normal sale\n",
            "replace_na(df, \"SaleCondition\", value=\"Normal\")\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 24,
         "metadata": {
            "editable": true,
            "lines_to_next_cell": 2,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Solution is correct!\n"
               ]
            }
         ],
         "source": [
            "assert df['CentralAir'].isna().sum() == 0\n",
            "assert df['EnclosedPorch'].isna().sum() == 0\n",
            "assert df['Fireplaces'].isna().sum() == 0\n",
            "assert df['SaleCondition'].isna().sum() == 0\n",
            "\n",
            "print(\"Solution is correct!\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Dane kategoryczne"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Jak już zdążyliśmy zauważyć, istnieją dwa główne rodzaje danych: numeryczne (*numerical data*) oraz kategoryczne (*categorical data*). Ten podział jest bardzo istotny. Dane numeryczne to żadna niespodzianka, po prostu mają swoją wartość, jak np. **GrLivArea**, czyli powierzchnia budynku/apartamentów. Dane kategoryczne to takie, którym w większości przypadków nie można przyporządkować wartości liczbowej (wyjątkiem są dane kategoryczne uporządkowane - *categorical ordinal*).\n",
            "\n",
            "Wyobraź sobie zmienną reprezentującą kolory o wartościach \"red\", \"green\" i \"blue. Jeżeli zakodowałbyś je np. jako $red = 0$, $green = 1$, $blue = 2$, to stwierdzasz tym samym, że w pewnym sensie $red < green < blue$. Raczej nie ma powodu, żeby tak sądzić. Jest to zmienna, która ma skończoną liczbę wartości, ale są one nieuporządkowane. Taki typ to zmienne *categorical nominal*.\n",
            "\n",
            "Szczególnym przypadkiem są zmienne binarne (*boolean*). Jest to u nas kolumna **CentralAir** (Central Air Conditioning). Z opisu w pliku [ames_description.txt](ames_description.txt) wiemy, że przyjmuje ona dokładnie dwie wartości kategoryczne: *No* oraz *Yes*. W takiej sytuacji wolno zakodować te wartości numerycznie jako 0 i 1. Stwierdzasz tym samym, że klimatyzacja albo jest, albo jej nie ma.\n",
            "\n",
            "Sytuacją podobną, chociaż mniej oczywistą, może być zmienna **Street**, opisująca typ drogi wiodącej do nieruchomości. Jeśli znowu spojrzymy do opisu danych, to można zauważyć, że ta zmienna może przyjmować tylko dwie różne wartości - *Grvl* i *Pave*. I tu też możemy sobie pozwolić na zakodowanie tych wartości jako 0 i 1. Stwierdzamy wtedy, że droga jest *utwardzona* (Pave) dla wartości 1. Oczywiście równie dobrze można by zakodować to odwrotnie i stwierdzić, że droga jest *nieutwardzona* (Grvl) gdy wartość wynosi 1."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "W Pandas typy numeryczne są oparte o NumPy (np. `np.int64`), a zmienne kategoryczne, napisy itp. są typu `object` (typ `Categorical` istnieje od pewnego czasu, ale nie jest jeszcze zbyt dobrze wspierany).\n",
            "\n",
            "Zmienne **MSSubClass** oraz **MoSold** są kategoryczne (tak wynika z informacji zawartej w pliku [ames_description.txt](ames_description.txt)), a są w naszych danych wprost liczbami. Przekształćmy je zatem do poprawnego typu."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 25,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T23:24:31.301156832Z",
               "start_time": "2023-09-15T23:24:31.205139018Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [],
         "source": [
            "df = df.replace(\n",
            "    {\n",
            "        \"MSSubClass\": {\n",
            "            20: \"SC20\",\n",
            "            30: \"SC30\",\n",
            "            40: \"SC40\",\n",
            "            45: \"SC45\",\n",
            "            50: \"SC50\",\n",
            "            60: \"SC60\",\n",
            "            70: \"SC70\",\n",
            "            75: \"SC75\",\n",
            "            80: \"SC80\",\n",
            "            85: \"SC85\",\n",
            "            90: \"SC90\",\n",
            "            120: \"SC120\",\n",
            "            150: \"SC150\",\n",
            "            160: \"SC160\",\n",
            "            180: \"SC180\",\n",
            "            190: \"SC190\",\n",
            "        },\n",
            "        \"MoSold\": {\n",
            "            1: \"Jan\",\n",
            "            2: \"Feb\",\n",
            "            3: \"Mar\",\n",
            "            4: \"Apr\",\n",
            "            5: \"May\",\n",
            "            6: \"Jun\",\n",
            "            7: \"Jul\",\n",
            "            8: \"Aug\",\n",
            "            9: \"Sep\",\n",
            "            10: \"Oct\",\n",
            "            11: \"Nov\",\n",
            "            12: \"Dec\",\n",
            "        },\n",
            "    }\n",
            ")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Oprócz tego zakodujemy zmienne kategoryczne uporządkowane (*categorical ordinal*) z tekstowych na kolejne liczby całkowite.\n",
            "\n",
            "Przykładowo zmienna **BsmtCond**, oceniająca stan piwnicy, ma następujące możliwe wartości:\n",
            "* *NA* (No) Basement\n",
            "* *Po* (Poor) - Severe cracking, settling, or wetness\n",
            "* *Fa* (Fair) - dampness or some cracking or settling\n",
            "* *TA* (Typical) - slight dampness allowed\n",
            "* *Gd* (Good)\n",
            "* *Ex* (Excellent)\n",
            "\n",
            "Do następujących wartości możemy dopasować pewną skalę punktową, bo są one naturalnie uporządkowane."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 26,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-15T23:44:44.949727320Z",
               "start_time": "2023-09-15T23:44:44.821182382Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "name": "stderr",
               "output_type": "stream",
               "text": [
                  "/var/folders/6n/s7fj6_r164d8m6lctbvln_cm0000gn/T/ipykernel_8077/863138428.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
                  "  df = df.replace(\n"
               ]
            }
         ],
         "source": [
            "df = df.replace(\n",
            "    {\n",
            "        \"Alley\": {\"None\": 0, \"Grvl\": 1, \"Pave\": 2},\n",
            "        \"BsmtCond\": {\"No\": 0, \"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"BsmtExposure\": {\"No\": 0, \"Mn\": 1, \"Av\": 2, \"Gd\": 3},\n",
            "        \"BsmtFinType1\": {\n",
            "            \"No\": 0,\n",
            "            \"Unf\": 1,\n",
            "            \"LwQ\": 2,\n",
            "            \"Rec\": 3,\n",
            "            \"BLQ\": 4,\n",
            "            \"ALQ\": 5,\n",
            "            \"GLQ\": 6,\n",
            "        },\n",
            "        \"BsmtFinType2\": {\n",
            "            \"No\": 0,\n",
            "            \"Unf\": 1,\n",
            "            \"LwQ\": 2,\n",
            "            \"Rec\": 3,\n",
            "            \"BLQ\": 4,\n",
            "            \"ALQ\": 5,\n",
            "            \"GLQ\": 6,\n",
            "        },\n",
            "        \"BsmtQual\": {\"No\": 0, \"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"ExterCond\": {\"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"ExterQual\": {\"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"FireplaceQu\": {\"No\": 0, \"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"Functional\": {\n",
            "            \"Sal\": 1,\n",
            "            \"Sev\": 2,\n",
            "            \"Maj2\": 3,\n",
            "            \"Maj1\": 4,\n",
            "            \"Mod\": 5,\n",
            "            \"Min2\": 6,\n",
            "            \"Min1\": 7,\n",
            "            \"Typ\": 8,\n",
            "        },\n",
            "        \"GarageCond\": {\"No\": 0, \"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"GarageQual\": {\"No\": 0, \"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"HeatingQC\": {\"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"KitchenQual\": {\"Po\": 1, \"Fa\": 2, \"TA\": 3, \"Gd\": 4, \"Ex\": 5},\n",
            "        \"LandSlope\": {\"Sev\": 1, \"Mod\": 2, \"Gtl\": 3},\n",
            "        \"LotShape\": {\"IR3\": 1, \"IR2\": 2, \"IR1\": 3, \"Reg\": 4},\n",
            "        \"PavedDrive\": {\"N\": 0, \"P\": 1, \"Y\": 2},\n",
            "        \"PoolQC\": {\"No\": 0, \"Fa\": 1, \"TA\": 2, \"Gd\": 3, \"Ex\": 4},\n",
            "        \"Street\": {\"Grvl\": 0, \"Pave\": 1},\n",
            "        \"Utilities\": {\"ELO\": 1, \"NoSeWa\": 2, \"NoSewr\": 3, \"AllPub\": 4},\n",
            "    }\n",
            ")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Przygotowanie danych do uczenia\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Nasz zbiór podzielimy na dwa podzbiory: treningowy (70%) i testowy (30%). Zbiór treningowy pozwoli nam utworzyć model regresji liniowej, natomiast testowy - oszacować jego jakość.\n",
            "\n",
            "Pamiętaj, że wyniki uzyskiwane przez model na danych treningowych nie odzwierciedlają tego, jak będzie on sobie radził na danych, których nie ma w zbiorze uczącym. Aby uzyskać taką informację, konieczne jest sprawdzenie, jak model radzi sobie na danych testowych. Daje nam to oszacowanie, jak dobrze model **generalizuje się** dla nowych danych.\n",
            "\n",
            "Wydzielimy sobie równeż zbiory kolumn z danymi numerycznymi i kategorycznymi, co później ułatwi nam odwoływanie się do nich.\n",
            "\n",
            "Funkcja `train_test_split` z biblioteki Scikit-Learn przyjmuje osobno macierze dla cech (*features*) i etykiet (*labels*), dlatego wyodrębniamy sobie z naszej tablicy kolumnę **SalePrice**, która zawiera ceny nieruchomości.\n",
            "\n",
            "---\n",
            "*Ciekawostka*\n",
            "\n",
            "Można zauważyć, że zmienna `y` jest małą literą, natomiast `X_train` czy `X_test` są z dużej. Są to konwencje pochodzące z matematyki:\n",
            "\n",
            "* wektor w matematyce często oznaczamy małą pogrubioną literą ($\\textbf{y}$) - w programowaniu natomiast oznaczamy po prostu małą literą - `y`\n",
            "* macierz w matematyce oznaczamy dużą pogrubioną literą ($\\textbf{X}$) - w programowaniu po prostu dużą literą - `X`\n",
            "\n",
            "Zbiór etykiet to w naszym przypadku wektor cen, więc zapisujemy `y` małą literą. Z drugiej strony `X` zawiera kolumny z cechami opisującymi poszczególne rekordy, a więc jest to macierz.\n",
            "\n",
            "---\n",
            "\n",
            "**Uwaga**: w eksperymentach ustalamy na sztywno wartość parametru `random_state`. [Doczytaj](https://scikit-learn.org/stable/glossary.html#term-random_state), dlaczego wykorzystywany jest ten parametr i co się dzieje, gdy jest on równy stałej wartości jak zero."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 27,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [],
         "source": [
            "from sklearn.model_selection import train_test_split\n",
            "\n",
            "y = df.pop(\"SalePrice\")\n",
            "\n",
            "categorical_features = df.select_dtypes(include=\"object\").columns\n",
            "numerical_features = df.select_dtypes(exclude=\"object\").columns\n",
            "\n",
            "X_train, X_test, y_train, y_test = train_test_split(\n",
            "    df, y, test_size=0.3, random_state=0\n",
            ")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Teraz trzeba dokonać transformacji naszych danych:\n",
            "- zmienne kategoryczne nieuporządkowane trzeba przetworzyć tak, aby nasz algorym był w stanie je obsłużyć, czyli je zakodować za pomocą **one-hot encoding**,\n",
            "- zmienne numeryczne dalej mogą mieć wartości brakujące, więc trzeba je uzupełnić, inaczej **imputować (impute)**,\n",
            "- zmienne numeryczne trzeba przeskalować do zakresu wartości $[0, 1]$ czyli je **znormalizować (normalization)** przez zastosowanie **min-max scaling**.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "\n",
            "### Kodowanie one-hot encoding\n",
            "\n",
            "Powyżej omawialiśmy zmienne kategoryczne. Typ *categorical ordinal* można zakodować kolejnymi liczbami całkowitymi, co jest oczywiście proste. Co jednak ze zmiennymi bez kolejności, typu *categorical nominal*? Trzeba je dalej przekształcić na liczby (żeby model był w stanie je przetworzyć), ale tak, aby nie nadać im implicite kolejności.\n",
            "\n",
            "Spójrzmy na kolumnę **Neighborhood**, oznaczającą poszczególne dzielnice. Dom znajduje się tylko w jednej dzielnicy, a w pozostałych go nie ma. Idea kodowania **one-hot encoding** polega na stworzeniu tylu zmiennych, ile jest możliwych wartości, a następnie w każdym wierszu przypisanie wartości 1 w tej kolumnie, z której była oryginalnie zmienna.\n",
            "\n",
            "Przykładowo, jeżeli mielibyśmy 3 wartości `[\"A\", \"B\", \"C\"]`, to powstają z nich 3 cechy (kolumny macierzy `X`) `[col_A, col_B, col_C]`. Wiersz z pierwotną wartością `\"B\"` będzie miał wartości tych cech `[0, 1, 0]`. W przypadku naszej zmiennej **Neighborhood** pojawią się osobne zmienne **Old Town**, **NoRidge**, **Gilbert** itd., a dla każdego wiersza dokładnie jedna z nich będzie miała wartość 1.\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "Jeżeli mamy dużo możliwych wartości, czyli zmienną o dużej **kardynalności (cardinality)**, to kolumn powstanie bardzo dużo. Do tego są **rzadkie (sparse)**, więc tracimy dużo pamięci na przechowywanie zer. Istnieją inne kodowania, które zajmują mniej miejsca, a implementuje je biblioteka [Category Encoders](https://contrib.scikit-learn.org/category_encoders/).\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Imputacja brakujących wartości numerycznych\n",
            "\n",
            "Wcześniej już napotkaliśmy wartości brakujące i postaraliśmy się uzupełnić je jak najlepiej potrafiliśmy, używając dokumentacji naszego zbioru. Nie gwarantuje to jednak usunięcia wszystkich braków. Nie zawsze w praktyce da się też tak łatwo znaleźć wartości do uzupełnienia. W przypadku zwykłych cech numerycznych możemy zastosować jedną z kilku bardzo popularnych strategii radzenia sobie z wartościami brakującymi:\n",
            "\n",
            "1. Usunąć kolumnę, która zawiera brakujące wartości.\n",
            "1. Usunąć wiersze, w których brakuje wartości.\n",
            "1. Zastąpić brakujące wartości innymi, np. średnią z kolumny, medianą albo wartością stałą.\n",
            "1. Przewidzieć brakujące wartości wykorzystując odpowiedni model uczenia maszynowego.\n",
            "\n",
            "Podejście 4 jest często zbyt czasochłonne. Opcje 1 i 2 prowadzą do utraty danych. My wypróbujemy sposób nr 3.\n",
            "\n",
            "Nie znaczy to jednak, że usunięcie wierszy czy kolumny jest zawsze złym podejściem. Usunięcie kolumny jest uzasadnione, jeśli ma ona naprawdę dużo wartości brakujących. W takich wypadkach ciężko z niej wyciągnąć jakąkolwiek sensowną informację. Usunięcie wierszy może być uzasadnione w przypadku, gdy mamy dużo rekordów i tylko niewielka część z nich posiada wartości brakujące (usunięcie kilku wierszy nie powinno powodować problemu).\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "Popularne algorytmy imputacji danych często są oparte [o algorytm najbliższych sąsiadów, czyli najbardziej podobne punkty](https://scikit-learn.org/stable/modules/impute.html#nearest-neighbors-imputation). Innym podejściem, iteracyjnie imputującym wartości, jest [algorytm MICE](https://www.numpyninja.com/post/mice-algorithm-to-impute-missing-values-in-a-dataset).\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Skalowanie\n",
            "\n",
            "Jest to bardzo ważny krok dla wielu modeli sztucznej inteligencji. Często takie modele mają pewne założenia co do danych wejściowych, a szczególnie popularnym założeniem jest, że wszystkie cechy mają wartości o podobnej skali. W szczególności regresja liniowa i logistyczna też czynią to założenie. Dlatego trzeba przeskalować nasze dane, żeby spełnić to założenie. Najprostsza metoda to `MinMaxScaler`, który przekształca wszystkie wartości do przedziału $[0, 1]$.\n",
            "\n",
            "Istnieją też inne metody, np. standaryzacja, którą możesz pamiętać ze statystyki (jej wynikiem jest Z-score). Polega na odjęciu średniej i podzieleniu przez odchylenie standardowe każdej cechy. Wynikiem przekształcenia są cechy o średniej 0 i odchyleniu standardowym 1.\n",
            "\n",
            "Więcej informacji na temat tego, dlaczego skalowanie jest tak istotne, możesz znaleźć [tutaj](https://analyticsindiamag.com/why-data-scaling-is-important-in-machine-learning-how-to-effectively-do-it/).\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "Porównanie różnych metod skalowania [możesz znaleźć tutaj](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html). Ciekawą metodą jest np. RobustScaler, który jest podobny do StandardScaler, ale używa mediany i kwartyli zamiast średniej i odchylenia standardowego. Są to tzw. robust statistics, czyli miary odporne na występowanie wartości odstających (outliers)."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Przetwarzanie danych z wykorzystaniem Scikit-Learn\n",
            "\n",
            "Mamy zatem do wykonania:\n",
            "- na zmiennych numerycznych 2 operacje do wykonania: imputacja i skalowanie,\n",
            "- na zmiennych kategorycznych: zastosowanie kodowania one-hot encoding.\n",
            "\n",
            "W Scikit-learn służą do tego następujące klasy:\n",
            "- `OneHotEncoder`, `SimpleImputer`, `MinMaxScaler` - transformacje, implementują metody `.fit()` i `.transform()`,\n",
            "- `Pipeline` - do układania transformacji sekwencyjnie,\n",
            "- `ColumnTransformer` - do układania transformacji równolegle, dla różnych kolumn.\n",
            "\n",
            "**Ważne:** jako, że zaraz skorzystamy z regresji liniowej, do klasy `OneHotEncoder` trzeba przekazać `drop=\"first\"`. Stworzy to 1 zmienną mniej, niż typowy one-hot encoding, np. `pd.get_dummies()`, gwarantując brak **idealnie współliniowych zmiennych (perfectly collinear features)**, co byłby niestabilny numerycznie. Dodatkowo, jako że przekształcamy już po podziale na zbiór treningowy i testowy, to możemy spotkać na zbiorze testowym nieliczne przypadki kategorii, których nie ma w zbiorze treningowym - kodujemy je wtedy po prostu jako wektory zer za pomocą `handle_unknown=\"ignore\"`.\n",
            "\n",
            "Na przykładzie `StandardScaler` (standaryzacja) rozpatrzmy, jak działają poszczególne metody.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Metoda `.fit()`\n",
            "\n",
            "Do wykonania standaryzacji potrzebujemy dla każdej z cech określić 2 wartości - średnią oraz odchylenie standardowe. Formuła standaryzacji dla przypomnienia:\n",
            "\n",
            "$$z = \\frac{x - \\mu}{\\sigma}$$\n",
            "\n",
            "Metodę `.fit()` wykonujemy tylko raz, dla **danych treningowych**. To powoduje, że obliczamy wartości $\\mu$ oraz $\\sigma$ dla każdej cechy, na podstawie wartości ze zbioru treningowego. Wyuczone wartości zostają zapisane w obiekcie `StandardScaler` i mogą być później używane do przeprowadzenia standaryzacji zarówno dla danych treningowych, jak i testowych.\n",
            "\n",
            "**Co, gdyby dla danych testowych przeprowadzić osobną standaryzację?**\n",
            "\n",
            "Będziemy, na przykład, standaryzować kolumnę **GrLivArea** - powierzchnię nieruchomości. Załóżmy, że z danych treningowych wyszłoby, że średnia jest równa $60m^2$, a odchylenie standardowe - $20m^2$. Wtedy wartości z przedziału $[40, 80]$ zostaną przekształcone do $[-1, 1]$. Nasz model wykorzysta to przekształcenie i będzie uważał, że wartości po transformacji w pobliżu $0$ oznaczają średniej wielkości apartamenty.\n",
            "\n",
            "Określiliśmy parametry modelu i dostajemy kilkadziesiąt budynków z jakiejś zamożnej dzielnicy dla predykcji. Średnia powierzchnia dla tych budynków to około $160m^2$. Osobno przeprowadzając standaryzację dla takich danych testowych, zaburzylibyśmy rozkład tej cechy, gdyż tym razem wartości wokół $0$ oznaczałyby dość duże mieszkania. Modele są niezwykle czułe na podobne zaburzenia - musimy przetwarzać dane spójnie, żeby nie doszło do podobnych sytuacji. \n",
            "\n",
            "**Czemu nie wywołać `.fit()` na wszystkich danych, a nie tylko treningowych?**\n",
            "\n",
            "Wydzieliliśmy dane testowe po to, żeby sprawdzać, jak model poradzi sobie z danymi, których do tej pory nigdy nie widział, bo to właśnie takie dane będzie on dostawać w praktyce, po wdrożeniu do realnego systemu. Ta ocena obejmuje też etap preprocessingu, w tym skalowania. Więc jeśli etap preprocessingu zobaczy dane testowe, to nie będziemy w stanie uczciwie estymować jego zachowania na nowych danych.\n",
            "\n",
            "Wykorzystanie danych testowych w procesie treningu to błąd **wycieku danych (data leakage)**. Skutkuje on niepoprawnym, nadmiernie optymistycznym oszacowaniem jakości modelu.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Metoda `.transform()`\n",
            "\n",
            "Przekształca dane za pomocą parametrów wyznaczonych w `.fit()`.\n",
            "\n",
            "### Metoda `.fit_transform()`\n",
            "\n",
            "Metoda, która najpierw wykonuje `.fit()`, a potem `.transform()` i zwraca wynik ostatniej. W przypadku niektórych transformacji wykorzystuje ich specyfikę i działa szybciej, niż sekwencyjne wywołanie `.fit()` oraz`.transform()`. Trzeba jednak pamiętać, że możemy tego użyć tylko na zbiorze treningowym - na zbiorze testowym wywołujemy już tylko `.transform()`.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 4 (0.5 punktu)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Stwórz pipeline'y dla zmiennych kategorycznych i numerycznych. Połącz je następnie z użyciem `ColumnTransformer`. \"Wytrenuj\" go na danych treningowych, a następnie przetransformuj dane treningowe oraz testowe.\n",
            "\n",
            "**Uwaga:** przekaż do `ColumnTransformer` parametr `verbose_feature_names_out=False`, żeby nie zmieniał on nazw cech. Ułatwi nam to późniejszą analizę wyników."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 28,
         "metadata": {
            "ExecuteTime": {
               "end_time": "2023-09-16T19:33:06.290225746Z",
               "start_time": "2023-09-16T19:33:04.939828812Z"
            },
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stderr",
               "output_type": "stream",
               "text": [
                  "/Users/maciejmakowski/studies/Artificial-Intelligence-Basics/.venv/lib/python3.12/site-packages/sklearn/preprocessing/_encoders.py:242: UserWarning: Found unknown categories in columns [12, 15, 17] during transform. These unknown categories will be encoded as all zeros\n",
                  "  warnings.warn(\n"
               ]
            }
         ],
         "source": [
            "from sklearn.compose import ColumnTransformer\n",
            "from sklearn.impute import SimpleImputer\n",
            "from sklearn.pipeline import Pipeline\n",
            "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n",
            "\n",
            "\n",
            "one_hot_encoder = OneHotEncoder(\n",
            "    drop=\"first\", sparse_output=False, handle_unknown=\"ignore\"\n",
            ")\n",
            "median_imputer = SimpleImputer(strategy=\"median\")\n",
            "min_max_scaler = MinMaxScaler()\n",
            "\n",
            "categorical_pipeline = Pipeline(\n",
            "    steps=[\n",
            "        (\"one-hot-encoder\", one_hot_encoder),\n",
            "    ]\n",
            ")\n",
            "\n",
            "numerical_pipeline = Pipeline(\n",
            "    steps=[\n",
            "        (\"imputer\", median_imputer),\n",
            "        (\"min-max-scaler\", min_max_scaler),\n",
            "    ]\n",
            ")\n",
            "\n",
            "column_transformer = ColumnTransformer(\n",
            "    verbose_feature_names_out=False,\n",
            "    transformers=[\n",
            "        (\"categorical\", categorical_pipeline, categorical_features),\n",
            "        (\"numerical\", numerical_pipeline, numerical_features),\n",
            "    ]\n",
            ")\n",
            "\n",
            "# fit and transform\n",
            "\n",
            "column_transformer.fit(X_train)\n",
            "\n",
            "X_train = column_transformer.transform(X_train)\n",
            "X_test = column_transformer.transform(X_test)\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 29,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Solution is correct!\n"
               ]
            }
         ],
         "source": [
            "for i in range(X_train.shape[1]):\n",
            "    assert -0.01 <= X_train[:, i].max() <= 1.01\n",
            "\n",
            "print(\"Solution is correct!\")"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 30,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
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                     "  --sklearn-color-text: black;\n",
                     "  --sklearn-color-line: gray;\n",
                     "  /* Definition of color scheme for unfitted estimators */\n",
                     "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
                     "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
                     "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
                     "  --sklearn-color-unfitted-level-3: chocolate;\n",
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                     "  @media (prefers-color-scheme: dark) {\n",
                     "    /* Redefinition of color scheme for dark theme */\n",
                     "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
                     "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
                     "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
                     "    --sklearn-color-icon: #878787;\n",
                     "  }\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 pre {\n",
                     "  padding: 0;\n",
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                     "\n",
                     "#sk-container-id-1 input.sk-hidden--visually {\n",
                     "  border: 0;\n",
                     "  clip: rect(1px 1px 1px 1px);\n",
                     "  clip: rect(1px, 1px, 1px, 1px);\n",
                     "  height: 1px;\n",
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                     "  overflow: hidden;\n",
                     "  padding: 0;\n",
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                     "\n",
                     "#sk-container-id-1 div.sk-dashed-wrapped {\n",
                     "  border: 1px dashed var(--sklearn-color-line);\n",
                     "  margin: 0 0.4em 0.5em 0.4em;\n",
                     "  box-sizing: border-box;\n",
                     "  padding-bottom: 0.4em;\n",
                     "  background-color: var(--sklearn-color-background);\n",
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                     "\n",
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                     "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
                     "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
                     "     so we also need the `!important` here to be able to override the\n",
                     "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
                     "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
                     "  display: inline-block !important;\n",
                     "  position: relative;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-text-repr-fallback {\n",
                     "  display: none;\n",
                     "}\n",
                     "\n",
                     "div.sk-parallel-item,\n",
                     "div.sk-serial,\n",
                     "div.sk-item {\n",
                     "  /* draw centered vertical line to link estimators */\n",
                     "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
                     "  background-size: 2px 100%;\n",
                     "  background-repeat: no-repeat;\n",
                     "  background-position: center center;\n",
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                     "\n",
                     "/* Parallel-specific style estimator block */\n",
                     "\n",
                     "#sk-container-id-1 div.sk-parallel-item::after {\n",
                     "  content: \"\";\n",
                     "  width: 100%;\n",
                     "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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                     "\n",
                     "#sk-container-id-1 div.sk-parallel {\n",
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                     "  justify-content: center;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  position: relative;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-parallel-item {\n",
                     "  display: flex;\n",
                     "  flex-direction: column;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
                     "  align-self: flex-end;\n",
                     "  width: 50%;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
                     "  align-self: flex-start;\n",
                     "  width: 50%;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
                     "  width: 0;\n",
                     "}\n",
                     "\n",
                     "/* Serial-specific style estimator block */\n",
                     "\n",
                     "#sk-container-id-1 div.sk-serial {\n",
                     "  display: flex;\n",
                     "  flex-direction: column;\n",
                     "  align-items: center;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  padding-right: 1em;\n",
                     "  padding-left: 1em;\n",
                     "}\n",
                     "\n",
                     "\n",
                     "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
                     "clickable and can be expanded/collapsed.\n",
                     "- Pipeline and ColumnTransformer use this feature and define the default style\n",
                     "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
                     "*/\n",
                     "\n",
                     "/* Pipeline and ColumnTransformer style (default) */\n",
                     "\n",
                     "#sk-container-id-1 div.sk-toggleable {\n",
                     "  /* Default theme specific background. It is overwritten whether we have a\n",
                     "  specific estimator or a Pipeline/ColumnTransformer */\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "}\n",
                     "\n",
                     "/* Toggleable label */\n",
                     "#sk-container-id-1 label.sk-toggleable__label {\n",
                     "  cursor: pointer;\n",
                     "  display: block;\n",
                     "  width: 100%;\n",
                     "  margin-bottom: 0;\n",
                     "  padding: 0.5em;\n",
                     "  box-sizing: border-box;\n",
                     "  text-align: center;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
                     "  /* Arrow on the left of the label */\n",
                     "  content: \"▸\";\n",
                     "  float: left;\n",
                     "  margin-right: 0.25em;\n",
                     "  color: var(--sklearn-color-icon);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "}\n",
                     "\n",
                     "/* Toggleable content - dropdown */\n",
                     "\n",
                     "#sk-container-id-1 div.sk-toggleable__content {\n",
                     "  max-height: 0;\n",
                     "  max-width: 0;\n",
                     "  overflow: hidden;\n",
                     "  text-align: left;\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-toggleable__content pre {\n",
                     "  margin: 0.2em;\n",
                     "  border-radius: 0.25em;\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
                     "  /* Expand drop-down */\n",
                     "  max-height: 200px;\n",
                     "  max-width: 100%;\n",
                     "  overflow: auto;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
                     "  content: \"▾\";\n",
                     "}\n",
                     "\n",
                     "/* Pipeline/ColumnTransformer-specific style */\n",
                     "\n",
                     "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Estimator-specific style */\n",
                     "\n",
                     "/* Colorize estimator box */\n",
                     "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
                     "#sk-container-id-1 div.sk-label label {\n",
                     "  /* The background is the default theme color */\n",
                     "  color: var(--sklearn-color-text-on-default-background);\n",
                     "}\n",
                     "\n",
                     "/* On hover, darken the color of the background */\n",
                     "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Label box, darken color on hover, fitted */\n",
                     "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Estimator label */\n",
                     "\n",
                     "#sk-container-id-1 div.sk-label label {\n",
                     "  font-family: monospace;\n",
                     "  font-weight: bold;\n",
                     "  display: inline-block;\n",
                     "  line-height: 1.2em;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-label-container {\n",
                     "  text-align: center;\n",
                     "}\n",
                     "\n",
                     "/* Estimator-specific */\n",
                     "#sk-container-id-1 div.sk-estimator {\n",
                     "  font-family: monospace;\n",
                     "  border: 1px dotted var(--sklearn-color-border-box);\n",
                     "  border-radius: 0.25em;\n",
                     "  box-sizing: border-box;\n",
                     "  margin-bottom: 0.5em;\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-estimator.fitted {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-0);\n",
                     "}\n",
                     "\n",
                     "/* on hover */\n",
                     "#sk-container-id-1 div.sk-estimator:hover {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
                     "\n",
                     "/* Common style for \"i\" and \"?\" */\n",
                     "\n",
                     ".sk-estimator-doc-link,\n",
                     "a:link.sk-estimator-doc-link,\n",
                     "a:visited.sk-estimator-doc-link {\n",
                     "  float: right;\n",
                     "  font-size: smaller;\n",
                     "  line-height: 1em;\n",
                     "  font-family: monospace;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  border-radius: 1em;\n",
                     "  height: 1em;\n",
                     "  width: 1em;\n",
                     "  text-decoration: none !important;\n",
                     "  margin-left: 1ex;\n",
                     "  /* unfitted */\n",
                     "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
                     "  color: var(--sklearn-color-unfitted-level-1);\n",
                     "}\n",
                     "\n",
                     ".sk-estimator-doc-link.fitted,\n",
                     "a:link.sk-estimator-doc-link.fitted,\n",
                     "a:visited.sk-estimator-doc-link.fitted {\n",
                     "  /* fitted */\n",
                     "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
                     "  color: var(--sklearn-color-fitted-level-1);\n",
                     "}\n",
                     "\n",
                     "/* On hover */\n",
                     "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
                     ".sk-estimator-doc-link:hover,\n",
                     "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
                     ".sk-estimator-doc-link:hover {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-3);\n",
                     "  color: var(--sklearn-color-background);\n",
                     "  text-decoration: none;\n",
                     "}\n",
                     "\n",
                     "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
                     ".sk-estimator-doc-link.fitted:hover,\n",
                     "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
                     ".sk-estimator-doc-link.fitted:hover {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-3);\n",
                     "  color: var(--sklearn-color-background);\n",
                     "  text-decoration: none;\n",
                     "}\n",
                     "\n",
                     "/* Span, style for the box shown on hovering the info icon */\n",
                     ".sk-estimator-doc-link span {\n",
                     "  display: none;\n",
                     "  z-index: 9999;\n",
                     "  position: relative;\n",
                     "  font-weight: normal;\n",
                     "  right: .2ex;\n",
                     "  padding: .5ex;\n",
                     "  margin: .5ex;\n",
                     "  width: min-content;\n",
                     "  min-width: 20ex;\n",
                     "  max-width: 50ex;\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  box-shadow: 2pt 2pt 4pt #999;\n",
                     "  /* unfitted */\n",
                     "  background: var(--sklearn-color-unfitted-level-0);\n",
                     "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
                     "}\n",
                     "\n",
                     ".sk-estimator-doc-link.fitted span {\n",
                     "  /* fitted */\n",
                     "  background: var(--sklearn-color-fitted-level-0);\n",
                     "  border: var(--sklearn-color-fitted-level-3);\n",
                     "}\n",
                     "\n",
                     ".sk-estimator-doc-link:hover span {\n",
                     "  display: block;\n",
                     "}\n",
                     "\n",
                     "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
                     "\n",
                     "#sk-container-id-1 a.estimator_doc_link {\n",
                     "  float: right;\n",
                     "  font-size: 1rem;\n",
                     "  line-height: 1em;\n",
                     "  font-family: monospace;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  border-radius: 1rem;\n",
                     "  height: 1rem;\n",
                     "  width: 1rem;\n",
                     "  text-decoration: none;\n",
                     "  /* unfitted */\n",
                     "  color: var(--sklearn-color-unfitted-level-1);\n",
                     "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
                     "  /* fitted */\n",
                     "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
                     "  color: var(--sklearn-color-fitted-level-1);\n",
                     "}\n",
                     "\n",
                     "/* On hover */\n",
                     "#sk-container-id-1 a.estimator_doc_link:hover {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-3);\n",
                     "  color: var(--sklearn-color-background);\n",
                     "  text-decoration: none;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-3);\n",
                     "}\n",
                     "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
                     "                                 Pipeline(steps=[(&#x27;one-hot-encoder&#x27;,\n",
                     "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
                     "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
                     "                                                                sparse_output=False))]),\n",
                     "                                 Index([&#x27;MSSubClass&#x27;, &#x27;MSZoning&#x27;, &#x27;LandContour&#x27;, &#x27;LotConfig&#x27;, &#x27;Neighborhood&#x27;,\n",
                     "       &#x27;Condition1&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
                     "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;Founda...\n",
                     "       &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;, &#x27;HalfBath&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;KitchenAbvGr&#x27;,\n",
                     "       &#x27;KitchenQual&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;Functional&#x27;, &#x27;Fireplaces&#x27;,\n",
                     "       &#x27;FireplaceQu&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;GarageQual&#x27;,\n",
                     "       &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;OpenPorchSF&#x27;,\n",
                     "       &#x27;EnclosedPorch&#x27;, &#x27;X3SsnPorch&#x27;, &#x27;ScreenPorch&#x27;, &#x27;PoolArea&#x27;, &#x27;PoolQC&#x27;,\n",
                     "       &#x27;MiscVal&#x27;, &#x27;YrSold&#x27;],\n",
                     "      dtype=&#x27;object&#x27;))],\n",
                     "                  verbose_feature_names_out=False)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
                     "                                 Pipeline(steps=[(&#x27;one-hot-encoder&#x27;,\n",
                     "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
                     "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
                     "                                                                sparse_output=False))]),\n",
                     "                                 Index([&#x27;MSSubClass&#x27;, &#x27;MSZoning&#x27;, &#x27;LandContour&#x27;, &#x27;LotConfig&#x27;, &#x27;Neighborhood&#x27;,\n",
                     "       &#x27;Condition1&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
                     "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;Founda...\n",
                     "       &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;, &#x27;HalfBath&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;KitchenAbvGr&#x27;,\n",
                     "       &#x27;KitchenQual&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;Functional&#x27;, &#x27;Fireplaces&#x27;,\n",
                     "       &#x27;FireplaceQu&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;GarageQual&#x27;,\n",
                     "       &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;OpenPorchSF&#x27;,\n",
                     "       &#x27;EnclosedPorch&#x27;, &#x27;X3SsnPorch&#x27;, &#x27;ScreenPorch&#x27;, &#x27;PoolArea&#x27;, &#x27;PoolQC&#x27;,\n",
                     "       &#x27;MiscVal&#x27;, &#x27;YrSold&#x27;],\n",
                     "      dtype=&#x27;object&#x27;))],\n",
                     "                  verbose_feature_names_out=False)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted 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 fitted sk-toggleable__label-arrow fitted\">categorical</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;MSSubClass&#x27;, &#x27;MSZoning&#x27;, &#x27;LandContour&#x27;, &#x27;LotConfig&#x27;, &#x27;Neighborhood&#x27;,\n",
                     "       &#x27;Condition1&#x27;, &#x27;Condition2&#x27;, &#x27;BldgType&#x27;, &#x27;HouseStyle&#x27;, &#x27;RoofStyle&#x27;,\n",
                     "       &#x27;RoofMatl&#x27;, &#x27;Exterior1st&#x27;, &#x27;Exterior2nd&#x27;, &#x27;MasVnrType&#x27;, &#x27;Foundation&#x27;,\n",
                     "       &#x27;Heating&#x27;, &#x27;CentralAir&#x27;, &#x27;Electrical&#x27;, &#x27;GarageType&#x27;, &#x27;GarageFinish&#x27;,\n",
                     "       &#x27;Fence&#x27;, &#x27;MiscFeature&#x27;, &#x27;MoSold&#x27;, &#x27;SaleType&#x27;, &#x27;SaleCondition&#x27;],\n",
                     "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted 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 fitted sk-toggleable__label-arrow fitted\">numerical</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;LotFrontage&#x27;, &#x27;LotArea&#x27;, &#x27;Street&#x27;, &#x27;Alley&#x27;, &#x27;LotShape&#x27;, &#x27;Utilities&#x27;,\n",
                     "       &#x27;LandSlope&#x27;, &#x27;OverallQual&#x27;, &#x27;OverallCond&#x27;, &#x27;YearBuilt&#x27;, &#x27;YearRemodAdd&#x27;,\n",
                     "       &#x27;MasVnrArea&#x27;, &#x27;ExterQual&#x27;, &#x27;ExterCond&#x27;, &#x27;BsmtQual&#x27;, &#x27;BsmtCond&#x27;,\n",
                     "       &#x27;BsmtExposure&#x27;, &#x27;BsmtFinType1&#x27;, &#x27;BsmtFinSF1&#x27;, &#x27;BsmtFinType2&#x27;,\n",
                     "       &#x27;BsmtFinSF2&#x27;, &#x27;BsmtUnfSF&#x27;, &#x27;TotalBsmtSF&#x27;, &#x27;HeatingQC&#x27;, &#x27;X1stFlrSF&#x27;,\n",
                     "       &#x27;X2ndFlrSF&#x27;, &#x27;LowQualFinSF&#x27;, &#x27;GrLivArea&#x27;, &#x27;BsmtFullBath&#x27;,\n",
                     "       &#x27;BsmtHalfBath&#x27;, &#x27;FullBath&#x27;, &#x27;HalfBath&#x27;, &#x27;BedroomAbvGr&#x27;, &#x27;KitchenAbvGr&#x27;,\n",
                     "       &#x27;KitchenQual&#x27;, &#x27;TotRmsAbvGrd&#x27;, &#x27;Functional&#x27;, &#x27;Fireplaces&#x27;,\n",
                     "       &#x27;FireplaceQu&#x27;, &#x27;GarageYrBlt&#x27;, &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;GarageQual&#x27;,\n",
                     "       &#x27;GarageCond&#x27;, &#x27;PavedDrive&#x27;, &#x27;WoodDeckSF&#x27;, &#x27;OpenPorchSF&#x27;,\n",
                     "       &#x27;EnclosedPorch&#x27;, &#x27;X3SsnPorch&#x27;, &#x27;ScreenPorch&#x27;, &#x27;PoolArea&#x27;, &#x27;PoolQC&#x27;,\n",
                     "       &#x27;MiscVal&#x27;, &#x27;YrSold&#x27;],\n",
                     "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
                  ],
                  "text/plain": [
                     "ColumnTransformer(transformers=[('categorical',\n",
                     "                                 Pipeline(steps=[('one-hot-encoder',\n",
                     "                                                  OneHotEncoder(drop='first',\n",
                     "                                                                handle_unknown='ignore',\n",
                     "                                                                sparse_output=False))]),\n",
                     "                                 Index(['MSSubClass', 'MSZoning', 'LandContour', 'LotConfig', 'Neighborhood',\n",
                     "       'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle',\n",
                     "       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'Founda...\n",
                     "       'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n",
                     "       'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces',\n",
                     "       'FireplaceQu', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'GarageQual',\n",
                     "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
                     "       'EnclosedPorch', 'X3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
                     "       'MiscVal', 'YrSold'],\n",
                     "      dtype='object'))],\n",
                     "                  verbose_feature_names_out=False)"
                  ]
               },
               "execution_count": 30,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "column_transformer"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Regresja liniowa"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Możemy teraz przejść do przewidywania wartości domów. Naszym narzędziem będzie tutaj **regresja liniowa (linear regression)**, czyli model postaci:\n",
            "$$\n",
            "\\hat{y} = ax + b\n",
            "$$\n",
            "gdzie $\\hat{y}$ to zmienna zależna, $x$ to zmienna niezależna (wartość cechy), a współczynniki obliczane są według wzorów opisanych [tutaj](https://www.vedantu.com/formula/linear-regression-formula), bez wątpienia znanych Ci z algebry liniowej i statystyki.\n",
            "\n",
            "Rozwinięciem regresji liniowej jest wielokrotna regresja liniowa (*multiple linear regression*), która pozwala na wykorzystanie więcej niż jednej cechy do predykcji wartości. W takim modelu predykcja to kombinacja liniowa cech i wag, gdzie każda cecha posiada własną wagę. Więcej o tym mechanizmie możesz przeczytać [tutaj](https://rankia.pl/analizy-gieldowe/co-to-jest-wielokrotna-regresja-liniowa-mlr/). Formalnie jest to model postaci:\n",
            "$$\n",
            "\\hat{y} = \\boldsymbol{w} \\cdot \\boldsymbol{x} + b = \\sum_{i=1}^{d} w_i x_i + b\n",
            "$$\n",
            "gdzie:\n",
            "- $d$ to **wymiarowość (dimensionality)**, czyli liczba cech\n",
            "- $\\boldsymbol{w}$ to wektor wag o długości $d$\n",
            "- $w_i$ to wagi poszczególnych cech\n",
            "- $b$ to **wyraz wolny (bias / intercept)**, punkt przecięcia ze środkiem układu współrzędnych\n",
            "\n",
            "Pozostaje pytanie, jak wyznaczyć wagi $\\boldsymbol{w}$ i wyraz wolny $b$. Można to robić na różne sposoby, przy czym klasyczna regresja liniowa minimalizuje **błąd średniokwadratowy (mean squared error, MSE)**. Jest to przykład **funkcji kosztu (loss function / cost function)**, a konkretnie **squared loss / L2 loss**. Ma on postać:\n",
            "$$\n",
            "L(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=1}^n \\left( y - \\hat{y} \\right)^2\n",
            "$$\n",
            "gdzie $\\hat{y}$ to wartość przewidywana przez model, $y$ - prawdziwa, a $n$ to liczba punktów w zbiorze.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "W Scikit-learn ten model implementuje klasa `LinearRegression`. Jej ważne cechy:\n",
            "- domyślnie uwzględnia intercept (bias) przez `fit_intercept=True`; jeżeli nasze dane są już wycentrowane, to jest to niepotrzebne i może powodować problemy numeryczne,\n",
            "- używa implementacji z pseudoodwrotnością Moore'a-Penrose'a (SVD),\n",
            "- nie pozwala na regularyzację, do tego trzeba użyć innych klas.\n",
            "\n",
            "Jak ocenić, jak taki model sobie radzi? Trzeba tutaj użyć pewnej **metryki (metric)**, czyli wyznacznika jakości modelu. Można na to patrzeć z wielu różnych perspektyw, w zależności od charakterystyki problemu. Tradycyjnie używa się **Root MSE (RMSE)**, czyli pierwiastka kwadratowego z MSE. Ma ważne zalety:\n",
            "- regresja liniowa z definicji modelu optymalizuje miarę MSE, więc używamy metryki dobrze związanej z modelem,\n",
            "- dzięki pierwiastkowaniu ma tę samą jednostkę, co przewidywane wartości. .\n",
            "\n",
            "Jest też dość czuła na wartości odstające, ale może to być korzystne, w zależności od zastosowania.\n",
            "\n",
            "$$\n",
            "RMSE(y, \\hat{y}) = \\sqrt{ \\frac{1}{N} \\sum_{i=1}^n (y_i - \\hat{y}_i)^2}\n",
            "$$\n",
            "\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "Minimalizując inne rodzaje błędu, otrzymujemy modele liniowe o innych parametrach, ale tej samej postaci funkcji. Typowo modele te są bardziej odporne na wartości odstające, ale bardziej kosztowne w treningu. Są to np. [quantile regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.QuantileRegressor.html) optymalizująca koszt L1 (*mean absolute error*) czy [Huber regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html), optymalizująca tzw. Huber loss (połączenie L1 i L2).\n",
            "\n",
            "Obliczanie regresji liniowej używa pseudoodwrotności Moore'a-Penrose'a i SVD. Objaśnia to dobrze [ten tutorial](https://sthalles.github.io/svd-for-regression/)."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 31,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "RMSE: 210781672.2011\n"
               ]
            }
         ],
         "source": [
            "from sklearn.metrics import root_mean_squared_error\n",
            "from sklearn.linear_model import LinearRegression\n",
            "\n",
            "# all variables are in range [0, 1], so we don't need an intercept\n",
            "reg_linear = LinearRegression(fit_intercept=False)\n",
            "reg_linear.fit(X_train, y_train)\n",
            "\n",
            "y_pred = reg_linear.predict(X_test)\n",
            "rmse = root_mean_squared_error(y_test, y_pred)\n",
            "\n",
            "print(f\"RMSE: {rmse:.4f}\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Czy taki błąd to duży, czy mały? Wszystko zależy od skali wartości przewidywanych. Trzeba pamiętać, że dokonaliśmy logarytmowania zmiennej docelowej, więc trzeba to sprawdzić po transformacji odwrotnej `np.expm1`. Po tej operacji wartość błędu będzie wyrażona w dolarach."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Zbyt małe i nadmierne dopasowanie"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "W trakcie trenowania modelu może dojść do sytuacji, w której zostanie on **przeuczony (overfitting)**. W takim wypadku model nadmiernie dostosowuje się do danych treningowych, \"zakuwając\" je. Daje wtedy bardzo dokładne wyniki na zbiorze treningowym, ale kiepskie na zbiorze testowym. Modele przeuczone słabo zatem się **generalizują (generalization)**.\n",
            "\n",
            "Dlatego wcześniej wydzieliliśmy zbiór testowy, za pomocą którego oceniamy skuteczność naszego modelu. Pozwala to uniknąć powyższego błędu. Przeuczenie bardzo często można rozpoznać właśnie po różnym zachowaniu modelu na danych treningowych i testowych. Jeśli z danymi treningowymi model radzi sobie dużo lepiej, niż z testowymi, to istnieje dużo ryzyko, że model został przeuczony i skupił się na zapamiętywaniu konkretnych przykładów, na których się uczył, niż na wyciąganiu z nich uniwersalnych wzorców. Taki model słabo się generalizuje i nie poradzi sobie z nowymi danymi.\n",
            "\n",
            "Sprawdza się to następująco:\n",
            "- obliczamy błąd treningowy oraz testowy,\n",
            "- jeżeli oba błędy są wysokie, to mamy zbyt małe dopasowanie (*underfitting*) i trzeba użyć pojemniejszego modelu,\n",
            "- jeżeli błąd treningowy jest dużo niższy od testowego, to mamy nadmierne dopasowanie (*overfitting*) i model trzeba regularyzować.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "W praktyce paradoksalnie często model o większej pojemności z mocną regularyzacją działa lepiej od prostszego modelu ze słabą regularyzacją. Wyjaśnianie, czemu tak jest, to otwarty problem naukowy, szczególnie w kontekście sieci neuronowych.\n",
            "\n",
            "Przeuczenie modelu jest bardzo istotnym problemem w sztucznej inteligencji i istnieje szereg metod, służących zapobieganiu tego zjawiska. Jedną z nich jest regularyzacja - do globalnej funkcji błędu dodawane są \"kary\" za tworzenie zbyt złożonych modeli. Typowe metody regularyzacji to L1 oraz L2, które penalizują wielkość parametrów obliczonych w trakcie treningu. Obie te wartości są tak naprawdę normami (odpowiednio `l1` i `l2`) wektorów wag modelu, przeskalowanymi przez określoną wartość. Dodawanie tych kar ma zapobiec przeuczeniu, bo typowo duże wagi w regresji liniowej i podobnych modelach oznaczają przeuczenie.\n",
            "\n",
            "Czemu tak jest? Przeuczenie bierze się z tego, że nasz model \"zakuwa\" zbiór treningowy, ucząc się **szumu (noise)** w danych, przypisując nadmierne znaczenie niewielkim różnicom w wartościach cech. Jeżeli cecha ma dużą wagę, to nawet niewielka zmiana jej wartości bardzo zmienia finalną predykcję (która jest kombinacją liniową). Dzięki regularyzacji, jeżeli model podczas treningu będzie chciał zwiększyć wagę dla cechy, to musi mu się to opłacać. Innymi słowy, zwiększenie wagi cechy musi zmniejszyć koszt (np. MSE) bardziej, niż wzrośnie kara z regularyzacji.\n",
            "\n",
            "Jak słusznie się domyślić, zbyt duże kary spowoduję z kolei niedouczenie (ang. *underfitting*). Więcej o konstrukcji i zastosowaniach regularyzacji L1 i L2 możesz przeczytać [tutaj](https://towardsdatascience.com/intuitions-on-l1-and-l2-regularisation-235f2db4c261).\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "W praktyce detekcja nadmiernego dopasowania nie musi być wcale taka oczywista. Nasz model może przeuczać się tylko na niektórych segmentach danych, dla nietrywialnych kombinacji cech etc. Testowanie modeli ML i detekcja overfittingu jest otwartym problemem badawczym, ale powstają już pierwsze narzędzia do tego, np. [Giskard](https://github.com/Giskard-AI/giskard)."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 5 (1.0 punkt)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Uzupełnij kod funkcji `assess_regression_model` o:\n",
            "- obliczenie predykcji na zbiorze treningowym oraz testowym,\n",
            "- transformacje eksponencjalne, żeby wrócić do oryginalnej jednostki (dolara),\n",
            "- obliczenie RMSE dla zbioru treningowego i testowego,\n",
            "- wypisywanie RMSE, zaokrąglonego do 2 miejsc po przecinku,\n",
            "- zwrócenie RMSE dla zbioru treningowego i testowego.\n",
            "\n",
            "Skomentuj wyniki. Czy następuje przeuczenie modelu? Oceń także sam błąd, czy subiektywnie to duża wartość, biorąc pod uwagę rozkład zmiennej docelowej (wartości i wykresy w sekcji EDA)?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 32,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "def assess_regression_model(model, X_train, X_test, y_train, y_test) -> tuple[float, float]:\n",
            "    # predict for train and test\n",
            "    y_train_pred = model.predict(X_train)\n",
            "    y_test_pred = model.predict(X_test)\n",
            "\n",
            "    # exponential transform for y_train, y_test and predictions\n",
            "    y_train = np.expm1(y_train)\n",
            "    y_test = np.expm1(y_test)\n",
            "\n",
            "    # calculate train and test RMSE\n",
            "    rmse_train = root_mean_squared_error(y_train, np.expm1(y_train_pred))\n",
            "    rmse_test = root_mean_squared_error(y_test, np.expm1(y_test_pred))\n",
            "    \n",
            "    # print train and test RMSE\n",
            "    print(f\"Train RMSE: {rmse_train:.2f}\")\n",
            "    print(f\"Test RMSE: {rmse_test:.2f}\")\n",
            "    return rmse_train, rmse_test\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 33,
         "metadata": {
            "editable": true,
            "lines_to_next_cell": 2,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Train RMSE: 16751.27\n",
                  "Test RMSE: 21480.47\n"
               ]
            }
         ],
         "source": [
            "rmse_train, rmse_test = assess_regression_model(reg_linear, X_train, X_test, y_train, y_test)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 34,
         "metadata": {
            "editable": true,
            "lines_to_next_cell": 2,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Solution is correct!\n"
               ]
            }
         ],
         "source": [
            "assert 16000 < rmse_train < 17000\n",
            "assert 21000 < rmse_test < 22000\n",
            "\n",
            "print(\"Solution is correct!\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Zauważamy, że różnica między RMSE na zbiorze treningowym i testowym jest rzędu 30%, co może sugerować, że model jest nieco przetrenowany, ale nie jest to różnica drastyczna.\n",
            "\n",
            "RMSE na zbiorze treningowym stanowi około 10%, natomiast na zbiorze testowym 12% wartości średniej ceny domu, co jest wynikiem całkiem zadowalającym.\n",
            "\n",
            "Odchylenie standardowe ceny domów wynosi około 80 tysięcy dolarów, co oznacza, że model całkiem dobrze przewiduje ceny domów, ale zdecydowanie ma pole do poprawy.\n",
            "\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Regresja regularyzowana (ridge, LASSO)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Regularyzacja zmniejsza pojemność modelu regresji liniowej, narzucając mniejsze wagi poprzez penalizację dużych wag w funkcji kosztu. Regresja liniowa z regularyzacją L2 nazywa się *ridge regression*, z regularyzacją L1 - *LASSO regression*, a z oboma naraz - *ElasticNet regression*. Formalnie mamy:\n",
            "$$\n",
            "L_{ridge}(y, \\hat{y}) = \\frac{1}{n} (y - \\hat{y})^2 + \\lambda ||\\boldsymbol{w}||_2^2\n",
            "$$\n",
            "$$\n",
            "L_{LASSO}(y, \\hat{y}) = \\frac{1}{n} (y - \\hat{y})^2 + \\alpha ||\\boldsymbol{w}||_1\n",
            "$$\n",
            "$$\n",
            "L_{ElasticNet}(y, \\hat{y}) = \\frac{1}{n} (y - \\hat{y})^2 + \\lambda ||\\boldsymbol{w}||_2^2 + \\alpha ||\\boldsymbol{w}||_1\n",
            "$$\n",
            "\n",
            "Jak widać, regularyzacja dodaje do zwykłego kosztu MSE dodatkowe wyrazy, penalizujące wielkość wag $\\boldsymbol{w}$. **Siłę regularyzacji (regularization strength)**, czyli jak mocna jest taka kara, wyznacza współczynnik, oznaczany typowo $\\lambda$ albo $\\alpha$. Jest to **hiperparametr (hyperparameter)**, czyli stała modelu, którą narzucamy z góry, przed treningiem. Nie jest on uczony z danych. Jak go dobrać, omówimy poniżej.\n",
            "\n",
            "Regresja ridge (L2) zmniejsza wagi i jest różniczkowalna (szybsza i łatwiejsza w treningu). Regresja LASSO (L1) dokonuje **selekcji cech (feature selection)**, zmniejszając często wagi cech dokładnie do zera, eliminując tym samym słabe cechy. Oba naraz realizuje model ElasticNet.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "W Scikit-learn implementują je klasy `Ridge`, `Lasso` oraz `ElasticNet`. Najważniejszy hiperparametr każdego z tych modeli to siła regularyzacji, która we wszystkich klasach to `alpha`. Scikit-learn definiuje regularyzację ElasticNet dość specyficznie, za pomocą parametru `l1_ratio`, który wyznacza, jaki ułamek siły regularyzacji przypada dla L1, a jaki dla L2:\n",
            "$$\n",
            "L_{ElasticNet}(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=1}^n \\left( y - \\hat{y} \\right)^2 + \\alpha \\cdot (1 - L1\\_ratio) \\cdot ||\\boldsymbol{w}||_2^2 + \\alpha \\cdot L1\\_ratio \\cdot ||\\boldsymbol{w}||_1 \\\\\n",
            "$$\n",
            "\n",
            "Inne ważne uwagi:\n",
            "- liczba iteracji `max_iter` wyznacza liczbę iteracji solwera; im więcej, tym dokładniejsze rozwiązanie, ale tym dłuższy czas obliczeń,\n",
            "- jeżeli `max_iter` będzie zbyt mała i algorytm nie osiągnie zbieżności, to dostaniemy ostrzeżenie, wtedy zwykle trzeba po prostu ją zwiększyć, np. 10-krotnie,\n",
            "- jeżeli nie potrzebujemy bardzo precyzyjnego rozwiązania, można ustawić większe `tol` dla przyspieszenia obliczeń.\n",
            "\n",
            "Jako że nasz model jest regularyzowany i nie ma ryzyka problemów numerycznych, to teraz już obliczamy intercept."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 35,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Train RMSE: 16870.91\n",
                  "Test RMSE: 18879.11\n",
                  "\n",
                  "Train RMSE: 79579.79\n",
                  "Test RMSE: 80091.99\n",
                  "\n"
               ]
            }
         ],
         "source": [
            "from sklearn.linear_model import Ridge, Lasso\n",
            "\n",
            "reg_ridge = Ridge(random_state=0)\n",
            "reg_lasso = Lasso(random_state=0)\n",
            "\n",
            "reg_ridge.fit(X_train, y_train)\n",
            "reg_lasso.fit(X_train, y_train)\n",
            "\n",
            "assess_regression_model(reg_ridge, X_train, X_test, y_train, y_test)\n",
            "print()\n",
            "assess_regression_model(reg_lasso, X_train, X_test, y_train, y_test)\n",
            "print()"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "W przypadku regularyzacji L2 domyślna siła regularyzacji (`alpha=1.0`) znacząco poprawiła wynik, natomiast w przypadku L1 mamy bardzo silny underfitting."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Tuning hiperparametrów, zbiór walidacyjny\n",
            "\n",
            "Praktycznie wszystkie modele ML mają hiperparametry, często liczne, które w zauważalny sposób wpływają na wyniki, a szczególnie na underfitting i overfitting. Ich wartości trzeba dobrać zatem dość dokładnie. Jak to zrobić? Proces doboru hiperparametrów nazywa się **tuningiem hiperparametrów** (*hyperparameter tuning*).\n",
            "\n",
            "Istnieje na to wiele sposobów. Większość z nich polega na tym, że trenuje się za każdym razem model z nowym zestawem hiperparametrów i wybiera się ten zestaw, który pozwala uzyskać najlepsze wyniki. Metody głównie różnią się między sobą sposobem doboru kandydujących zestawów hiperparametrów.\n",
            "\n",
            "Najprostsze i najpopularniejsze to:\n",
            "\n",
            "* **pełne przeszukiwanie** (*grid search*) - definiujemy możliwe wartości dla różnych hiperparametrów, a metoda sprawdza ich wszystkie możliwe kombinacje (czyli siatkę),\n",
            "* **losowe przeszukiwanie** (*randomized search*) - definiujemy możliwe wartości jak w pełnym przeszukiwaniu, ale sprawdzamy tylko ograniczoną liczbę losowo wybranych kombinacji."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "Jak ocenić, jak dobry jest jakiś zestaw hiperparametrów? Nie możemy sprawdzić tego na zbiorze treningowym - wyniki byłyby zbyt optymistyczne. Nie możemy wykorzystać zbioru testowego - mielibyśmy data leakage, bo wybieralibyśmy model explicite pod nasz zbiór testowy. Trzeba zatem osobnego zbioru, na którym będziemy na bieżąco sprawdzać jakość modeli dla różnych hiperparametrów. Jest to **zbiór walidacyjny** (*validation set*).\n",
            "\n",
            "Zbiór taki wycina się ze zbioru treningowego. Dzielimy zatem nasze dane nie na dwie, ale trzy części: treningową, walidacyjną i testową. Typowe proporcje to 60-20-20% lub 80-10-10%.\n",
            "\n",
            "Metody tuningu hiperparametrów są zaimplementowane w Scikit-Learn jako `GridSearchCV` oraz `RandomizedSearchCV`. Są też bardziej wyspecjalizowane metody dla konkretnych modeli, które są dla nich typowo o wiele szybsze.\n",
            "\n",
            "**Uwaga:** warto zauważyć, że liczba możliwych kombinacji rośnie gwałtownie wraz z liczbą hiperparametrów i ich możliwych wartości. Mając siatkę na 3 hiperparametry po 10 możliwych wartości dla każdego, otrzymujemy 1000 możliwych kombinacji. W pracy w ML płacą nam też za to, że wiemy, jakie siatki dobrać :)\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "Szczególnie inteligentne są metody tuningu z grupy metod optymalizacji bayesowskiej (Bayesian hyperparameter optimization / Bayesian HPO). Są to np. procesy Gaussowskie oraz Tree Parzen Estimator (TPE). Wykorzystują one dość zaawansowaną statystykę, aby zamodelować, jak poszczególne hiperparametry wpływają na wynik i dobierają takie kolejne kombinacje hiperparametrów, które są ich zdaniem najbardziej obiecujące. W szczególności wiele z tych metod traktuje dobór hiperparametrów jak problem regresji, gdzie parametrami są hiperparametry modelu, które dobieramy.\n",
            "\n",
            "Takich metod szczególnie często używa się przy tuningu hiperparametrów dla sieci neuronowej, gdyż jej wytrenowanie jest czasochłonne, a więc nie możemy pozwolić sobie na sprawdzenie licznych kombinacji, bo zbyt dużo by nas to kosztowało.\n",
            "\n",
            "Ta metoda została zaimplementowana w wielu frameworkach, jak np. Optuna czy Hyperopt. Więcej można o nich przeczytać [tutaj](https://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f)."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Walidacja skrośna\n",
            "\n",
            "Jednorazowy podział zbioru na części nazywa się *split validation* lub *holdout*. Używamy go, gdy mamy sporo danych, i 10-20% zbioru jako dane walidacyjne czy testowe to dość dużo, żeby mieć przyzwoite oszacowanie. Zbyt mały zbiór walidacyjny czy testowy da nam mało wiarygodne wyniki - nie da się nawet powiedzieć, czy zbyt pesymityczne, czy optymistyczne! W praktyce niestety często mamy mało danych. Trzeba zatem jakiejś magicznej metody, która stworzy nam więcej zbiorów walidacyjnych z tej samej ilości danych.\n",
            "\n",
            "Taką metodą jest **walidacja skrośna** (*cross-validation, CV*). Polega na tym, że dzielimy zbiór na K równych podzbiorów, tzw. *foldów*. Każdy podzbiór po kolei staje się zbiorem walidacyjnym, a pozostałe łączymy w zbiór treningowy. Przykładowo, jeżeli mamy 5 foldów (1, 2, 3, 4, 5), to będziemy mieli po kolei:\n",
            "- zbiór treningowy: (2, 3, 4, 5), walidacyjny: (1)\n",
            "- zbiór treningowy: (1, 3, 4, 5), walidacyjny: (2)\n",
            "- zbiór treningowy: (1, 2, 4, 5), walidacyjny: (3)\n",
            "- zbiór treningowy: (1, 2, 3, 5), walidacyjny: (4)\n",
            "- zbiór treningowy: (1, 2, 3, 4), walidacyjny: (5)\n",
            "\n",
            "Trenujemy zatem K modeli dla tego samego zestawu hiperparametrów i każdy testujemy na zbiorze walidacyjnym. Mamy K wyników dla zbiorów walidacyjnych, które możemy uśrednić (i ew. obliczyć odchylenie standardowe). Takie wyniki są znacznie bardziej wiarygodne zgodnie ze statystyką (moc statystyczna itp.). Typowo używa się 5 lub 10 foldów, co jest dobrym balansem między liczbą modeli do wytrenowania i wielkością zbiorów walidacyjnych.\n",
            "\n",
            "Szczególnym przypadkiem jest Leave-One-Out Cross-Validation (LOOCV), w którym ilość podzbiorów (*foldów*) jest równa ilości rekordów. Czyli w danej chwili tylko 1 przykład jest zbiorem walidacyjnym. Daje to możliwość prawie całkowitego wykorzystania naszych danych (w każdej iteracji musimy wydzielić tylko 1 przykład na zbiór walidacyjny, cała reszta jest naszym zbiorem treningowym), ale wprowadza ogromny koszt obliczeniowy. Jest to opłacalne tylko w szczególnych przypadkach.\n",
            "\n",
            "Można zauważyć, że w nazwach klas do tuningu parametrów, wspomnianych wyżej, mamy sufiks `CV` - to jest właśnie *Cross Validation*.\n",
            "\n",
            "#### Dla zainteresowanych\n",
            "\n",
            "Walidacji skrośnej można użyć także do testowania, tworząc wiele zbiorów testowych. Można połączyć obie techniki, co daje tzw. [nested cross-validation](https://vitalflux.com/python-nested-cross-validation-algorithm-selection/). Jest to bardzo kosztowna, ale jednocześnie bardzo precyzyjna technika."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### RidgeCV, LassoCV, ElasticNetCV\n",
            "\n",
            "W przypadku regresji liniowej istnieją bardzo wydajne implementacje walidacji skrośnej, głównie dzięki prostocie tego modelu. W Scikit-learn są to odpowiednio `RidgeCV`, `LassoCV` oraz `ElasticNetCV`.\n",
            "\n",
            "`RidgeCV` domyślnie wykorzystuje efektywną implementację Leave-One-Out Cross-Validation (LOOCV). Jest to możliwe dzięki pewnym sztuczkom opartym na algebrze liniowej, wyjaśnionych [w dokumentacji w kodzie](https://github.com/scikit-learn/scikit-learn/blob/8c9c1f27b7e21201cfffb118934999025fd50cca/sklearn/linear_model/_ridge.py#L1547) (dla zainteresowanych). Co ważne, jest to operacja o wiele szybsza niż osobne grid search + ridge regression, a nawet od `RidgeCV` z mniejszą liczbą foldów.\n",
            "\n",
            "`LassoCV` oraz `ElasticNetCV` iterują od najmniejszych do największych wartości `alpha` (siły regularyzacji), używając rozwiązania dla mniejszej siły regularyzacji jako punktu początkowego dla kolejnej wartości. Odpowiada to po prostu dość inteligentnemu wyborowi punktu startowego w optymalizacji funkcji kosztu, a znacznie obniża koszt obliczeniowy."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 6 (1.0 punkt)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Użyj klas `RidgeCV` oraz `LassoCV` do tuningu hiperparametrów.\n",
            "\n",
            "Dla `RidgeCV` sprawdź 1000 wartości `[0.1, 100]` w skali liniowej - przyda się `np.linspace()`. Użyj LOOCV.\n",
            "\n",
            "Dla `LassoCV` Scikit-learn sam dobierze wartości, musisz podać tylko liczbę wartości alfa do sprawdzenia - użyj 1000. Użyj 5-fold CV. Pamiętaj o podaniu `random_state=0` - solver jest niedeterministyczny.\n",
            "\n",
            "Wypisz znalezione optymalne wartości siły regularyzacji `.alpha_` dla obu modeli, zaokrąglone do 4 miejsca po przecinku dla czytelności.\n",
            "\n",
            "Wartości błędu przypisz do zmiennych: `reg_train_rmse`, `reg_test_rmse`, `lass_train_rmse`, `lasso_test_rmse`.\n",
            "Wartości $\\alpha$ przypisz do zmiennych `reg_ridge_alpha` oraz `reg_lasso_alpha`.\n",
            "\n",
            "---\n",
            "\n",
            "***Ciekawostka***\n",
            "\n",
            "Atrybuty z `_` (*underscore*) na końcu w Scikit-Learn oznaczają, że zostały one wyliczone podczas treningu (`.fit()`). W powyższym przypadku optymalny współczynnik regularyzacji `.alpha_` został wyznaczony dopiero po przeprowadzeniu tuningu hiperparametrów.\n",
            "\n",
            "Jeśli zajrzeć do [dokumentacji](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) dla klasy `LinearRegression`, to można zauważyć takie atrybuty jak `.coef_` przechowujący wyznaczone współczynniki cech, czy `.intercept_` - wyraz wolny.\n",
            "\n",
            "Takie atrybuty pozwalają przeprowadzić dogłębniejszą analizę wytrenowanego modelu.\n",
            "\n",
            "---\n",
            "\n",
            "Przetestuj modele z użyciem `assess_regression_model()`. Skomentuj wyniki. Czy udało się wyeliminować overfitting?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 36,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Train RMSE: 17212.41\n",
                  "Test RMSE: 18758.43\n",
                  "\n",
                  "Train RMSE: 18108.04\n",
                  "Test RMSE: 18664.62\n",
                  "\n",
                  "RidgeCV alpha: 2.9000\n",
                  "LassoCV alpha: 0.0003\n"
               ]
            }
         ],
         "source": [
            "from sklearn.linear_model import RidgeCV, LassoCV\n",
            "\n",
            "red_ridge_cv_alpha_values = np.linspace(0.1, 100, 1000)\n",
            "reg_ridge_cv = RidgeCV(alphas=red_ridge_cv_alpha_values)\n",
            "\n",
            "reg_lasso_cv = LassoCV(n_alphas=1000, cv=5, random_state=0)\n",
            "\n",
            "reg_ridge_cv.fit(X_train, y_train)\n",
            "reg_lasso_cv.fit(X_train, y_train)\n",
            "\n",
            "reg_ridge_alpha = reg_ridge_cv.alpha_\n",
            "reg_lasso_alpha = reg_lasso_cv.alpha_\n",
            "\n",
            "ridge_train_rmse, ridge_test_rmse = assess_regression_model(reg_ridge_cv, X_train, X_test, y_train, y_test)\n",
            "print()\n",
            "lasso_train_rmse, lasso_test_rmse = assess_regression_model(reg_lasso_cv, X_train, X_test, y_train, y_test)\n",
            "print()\n",
            "\n",
            "print(f\"RidgeCV alpha: {reg_ridge_alpha:.4f}\")\n",
            "print(f\"LassoCV alpha: {reg_lasso_alpha:.4f}\")"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 37,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Solution is correct!\n"
               ]
            }
         ],
         "source": [
            "assert 17000 < ridge_train_rmse < 18000\n",
            "assert 18000 < ridge_test_rmse < 19000\n",
            "\n",
            "assert 18000 < lasso_train_rmse < 19000\n",
            "assert 18000 < lasso_test_rmse < 19000\n",
            "\n",
            "assert 2 < reg_ridge_alpha < 3\n",
            "assert 0 < reg_lasso_alpha < 0.1\n",
            "\n",
            "print(\"Solution is correct!\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "RMSE w obu modelach jest zbliżone, co sugeruje, że udało się wyeliminować problem overfittingu\n",
            "\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Regresja wielomianowa"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Regresja wielomianowa to po prostu dodanie wielomianów cech do naszych danych:\n",
            "$$\n",
            "[a, b, c, d] -> [a, b, c, d, a^2, b^2, c^2, d^2, ab, ac, ad, bc, bd, cd]\n",
            "$$\n",
            "\n",
            "Pozwala to na uwzględnienie bardziej złożonych kombinacji cech, których sama regresja liniowa, ze względu na swoją prostotę, nie jest w stanie uwzględnić.\n",
            "\n",
            "W Scikit-learn regresja wielomianowa składa się z 2 osobnych kroków: wygenerowania cech wielomianowych i użycia zwykłej regresji liniowej. Pozwala to na użycie tej transformacji dla dowolnych algorytmów, nie tylko regresji liniowej.\n",
            "\n",
            "Kwestią sporną jest, czy jest sens przeprowadzać taką transformację dla zmiennych po one-hot encodingu. Potęgi na pewno nie mają sensu, natomiast interakcje realizują po prostu operację koniunkcji (AND), ale łatwo prowadzi to do eksplozji wymiarowości. Dla uproszczenia poniżej zastosujemy transformację dla wszystkich cech.\n",
            "\n",
            "Warto pamiętać, że jeżeli używamy modelu, który sam dodaje intercept (jak regresja liniowa), to trzeba przekazać `include_bias=False`. Żeby wymiarowość zbytnio nam nie urosła, użyjemy `interaction_only=True`."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 38,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Train RMSE: 12856.88\n",
                  "Test RMSE: 18298.30\n",
                  "\n",
                  "Ridge + polynomial features alpha: 84.8000\n"
               ]
            }
         ],
         "source": [
            "from sklearn.preprocessing import PolynomialFeatures\n",
            "\n",
            "poly_features = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)\n",
            "poly_features.fit(X_train)\n",
            "\n",
            "X_train_poly = poly_features.transform(X_train)\n",
            "X_test_poly = poly_features.transform(X_test)\n",
            "\n",
            "reg_ridge_cv_poly = RidgeCV(alphas=np.linspace(0.1, 100, 1000))\n",
            "reg_ridge_cv_poly.fit(X_train_poly, y_train)\n",
            "\n",
            "assess_regression_model(reg_ridge_cv_poly, X_train_poly, X_test_poly, y_train, y_test)\n",
            "print()\n",
            "print(f\"Ridge + polynomial features alpha: {reg_ridge_cv_poly.alpha_:.4f}\")"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Co ciekawe, model bardziej zbliżył się do przeuczenia, ale błąd testowy zmalał. Jest to niezbyt częste, ale możliwe."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "## Regresja logistyczna\n",
            "\n",
            "Regresja logistyczna jest modelem, który pozwala na przewidywanie wartości zmiennych dychotomicznych w oparciu o jedną lub większą liczbę cech. Funkcją bazową regresji logistycznej jest funkcja logistyczna. Bardzo ciekawe podsumowanie dotyczące matematyki stojącej za regresją logistyczną znajdziesz [tu](https://philippmuens.com/logistic-regression-from-scratch)."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Do klasyfikacji wykorzystamy zbiór [Bank Marketing](https://archive.ics.uci.edu/ml/datasets/bank+marketing), w którym przewiduje się, czy dana osoba będzie zainteresowana lokatą terminową w banku. Precyzyjny targetowany marketing jest ważny z perspektywy biznesu, bo w praktyce chce się reklamować tak mało, jak to możliwe. Bank zarabia tylko na tych osobach, które są faktycznie zainteresowane reklamą, a pozostałych można łatwo zrazić zbyt dużą liczbą reklam, więc precyzyjna ocena przynosi tu realne zyski.\n",
            "\n",
            "Zbiór posiada dwie wersje, uproszczoną oraz rozszerzoną o dodatkowe atrybuty socjoekonomiczne (np. sytuację ekonomiczną w planowanym momencie reklamy). Wykorzystamy tę drugą, bo są to bardzo wartościowe cechy. Dodatkowo każda wersja posiada pełny zbiór (ok. 45 tysięcy przykładów) oraz pomniejszony (ok. 4 tysiąca przykładów). Dzięki skalowalności regresji logistycznej możemy bez problemu wykorzystać pełny zbiór z dodatkowymi cechami.\n",
            "\n",
            "Opisy zmiennych znajdują się w pliku [bank_marketing_description.txt](bank_marketing_description.txt)."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 7 (1.0 punkt)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "*Wczytywanie i czyszczenie danych*\n",
            "\n",
            "1. Załaduj zbiór danych z pliku [bank_marketing_data.csv](bank_marketing_data.csv) do DataFrame'a. Zwróć uwagę, że separatorem jest średnik (argument `sep`).\n",
            "2. Usuń kolumny:\n",
            "    - `default`, czy klient ma zadłużenie na karcie kredytowej; ma tylko 3 wartości `yes`,\n",
            "    - `duration`, czas trwania ostatniego telefonu reklamowego; autorzy sugerują usunięcie w opisie zbioru, bo nie znamy tej wartości przed wykonaniem telefonu,\n",
            "    - `pdays`, liczba dni od ostatniego telefonu reklamowego w ramach danej kampanii marketingowej; jeżeli to pierwszy kontakt, to wartość to 999, i ciężko byłoby włączyć taką cechę do modelu, a mamy już i tak informację o tym, czy to pierwszy kontakt z klientem w zmiennej `previous`,\n",
            "    - `poutcome`, wynik poprzedniej kampanii; w zdecydowanej większości przypadków to `nonexistent`.\n",
            "3. Dokonaj filtrowania wierszy:\n",
            "    - usuń wiersze z `education` na poziomie `illiterate`, jest ich tylko kilkanaście.\n",
            "4. Zakoduj odpowiednio zmienne `education`, `contact`, `month`, `day_of_week` i `y`. Dla ułatwienia słowniki tych zmiennych są w zmiennych poniżej.\n",
            "5. Wyodrębnij kolumnę `y` do zmiennej `y` (pamiętaj o usunięciu jej z DataFrame'a)."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 39,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "education_mapping = {\n",
            "    \"basic.4y\": \"primary\",\n",
            "    \"basic.6y\": \"primary\",\n",
            "    \"basic.9y\": \"primary\",\n",
            "    \"high.school\": \"secondary\",\n",
            "    \"professional.course\": \"secondary\",\n",
            "    \"university.degree\": \"tertiary\",\n",
            "}\n",
            "\n",
            "contact_mapping = {\n",
            "    \"telephone\": 0,\n",
            "    \"cellular\": 1,\n",
            "}\n",
            "\n",
            "month_mapping = {\n",
            "    \"jan\": 1,\n",
            "    \"feb\": 2,\n",
            "    \"mar\": 3,\n",
            "    \"apr\": 4,\n",
            "    \"may\": 5,\n",
            "    \"jun\": 6,\n",
            "    \"jul\": 7,\n",
            "    \"aug\": 8,\n",
            "    \"sep\": 9,\n",
            "    \"oct\": 10,\n",
            "    \"nov\": 11,\n",
            "    \"dec\": 12,\n",
            "}\n",
            "\n",
            "day_of_week_mapping = {\n",
            "    \"mon\": 1,\n",
            "    \"tue\": 2,\n",
            "    \"wed\": 3,\n",
            "    \"thu\": 4,\n",
            "    \"fri\": 5,\n",
            "}\n",
            "\n",
            "y_mapping = {\n",
            "    \"no\": 0,\n",
            "    \"yes\": 1,\n",
            "}"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 40,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "df = pd.read_csv(\"bank_marketing_data.csv\", sep=\";\")\n",
            "\n",
            "df = df.drop(['default', 'duration', 'pdays', 'poutcome'], axis=\"columns\")\n",
            "\n",
            "df = df.loc[~df[\"education\"].isin([\"illiterate\"]), :]\n",
            "\n",
            "df[\"education\"] = df[\"education\"].map(education_mapping)\n",
            "df[\"contact\"] = df[\"contact\"].map(contact_mapping)\n",
            "df[\"month\"] = df[\"month\"].map(month_mapping)\n",
            "df[\"day_of_week\"] = df[\"day_of_week\"].map(day_of_week_mapping)\n",
            "df[\"y\"] = df[\"y\"].map(y_mapping)\n",
            "\n",
            "replace_na(df, \"education\", value=\"unknown\")\n",
            "\n",
            "y = df.pop(\"y\")"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 41,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "assert 'default' not in df.columns\n",
            "assert 'duration' not in df.columns\n",
            "assert 'pdays' not in df.columns\n",
            "assert 'poutcome' not in df.columns\n",
            "assert 'y' not in df.columns\n",
            "\n",
            "assert \"illiterate\" not in df['education'].unique()\n",
            "\n",
            "assert sorted(['primary', 'secondary', 'tertiary', 'unknown']) == sorted(df['education'].unique())\n",
            "assert [3,4,5,6,7,8,9,10,11,12] == sorted(df['month'].unique())\n",
            "assert [1,2,3,4,5] == sorted(df['day_of_week'].unique())\n",
            "assert [0,1] == sorted(df['contact'].unique())"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 8 (0.5 punktu)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "*Exploratory Data Analysis (EDA)*\n",
            "\n",
            "1. Sprawdź, czy są jakieś wartości brakujące za pomocą biblioteki `missingno`. Jeżeli tak, to sprawdź w dokumentacji zbioru, jaka byłaby sensowna wartość do ich uzupełnienia.\n",
            "2. Narysuj wykres (bar plot) z częstością klas. Uwzględnij częstość na wykresie ([to może się przydać](https://stackoverflow.com/a/68107610/9472066)). Pamiętaj o tytule i opisaniu osi."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 42,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "data": {
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",
                  "text/plain": [
                     "<Figure size 2500x1000 with 3 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "import missingno as msno\n",
            "\n",
            "msno.bar(df)\n",
            "\n",
            "replace_na(df, \"education\", value=\"unknown\")\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 43,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "data": {
                  "image/png": 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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "y.hist(bins=2, edgecolor='black')\n",
            "\n",
            "plt.title('Distribution of Loan Tendency')\n",
            "plt.xlabel('Loan Tendency (0: Does Not, 1: Takes Loan)')\n",
            "plt.ylabel('Frequency')\n",
            "\n",
            "plt.show()\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "Jak widać, będziemy tu mieli do czynienia z problemem klasyfikacji niezbalansowanej. Na szczęście funkcja kosztu w regresji logistycznej pozwala na dodanie **wag klas (class weights)**, aby przypisać większą wagę interesującej nas klasie pozytywnej. Scikit-learn dla wartości `class_weights=\"balanced\"` obliczy wagi odwrotnie proporcjonalne do częstości danej klasy w zbiorze."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 9 (1.0 punkt)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "*Podział i preprocessing danych*\n",
            "\n",
            "1. Dokonaj podziału zbioru na treningowy i testowy w proporcjach 75%-25%. Pamiętaj o użyciu podziału ze stratyfikacją (argument `stratify`), aby zachować proporcje klas. Ustaw `random_state=0`.\n",
            "2. Stwórz `ColumnTransformer`, przetwarzający zmienne kategoryczne za pomocą `OneHotEncoder` (teraz już nie musimy robić `drop=\"first\"`), a numeryczne za pomocą `StandardScaler`. Zaaplikuj go do odpowiednich kolumn.\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 44,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "from sklearn.compose import ColumnTransformer\n",
            "from sklearn.pipeline import Pipeline\n",
            "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
            "\n",
            "categorical_features = df.select_dtypes(include=\"object\").columns\n",
            "numerical_features = df.select_dtypes(exclude=\"object\").columns\n",
            "\n",
            "X_train, X_test, y_train, y_test = train_test_split(\n",
            "    df, y, test_size=0.25, random_state=0, stratify=y\n",
            ")\n",
            "\n",
            "categorical_pipeline = Pipeline(\n",
            "    steps=[\n",
            "        (\"one-hot-encoder\", OneHotEncoder(sparse_output=False, handle_unknown=\"ignore\")),\n",
            "    ]\n",
            ")\n",
            "\n",
            "numerical_pipeline = Pipeline(\n",
            "    steps=[\n",
            "        (\"scaler\", StandardScaler()),\n",
            "    ]\n",
            ")\n",
            "\n",
            "column_transformer = ColumnTransformer(\n",
            "    transformers=[\n",
            "        (\"categorical\", categorical_pipeline, categorical_features),\n",
            "        (\"numerical\", numerical_pipeline, numerical_features),\n",
            "    ]\n",
            ")\n",
            "\n",
            "column_transformer.fit(X_train)\n",
            "\n",
            "X_train = column_transformer.transform(X_train)\n",
            "X_test = column_transformer.transform(X_test)\n",
            "\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 45,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "assert X_train.shape == (30877, 37)\n",
            "assert X_test.shape == (10293, 37)\n",
            "\n",
            "assert X_train[:,0].min() == 0\n",
            "assert X_train[:,0].max() == 1\n",
            "assert -3 < X_train[:,26].min() < -2\n",
            "assert 5 < X_train[:,26].max() < 6\n",
            "\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 46,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "data": {
                  "text/html": [
                     "<style>#sk-container-id-2 {\n",
                     "  /* Definition of color scheme common for light and dark mode */\n",
                     "  --sklearn-color-text: black;\n",
                     "  --sklearn-color-line: gray;\n",
                     "  /* Definition of color scheme for unfitted estimators */\n",
                     "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
                     "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
                     "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
                     "  --sklearn-color-unfitted-level-3: chocolate;\n",
                     "  /* Definition of color scheme for fitted estimators */\n",
                     "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
                     "  --sklearn-color-fitted-level-1: #d4ebff;\n",
                     "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
                     "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
                     "\n",
                     "  /* Specific color for light theme */\n",
                     "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
                     "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
                     "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
                     "  --sklearn-color-icon: #696969;\n",
                     "\n",
                     "  @media (prefers-color-scheme: dark) {\n",
                     "    /* Redefinition of color scheme for dark theme */\n",
                     "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
                     "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
                     "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
                     "    --sklearn-color-icon: #878787;\n",
                     "  }\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 pre {\n",
                     "  padding: 0;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 input.sk-hidden--visually {\n",
                     "  border: 0;\n",
                     "  clip: rect(1px 1px 1px 1px);\n",
                     "  clip: rect(1px, 1px, 1px, 1px);\n",
                     "  height: 1px;\n",
                     "  margin: -1px;\n",
                     "  overflow: hidden;\n",
                     "  padding: 0;\n",
                     "  position: absolute;\n",
                     "  width: 1px;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-dashed-wrapped {\n",
                     "  border: 1px dashed var(--sklearn-color-line);\n",
                     "  margin: 0 0.4em 0.5em 0.4em;\n",
                     "  box-sizing: border-box;\n",
                     "  padding-bottom: 0.4em;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-container {\n",
                     "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
                     "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
                     "     so we also need the `!important` here to be able to override the\n",
                     "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
                     "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
                     "  display: inline-block !important;\n",
                     "  position: relative;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-text-repr-fallback {\n",
                     "  display: none;\n",
                     "}\n",
                     "\n",
                     "div.sk-parallel-item,\n",
                     "div.sk-serial,\n",
                     "div.sk-item {\n",
                     "  /* draw centered vertical line to link estimators */\n",
                     "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
                     "  background-size: 2px 100%;\n",
                     "  background-repeat: no-repeat;\n",
                     "  background-position: center center;\n",
                     "}\n",
                     "\n",
                     "/* Parallel-specific style estimator block */\n",
                     "\n",
                     "#sk-container-id-2 div.sk-parallel-item::after {\n",
                     "  content: \"\";\n",
                     "  width: 100%;\n",
                     "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
                     "  flex-grow: 1;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-parallel {\n",
                     "  display: flex;\n",
                     "  align-items: stretch;\n",
                     "  justify-content: center;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  position: relative;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-parallel-item {\n",
                     "  display: flex;\n",
                     "  flex-direction: column;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
                     "  align-self: flex-end;\n",
                     "  width: 50%;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
                     "  align-self: flex-start;\n",
                     "  width: 50%;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
                     "  width: 0;\n",
                     "}\n",
                     "\n",
                     "/* Serial-specific style estimator block */\n",
                     "\n",
                     "#sk-container-id-2 div.sk-serial {\n",
                     "  display: flex;\n",
                     "  flex-direction: column;\n",
                     "  align-items: center;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  padding-right: 1em;\n",
                     "  padding-left: 1em;\n",
                     "}\n",
                     "\n",
                     "\n",
                     "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
                     "clickable and can be expanded/collapsed.\n",
                     "- Pipeline and ColumnTransformer use this feature and define the default style\n",
                     "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
                     "*/\n",
                     "\n",
                     "/* Pipeline and ColumnTransformer style (default) */\n",
                     "\n",
                     "#sk-container-id-2 div.sk-toggleable {\n",
                     "  /* Default theme specific background. It is overwritten whether we have a\n",
                     "  specific estimator or a Pipeline/ColumnTransformer */\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "}\n",
                     "\n",
                     "/* Toggleable label */\n",
                     "#sk-container-id-2 label.sk-toggleable__label {\n",
                     "  cursor: pointer;\n",
                     "  display: block;\n",
                     "  width: 100%;\n",
                     "  margin-bottom: 0;\n",
                     "  padding: 0.5em;\n",
                     "  box-sizing: border-box;\n",
                     "  text-align: center;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
                     "  /* Arrow on the left of the label */\n",
                     "  content: \"▸\";\n",
                     "  float: left;\n",
                     "  margin-right: 0.25em;\n",
                     "  color: var(--sklearn-color-icon);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "}\n",
                     "\n",
                     "/* Toggleable content - dropdown */\n",
                     "\n",
                     "#sk-container-id-2 div.sk-toggleable__content {\n",
                     "  max-height: 0;\n",
                     "  max-width: 0;\n",
                     "  overflow: hidden;\n",
                     "  text-align: left;\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-toggleable__content pre {\n",
                     "  margin: 0.2em;\n",
                     "  border-radius: 0.25em;\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
                     "  /* Expand drop-down */\n",
                     "  max-height: 200px;\n",
                     "  max-width: 100%;\n",
                     "  overflow: auto;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
                     "  content: \"▾\";\n",
                     "}\n",
                     "\n",
                     "/* Pipeline/ColumnTransformer-specific style */\n",
                     "\n",
                     "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Estimator-specific style */\n",
                     "\n",
                     "/* Colorize estimator box */\n",
                     "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
                     "#sk-container-id-2 div.sk-label label {\n",
                     "  /* The background is the default theme color */\n",
                     "  color: var(--sklearn-color-text-on-default-background);\n",
                     "}\n",
                     "\n",
                     "/* On hover, darken the color of the background */\n",
                     "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Label box, darken color on hover, fitted */\n",
                     "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Estimator label */\n",
                     "\n",
                     "#sk-container-id-2 div.sk-label label {\n",
                     "  font-family: monospace;\n",
                     "  font-weight: bold;\n",
                     "  display: inline-block;\n",
                     "  line-height: 1.2em;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-label-container {\n",
                     "  text-align: center;\n",
                     "}\n",
                     "\n",
                     "/* Estimator-specific */\n",
                     "#sk-container-id-2 div.sk-estimator {\n",
                     "  font-family: monospace;\n",
                     "  border: 1px dotted var(--sklearn-color-border-box);\n",
                     "  border-radius: 0.25em;\n",
                     "  box-sizing: border-box;\n",
                     "  margin-bottom: 0.5em;\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-0);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-estimator.fitted {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-0);\n",
                     "}\n",
                     "\n",
                     "/* on hover */\n",
                     "#sk-container-id-2 div.sk-estimator:hover {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-2);\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-2);\n",
                     "}\n",
                     "\n",
                     "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
                     "\n",
                     "/* Common style for \"i\" and \"?\" */\n",
                     "\n",
                     ".sk-estimator-doc-link,\n",
                     "a:link.sk-estimator-doc-link,\n",
                     "a:visited.sk-estimator-doc-link {\n",
                     "  float: right;\n",
                     "  font-size: smaller;\n",
                     "  line-height: 1em;\n",
                     "  font-family: monospace;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  border-radius: 1em;\n",
                     "  height: 1em;\n",
                     "  width: 1em;\n",
                     "  text-decoration: none !important;\n",
                     "  margin-left: 1ex;\n",
                     "  /* unfitted */\n",
                     "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
                     "  color: var(--sklearn-color-unfitted-level-1);\n",
                     "}\n",
                     "\n",
                     ".sk-estimator-doc-link.fitted,\n",
                     "a:link.sk-estimator-doc-link.fitted,\n",
                     "a:visited.sk-estimator-doc-link.fitted {\n",
                     "  /* fitted */\n",
                     "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
                     "  color: var(--sklearn-color-fitted-level-1);\n",
                     "}\n",
                     "\n",
                     "/* On hover */\n",
                     "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
                     ".sk-estimator-doc-link:hover,\n",
                     "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
                     ".sk-estimator-doc-link:hover {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-3);\n",
                     "  color: var(--sklearn-color-background);\n",
                     "  text-decoration: none;\n",
                     "}\n",
                     "\n",
                     "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
                     ".sk-estimator-doc-link.fitted:hover,\n",
                     "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
                     ".sk-estimator-doc-link.fitted:hover {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-3);\n",
                     "  color: var(--sklearn-color-background);\n",
                     "  text-decoration: none;\n",
                     "}\n",
                     "\n",
                     "/* Span, style for the box shown on hovering the info icon */\n",
                     ".sk-estimator-doc-link span {\n",
                     "  display: none;\n",
                     "  z-index: 9999;\n",
                     "  position: relative;\n",
                     "  font-weight: normal;\n",
                     "  right: .2ex;\n",
                     "  padding: .5ex;\n",
                     "  margin: .5ex;\n",
                     "  width: min-content;\n",
                     "  min-width: 20ex;\n",
                     "  max-width: 50ex;\n",
                     "  color: var(--sklearn-color-text);\n",
                     "  box-shadow: 2pt 2pt 4pt #999;\n",
                     "  /* unfitted */\n",
                     "  background: var(--sklearn-color-unfitted-level-0);\n",
                     "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
                     "}\n",
                     "\n",
                     ".sk-estimator-doc-link.fitted span {\n",
                     "  /* fitted */\n",
                     "  background: var(--sklearn-color-fitted-level-0);\n",
                     "  border: var(--sklearn-color-fitted-level-3);\n",
                     "}\n",
                     "\n",
                     ".sk-estimator-doc-link:hover span {\n",
                     "  display: block;\n",
                     "}\n",
                     "\n",
                     "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
                     "\n",
                     "#sk-container-id-2 a.estimator_doc_link {\n",
                     "  float: right;\n",
                     "  font-size: 1rem;\n",
                     "  line-height: 1em;\n",
                     "  font-family: monospace;\n",
                     "  background-color: var(--sklearn-color-background);\n",
                     "  border-radius: 1rem;\n",
                     "  height: 1rem;\n",
                     "  width: 1rem;\n",
                     "  text-decoration: none;\n",
                     "  /* unfitted */\n",
                     "  color: var(--sklearn-color-unfitted-level-1);\n",
                     "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
                     "  /* fitted */\n",
                     "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
                     "  color: var(--sklearn-color-fitted-level-1);\n",
                     "}\n",
                     "\n",
                     "/* On hover */\n",
                     "#sk-container-id-2 a.estimator_doc_link:hover {\n",
                     "  /* unfitted */\n",
                     "  background-color: var(--sklearn-color-unfitted-level-3);\n",
                     "  color: var(--sklearn-color-background);\n",
                     "  text-decoration: none;\n",
                     "}\n",
                     "\n",
                     "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
                     "  /* fitted */\n",
                     "  background-color: var(--sklearn-color-fitted-level-3);\n",
                     "}\n",
                     "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
                     "                                 Pipeline(steps=[(&#x27;one-hot-encoder&#x27;,\n",
                     "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
                     "                                                                sparse_output=False))]),\n",
                     "                                 Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;], dtype=&#x27;object&#x27;)),\n",
                     "                                (&#x27;numerical&#x27;,\n",
                     "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
                     "                                 Index([&#x27;age&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;, &#x27;day_of_week&#x27;, &#x27;campaign&#x27;, &#x27;previous&#x27;,\n",
                     "       &#x27;emp.var.rate&#x27;, &#x27;cons.price.idx&#x27;, &#x27;cons.conf.idx&#x27;, &#x27;euribor3m&#x27;,\n",
                     "       &#x27;nr.employed&#x27;],\n",
                     "      dtype=&#x27;object&#x27;))])</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
                     "                                 Pipeline(steps=[(&#x27;one-hot-encoder&#x27;,\n",
                     "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
                     "                                                                sparse_output=False))]),\n",
                     "                                 Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;], dtype=&#x27;object&#x27;)),\n",
                     "                                (&#x27;numerical&#x27;,\n",
                     "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
                     "                                 Index([&#x27;age&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;, &#x27;day_of_week&#x27;, &#x27;campaign&#x27;, &#x27;previous&#x27;,\n",
                     "       &#x27;emp.var.rate&#x27;, &#x27;cons.price.idx&#x27;, &#x27;cons.conf.idx&#x27;, &#x27;euribor3m&#x27;,\n",
                     "       &#x27;nr.employed&#x27;],\n",
                     "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">categorical</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;job&#x27;, &#x27;marital&#x27;, &#x27;education&#x27;, &#x27;housing&#x27;, &#x27;loan&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">numerical</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;age&#x27;, &#x27;contact&#x27;, &#x27;month&#x27;, &#x27;day_of_week&#x27;, &#x27;campaign&#x27;, &#x27;previous&#x27;,\n",
                     "       &#x27;emp.var.rate&#x27;, &#x27;cons.price.idx&#x27;, &#x27;cons.conf.idx&#x27;, &#x27;euribor3m&#x27;,\n",
                     "       &#x27;nr.employed&#x27;],\n",
                     "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
                  ],
                  "text/plain": [
                     "ColumnTransformer(transformers=[('categorical',\n",
                     "                                 Pipeline(steps=[('one-hot-encoder',\n",
                     "                                                  OneHotEncoder(handle_unknown='ignore',\n",
                     "                                                                sparse_output=False))]),\n",
                     "                                 Index(['job', 'marital', 'education', 'housing', 'loan'], dtype='object')),\n",
                     "                                ('numerical',\n",
                     "                                 Pipeline(steps=[('scaler', StandardScaler())]),\n",
                     "                                 Index(['age', 'contact', 'month', 'day_of_week', 'campaign', 'previous',\n",
                     "       'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m',\n",
                     "       'nr.employed'],\n",
                     "      dtype='object'))])"
                  ]
               },
               "execution_count": 46,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "column_transformer"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "### Metryki klasyfikacji binarnej\n",
            "\n",
            "W klasyfikacji binarnej mamy tylko dwie klasy, z konwencji oznaczamy jedną klasę jako negatywną, a drugą - pozytywną. W naszym przypadku klasą negatywną będą osoby niezainteresowane lokatą - nie chcemy im pokazywać naszych reklam, bo to będzie raczej nieskuteczne, a reklama kosztuje. Naszym targetem będą osoby oznaczone klasą pozytywną.\n",
            "\n",
            "Wytrenowaliśmy model, ale jak sprawdzić jakość jego działania? Metryki z regresji raczej za wiele nam nie pomogą. Potrzebujemy zdefiniować nowe.\n",
            "\n",
            "#### Celność, dokładność (*Accuracy*)\n",
            "\n",
            "Najprostszym sposobem oceny klasyfikacji jest sprawdzić, w ilu przypadkach się mylimy, a w ilu model odpowiada poprawnie. Ta metryka jest zwana ***accuracy***. Ma ona jednak zasadniczą wadę - kompletnie nie radzi sobie z klasami niezbalansowanymi.\n",
            "\n",
            "Prosty przypadek - mamy zbiór danych, który pozwala na podstawie różnych parametrów medycznych wykryć rzadką chorobę, która zdarza się u 0.01% ludzi. Weźmy prosty klasyfikator, który zawsze zwraca klasę negatywną. Niby jest w oczywisty sposób kompletnie nieprzydatny, ale jednak dla losowej próbki ludzi dostanie ***celność*** równą 99.99%, bo, rzeczywiście, u większości tej choroby nie będzie.\n",
            "\n",
            "Potrzebujemy bardziej skomplikowanej metryki, której nie da się tak łatwo oszukać.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "\n",
            "#### Macierz pomyłek (*Confusion Matrix*)\n",
            "\n",
            "Żeby zdefiniować taką metodę oceny klasyfikacji, musimy najpierw rozważyć jakie sytuacje mogą zdarzyć się przy klasyfikacji binarnej. Spójrzmy na tablicę poniżej:\n",
            "\n",
            "<div>\n",
            "<img src=\"confusion-matrix.png\" width=\"500\"/>\n",
            "</div>\n",
            "\n",
            "Występują tutaj przypadki:\n",
            "* ***prawdziwie pozytywne*** (*true positive*) - model zwrócił klasę pozytywną (*positive*), i jest to prawda (*true*)\n",
            "* ***prawdziwie negatwyne*** (*true negative*) - model zwrócił klasę negatywną (*negative*), i jest to prawda (*true*)\n",
            "* ***fałszywie negatywne*** (*false negative*) - model zwrócił klasę negatywną (*negative*), ale nie jest to prawda (*false*)\n",
            "* ***fałszywie pozytywne*** (*false positive*) - model zwrócił klasę pozytywną (*positive*), ale nie jest to prawda (*false*)\n",
            "\n",
            "Mając powyższe punkty - możemy zdefiniować ***celność*** następująco:\n",
            "\n",
            "$$\n",
            "accuracy = \\frac{TP + TN}{TP + TN + FP + FN}\n",
            "$$\n",
            "\n",
            "czyli ilość przypadków, w których poprawnie zidentykowaliśmy klasę, podzieloną przez ilość wszystkich przypadków.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "\n",
            "#### Precyzja (miara predykcyjna dodatnia) i czułość (*Precision & Recall*)\n",
            "\n",
            "Jednak jak zauważyliśmy wcześniej, istnieją sytuacje, w których nie jest to właściwe podejście.\n",
            "\n",
            "Zdecydowanie ciekawszą dla nas metryką może być stwierdzenie jaką część rekordów z klasą pozytywną model poprawnie rozpoznał. Pozwoli to nam powiedzieć, jak czuły jest nasz model na klasę pozytywną. Ta metryka nazywa się czułością (***recall***):\n",
            "\n",
            "$$\n",
            "recall = \\frac{TP}{TP + FN}\n",
            "$$\n",
            "\n",
            "Jest o ilość przypadków, w których poprawnie rozpoznaliśmy klasę pozytywną, podzielona przez ilość wszystkich przypadków z klasą pozytywną.\n",
            "\n",
            "Drugą korzystną dla nas metryką będzie stwierdzenie ile z osób, które zakwalifikowaliśmy do klasy pozytywne, rzeczywiście do niej należy. Pozwoli to oszacować, jak często mylimy się oznaczając rekord klasą pozytywną. Ta metryka nazywa się precyzją (***precision***):\n",
            "\n",
            "$$\n",
            "precision = \\frac{TP}{TP + FP}\n",
            "$$\n",
            "\n",
            "Jest to ilość przypadków, w których poprawnie rozpoznaliśmy klasę pozytywną, podzielona przez ilość wszystkich przypadków, w których zwróciliśmy klasę pozytywną.\n",
            "\n",
            "Ta metryka może być bardzo pomocna, na przykład, przy klasyfikacji spamu. Gorzej będzie, jeśli wrzucimy ważnego maila do spamu, niż przegapimy jakąś reklamę. Chcemy, aby jeśli coś zostało zaklasyfikowane jako spam, rzeczywiście nim było - chcemy jak najwyższą precyzję.\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "source": [
            "#### F1 score\n",
            "\n",
            "Powyższe metryki mają wadę - pojedynczo można je łatwo oszukać:\n",
            "\n",
            "* Czy chcemy idealną ***precyzję***? - wystarczy zawsze zwracać klasę negatywną (ważny mail nie trafi do spamu, jeśli żadnego z nich tam nie wrzucimy).\n",
            "* Czy chcemy idealną ***czułość***? - zawsze zwracamy klasę pozytywną (na pewno nie pominiemy chorego pacjenta, jeśli każdemu powiemy, że jest chory).\n",
            "\n",
            "Musimy stosować je w parze. Dla prostoty, często agregujemy je do jednej zagregowanej miary za pomocą średniej harmonicznej. W przypadku liczb z zakresu $[0, 1]$ (a z takimi mamy do czynienia), ona ma taką własność, że wartość wynikowa zawsze będzie bliższa mniejszej wartości. I im większa jest między nimi różnica, tym bardziej jest to widoczne. Przykładowo, dla pary $(100\\%, 0\\%)$ średnia harmoniczna wynosi $0\\%$. Średnia harmoniczna z ***precyzji*** i ***czułości*** nazywana jest ***miarą F1*** (*F1 score*):\n",
            "\n",
            "$$\n",
            "F_1 = \\frac{2 \\cdot precision \\cdot recall}{precision + recall}\n",
            "$$\n",
            "\n",
            "[Ten tutorial](https://mlu-explain.github.io/precision-recall/) ma świetne wizualizację, które w interaktywny sposób prezentują działanie powyższych metryk.\n",
            "\n",
            "**Uwaga**:  indeks dolny w mierze $F_1$ oznacza, że mamy do czyninia z miarą, która daje taką samą wagę precyzji i czułości, ale w ogólnym przypadku jest to parametr, za pomocą którego możemy promować miarę, która ma dla nas większe znaczenie."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 10 (2.0 punkty)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "*Trening, tuning i analiza modeli*\n",
            "\n",
            "1. Wytrenuj podstawowy model regresji logistycznej z użyciem `LogisticRegression`. Użyj wag klas (`class_weights=\"balanced\"`). Przetestuj model, wypisując pecyzję, czułość oraz miarę F1 w procentach. **Uwaga:** Scikit-learn domyślnie stosuje tutaj regularyzację L2, więc przekaż `penalty=\"None\"`.\n",
            "2. Dokonaj tuningu modelu z regularyzacją L2 za pomocą `LogisticRegressionCV`:\n",
            "    - sprawdź 100 wartości, wystarczy podać liczbę do `Cs`,\n",
            "    - użyj 5-krotnej walidacji krzyżowej,\n",
            "    - wybierz najlepszy model według metryki F1 (parametr `scoring`),\n",
            "    - pamiętaj o `class_weights=\"balanced\"` i `random_state=0`,\n",
            "    - użyj `n_jobs=-1` dla przyspieszenia obliczeń (`-1` znaczy, że użyjemy wszystkich rdzeni do obliczeń),\n",
            "    - przetestuj model, wypisując precyzję, czułość i miarę F1 w procentach.\n",
            "    - **uwaga:** Scikit-learn stosuje tutaj konwencję, gdzie parametr `C` to odwrotność siły regularyzacji - im mniejszy, tym silniejsza regularyzacja.\n",
            "3. Dokonaj analogicznego tuningu, ale dla regularyzacji L1. Użyj solwera SAGA. Przetestuj model, wypisując precyzję, czułość i miarę F1 w procentach. Przypisz wyniki do zmiennych:\n",
            "   - `l0_precision`, `l0_recall`, `l0_f1` - dla braku regularyzacji,\n",
            "   - `l1_precision`, `l1_recall`, `l1_f1` - dla regularyzacji L1,\n",
            "   - `l2_precision`, `l2_recall`, `l2_f1` - dla regularyzacji L2.\n",
            "5. Dokonaj analizy wytrenowanych modeli:\n",
            "    - Oblicz miarę F1 na zbiorze treningowym modelu bez żadnej regularyzacji i porównaj go z wynikiem testowym; czy występuje tutaj overfitting?\n",
            "    - Wartości przypisz do zmiennych `f1_train` oraz `f1_test`. \n",
            "    - Czy twoim zdaniem tworzenie modeli z regularyzacją ma sens w tym przypadku?\n",
            "\n",
            "Napisz co, w twojej opinii, jest ważniejsze dla naszego problemu, ***precision*** czy ***recall***? Jak moglibyśmy, nie zmieniając modelu, zmienić ich stosunek?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 47,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "No regularization\n",
                  "\n",
                  "Precision: 26.35158109486569\n",
                  "Recall: 66.86798964624676\n",
                  "F1: 37.80487804878049\n",
                  "\n",
                  "L2 regularization\n",
                  "\n",
                  "Precision: 26.358695652173914\n",
                  "Recall: 66.95427092320966\n",
                  "F1: 37.82598098951986\n",
                  "\n",
                  "L1 regularization\n",
                  "\n",
                  "Precision: 26.3784887678693\n",
                  "Recall: 66.86798964624676\n",
                  "F1: 37.83256041005614\n"
               ]
            }
         ],
         "source": [
            "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
            "from sklearn.metrics import precision_score, recall_score, f1_score, make_scorer\n",
            "\n",
            "def assess_logistic_regression_model(model, X_test, y_test) -> tuple[float, float, float]:\n",
            "    # predict for train and test\n",
            "    y_pred = model.predict(X_test)\n",
            "\n",
            "    # calculate test precision, recall and f1\n",
            "    precision = precision_score(y_test, y_pred)\n",
            "    recall = recall_score(y_test, y_pred)\n",
            "    f1 = f1_score(y_test, y_pred)\n",
            "    print(f\"Precision: {precision * 100}\")\n",
            "    print(f\"Recall: {recall * 100}\")\n",
            "    print(f\"F1: {f1 * 100}\")\n",
            "    return precision, recall, f1\n",
            "\n",
            "reg_logical = LogisticRegression(penalty=None, class_weight='balanced')\n",
            "reg_logical.fit(X_train, y_train)\n",
            "\n",
            "print(\"No regularization\\n\")\n",
            "l0_precision, l0_recall, l0_f1 = assess_logistic_regression_model(reg_logical, X_test, y_test)\n",
            "\n",
            "reg_logical_l2 = LogisticRegressionCV(\n",
            "    Cs=100,\n",
            "    cv=5,\n",
            "    scoring=make_scorer(f1_score),\n",
            "    class_weight='balanced',\n",
            "    n_jobs=-1,\n",
            ")\n",
            "reg_logical_l2.fit(X_train, y_train)\n",
            "\n",
            "print(\"\\nL2 regularization\\n\")\n",
            "l2_precision, l2_recall, l2_f1 = assess_logistic_regression_model(reg_logical_l2, X_test, y_test)\n",
            "\n",
            "reg_logical_l1 = LogisticRegressionCV(\n",
            "    Cs=100,\n",
            "    cv=5,\n",
            "    scoring=make_scorer(f1_score),\n",
            "    class_weight='balanced',\n",
            "    n_jobs=-1,\n",
            "    penalty='l1',\n",
            "    solver=\"saga\"\n",
            ")\n",
            "reg_logical_l1.fit(X_train, y_train)\n",
            "\n",
            "print(\"\\nL1 regularization\\n\")\n",
            "l1_precision, l1_recall, l1_f1 = assess_logistic_regression_model(reg_logical_l1, X_test, y_test)\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 48,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "assert 0.26 < l0_precision < 0.27\n",
            "assert 0.66 < l0_recall < 0.67\n",
            "assert 0.37 < l0_f1 < 0.38\n",
            "\n",
            "assert 0.26 < l1_precision < 0.27\n",
            "assert 0.66 < l1_recall < 0.67\n",
            "assert 0.37 < l1_f1 < 0.38\n",
            "\n",
            "assert 0.26 < l2_precision < 0.27\n",
            "assert 0.66 < l2_recall < 0.67\n",
            "assert 0.37 < l2_f1 < 0.38"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 49,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "f1_test = l0_f1\n",
            "\n",
            "y_pred_train = reg_logical.predict(X_train)\n",
            "f1_train = f1_score(y_train, y_pred_train)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 50,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "assert 0.38 < f1_train < 0.39\n",
            "assert 0.37 < f1_test < 0.38"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Różnica między modelem bez regularyzacji a modelami z regularyzacją jest niewielka, co sugeruje, że model bez regularyzacji nie jest przetrenowany.\n",
            "\n",
            "W naszym przypadku wazniejsza jest precyzja niż recall, ponieważ chcemy, aby na jak najmniej osób, które nie chcą pożyczki, zostały przeznaczone środki na kampanię marketingową.\n",
            "\n",
            "Aby zmienić stosunek precyzji do recall'u nalezałoby zmienić próg kwalifikowania osoby jako zainteresowanej."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "### Zadanie 11 (2.0 punkty)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "*Dodanie cech wielomianowych do regresji logistycznej*\n",
            "\n",
            "1. Stwórz nowy pipeline do przetwarzania danych do regresji logistycznej, dodając `PolynomialFeatures` do zmiennych numerycznych przed standaryzacją. Wygeneruj cechy o stopniu 2, interakcje oraz potęgi, nie generuj interceptu.\n",
            "2. Wytrenuj model regresji logistycznej bez regularyzacji na takim powiększonym zbiorze.\n",
            "   - Wypisz F1 treningowy oraz testowy w procentach.\n",
            "   - Wartości F1 na tych zbiorach przypisz do zmiennych `f1_train` oraz `f1_test`.\n",
            "4. Zdecyduj, czy jest sens tworzyć modele z regularyzacją. Jeżeli tak, to wytrenuj i dokonaj tuningu takich modeli. Jeżeli nie, to uzasadnij czemu."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 51,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "No regularization\n",
                  "\n",
                  "Train F1: 44.33773509403762\n",
                  "Test F1: 43.54936402180496\n"
               ]
            }
         ],
         "source": [
            "from sklearn.preprocessing import PolynomialFeatures\n",
            "\n",
            "X_train, X_test, y_train, y_test = train_test_split(\n",
            "    df, y, test_size=0.25, random_state=0, stratify=y\n",
            ")\n",
            "\n",
            "categorical_pipeline = Pipeline(\n",
            "    steps=[\n",
            "        (\"one-hot-encoder\", OneHotEncoder(sparse_output=False, handle_unknown=\"ignore\")),\n",
            "    ]\n",
            ")\n",
            "\n",
            "numerical_pipeline = Pipeline(\n",
            "    steps=[\n",
            "        (\"scaler\", StandardScaler()),\n",
            "        (\"poly\", PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)),\n",
            "    ]\n",
            ")\n",
            "\n",
            "column_transformer = ColumnTransformer(\n",
            "    transformers=[\n",
            "        (\"categorical\", categorical_pipeline, categorical_features),\n",
            "        (\"numerical\", numerical_pipeline, numerical_features),\n",
            "    ]\n",
            ")\n",
            "\n",
            "column_transformer.fit(X_train)\n",
            "\n",
            "X_train = column_transformer.transform(X_train)\n",
            "X_test = column_transformer.transform(X_test)\n",
            "\n",
            "reg_logical = LogisticRegression(penalty=None, class_weight='balanced', max_iter=1000)\n",
            "reg_logical.fit(X_train, y_train)\n",
            "\n",
            "y_pred_train = reg_logical.predict(X_train)\n",
            "y_pred = reg_logical.predict(X_test)\n",
            "\n",
            "f1_train = f1_score(y_train, y_pred_train)\n",
            "f1_test = f1_score(y_test, y_pred)\n",
            "\n",
            "print(\"No regularization\\n\")\n",
            "\n",
            "print(f\"Train F1: {f1_train * 100}\")\n",
            "print(f\"Test F1: {f1_test * 100}\")\n"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 52,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "assert 0.44 < f1_train < 0.45\n",
            "assert 0.43 < f1_test < 0.44"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "Wysępuje bardzo mała różnica między rezultatem dla zbioru testowego i treningowego. Z tego tytułu wydaje się, że regularyzacja nie będzie potrzebna.\n",
            "\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "source": [
            "## Zadanie 12 dodatkowe (3 punkty)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {
            "editable": true,
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               "slide_type": ""
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         "source": [
            "Z formalnego, statystycznego punktu widzenia regresja liniowa czyni szereg założeń ([Wikipedia](https://en.wikipedia.org/wiki/Linear_regression#Assumptions)):\n",
            "1. Liniowość - relacja w danych może być reprezentowana jako `y=Xw`.\n",
            "2. Normalność błędów - błędy (rezydua) mają rozkład normalny, wycentrowany na zerze.\n",
            "3. Homoskedastyczność (stała wariancja) - wariancja błędu nie zależy od wartości docelowych `y`. Innymi słowy, nasz błąd będzie w przybliżeniu miał podobny \"rozrzut\" dla małych i dużych wartości `y`.\n",
            "4. Niezależność błędów - błąd i `y` są niezależne (w sensie statystycznym). Innymi słowy, nie ma między nimi bezpośredniej relacji. Jeżeli nie pracujemy z szeregami czasowymi, to to założenie po prostu jest spełnione.\n",
            "5. Brak współliniowości zmiennych - nie ma idealnej korelacji cech.\n",
            "\n",
            "Testowanie tych własności nie zawsze jest oczywiste, a w szczególności Scikit-learn oferuje tutaj dość mało opcji, bo pochodzą one głównie z tradycyjnej statystyki.\n",
            "\n",
            "1. Liniowość:\n",
            "  - numerycznie: wysoki współczynnik dopasowania modelu $R^2$ na zbiorze treningowym, niski błąd (RMSE) na zbiorze treningowym oraz testowym\n",
            "  - testem statystycznym: [Rainbow test](https://www.statsmodels.org/dev/generated/statsmodels.stats.diagnostic.linear_rainbow.html) lub [Harvey Collier test](https://www.statsmodels.org/stable/generated/statsmodels.stats.diagnostic.linear_harvey_collier.html)\n",
            "  - graficznie: możliwe kiedy mamy 1/2 zmienne i da się narysować wykres zmiennej zależnej względem cech\n",
            "2. Normalność błędów:\n",
            "  - graficznie: robimy histogram rezyduów, powinien mieć kształt rozkładu normalnego i być wycentrowany na zerze\n",
            "  - testem statystycznym: [Jarque-Bera test](https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test), [Omnibus normality test](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html)\n",
            "3. Homoskedastyczność:\n",
            "  - graficznie: robimy scatter plot rezyduów dla wartości przewidywanych od najmniejszej do największej, nie powinno być na nim żadnych widocznych wzorców czy kształtów; [przykład 1](https://towardsdatascience.com/multivariant-linear-regression-e636a4f99b40), [przykład 2](https://www.vexpower.com/brief/homoskedasticity)\n",
            "  - testem statystycznym: [Breusch–Pagan test](https://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test) lub [Goldfeld-Quandt test](https://en.wikipedia.org/wiki/Goldfeld%E2%80%93Quandt_test)\n",
            "4. Niezależność błędów - nie omawiam, bo dotyczy tylko szeregów czasowych.\n",
            "5. Brak współliniowości zmiennych: numerycznie, sprawdzić korelacje zmiennych, lub współczynnik uwarunkowania macierzy `X`\n",
            "\n",
            "\n",
            "W ramach zadania wytrenuj model regresji liniowej dla zbioru danych Ames Housing z użyciem biblioteki Statsmodels: [OLS docs](https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html), [OLS](https://www.statsmodels.org/dev/examples/notebooks/generated/ols.html), [Regression diagnostics](https://www.statsmodels.org/dev/examples/notebooks/generated/regression_diagnostics.html). Wytrenuj najpierw model bez regularyzacji, a następnie z regularyzacją L2 oraz L1. Nie przeprowadzaj tuningu, użyj tych wartości siły regularyzacji, które wyznaczyliśmy wcześniej.\n",
            "\n",
            "Przetestuj założenia za pomocą testów statystycznych: Harvey Collier, Jarque-Bera, Breusch–Pagan. Współliniowość zmiennych zweryfikuj z użyciem współczynnika uwarunkowania. Zastosuj poziom istotności $\\alpha=0.05$.\n",
            "\n",
            "Czy założenia są spełnione w przypadku podstawowego modelu i/lub modeli z regularyzacją? Czy modele regularyzowane w lepszym stopniu spełniają założenia?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 53,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": [
               "ex"
            ]
         },
         "outputs": [],
         "source": [
            "# TODO solution"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": null,
         "metadata": {
            "editable": true,
            "slideshow": {
               "slide_type": ""
            },
            "tags": []
         },
         "outputs": [],
         "source": []
      }
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