{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Son güncelleme: **09.06.2022**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTLAR:**\n",
    "\n",
    "- Görsellerin bir kısmı değerli hocam Sait Ölmez'in(Sabancı Üniversitesi) sunumlarından bir kısmı da internetten çeşitli kaynaklardan alınmıştır.\n",
    "- Verdiğim linkler çalışmıyorsa lütfen benle iletişime geçin: volkan.yurtseven@hotmail.com\n",
    "- Benim python ayarlarım startup olarak kendi paketimdeki magic ve extension fonksiyonlarımı otomatik yüklüyor. Ayrıca kod kalabalıklığı olmaması ve reusability adına, yine kendi paketimdeki modülleri de kullanıyorum. Nelerin yüklendiğini görmek ve sizde de benzer çıktıların oluşmasını sağlamak için <a href=\"https://mvolkanyurtseven.medium.com/top-n-useful-python-tips-tricks-e3a163e56749\">buraya</a> bakınız. Burada **extension methods** ve **magic functions** ile **Your own utility package** maddelerine bakınız. Bunlar çok faydalı özellikler olup, aynı zamanda bu notebookun hatasız çalışması için de gereklidir. Benim anlattığım şekilde kurmayı başaramazsanız bile, ilgili kodları alıp bu notebookun başına yapıştırsanız da olur.\n",
    "- Bu notebookta ML sürecinin detaylarına odaklanacağız. Biraz uzun, hatta çok uzun bir notebook olacak. Çünkü tüm ML conceptelerinin detayını burda vermeye çalışacağım. Bununla beraber tüm anlatılacak conceptlere uygun bir dataseti bulmak oldukça zor olduğu için yer yer başka notebooklara linkler vereceğim.\n",
    "- Bu notebooktaki ana amacımız conceptleri vermeye çalışmak olacak; en iyi modeli kurmaya çalışmak değil. Eminim biraz daha uğraştığınızda burdakinden daha iyi sonuçlar veren bir model kurabilirsiniz.\n",
    "- Dosya çok daha büyük olmasın diye AutoEDA araçlarını(Dataprep, pandas-profiling v.s) da kullanmayacağız.\n",
    "- NOT:Dosya yine de çok büyük oldu(nbextensions aktifleşemeyecek ve sol panelde TOC görünmeyecek kadar büyüdü), o yüzden **`Part I ve Part II`** olmak üzere iki kısma ayrırdım. İlk kısımda Pipelinelara kadar olan olan bölümler, ikinci kısımda ise pipeline, model evaluation ve deployment bölümleri olacak.\n",
    "- NOT: Ara ara yapacağım güncellemeler sonucunda outputlar farklı olabilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelleme öncesi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Öncelikle gerekli kütüphaneleri import edelim. Modellemeyle ilgili kütüphaneleri ise ileride yavaş yavaş dahil edeceğiz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:37.582572Z",
     "start_time": "2022-06-03T09:41:32.683676Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:48.255197Z",
     "start_time": "2022-06-03T09:41:37.596531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all warnings will be shown\n"
     ]
    }
   ],
   "source": [
    "#kendi paketimi de yüklüyorum\n",
    "from mypyext import dataanalysis as da\n",
    "from mypyext import ml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Öncelikle yeni bir notebook açtığımız için datamızı baştan okuyalım ve train/test ayrımını baştan yapalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:48.916420Z",
     "start_time": "2022-06-03T09:41:48.264166Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "dfheart=pd.read_csv(\"https://raw.githubusercontent.com/VolkiTheDreamer/dataset/master/Classification/Heart.csv\")\n",
    "del dfheart[\"Unnamed: 0\"]\n",
    "X=dfheart.iloc[:,:-1]\n",
    "y=dfheart.iloc[:,-1]\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2, stratify=dfheart.Thal.fillna(\"NA\"), random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:48.962298Z",
     "start_time": "2022-06-03T09:41:48.934373Z"
    }
   },
   "outputs": [],
   "source": [
    "target=[\"AHD\"]\n",
    "nums=[\"Age\",\"RestBP\",\"Chol\",\"MaxHR\",\"Oldpeak\",\"Ca\"]\n",
    "cats=list(dfheart.columns).removeItemsFromList_(nums+target,False)\n",
    "ords=[\"ChestPain\",\"RestECG\"]\n",
    "noms=cats.removeItemsFromList_(ords,False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelleme (Part I'den devam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data temini ve EDA aşamalarını yaptığımız Part I için <a href=\"E2E_Classification_ML_Main_Concepts_Part_I.ipynb\">buraya</a> tıklayınız."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline ve ColumnTransformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genel Bilgiler ve Standart Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA aşamasında veya model kurma aşamasında yaptığımız çeşitli preprocessing ve feature engineering işlemleri vardı. Bunları train/test ayrımından sonra train set üzerinde yapıyorsak aynı işlemleri bir de test verisi için de yapmak gerekir, ki bu aynı kodların tekrar yazılması demektir. Üstelik eğitim setindeki çeşitli bilgilerin bir değişkende depolanmasını da gerektirir, ki bu değişkenleri test seti üzerinde de kullanalım. Özetle uzun iş. Bunun için **Pipeline** diye bir sınıf/obje geliştirilmiş. Uygulanacak işlemler belli sıralar halinde yazılır, bir nevi bir fonksiyon yazılır, ve bu fonksiyon hem train data setinde hem de test seti üzerinde uygulanır. Tabiki train setinde önce fit sonra transform yapılırken, test üzerinde sadece transform uygulanır.\n",
    "\n",
    "Bir veri seti için birden fazla pipeline kurgulanabileceği gibi, farklı pipelinelar birleşip tek bir pipeline da oluşturabilir. Böyle durumlarda genelde arada bir yerde **ColumnTransformer** nesnesi de kullanılır, ki bunu birazdan göreceğiz.\n",
    "\n",
    "Şimdi bunlar yakından bakalım:\n",
    "\n",
    "- **`Pipeline`**: Ardışık işlemleri pratik bir şekilde yapmak için kullanılır.\n",
    "    - İçine aldığı tüm işlemleri **`arka arkaya`** çalıştırır.\n",
    "    - İlk transformasyon sonrasında kolon isimleri kaybolur, çünkü artık elimizde bir numpy array vardır, o yüzden sonraki aşamalarda ilgili kolonların indeks numarasıyla gitmek gerekecektir. Özellikle alt pipelinelerda bu durum sorun yaratabilmektedir. Burada dikkat edilecek nokta şu: ColumnTransformerlara parça parça gönderildikleri için indeks numaralarını bu parçalar içindeki konumuna göre belirlemek olacaktır, ana dataframedeki pozisyonuna göre değil.\n",
    "    - Son aşama fit(estimator, yani classifier veya regressor), önceki aşamalar fit_transform(preprocessor, feature selector v.s) mantığıyla çalışan işlemler olmalıdır.\n",
    "    - Bazı durumlarda birden fazla alt pipeline ana pipeline içine konulabilir.\n",
    "    - Pipelinlar sadece X’ler üzerinde çalışmalı, target için değil. Target için de bir işlem gerekliyse bunu pipeline dışında yapmak gerekir. Örneğin LabelEncoder. Tabiki train setinde fit_transform, test setinde sadece transform olacak şekilde. \n",
    "    - Bir işlem ve bu işleme ait ayırdedici bir isim olmak üzere iki elemanlı tuplelar şeklinde tanımlanır<br>    \n",
    "    Kullanım şekli aşağıdaki gibidir.\n",
    "    <pre>\n",
    "    mypipe = Pipeline([\n",
    "    ('isim', Transformer1),    \n",
    "    ('isim', Transformer2),    \n",
    "    ('isim', Estimator()),\n",
    "    ])\n",
    "    \n",
    "    #örnek\n",
    "    mypipe = Pipeline([\n",
    "    ('imp', SimpleImputer(strategy=\"median\")),    \n",
    "    ('scl', StandartScaler()), \n",
    "    ('clf', LogisticRegression(C=1, max_iter=1000)),\n",
    "    ])\n",
    "    </pre>\n",
    "    \n",
    "- **`ColumnTransformer`**: Farklı kolonlar için farklı işlemler yapılmak istendiğinde kullanılır.\n",
    "    - İçine aldığı işlemleri **`paralel`** olarak çalıştırır. Ancak sonuçlar, işlem sırasına göre yanyana konur. Dolayısıyla bir columntransformerı takip eden başka bir işlem varsa, ondan sonraki kolon numaralarını verirken yeni sırasına göre vermek gerekir.\n",
    "    - İçine aldığı sınıfların fit_transform metoduna sahip olan sınıflar olması gerekir. Custom bir fonksiyonu çağırmak isterseniz **FunctionTransformer**(\\*) sınıfından destek alınabilmektedir. Daha ileri durumlarda ise **class tanımı** yapılabilir. Mesela outlier handling veya log transformasyon yapacaksanız bunlardan birine ihtiyacınız olacaktır. Peki hangisi ne zaman? Az aşağıda bunun cevabını bulacaksınız. Bu ikisi için de <a href=\"https://towardsdatascience.com/custom-transformers-and-ml-data-pipelines-with-python-20ea2a7adb65\">şuradan</a> detay bilgi alabilirsiniz.\n",
    "    - Birden fazla ColumnTransform kullanılabilir ve her birinin sonucu ayrı ayrı görülebilir(Pipeline'ı fit etmeden, sadece columtransformerı fit_transform ederek. Bu da debugging imkanı sunar)\n",
    "    - ColumnTransfomer içine bir alt pipeline verilebilir veya tek adımlık birşeyse direkt transformerın kendisi(Ör:OneHotEncoder) direkt verilebilir, veyahut da custom bir işlem yapılacaksa, FunctionTransformerla wrap edilmiş bir fonksiyon verilebilir.\n",
    "    - İşlemin adı, transformerın/alt pipelineın/custom_fonksiyonun kendisi ve hangi kolonları göndereceğiniz şeklinde 3 elemanlı bir tuple listesi alır(\\*\\*).<br>    \n",
    "    Kullanım şekli aşağıdaki gibidir.\n",
    "    <pre>\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('isim', Transformer1/Pipeline/Fonksiyon, kolonad/kolonindeks listesi),\n",
    "                                ('isim', Transformer1/Pipeline/Fonksiyon, kolonad/kolonindeks listesi),         \n",
    "                                ('isim', Transformer1/Pipeline/Fonksiyon, kolonad/kolonindeks listesi)\n",
    "                                 ])\n",
    "    \n",
    "    #örnek\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  FunctionTransformer(mycustomFunc), noms), #custom function ve kolon listesi\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[[Kötü', 'Orta', 'İyi']]), [3]), #standart bir transformer ve kolon indeks listesi        \n",
    "                                ('numerics',  num_pipe, nums) #pipeline ve kolon listesi\n",
    "                                 ])  \n",
    "\n",
    "    </pre>    \n",
    "    \n",
    "Tüm yapı yine nihai olarak bir **`Gridsearch`** içine konabilir. Daha önce Gridsearch içine bir estimator konarken, şimdi Pipeline konacak, tek farkı bu.    \n",
    "\n",
    "\n",
    "<span style=\"font-size:small\">* Custom Function'ınız ekstra parametre de alıyorsa bunu <strong>kw_args=dict(parametreniz=değeri)</strong> şeklinde gönderebilirsiniz.</span><br>\n",
    "<span style=\"font-size:small\">** kolon bilgisi olarak bunlara ek olarak <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html\">make_columns_selector</a> ile belli tiplerdeki kolonları da gönderebiliyoruz.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi kendi pipeline'ımızı kuralım ama öncesinde yardımcı sınıf ve fonksiyonlarımızı yazalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:49.054054Z",
     "start_time": "2022-06-03T09:41:48.973273Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "def numericImputer(X:pd.DataFrame):\n",
    "    #integer için median, float için mean olacak\n",
    "    #bunu bilerek hatalı bir şekilde hazırlıyorum, çünkü test datasındaki nullar replace edilirken kendi ortalaması ile replace edilecek, trainiki ile değil\n",
    "    X.loc[:,X.select_dtypes(np.float64).columns]=X.loc[:,X.select_dtypes(np.float64).columns].fillna(X.select_dtypes(np.float64).mean())\n",
    "    X.loc[:,X.select_dtypes(np.int64).columns]=X.loc[:,X.select_dtypes(np.int64).columns].fillna(X.select_dtypes(np.int64).median())\n",
    "    return X.values #numpy döndürsün diye   \n",
    "    \n",
    "class OutlierHandler(BaseEstimator, TransformerMixin):\n",
    "    # Class Constructor, bi parametre alacaksa onu da veriyoruz\n",
    "    # normalde columntaransformden zaten bi kolon listesi gelecek(nums), ama biz hepsini değil, sadece bi kısmını kullanacağız\n",
    "    # aslında olur da ilerde diğer kolonlarda da outlier gelir diye hepsine yapmak lazım ama bu, işlem süresini uzatacaktır\n",
    "    def __init__(self, featureindices):\n",
    "        self.featureindices = featureindices\n",
    "  \n",
    "    def fit(self, X:np.array, y = None):\n",
    "        # y’yi kullamayacak olsak bile ilgili fonk içinde bulundururuz, None atarız\n",
    "        Q1s = np.quantile(X[:,self.featureindices],0.25,axis=0)\n",
    "        Q3s = np.quantile(X[:,self.featureindices],0.75,axis=0)\n",
    "        IQRs = Q3s-Q1s\n",
    "        self.top=(Q3s + 1.5 * IQRs)\n",
    "        self.bottom=(Q1s - 1.5 * IQRs)\n",
    "        return self \n",
    "    \n",
    "    def transform(self, X:np.array, y = None ):\n",
    "        X[:,self.featureindices]=np.where(X[:,self.featureindices]>self.top,self.top,X[:,self.featureindices])\n",
    "        X[:,self.featureindices]=np.where(X[:,self.featureindices]<self.bottom,self.bottom,X[:,self.featureindices])\n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burda outlierHandler'ı neden şöyle yaratmadığımızı merak etmiş olabilirsiniz.\n",
    "\n",
    "<pre>\n",
    "def outlierHandler(X):\n",
    "    Q1 = X[\"RestBP\"].quantile(0.25)\n",
    "    Q3 = X[\"RestBP\"].quantile(0.75)\n",
    "    IQR = Q3-Q1\n",
    "    topRestBP=(Q3 + 1.5 * IQR)\n",
    "    bottomRestBP=(Q1 - 1.5 * IQR)\n",
    "    X[\"RestBP\"]=np.where(X[\"RestBP\"]>topRestBP,topRestBP,X[\"RestBP\"])\n",
    "    X[\"RestBP\"]=np.where(X[\"RestBP\"]&lt;bottomRestBP,bottomRestBP,X[\"RestBP\"])\n",
    "    return X\n",
    "</pre>          \n",
    "\n",
    "Tıpkı numericImputer'da yaptığımız gibi yani, ki aslında numericImputer'ın commentinde bunun hatalı olduğunu belirtmiştim, aynı hatayı outlierHandleda yapmıyoruz. Bunun sebebi meşhur **data leakage** konusu. Bu şekilde yaparsak hem train hem test datası için IQR hesabı ayrı ayrı yapılır, yani fit edilir, halbuki biz hep ne diyoruz; train datası için fit_transform, test datası için ise sadece transform yapılır. O yüzden ayrı bir sınıf yaptık, train datası burda fit_transform edilirken, test ise sadece transform edilmiş olacak. Ne demek bu, yani test datasının transform olması ne demek? Kendisine iletilen bir bilgiyi kullanması demek, peki nedir bu bilgi, yani neyle transform edecek? Train seti üzerinden hesaplanan ve classın(aslında bu classtan ürettiğimiz objenin) üzerinde taşıdığı top ve bottom bilgisiyle. Bunu class kullanmadan yapamazdık, classın(objenin) bu bilgiyi kendi üzerinde taşıyarak getirmesi lazım. \n",
    "\n",
    "numericImputer fonksiyonunda bu hatayı bilerek yaptık ki hem FunctionTransformer kullanımını gösterelim hem de class inheritance ile arasındaki farkı. Çok yıkıcı bi etkisi olmayacağı ve burda amacımız en ideal modeli kurmak olmadığı için bu hataya katlanabiliriz. \n",
    "\n",
    "**Özetle, eğer işleme soktuğumuz verinin üstünde daha sonra kullanılacak üzere bir hesaplama yapacaksak burda class kullanırız, aksi halde function yazmamız yeterlidir.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu arada yazdığımız classın doğru sonuç üretip üretmediğini test edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:49.099930Z",
     "start_time": "2022-06-03T09:41:49.067020Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age', 'RestBP', 'Chol', 'MaxHR', 'Oldpeak', 'Ca']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RestBP ve Chol'ün indekisine bakalım, 1 ve 2\n",
    "nums "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OutlierHandler'ımız kontrol edelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:49.163762Z",
     "start_time": "2022-06-03T09:41:49.112896Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([170.   , 377.875]), array([ 90.   , 110.875]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ouh=OutlierHandler(featureindices=[1,2]) #RestBP ve Cholün indeksleri\n",
    "kontroldata=ouh.fit_transform(X_train[nums].values)\n",
    "ouh.top,ouh.bottom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RestBP'nin top değeri(maxla karışmasın, IQR yöntemine göre hesaplanan top değeri) 170, Chol'ün 377.875miş. Diğer iki rakam da bottom değerleri."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:49.211631Z",
     "start_time": "2022-06-03T09:41:49.176724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(192, 170.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#şimdi bi max değerleri kontrol edelim\n",
    "X_train[\"RestBP\"].max(), kontroldata[:,1].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evet orjinal sette 192 olan bir değer top değer olan 170 ile replace edilmiş."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:49.273477Z",
     "start_time": "2022-06-03T09:41:49.224615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 27],\n",
       "       [ 44],\n",
       "       [ 45],\n",
       "       [110],\n",
       "       [111],\n",
       "       [220],\n",
       "       [240]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 30],\n",
       "       [ 48],\n",
       "       [ 78],\n",
       "       [198],\n",
       "       [219]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hangi elemanlar değişmiş görelim\n",
    "np.argwhere((X_train.RestBP.values==kontroldata[:,1])==False)\n",
    "np.argwhere((X_train.Chol.values==kontroldata[:,2])==False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi pipelineımızı kurmaya devam edelim. Buradan itibaren çok sayıda senaryoya bakacağız ve karşılaştırmalar yapacağımız ve tutarlı sonuçlar almak istediğimiz için gerekli her yerde **random_state** parametresini kullanmak gerekecek."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:41:49.870869Z",
     "start_time": "2022-06-03T09:41:49.295408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;,\n",
       "                                                  OutlierHandler(featureindices=[1])),\n",
       "                                                 (&#x27;scl&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 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 sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label 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 sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 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 sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierHandler</label><div class=\"sk-toggleable__content\"><pre>OutlierHandler(featureindices=[1])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ct',\n",
       "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
       "                                   transformers=[('nominals',\n",
       "                                                  Pipeline(steps=[('csi',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('ohe',\n",
       "                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                 handle_unknown='ignore'))]),\n",
       "                                                  ['Sex', 'Fbs', 'ExAng',\n",
       "                                                   'Slope', 'Thal']),\n",
       "                                                 ('ordinals',\n",
       "                                                  OrdinalEncoder(categories=[['typical',\n",
       "                                                                              'asymptomatic',\n",
       "                                                                              'nonanginal',\n",
       "                                                                              'nontypi...\n",
       "                                                  Pipeline(steps=[('nsi',\n",
       "                                                                   FunctionTransformer(func=<function numericImputer at 0x0000028493040040>)),\n",
       "                                                                  ('ouh',\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  ('scl',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Age', 'RestBP', 'Chol',\n",
       "                                                   'MaxHR', 'Oldpeak',\n",
       "                                                   'Ca'])])),\n",
       "                ('fs',\n",
       "                 SelectKBest(score_func=<function mutual_info_classif at 0x00000284930AE790>)),\n",
       "                ('clf',\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer, OrdinalEncoder, OneHotEncoder, StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(drop=\"first\",handle_unknown='ignore'))\n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer)), #tüm kolonlarda yapsın(belli kolonlar da verebilirdik)\n",
    "                   (\"ouh\", OutlierHandler(featureindices=[1])), #sadece belli kolonlarda yapsın(tümünü de verebilirdik)\n",
    "                   (\"scl\", StandardScaler()) #bunu nihai pipeline içinde yapmayı da deneyebilirsiniz\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms), #sub pipeline vererek\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                # ordinallere imputing yapmamış gibi duruyoruz ama zaten sadece bi tane var, onda da missing yok\n",
    "                                ('numerics',  num_pipe, nums) #sub pipeline vererek\n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\") \n",
    "\n",
    "pipe = Pipeline(steps=[('ct', coltrans), #fit_trasnformlu bir işlem\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), #fit_transformlu bir işlem\n",
    "                       ('clf', LogisticRegression(C=1,max_iter=1000,random_state=42)) #sadece fitli bir işlem en sonda yer alıyor\n",
    "                       ])\n",
    "pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pipeline'dakilerin ardışık/seri, ColumTransformdakilerin paralel** resmedildiğine dikkat ettiniz mi? Bu akış gerçekten önemli. Eğer bu ayrımı iyi anlayamazsanız, hatalarla debelenir durursunuz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**çıktı olarak oluşan array ve `remainder` parametresi**\n",
    "\n",
    "remainder ile, Columtransformerda işlem yapılmasını istediğimiz kolonlar dışındaki kolonlara ne yapılması gerektiğini söyleriz. Default değeri \"drop\" olup, bunların nihai array'de yer almaması gerektiğin söylemiş olurken, \"passthrough\" dersek de en sağa eklenmesini söylemiş oluruz. \n",
    "\n",
    "Şöyle ki yeni oluşan array'imizdeki kolonların sıralaması şöyledir: \n",
    "- en solda noms\n",
    "- onun hemen sağında ords\n",
    "- onun da sağında nums\n",
    "- varsa diğer kolonlar da en sağa gider, bizde bi tane ordinal feature(RestECG) vardı işleme tabi tutmadığımız, o en sağa gidecek."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi, bakalım nasıl bir çıktımız var. Önce coltransı fit_transform edip görelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:06.224116Z",
     "start_time": "2022-06-03T09:41:49.883833Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        1.        ,  0.        ,  2.        ,  1.33663279,  1.19595024,\n",
       "        0.51055787,  0.95924191, -0.87266506,  0.31294034,  0.        ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct=coltrans.fit_transform(X_train)\n",
    "ct[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bunların kolon isimlerini <a href=\"https://stackoverflow.com/questions/54646709/sklearn-pipeline-get-feature-names-after-onehotencode-in-columntransformer\">burada</a> belirtilen yöntemlerle kolaylıkla elde edebilirdik ancak custom fonksiyon kullandığımız için bu şekilde yapamıyoruz, onun yerine benim ml modülümdeki aşağıdaki fonksiyonu kullanabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:06.256031Z",
     "start_time": "2022-06-03T09:42:06.235095Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OHE_Sex_1',\n",
       " 'OHE_Fbs_1',\n",
       " 'OHE_ExAng_1',\n",
       " 'OHE_Slope_2',\n",
       " 'OHE_Slope_3',\n",
       " 'OHE_Thal_normal',\n",
       " 'OHE_Thal_reversable',\n",
       " 'ChestPain',\n",
       " 'Age',\n",
       " 'RestBP',\n",
       " 'Chol',\n",
       " 'MaxHR',\n",
       " 'Oldpeak',\n",
       " 'Ca',\n",
       " 'RestECG']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.get_feature_names_from_columntransformer(coltrans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coltransın her bir aşamasında ne elde ettiğimizi de görebiliriz. İlgili satırın altındakileri commentleyip, sadece coltransı fit_transform yapmak yeterlidir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ana pipeline içindeki son satırdaki estimator kodunu commentleyip pipeline için de fit_transform yapılabilir, böylece feature selection sonucunu da görebiliriz. Aşağıda buna ait sonuçlar var."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:12.918216Z",
     "start_time": "2022-06-03T09:42:06.266006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  1.        ,  0.        ,  2.        ,\n",
       "        0.51055787,  0.95924191, -0.87266506,  0.31294034,  0.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline(steps=[('ct', coltrans), #fit_trasnformlu bir işlem\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), #fit_transformlu bir işlem\n",
    "#                        ('clf', LogisticRegression(C=1,max_iter=1000,random_state=42)) #sadece fitli bir işlem en sonda yer alıyor\n",
    "                       ])\n",
    "\n",
    "pipe.fit_transform(X_train,y_train)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LogisticRegression satırını tekrar uncomment yapıp, o hücreyi tekrar çalıştırdıktan sonra artık gönül rahatlığı ile modelimizi eğitebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:14.082098Z",
     "start_time": "2022-06-03T09:42:12.930174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;,\n",
       "                                                  OutlierHandler(featureindices=[1])),\n",
       "                                                 (&#x27;scl&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierHandler</label><div class=\"sk-toggleable__content\"><pre>OutlierHandler(featureindices=[1])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;RestECG&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ct',\n",
       "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
       "                                   transformers=[('nominals',\n",
       "                                                  Pipeline(steps=[('csi',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('ohe',\n",
       "                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                 handle_unknown='ignore'))]),\n",
       "                                                  ['Sex', 'Fbs', 'ExAng',\n",
       "                                                   'Slope', 'Thal']),\n",
       "                                                 ('ordinals',\n",
       "                                                  OrdinalEncoder(categories=[['typical',\n",
       "                                                                              'asymptomatic',\n",
       "                                                                              'nonanginal',\n",
       "                                                                              'nontypi...\n",
       "                                                  Pipeline(steps=[('nsi',\n",
       "                                                                   FunctionTransformer(func=<function numericImputer at 0x0000028493040040>)),\n",
       "                                                                  ('ouh',\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  ('scl',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Age', 'RestBP', 'Chol',\n",
       "                                                   'MaxHR', 'Oldpeak',\n",
       "                                                   'Ca'])])),\n",
       "                ('fs',\n",
       "                 SelectKBest(score_func=<function mutual_info_classif at 0x00000284930AE790>)),\n",
       "                ('clf',\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline(steps=[('ct', coltrans), #fit_trasnformlu bir işlem\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), #fit_transformlu bir işlem\n",
    "                       ('clf', LogisticRegression(C=1,max_iter=1000,random_state=42)) #sadece fitli bir işlem en sonda yer alıyor\n",
    "                       ])\n",
    "pipe.fit(X_train,y_train) #Bu sefer sadece fit yapıyoruz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hangi featurelar seçilmiş, bir de onlara bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:14.128968Z",
     "start_time": "2022-06-03T09:42:14.093071Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OHE_Sex_1',\n",
       " 'OHE_Fbs_1',\n",
       " 'OHE_ExAng_1',\n",
       " 'OHE_Slope_2',\n",
       " 'OHE_Thal_normal',\n",
       " 'OHE_Thal_reversable',\n",
       " 'ChestPain',\n",
       " 'Chol',\n",
       " 'MaxHR',\n",
       " 'Ca']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selecteds=pipe[\"fs\"].get_support()\n",
    "[x for e,x in enumerate(ml.get_feature_names_from_columntransformer(coltrans)) if e in np.argwhere(selecteds==True).ravel()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:14.159889Z",
     "start_time": "2022-06-03T09:42:14.138954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OHE_Slope_3', 'Age', 'RestBP', 'Oldpeak', 'RestECG']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seçilmeyenler\n",
    "[x for e,x in enumerate(ml.get_feature_names_from_columntransformer(coltrans)) if e not in np.argwhere(selecteds==True).ravel()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Her ne kadar fit etmiş olsak da önceki aşamalar için arka planda fit_transform çalışır ama daha önce belirttiğimiz gibi son aşama mutlaka sadece fit'li bir estimator olmalıdır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eğer isim parametreleri(csi, ohe fs, clf vs) kullanılmayacaksa utility function olan **make_pipeline ve make_colum_taransfomer** kullanılabilir. Peki isimlere ne zaman ihtiyaç duyarız? Mesela grid search yapacaksak, parametreleri gönderirken pipeline isimlerini kullanırız, ki bu örneği de birazdan göreceğiz. Şimdi bu utility functionlara bakalım. Bu arada, bunlarda transformerlar artık bir list içinde yazılmıyor, doğrudan yazılıyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:14.618664Z",
     "start_time": "2022-06-03T09:42:14.171856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;pipeline-1&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;as...\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;outlierhandler&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;selectkbest&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;pipeline-1&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehotencoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;as...\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;outlierhandler&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;standardscaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;selectkbest&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;logisticregression&#x27;,\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;pipeline-1&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;pipeline-2&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;functiontransformer&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;outlierhandler&#x27;,\n",
       "                                                  OutlierHandler(featureindices=[1])),\n",
       "                                                 (&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-1</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline-2</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierHandler</label><div class=\"sk-toggleable__content\"><pre>OutlierHandler(featureindices=[1])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('pipeline-1',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehotencoder',\n",
       "                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                 handle_unknown='ignore'))]),\n",
       "                                                  ['Sex', 'Fbs', 'ExAng',\n",
       "                                                   'Slope', 'Thal']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(categories=[['typical',\n",
       "                                                                              'as...\n",
       "                                                                   FunctionTransformer(func=<function numericImputer at 0x0000028493040040>)),\n",
       "                                                                  ('outlierhandler',\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  ('standardscaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Age', 'RestBP', 'Chol',\n",
       "                                                   'MaxHR', 'Oldpeak',\n",
       "                                                   'Ca'])])),\n",
       "                ('selectkbest',\n",
       "                 SelectKBest(score_func=<function mutual_info_classif at 0x00000284930AE790>)),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(C=1, max_iter=1000, random_state=42))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#makepipeline versiyonu\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "num_pipeline = make_pipeline(\n",
    "    (SimpleImputer(strategy=\"median\")),    \n",
    "    (StandardScaler()),\n",
    "    (LogisticRegression(C=1,max_iter=1000,random_state=42))\n",
    "    )\n",
    "\n",
    "\n",
    "cat_pipe=make_pipeline( \n",
    "                   (SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (OneHotEncoder(drop=\"first\",handle_unknown='ignore'))     \n",
    "                  )\n",
    "\n",
    "num_pipe=make_pipeline(  \n",
    "                   (FunctionTransformer(numericImputer)),\n",
    "                   (OutlierHandler(featureindices=[1])), \n",
    "                   (StandardScaler())\n",
    "                  )\n",
    "\n",
    "coltrans = make_column_transformer(\n",
    "                                (cat_pipe, noms),\n",
    "                                (OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                (num_pipe, nums)\n",
    "                                 ,remainder=\"passthrough\") \n",
    "\n",
    "pipe2 = make_pipeline((coltrans),\n",
    "                     (SelectKBest(score_func=mutual_info_classif,k=10)),\n",
    "                     (LogisticRegression(C=1,max_iter=1000,random_state=42))\n",
    "                    )\n",
    "pipe2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchlü kullanım"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basit versiyon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi bir de gridsearch yapalım, burda önemli bi notasyon var, ondan bahsedeyim. pipelinenın hangi stepine parametre göndereceksek o stepin adını __ takip eder, sonra da parametre adı. Columntransformer da kullanacaksak zincirleme __ ifadesi kullanılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:42:15.662868Z",
     "start_time": "2022-06-03T09:42:14.631623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordinals&#x27;,\n",
       "                                                                         OrdinalE...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordinals&#x27;,\n",
       "                                                                         OrdinalE...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;Che...\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;,\n",
       "                                                  OutlierHandler(featureindices=[1])),\n",
       "                                                 (&#x27;scl&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierHandler</label><div class=\"sk-toggleable__content\"><pre>OutlierHandler(featureindices=[1])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordinals',\n",
       "                                                                         OrdinalE...\n",
       "             param_grid=[{'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l1'], 'clf__solver': ['liblinear'],\n",
       "                          'ct__nominals__ohe__drop': ['first', None],\n",
       "                          'fs__k': [9, 10]},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__nominals__ohe__drop': ['first', None],\n",
       "                          'fs__k': [9, 10]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(handle_unknown='ignore'))     \n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer)),\n",
    "                   (\"ouh\", OutlierHandler(featureindices=[1])),\n",
    "                   (\"scl\", StandardScaler())\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms), \n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                ('numerics',  num_pipe, nums) \n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\") \n",
    "\n",
    "pipe = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif)), \n",
    "                       ('clf', LogisticRegression(max_iter=1000,random_state=42)) \n",
    "                       ])\n",
    "\n",
    "c_range = [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1]\n",
    "params = [{'clf__C'      : c_range,\n",
    "           'clf__penalty': ['l1'], \n",
    "           'clf__solver' : ['liblinear'],\n",
    "           'fs__k': [9,10],\n",
    "           'ct__nominals__ohe__drop': [\"first\",None] \n",
    "          },\n",
    "          \n",
    "          {'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs', ],\n",
    "           'fs__k': [9,10],\n",
    "           'ct__nominals__ohe__drop': [\"first\",None]\n",
    "          }  \n",
    "         ]\n",
    "\n",
    "mycv = RepeatedKFold(n_splits=3, n_repeats=5, random_state=1)\n",
    "gs1 = GridSearchCV(estimator = pipe, param_grid = params, cv = mycv, n_jobs=-1, verbose = 1, scoring = 'accuracy') \n",
    "gs1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eğitelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:44:10.054825Z",
     "start_time": "2022-06-03T09:42:15.672838Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 96 candidates, totalling 1440 fits\n",
      "Wall time: 2min 53s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordinals&#x27;,\n",
       "                                                                         OrdinalE...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordinals&#x27;,\n",
       "                                                                         OrdinalE...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__nominals__ohe__drop&#x27;: [&#x27;first&#x27;, None],\n",
       "                          &#x27;fs__k&#x27;: [9, 10]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;Che...\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;,\n",
       "                                                  OutlierHandler(featureindices=[1])),\n",
       "                                                 (&#x27;scl&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierHandler</label><div class=\"sk-toggleable__content\"><pre>OutlierHandler(featureindices=[1])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordinals',\n",
       "                                                                         OrdinalE...\n",
       "             param_grid=[{'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l1'], 'clf__solver': ['liblinear'],\n",
       "                          'ct__nominals__ohe__drop': ['first', None],\n",
       "                          'fs__k': [9, 10]},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__nominals__ohe__drop': ['first', None],\n",
       "                          'fs__k': [9, 10]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gs1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçları dataframe üzerinde görelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:44:10.150562Z",
     "start_time": "2022-06-03T09:44:10.063803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=2c49e3b0-4610-443e-a2eb-4700f3256b0f style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('2c49e3b0-4610-443e-a2eb-4700f3256b0f').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>param_ct__nominals__ohe__drop</th>\n",
       "      <th>param_fs__k</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.2</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>first</td>\n",
       "      <td>10</td>\n",
       "      <td>0.838807</td>\n",
       "      <td>0.034568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.1</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>0.837150</td>\n",
       "      <td>0.035461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>first</td>\n",
       "      <td>9</td>\n",
       "      <td>0.835504</td>\n",
       "      <td>0.034689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>first</td>\n",
       "      <td>9</td>\n",
       "      <td>0.833848</td>\n",
       "      <td>0.033036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.05</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>0.832253</td>\n",
       "      <td>0.047211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "   param_clf__C param_clf__penalty param_clf__solver  \\\n",
       "69          0.2                 l2             lbfgs   \n",
       "63          0.1                 l2             lbfgs   \n",
       "76          0.3                 l2             lbfgs   \n",
       "72          0.3                 l2         newton-cg   \n",
       "54         0.05                 l2             lbfgs   \n",
       "\n",
       "   param_ct__nominals__ohe__drop param_fs__k  mean_test_score  std_test_score  \n",
       "69                         first          10         0.838807        0.034568  \n",
       "63                          None          10         0.837150        0.035461  \n",
       "76                         first           9         0.835504        0.034689  \n",
       "72                         first           9         0.833848        0.033036  \n",
       "54                          None           9         0.832253        0.047211  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(gs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bir işlemin yapıldığı ve yapılmadığı senaryo hazırlama"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi bir de gridsearchümüz outlier handling yapsın ve yapmasın seçeneğini nasıl kurgulayacağımızı görelim, ki bu diğer işlemlerin de yap/yapma seçenekleri için bir örnek teşkil edecektir.\n",
    "\n",
    "Bunun için `DummyTransformer` adında bir class tanımlayıp bunu **placeholder** olarak kullanacağız. Bu placeholder'a bi yapmak istediğimiz işlemi(bizim örnekte Outlierhandler) bir de **None** değerini göndeririz.\n",
    "\n",
    "**Uyarı:** DummyTransfomer kullanmaya başladığınızda yukarıda yaptığımız gibi sadece coltrans'ın ara sonucuna bakma şansımız kalmıyor, hata alırsınız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:44:11.345412Z",
     "start_time": "2022-06-03T09:44:10.159539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordin...\n",
       "             param_grid=[{'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l1'], 'clf__solver': ['liblinear'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None]},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class DummyTransformer(TransformerMixin,BaseEstimator):\n",
    "    def fit(self,X,y=None): pass\n",
    "    def transform(self,X,y=None): pass\n",
    "    \n",
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(drop=\"first\",handle_unknown='ignore'))     \n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer)), \n",
    "                   (\"ouh\", DummyTransformer()), \n",
    "                   (\"scl\", StandardScaler())\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms),\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                ('numerics',  num_pipe, nums)\n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\")\n",
    "\n",
    "pipe = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), \n",
    "                       ('clf', LogisticRegression(max_iter=1000,random_state=42)) \n",
    "                       ])\n",
    "\n",
    "params = [{'clf__C'      : c_range,\n",
    "           'clf__penalty': ['l1'], \n",
    "           'clf__solver' : ['liblinear'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None]\n",
    "          },\n",
    "          \n",
    "          {'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None]\n",
    "          }  \n",
    "         ]\n",
    "\n",
    "mycv = RepeatedKFold(n_splits=3, n_repeats=5, random_state=1)\n",
    "gs2 = GridSearchCV(estimator = pipe, param_grid = params, cv = mycv, n_jobs=-1, verbose = 1, scoring = 'accuracy') \n",
    "gs2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eğitelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:45:09.031141Z",
     "start_time": "2022-06-03T09:44:11.355347Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 15 folds for each of 48 candidates, totalling 720 fits\n",
      "Wall time: 1min 7s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "             param_grid=[{&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;], &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-99\" type=\"checkbox\" ><label for=\"sk-estimator-id-99\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-100\" type=\"checkbox\" ><label for=\"sk-estimator-id-100\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-103\" type=\"checkbox\" ><label for=\"sk-estimator-id-103\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-104\" type=\"checkbox\" ><label for=\"sk-estimator-id-104\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-105\" type=\"checkbox\" ><label for=\"sk-estimator-id-105\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-106\" type=\"checkbox\" ><label for=\"sk-estimator-id-106\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-107\" type=\"checkbox\" ><label for=\"sk-estimator-id-107\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-108\" type=\"checkbox\" ><label for=\"sk-estimator-id-108\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-109\" type=\"checkbox\" ><label for=\"sk-estimator-id-109\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordin...\n",
       "             param_grid=[{'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l1'], 'clf__solver': ['liblinear'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None]},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gs2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçları görelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:45:09.092934Z",
     "start_time": "2022-06-03T09:45:09.040078Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=b1292a74-72a9-4cf3-b32a-40ffb8f7ec1a style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('b1292a74-72a9-4cf3-b32a-40ffb8f7ec1a').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>param_ct__numerics__ouh</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>0.834671</td>\n",
       "      <td>0.037582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>0.832202</td>\n",
       "      <td>0.039706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.5</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>0.831389</td>\n",
       "      <td>0.040924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.2</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>0.831379</td>\n",
       "      <td>0.031677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>None</td>\n",
       "      <td>0.829712</td>\n",
       "      <td>0.037273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "   param_clf__C param_clf__penalty param_clf__solver  \\\n",
       "38          0.3                 l2             lbfgs   \n",
       "36          0.3                 l2         newton-cg   \n",
       "42          0.5                 l2             lbfgs   \n",
       "32          0.2                 l2         newton-cg   \n",
       "11          0.3                 l1         liblinear   \n",
       "\n",
       "               param_ct__numerics__ouh  mean_test_score  std_test_score  \n",
       "38  OutlierHandler(featureindices=[1])         0.834671        0.037582  \n",
       "36  OutlierHandler(featureindices=[1])         0.832202        0.039706  \n",
       "42  OutlierHandler(featureindices=[1])         0.831389        0.040924  \n",
       "32  OutlierHandler(featureindices=[1])         0.831379        0.031677  \n",
       "11                                None         0.829712        0.037273  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(gs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Farklı transformerler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi de scaling olarak bi StandartScaler bir de MinMaxScaler yap diyelim. Yine benzer şekilde farklı işlemler için de benzer mantık uygulanabilir. Ör: Imputation için bi SimpleImputer bir de IterativeImputer kullanmak, veya feature selection için SelectKBest ve RFE kullanmak veya custom fonk içinde korelasyon seçici kullanmak gibi.\n",
    "\n",
    "Biz burdaki örnekte 3 farklı scaler deneyelim. Yukardaki dummytransformer'ı bunun için de kullanacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:45:10.524146Z",
     "start_time": "2022-06-03T09:45:09.100915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-110\" type=\"checkbox\" ><label for=\"sk-estimator-id-110\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-111\" type=\"checkbox\" ><label for=\"sk-estimator-id-111\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   DummyTransformer())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-112\" type=\"checkbox\" ><label for=\"sk-estimator-id-112\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, DummyTransformer())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-113\" type=\"checkbox\" ><label for=\"sk-estimator-id-113\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-114\" type=\"checkbox\" ><label for=\"sk-estimator-id-114\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-115\" type=\"checkbox\" ><label for=\"sk-estimator-id-115\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-116\" type=\"checkbox\" ><label for=\"sk-estimator-id-116\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-117\" type=\"checkbox\" ><label for=\"sk-estimator-id-117\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-118\" type=\"checkbox\" ><label for=\"sk-estimator-id-118\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-119\" type=\"checkbox\" ><label for=\"sk-estimator-id-119\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-120\" type=\"checkbox\" ><label for=\"sk-estimator-id-120\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-121\" type=\"checkbox\" ><label for=\"sk-estimator-id-121\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordin...\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler,RobustScaler\n",
    "\n",
    "class DummyTransformer(TransformerMixin,BaseEstimator):\n",
    "    def fit(self,X,y=None): pass\n",
    "    def transform(self,X,y=None): pass\n",
    "    \n",
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(drop=\"first\",handle_unknown='ignore'))     \n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer)), \n",
    "                   (\"ouh\", DummyTransformer()), \n",
    "                   (\"scl\", DummyTransformer())\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms),\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                ('numerics',  num_pipe, nums)\n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\")\n",
    "\n",
    "pipe = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), \n",
    "                       ('clf', LogisticRegression(max_iter=1000,random_state=42)) \n",
    "                       ])\n",
    "\n",
    "params = [{\n",
    "           'clf__C'      : c_range,\n",
    "           'clf__penalty': ['l1'], \n",
    "           'clf__solver' : ['liblinear'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],\n",
    "           'ct__numerics__scl': [StandardScaler(),MinMaxScaler(),RobustScaler()]\n",
    "          },\n",
    "          \n",
    "          {\n",
    "           'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],\n",
    "           'ct__numerics__scl': [StandardScaler(),MinMaxScaler(),RobustScaler()]\n",
    "          }  \n",
    "         ]\n",
    "\n",
    "mycv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=1)\n",
    "gs3 = GridSearchCV(estimator = pipe, param_grid = params, cv = mycv, n_jobs=-1, verbose = 1, scoring = 'accuracy') \n",
    "gs3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T10:19:30.751715Z",
     "start_time": "2022-06-03T10:19:29.517051Z"
    }
   },
   "source": [
    "Eğitelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:46:44.619385Z",
     "start_time": "2022-06-03T09:45:10.532093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 9 folds for each of 144 candidates, totalling 1296 fits\n",
      "Wall time: 2min 10s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-126\" type=\"checkbox\" ><label for=\"sk-estimator-id-126\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordin...\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-127\" type=\"checkbox\" ><label for=\"sk-estimator-id-127\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   DummyTransformer())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-128\" type=\"checkbox\" ><label for=\"sk-estimator-id-128\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, DummyTransformer())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-129\" type=\"checkbox\" ><label for=\"sk-estimator-id-129\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-130\" type=\"checkbox\" ><label for=\"sk-estimator-id-130\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-131\" type=\"checkbox\" ><label for=\"sk-estimator-id-131\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-132\" type=\"checkbox\" ><label for=\"sk-estimator-id-132\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-133\" type=\"checkbox\" ><label for=\"sk-estimator-id-133\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-134\" type=\"checkbox\" ><label for=\"sk-estimator-id-134\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-135\" type=\"checkbox\" ><label for=\"sk-estimator-id-135\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-136\" type=\"checkbox\" ><label for=\"sk-estimator-id-136\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-137\" type=\"checkbox\" ><label for=\"sk-estimator-id-137\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-138\" type=\"checkbox\" ><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-139\" type=\"checkbox\" ><label for=\"sk-estimator-id-139\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-140\" type=\"checkbox\" ><label for=\"sk-estimator-id-140\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=3, random_state=1),\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordin...\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 1],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                MinMaxScaler(),\n",
       "                                                RobustScaler()]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gs3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçları görelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:46:44.681276Z",
     "start_time": "2022-06-03T09:46:44.627363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=ec96626c-e0c2-4606-861a-9778bd2dd9db style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('ec96626c-e0c2-4606-861a-9778bd2dd9db').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>param_ct__numerics__ouh</th>\n",
       "      <th>param_ct__numerics__scl</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>0.837431</td>\n",
       "      <td>0.044272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>0.837397</td>\n",
       "      <td>0.039597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>0.836025</td>\n",
       "      <td>0.044538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>None</td>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>0.836008</td>\n",
       "      <td>0.044944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>0.833316</td>\n",
       "      <td>0.039627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "    param_clf__C param_clf__penalty param_clf__solver  \\\n",
       "119          0.3                 l2             lbfgs   \n",
       "110          0.3                 l2         newton-cg   \n",
       "117          0.3                 l2             lbfgs   \n",
       "111          0.3                 l2         newton-cg   \n",
       "116          0.3                 l2             lbfgs   \n",
       "\n",
       "                param_ct__numerics__ouh param_ct__numerics__scl  \\\n",
       "119                                None          RobustScaler()   \n",
       "110  OutlierHandler(featureindices=[1])          RobustScaler()   \n",
       "117                                None        StandardScaler()   \n",
       "111                                None        StandardScaler()   \n",
       "116  OutlierHandler(featureindices=[1])          RobustScaler()   \n",
       "\n",
       "     mean_test_score  std_test_score  \n",
       "119         0.837431        0.044272  \n",
       "110         0.837397        0.039597  \n",
       "117         0.836025        0.044538  \n",
       "111         0.836008        0.044944  \n",
       "116         0.833316        0.039627  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(gs3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearch içindeki çoklu dictionary kullanma yöntemini iyi anlamak önemli, aksi halde gereksiz sayıda çok deneme yaptırmış olursunuz. Mesela Outlierhandling yapacaksınız diyelim, bunun için bir de RobustScaler yaptırmaya gerek yok(çünkü bu zaten outlierlara çözüm olsun diye kullanılıyor), biz yukarıda yaptık ama bu doğru bi kullanım şekli olmadı, onun yerine şu şekilde yapmak daha mantıklı."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:46:44.713188Z",
     "start_time": "2022-06-03T09:46:44.689200Z"
    }
   },
   "outputs": [],
   "source": [
    "params = [{\n",
    "           'clf__C'      : c_range,\n",
    "           'clf__penalty': ['l1'], \n",
    "           'clf__solver' : ['liblinear'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],\n",
    "           'ct__numerics__scl': [StandardScaler(),MinMaxScaler()] #burdan robust'u çıkaralım\n",
    "          },\n",
    "          {\n",
    "           'clf__C'      : c_range,\n",
    "           'clf__penalty': ['l1'], \n",
    "           'clf__solver' : ['liblinear'],\n",
    "#            'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],#bunu commentleyelim\n",
    "           'ct__numerics__scl': [StandardScaler(),RobustScaler()]\n",
    "          },              \n",
    "          {\n",
    "           'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],\n",
    "           'ct__numerics__scl': [StandardScaler(),MinMaxScaler()]#burdan robust'u çıkaralım\n",
    "          },\n",
    "          {\n",
    "           'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs'],\n",
    "#            'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],#bunu commentleyelim\n",
    "           'ct__numerics__scl': [StandardScaler(),RobustScaler()]\n",
    "          }      \n",
    "         ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Farklı Classifierlar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diyelim ki birden fazla algoritma kullanmak istiyoruz, ki gerçek hayatta böyle olacak, olmalı. Bunlar için ayrı ayrı griddsearchler yapmak yerine tek bir gridsearch içinde bu işi gerçekleştirebiliriz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu sefer bir de DummyEstimator adına bir placeholder sınıf yaratıyoruz. Burda ayrıca classifierın duyarlı olduğu şeye göre bazı işlemleri yapar veya yapmayız. Mesela DecisionTreeler konusunda göreceğiz ki, bu algoritma scalinge veya outlierlara duyarlı değil, o yüzden bu işlem(ler)i yapmaya gerek yok ama yine de bu işleme ait placeholder olan dummytransformer için None değerini verdiğimiz bir satır yazmalıyız, yoksa hata alırız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:46:46.222105Z",
     "start_time": "2022-06-03T09:46:44.738068Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slo...\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                          &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                          &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                          &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slo...\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                          &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                          &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                          &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;ChestPain&#x27;]),\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   DummyTransformer())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, DummyEstimator())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, DummyTransformer())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-145\" type=\"checkbox\" ><label for=\"sk-estimator-id-145\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-146\" type=\"checkbox\" ><label for=\"sk-estimator-id-146\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-147\" type=\"checkbox\" ><label for=\"sk-estimator-id-147\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-148\" type=\"checkbox\" ><label for=\"sk-estimator-id-148\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-149\" type=\"checkbox\" ><label for=\"sk-estimator-id-149\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-150\" type=\"checkbox\" ><label for=\"sk-estimator-id-150\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-151\" type=\"checkbox\" ><label for=\"sk-estimator-id-151\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-152\" type=\"checkbox\" ><label for=\"sk-estimator-id-152\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-153\" type=\"checkbox\" ><label for=\"sk-estimator-id-153\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-154\" type=\"checkbox\" ><label for=\"sk-estimator-id-154\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-155\" type=\"checkbox\" ><label for=\"sk-estimator-id-155\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-156\" type=\"checkbox\" ><label for=\"sk-estimator-id-156\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-157\" type=\"checkbox\" ><label for=\"sk-estimator-id-157\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyEstimator</label><div class=\"sk-toggleable__content\"><pre>DummyEstimator()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score='raise',\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slo...\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'clf': [DecisionTreeClassifier(random_state=42)],\n",
       "                          'clf__criterion': ['gini', 'entropy'],\n",
       "                          'clf__max_depth': [2, 3],\n",
       "                          'clf__min_samples_split': [2, 4],\n",
       "                          'ct__numerics__ouh': [None],\n",
       "                          'ct__numerics__scl': [None]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "class DummyTransformer(TransformerMixin,BaseEstimator):\n",
    "    def fit(self,X,y=None): pass\n",
    "    def transform(self,X,y=None): pass\n",
    "    \n",
    "class DummyEstimator(BaseEstimator):\n",
    "    def fit(self,X,y=None): pass\n",
    "    def score(self,X,y=None): pass    \n",
    "    \n",
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(drop=\"first\",handle_unknown='ignore'))     \n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer)), \n",
    "                   (\"ouh\", DummyTransformer()), \n",
    "                   (\"scl\", DummyTransformer())\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms),\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                ('numerics',  num_pipe, nums)\n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\")\n",
    "\n",
    "pipe = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), \n",
    "                       ('clf', DummyEstimator()) \n",
    "                       ])\n",
    "\n",
    "params = [          \n",
    "          {\n",
    "           'clf'         : [LogisticRegression(max_iter=1000,random_state=42)],\n",
    "           'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],\n",
    "           'ct__numerics__scl': [StandardScaler(),RobustScaler()]\n",
    "          } ,\n",
    "    \n",
    "          {\n",
    "           'clf'         : [DecisionTreeClassifier(random_state=42)],\n",
    "           'clf__criterion': ['gini', 'entropy'],\n",
    "           'clf__max_depth': [2,3],\n",
    "           'clf__min_samples_split':[2,4],\n",
    "           'ct__numerics__ouh': [None], #bu parametrleri None olarak vermemiz lazım, hiçvermezsek hata alırız\n",
    "           'ct__numerics__scl': [None] #bu parametrleri None olarak vermemiz lazım, hiçvermezsek hata alırız\n",
    "          }  \n",
    "         ]\n",
    "\n",
    "mycv = RepeatedKFold(n_splits=2, n_repeats=3, random_state=1) #uzun sürmesin diye burdaki rakamları biraz düşürdüm\n",
    "gs4 = GridSearchCV(estimator = pipe, param_grid = params, cv = mycv, n_jobs=-1, verbose = 1, scoring = 'accuracy',\n",
    "                  error_score='raise') \n",
    "gs4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eğitelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:20.422595Z",
     "start_time": "2022-06-03T09:46:46.233070Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 6 folds for each of 72 candidates, totalling 432 fits\n",
      "Wall time: 42.7 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slo...\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                          &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                          &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                          &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-158\" type=\"checkbox\" ><label for=\"sk-estimator-id-158\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slo...\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                          &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                          &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                          &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-159\" type=\"checkbox\" ><label for=\"sk-estimator-id-159\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;ChestPain&#x27;]),\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   DummyTransformer())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, DummyEstimator())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-160\" type=\"checkbox\" ><label for=\"sk-estimator-id-160\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, DummyTransformer())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-161\" type=\"checkbox\" ><label for=\"sk-estimator-id-161\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-162\" type=\"checkbox\" ><label for=\"sk-estimator-id-162\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-163\" type=\"checkbox\" ><label for=\"sk-estimator-id-163\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-164\" type=\"checkbox\" ><label for=\"sk-estimator-id-164\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-165\" type=\"checkbox\" ><label for=\"sk-estimator-id-165\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-166\" type=\"checkbox\" ><label for=\"sk-estimator-id-166\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-167\" type=\"checkbox\" ><label for=\"sk-estimator-id-167\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-168\" type=\"checkbox\" ><label for=\"sk-estimator-id-168\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-169\" type=\"checkbox\" ><label for=\"sk-estimator-id-169\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-170\" type=\"checkbox\" ><label for=\"sk-estimator-id-170\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-171\" type=\"checkbox\" ><label for=\"sk-estimator-id-171\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-172\" type=\"checkbox\" ><label for=\"sk-estimator-id-172\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-173\" type=\"checkbox\" ><label for=\"sk-estimator-id-173\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyEstimator</label><div class=\"sk-toggleable__content\"><pre>DummyEstimator()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score='raise',\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slo...\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'clf': [DecisionTreeClassifier(random_state=42)],\n",
       "                          'clf__criterion': ['gini', 'entropy'],\n",
       "                          'clf__max_depth': [2, 3],\n",
       "                          'clf__min_samples_split': [2, 4],\n",
       "                          'ct__numerics__ouh': [None],\n",
       "                          'ct__numerics__scl': [None]}],\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gs4.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:20.547265Z",
     "start_time": "2022-06-03T09:47:20.433568Z"
    }
   },
   "source": [
    "Sonuçları görelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:20.687889Z",
     "start_time": "2022-06-03T09:47:20.557239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=1428d45c-1e81-4221-a504-cd20f7c1a384 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('1428d45c-1e81-4221-a504-cd20f7c1a384').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_clf</th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>param_ct__numerics__ouh</th>\n",
       "      <th>param_ct__numerics__scl</th>\n",
       "      <th>param_clf__criterion</th>\n",
       "      <th>param_clf__max_depth</th>\n",
       "      <th>param_clf__min_samples_split</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42, solver='newton-cg')</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>None</td>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.829201</td>\n",
       "      <td>0.020615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42, solver='newton-cg')</td>\n",
       "      <td>0.2</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>None</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.825069</td>\n",
       "      <td>0.022591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42, solver='newton-cg')</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.825069</td>\n",
       "      <td>0.022082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42, solver='newton-cg')</td>\n",
       "      <td>0.2</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.823691</td>\n",
       "      <td>0.022717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42, solver='newton-cg')</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>None</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.823691</td>\n",
       "      <td>0.015584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "                                            param_clf param_clf__C  \\\n",
       "58  LogisticRegression(C=1, max_iter=1000, random_...            1   \n",
       "35  LogisticRegression(C=1, max_iter=1000, random_...          0.2   \n",
       "60  LogisticRegression(C=1, max_iter=1000, random_...            1   \n",
       "39  LogisticRegression(C=1, max_iter=1000, random_...          0.2   \n",
       "59  LogisticRegression(C=1, max_iter=1000, random_...            1   \n",
       "\n",
       "   param_clf__penalty param_clf__solver             param_ct__numerics__ouh  \\\n",
       "58                 l2         newton-cg                                None   \n",
       "35                 l2         newton-cg                                None   \n",
       "60                 l2             lbfgs  OutlierHandler(featureindices=[1])   \n",
       "39                 l2             lbfgs                                None   \n",
       "59                 l2         newton-cg                                None   \n",
       "\n",
       "   param_ct__numerics__scl param_clf__criterion param_clf__max_depth  \\\n",
       "58        StandardScaler()                  NaN                  NaN   \n",
       "35          RobustScaler()                  NaN                  NaN   \n",
       "60        StandardScaler()                  NaN                  NaN   \n",
       "39          RobustScaler()                  NaN                  NaN   \n",
       "59          RobustScaler()                  NaN                  NaN   \n",
       "\n",
       "   param_clf__min_samples_split  mean_test_score  std_test_score  \n",
       "58                          NaN         0.829201        0.020615  \n",
       "35                          NaN         0.825069        0.022591  \n",
       "60                          NaN         0.825069        0.022082  \n",
       "39                          NaN         0.823691        0.022717  \n",
       "59                          NaN         0.823691        0.015584  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(gs4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu yukarıda kullandığımız DummyEstimator yerine estimatorleri bir list içine alıp döngüyle dolaşıp, her döngü içinde gridsearch yapmak gibe seçenekler veya aşağıdaki linkteki öneriler de var ama bana en sade ve pratiği bizim kullandığımız gibi geliyor. Tercih size kalmış.\n",
    "\n",
    "- http://www.davidsbatista.net/blog/2018/02/23/model_optimization/\n",
    "- https://stackoverflow.com/questions/50285973/pipeline-multiple-classifiers\n",
    "- https://stackoverflow.com/questions/50265993/alternate-different-models-in-pipeline-for-gridsearchcv\n",
    "- https://stackoverflow.com/questions/42266737/parallel-pipeline-to-get-best-model-using-gridsearch/42271829"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HalvingRandomizedSearch ile hızlandırma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:26.362240Z",
     "start_time": "2022-06-03T09:47:20.696863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_iterations: 2\n",
      "n_required_iterations: 2\n",
      "n_possible_iterations: 2\n",
      "min_resources_: 60\n",
      "max_resources_: 242\n",
      "aggressive_elimination: False\n",
      "factor: 3\n",
      "----------\n",
      "iter: 0\n",
      "n_candidates: 4\n",
      "n_resources: 60\n",
      "Fitting 6 folds for each of 4 candidates, totalling 24 fits\n",
      "----------\n",
      "iter: 1\n",
      "n_candidates: 2\n",
      "n_resources: 180\n",
      "Fitting 6 folds for each of 2 candidates, totalling 12 fits\n",
      "Wall time: 4.92 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>HalvingRandomSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "                      error_score=&#x27;raise&#x27;,\n",
       "                      estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                                 ColumnTransformer(n_jobs=-1,\n",
       "                                                                   remainder=&#x27;passthrough&#x27;,\n",
       "                                                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                                  [&#x27;Sex&#x27;,\n",
       "                                                                                   &#x27;Fbs&#x27;,\n",
       "                                                                                   &#x27;Ex...\n",
       "                                                            &#x27;lbfgs&#x27;],\n",
       "                                            &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                                  None],\n",
       "                                            &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                                  RobustScaler()]},\n",
       "                                           {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                                            &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;,\n",
       "                                                               &#x27;entropy&#x27;],\n",
       "                                            &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                                            &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                                            &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                                            &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "                      scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-174\" type=\"checkbox\" ><label for=\"sk-estimator-id-174\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HalvingRandomSearchCV</label><div class=\"sk-toggleable__content\"><pre>HalvingRandomSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "                      error_score=&#x27;raise&#x27;,\n",
       "                      estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                                 ColumnTransformer(n_jobs=-1,\n",
       "                                                                   remainder=&#x27;passthrough&#x27;,\n",
       "                                                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                                  [&#x27;Sex&#x27;,\n",
       "                                                                                   &#x27;Fbs&#x27;,\n",
       "                                                                                   &#x27;Ex...\n",
       "                                                            &#x27;lbfgs&#x27;],\n",
       "                                            &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                                  None],\n",
       "                                            &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                                  RobustScaler()]},\n",
       "                                           {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                                            &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;,\n",
       "                                                               &#x27;entropy&#x27;],\n",
       "                                            &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                                            &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                                            &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                                            &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "                      scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-175\" type=\"checkbox\" ><label for=\"sk-estimator-id-175\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;ChestPain&#x27;]),\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   DummyTransformer())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, DummyEstimator())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-176\" type=\"checkbox\" ><label for=\"sk-estimator-id-176\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, DummyTransformer())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-177\" type=\"checkbox\" ><label for=\"sk-estimator-id-177\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-178\" type=\"checkbox\" ><label for=\"sk-estimator-id-178\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-179\" type=\"checkbox\" ><label for=\"sk-estimator-id-179\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-180\" type=\"checkbox\" ><label for=\"sk-estimator-id-180\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-181\" type=\"checkbox\" ><label for=\"sk-estimator-id-181\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-182\" type=\"checkbox\" ><label for=\"sk-estimator-id-182\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-183\" type=\"checkbox\" ><label for=\"sk-estimator-id-183\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-184\" type=\"checkbox\" ><label for=\"sk-estimator-id-184\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-185\" type=\"checkbox\" ><label for=\"sk-estimator-id-185\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-186\" type=\"checkbox\" ><label for=\"sk-estimator-id-186\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-187\" type=\"checkbox\" ><label for=\"sk-estimator-id-187\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-188\" type=\"checkbox\" ><label for=\"sk-estimator-id-188\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-189\" type=\"checkbox\" ><label for=\"sk-estimator-id-189\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyEstimator</label><div class=\"sk-toggleable__content\"><pre>DummyEstimator()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "HalvingRandomSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "                      error_score='raise',\n",
       "                      estimator=Pipeline(steps=[('ct',\n",
       "                                                 ColumnTransformer(n_jobs=-1,\n",
       "                                                                   remainder='passthrough',\n",
       "                                                                   transformers=[('nominals',\n",
       "                                                                                  Pipeline(steps=[('csi',\n",
       "                                                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                                  ('ohe',\n",
       "                                                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                                                 handle_unknown='ignore'))]),\n",
       "                                                                                  ['Sex',\n",
       "                                                                                   'Fbs',\n",
       "                                                                                   'Ex...\n",
       "                                                            'lbfgs'],\n",
       "                                            'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                                  None],\n",
       "                                            'ct__numerics__scl': [StandardScaler(),\n",
       "                                                                  RobustScaler()]},\n",
       "                                           {'clf': [DecisionTreeClassifier(random_state=42)],\n",
       "                                            'clf__criterion': ['gini',\n",
       "                                                               'entropy'],\n",
       "                                            'clf__max_depth': [2, 3],\n",
       "                                            'clf__min_samples_split': [2, 4],\n",
       "                                            'ct__numerics__ouh': [None],\n",
       "                                            'ct__numerics__scl': [None]}],\n",
       "                      scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import HalvingRandomSearchCV,RandomizedSearchCV\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "np.random.seed(42)\n",
    "params = [          \n",
    "          {\n",
    "           'clf'         : [LogisticRegression(max_iter=1000,random_state=42)],\n",
    "           'clf__C'      : c_range, \n",
    "           'clf__penalty': ['l2'], \n",
    "           'clf__solver' : ['newton-cg', 'lbfgs'],\n",
    "           'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),None],\n",
    "           'ct__numerics__scl': [StandardScaler(),RobustScaler()]\n",
    "          } ,\n",
    "    \n",
    "          {\n",
    "           'clf'         : [DecisionTreeClassifier(random_state=42)],\n",
    "           'clf__criterion': ['gini', 'entropy'],\n",
    "           'clf__max_depth': [2,3],\n",
    "           'clf__min_samples_split':[2,4],\n",
    "           'ct__numerics__ouh': [None], #bu parametrleri None olarak vermemiz lazım, hiçvermezsek hata alırız\n",
    "           'ct__numerics__scl': [None]\n",
    "          }  \n",
    "         ]         \n",
    "\n",
    "mycv = RepeatedKFold(n_splits=2, n_repeats=3, random_state=1)\n",
    "hrs = HalvingRandomSearchCV(estimator = pipe, param_distributions = params, cv = mycv, n_jobs=-1, verbose = 1, \n",
    "                           scoring = 'accuracy',error_score='raise',min_resources=60,factor=3) \n",
    "\n",
    "hrs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçları görelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:26.441029Z",
     "start_time": "2022-06-03T09:47:26.372208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=d0305438-4f09-4733-b4ec-8ce31ed7bb6f style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('d0305438-4f09-4733-b4ec-8ce31ed7bb6f').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_ct__numerics__scl</th>\n",
       "      <th>param_ct__numerics__ouh</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>None</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>l2</td>\n",
       "      <td>1</td>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.818519</td>\n",
       "      <td>0.017762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>None</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>l2</td>\n",
       "      <td>1</td>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.805556</td>\n",
       "      <td>0.062113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>None</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.089581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>None</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.021276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>l2</td>\n",
       "      <td>0.001</td>\n",
       "      <td>LogisticRegression(C=1, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.175066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "  param_ct__numerics__scl             param_ct__numerics__ouh  \\\n",
       "5        StandardScaler()                                None   \n",
       "1        StandardScaler()                                None   \n",
       "2        StandardScaler()                                None   \n",
       "4        StandardScaler()                                None   \n",
       "0        StandardScaler()  OutlierHandler(featureindices=[1])   \n",
       "\n",
       "  param_clf__solver param_clf__penalty param_clf__C  \\\n",
       "5             lbfgs                 l2            1   \n",
       "1             lbfgs                 l2            1   \n",
       "2         newton-cg                 l2         0.05   \n",
       "4         newton-cg                 l2         0.05   \n",
       "0             lbfgs                 l2        0.001   \n",
       "\n",
       "                                           param_clf  mean_test_score  \\\n",
       "5  LogisticRegression(C=1, max_iter=1000, random_...         0.818519   \n",
       "1  LogisticRegression(C=1, max_iter=1000, random_...         0.805556   \n",
       "2  LogisticRegression(C=1, max_iter=1000, random_...         0.788889   \n",
       "4  LogisticRegression(C=1, max_iter=1000, random_...         0.788889   \n",
       "0  LogisticRegression(C=1, max_iter=1000, random_...         0.550000   \n",
       "\n",
       "   std_test_score  \n",
       "5        0.017762  \n",
       "1        0.062113  \n",
       "2        0.089581  \n",
       "4        0.021276  \n",
       "0        0.175066  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(hrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimatorler arasında seçim yapma ve oylama"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Farklı classfierlar denediysek, ki gerçek hayatta deniyor olacaksınız, böyle bir sürecimiz olacak ancak biz bu kısmı şimdilik yapmayacağız. Diğer algoritmaları ve özellikle ensemble modelleri gördüğümüz notebooklarda yapacağız. Ve günün sonunda ideal yapı şöyle olacak.\n",
    "\n",
    "Ana pipeline içinde grid/randomize search ve paramgrid içinde de birden çok estimator olur. Buradaki işlem, best_modeli almak yerine en iyi 3 modeli alıp, bunları bir voting mekanizmasına tabi tutmak olacak. \n",
    "\n",
    "En iyi estimatorleri aşağıdaki fonksiyonla bulabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:26.565688Z",
     "start_time": "2022-06-03T09:47:26.447014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=e980cdd3-8e08-404a-95f6-9bf8101b8fbf style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('e980cdd3-8e08-404a-95f6-9bf8101b8fbf').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAX of mean_test_score</th>\n",
       "      <th>MIN of mean_fit_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_clf</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.829201</td>\n",
       "      <td>0.394655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.785124</td>\n",
       "      <td>0.365629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "                        MAX of mean_test_score  MIN of mean_fit_time\n",
       "param_clf                                                           \n",
       "LogisticRegression                    0.829201              0.394655\n",
       "DecisionTreeClassifier                0.785124              0.365629"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.compareEstimatorsInGridSearch(gs4,tableorplot='table')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu custom fonksiyon ile hem eğitim performans sonuçlarını hem de eğitim sürelerini görmüş olursunuz. Nihai modelinizde ikisini de göz önünde bulundurmakta fayda var.\n",
    "\n",
    "Bu arada sonuçlara grafik olarak da bakabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:33.075864Z",
     "start_time": "2022-06-03T09:47:26.573668Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.compareEstimatorsInGridSearch(gs4,tableorplot='plot',figsize=(4,4))        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformerlar için farklı sıralamalar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Farklı alternatifler için birkaç gridsearch çalıştırmak gerekebilir. Örneğin birinde önce feature selection sonra encoding, diğerinde tam tersi gibi. Bu alıştırmayı da size bırakıyorum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaynaklar\n",
    "\n",
    "- https://scikit-learn.org/stable/modules/compose.html\n",
    "- https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html\n",
    "- https://stackoverflow.com/questions/67858871/pass-arguments-to-functiontransformer-in-pipeline\n",
    "- https://www.geeksforgeeks.org/prediction-using-columntransformer-onehotencoder-and-pipeline/\n",
    "- https://www.youtube.com/watch?v=irHhDMbw3xo\n",
    "- https://towardsdatascience.com/pipeline-columntransformer-and-featureunion-explained-f5491f815f\n",
    "- https://machinelearningmastery.com/create-custom-data-transforms-for-scikit-learn/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi, pipelinımız da oluştuğuna ve en iyi modelimizi seçebilecek durumda olduğumuza göre artık test datası ile tahminleme yapıp modelimizi değerlendirmeye geçebiliriz."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi burdaki ilk soru şu olabilir, biz zaten train seti üzerinden bir score ölçümü yapmıştık, o değerlendirme değil miydi, daha ne değerlendirmesi yapacağız? Aslında adı üzerinde; bu skor, train setinin skoruydu. Modelimizin generalization(genelleştirilmiş) performansını test setinin skoru verir, traininki değil. \n",
    "\n",
    "Test performansı genellikle train setinin performansından biraz daha kötü olacaktır. Bu normal olup, hyperparametrelerle biraz daha oynamaya çalışmanıza gerek yoktur. Bu arada bazen ender olarak test setin performansı traininkinden de iyi çıkabilir. \n",
    "\n",
    "Ancak train ve testin skorları arasında normal sayılamayacak büyüklükte fark varsa, modelimizin **overfit** ediyor diyeceğiz ve bu problemi çözmeye çalışacağız. Ama aslında daha öncesinde şuna bakacağız: Train setimizin performansı beklentimizin(veya varsa bir baseline modelin) yeterince üstünde mi? Değilse, bu sefer de modelimiz **underfit** ediyor diyeceğiz ve test setinin ölçümüne hiç geçmeyeceğiz bile. Hadi şimdi bu iki kavrama ve bunların olması durumunda ne tür aksiyonlar alacağımıza bakalım."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias-Variance Tradeoff & Overfitting/Underfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train setimizin eğitimi sonucunda çıkan skor beklediğimizin çok altındaysa(classification için böyle, regressionda ise tam tersi) modelimiz `underfit` ediyor ve `high bias` bir modelimiz var demektir. Neden high bias, çünkü modelimiz onu gerektirdiği varsayımlara karşı önyargılıdır(biased), varsayımları çok basite indirgiyordur. \n",
    "\n",
    "Aynı şekilde modelimiz test datasında train datasına göre çok kötü performe ediyorsa `overfit` ediyordur ve `high variance` bir modelimiz vardır deriz, buna high variance denme sebebi de, test seti verdiğimizde çıkan hataların çok dalgalanması, yani yüksek variancelı olmasındandır. Bu isimlendirmeyle alakalı <a href=\"https://datascience.stackexchange.com/questions/45578/why-underfitting-is-called-high-bias-and-overfitting-is-called-high-variance\">şurda</a> güzel bi açıklama mevcut, oraya da bakmanızı tavsiye ederim.\n",
    "\n",
    "Bias ve variance iki zıt olgudur, biri artarken biri düşer. Amacımız ikisini de düşürmek ama birini düşürürken diğeri arttığı için optimum noktayı bulmaya çalışırız."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir başka yaygın gösterim şöyledir.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Özetleyecek olursak, bu iki öğrenme sorununun sebepleri ve çözüm önerileri şöyledir:\n",
    "\n",
    "**Overfitting;**\n",
    "\n",
    "*Sebepleri:*\n",
    "\n",
    "-\tModel, gürültülü veriyi(hata ve outlierları) ezberlemiştir\n",
    "-\tElimizde az miktarda data vardır, zaten bir kısmını da teste ayırmışızdır, öğrenilecek data iyice azalmıştır, data en küçük ayrıntısına kadar ezberlenmiştir.\n",
    "-\tGereksiz karmaşıklıkta bir model kullanmışızdır, halbuki ihtiyaç basit modeldir\n",
    "-\tÇok feature vardır\n",
    "\n",
    "*Nasıl anlarız:*\n",
    "\n",
    "-\tTrain accuracy >> test accuracy\n",
    "\n",
    "*Çözüm:*\n",
    "\n",
    "-\tOutlier handling -> noise düşürme\n",
    "-   Hatalı kayıtların temizlenmesi\n",
    "-\tVeri azsa artırılabilir\n",
    "-\tModel basitleştirilebilir\n",
    "-\tKolon çoksa feature selection ve/veya PCA yapılabilir\n",
    "-\tLinear modelse regularization eklenebilir, tree based bir modelse arama derinlik düşürülebilir\n",
    "-   Ensemble model kullanılabilir\n",
    "\n",
    "**Underfitting**\n",
    "\n",
    "*Sebepleri:*\n",
    "\n",
    "-\tElimizde az miktarda data vardır, zaten bir kısmını da teste ayırmışızdır, öğrenilecek data iyice azalmıştır, data yeterli gelmemiştir.\n",
    "-\tKarmaşık bir model gerekliyken basit bir model kurmuşuzdur\n",
    "\n",
    "*Nasıl anlarız:*\n",
    "\n",
    "-\tEğitim setinin performansı çok düşüktür. Testinkine bakmaya gerek bile olmaz.\n",
    "\n",
    "*Çözümler:*\n",
    "\n",
    "-\tDaha karmaşık model kullanılabilir\n",
    "-\tYeni feature eklenebilir\n",
    "-\tLinear modelse regularization miktarını düşürebilir, tree based bir modelse arama derinliğini artırabiliriz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test datası tahmini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buraya kadar bulduğumuz skorlar, hala model selection aşamasında olduğumuz için train setinin skoruydu. Tekrar hatırlatmak gerekirse gridsearch sonucunda çıkan **best_score_** değeri, cross validation yaparken ayırdığımız validation setlerinin ortalama skorudur. \n",
    "\n",
    "Şimdi kısa bir ara verip test datamızı tahminleyelim, sonra farklı ölçüm metrikleriyle ve yöntemleriyle devam edeceğiz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:33.105785Z",
     "start_time": "2022-06-03T09:47:33.084840Z"
    }
   },
   "outputs": [],
   "source": [
    "#öncelikle nolur nolmaz yedeğimizi alalım\n",
    "X_test_orj=X_test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pipelinsız tahmin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu yöntem çok zahmetli olacaktır. X_test üzerinde, train setinde yaptığımız tüm manuel processingleri yapmayı gerektiriyor. Normalde test tahminlemeyi bu şekilde yapmayız, belki bunu sadece eğitim amaçlı kaynaklarda görebilirsiniz. Ama burda yapsaydık şu şekilde yapardık(kodsuz), böylece bunun ne kadar zahmetli olduğunu siz de görün.\n",
    "\n",
    "- X_test[nums] = numimp.transform(X_test[nums]) diyerek numeriklerin imputationuını yapardık\n",
    "- aynısını categorikler için yapardık\n",
    "- sonra outlierhandlingi yapardık\n",
    "- sonra feature selection yapardık\n",
    "- sonra nominallerin onehot ecnodingini, chestpain'in ordinal encodingini yapar ve bunlarla diğer ordinal kolonları birleştirirdik, ama burası bizim için biraz daha zahmetli olurdu, zira artık kolon bilgilerini kaybettiğimiz için bunların kolon indekslerini da bulup öyle işleme sokmak zorunda kalırdık\n",
    "- sonra scaling yapardık\n",
    "- son olarak da tahminlememizi yapardık"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pipelinelı tahmin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pipelinelı bir model kurduğumuzda işlerin ne kadar kolay olduğunu görelim. Yukardaki gridsearchlerden birinin best estimatorünü alıp onunla doğrudan tahminleme yapacağız. Hatta best_estimator_ almaya da gerek yok, çünkü zaten **refit=True** idi, yani şuan **gs4=gs4.best_estimator_**. O kendi içinde tüm transformerları kendi içinde yapıyor olacak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:33.215485Z",
     "start_time": "2022-06-03T09:47:33.114762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819672131147541"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generalization performance skoru\n",
    "gs4.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu kadar basit. Harika değil mi :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train scorumuz 0.83 civarıydı, sanki hafiften bi overfitting var gibi ama şimdi bi learning curve'e bakalım, daha derin yorumlamalarda bulunalım."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learning curve train seti ile çizdirilir. Amaç, daha fazla veri eklendiğinde cross-validation sonucu iyileşme trendine giriyor mu, onu görmektir. Zira hem overfitting hem underfitting çözümünde ne demiştik; veri miktarı artırılırsa iyileşme bekleriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:44.842384Z",
     "start_time": "2022-06-03T09:47:33.225473Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plot_learning_curve(gs4.best_estimator_,\"Learnig curve\",X_train,y_train,cv=mycv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Soldaki grafikten analışıyor ki, cv score 100'e kadar artma eğiliminde, ama ondan sonraki sample sayısını artırmanın bi esprisi yok, zira düşüş başlıyor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/\n",
    "- https://towardsdatascience.com/learning-curve-to-identify-overfitting-underfitting-problems-133177f38df5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ölçüm metrikleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu skor, toplam yaptığımız tahmin adedinin ne kadarı doğru(binary classfication için hem 1'ler hem 0'lar) bunu gösterir.   Yukarıda **score** metodu ile yaptığımız işlem bize accuracy'yi döndürdü, çünkü gridsearchü yaparken scoring olarak accuracy seçmiştik. Bununla birlikte sklern.metrics içinde de bu skoru veren bir fonksiyon vardır, ama parametre olarak **y_true**, yani y_test ve tahminlediğimiz labelları yani **y_pred**leri alır. Bu bazen $\\bar{y}$ (y_hat) olarak da adlandırılır. Bunun için **predict** metoduyla tahminleme yaparız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:44.968040Z",
     "start_time": "2022-06-03T09:47:44.853348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819672131147541"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "('Yes', 'Yes')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred= gs4.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)\n",
    "y_pred[0],y_test.values[0] #ilk instance için kontrol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regresyon notebookuna baktıysanız ve işleyişi öğrendiyseniz görmüşsünüzdür ki, aslında bu algoritma bi olasılık hesabı yapıyor, bu hesaplama sonucu %50 üzerindeyse 1(Yes/True), altındaysa 0(No/False) değeri üretiliyor. Peki bu olasılık değerlerini nasıl bulacağız? **predict_proba** metodu ile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:45.077750Z",
     "start_time": "2022-06-03T09:47:44.979013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01368107, 0.98631893])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs4.predict_proba(X_test)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mesela burda ilk kayda %98 oranında Yes olur dediği için Yes atanmış olduğunu görüyoruz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daha anlaşılır bir şekilde gösterelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:45.312119Z",
     "start_time": "2022-06-03T09:47:45.085731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=17e8c84e-66ae-4c72-bf11-172834fb3c6e style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('17e8c84e-66ae-4c72-bf11-172834fb3c6e').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Prob(No)</th>\n",
       "      <th>Prob(Yes)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.013681</td>\n",
       "      <td>0.986319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.845689</td>\n",
       "      <td>0.154311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.409790</td>\n",
       "      <td>0.590210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.810487</td>\n",
       "      <td>0.189513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.120649</td>\n",
       "      <td>0.879351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "    Actual Predicted  Prob(No)  Prob(Yes)\n",
       "2      Yes       Yes  0.013681   0.986319\n",
       "214    Yes        No  0.845689   0.154311\n",
       "267    Yes       Yes  0.409790   0.590210\n",
       "61      No        No  0.810487   0.189513\n",
       "174    Yes       Yes  0.120649   0.879351"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = gs4.predict_proba(X_test)\n",
    "data = {'Actual'   : y_test,\n",
    "        'Predicted': gs4.predict(X_test),\n",
    "        'Prob(No)'  : probs[:,0],\n",
    "        'Prob(Yes)'  : probs[:,1]  \n",
    "        }\n",
    "\n",
    "dfprobs = pd.DataFrame(data)\n",
    "dfprobs.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu arada istersek tahminlerimizi predict ile değil predict_proba'nın sonucuna göre de yapabiliriz. Mesela %50 değil de %80in üzerindekilere Yes diyelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:45.344034Z",
     "start_time": "2022-06-03T09:47:45.321102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'Yes', 'Yes',\n",
       "       'No', 'No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'No', 'Yes', 'Yes',\n",
       "       'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No',\n",
       "       'No', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No',\n",
       "       'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'Yes',\n",
       "       'No', 'Yes', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No'],\n",
       "      dtype='<U3')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(probs[:,1]>0.8,\"Yes\",\"No\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biraz aşağıdaki farklı thresholdlar için sonuçların ne olacağına da bakacağız, ve bunun daha derin bir anlamı olacak."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Targettaki classlar arasında dengeli bir dağılım varsa, accuracy genelde yeterlidir. Doğru bildiğimiz tahminlerin toplam kayıt sayısına olan oranı verir demiştik. Ancak diyelim ki verisetinizdeki 100bin satırın, 99bini kanserli değil, bini kanserli diye etiketlenmiş olsun. Yani mevcut durumda kansersiz kişi oranı %99. Siz böyle bir durumda hiç model çalıştırmadan tüm kayıtlar için \"kanserli değil\" tahminini yaptığınızda sözde modelinizin(!) accuracy oranı %99 olacak. Ama bu tahminler gerçekten o kadar başarılı mıdır? Aslında çok başarısız bir tahminleme yönteminiz var, ama bunu nasıl ifade etmek gerekir?\n",
    "\n",
    "Böyle durumlar için başka metriklere ihtiyaç duyulur. Bunlar için de öncelikle bir `confusion matrix` çıkarılır. Gerçek(Actual) Pozitiflerin(**True positive:TP** de denir), ki bunlara genellikle 1 olarak class etiketi verilir, ve Gerçek Negatiflerin(**True Negative:TN,** etiketi 0) ne kadarını doğru, ne kadarını yanlış tahmin etmişiz, bunu göstermek için idealdir. Genelde aşağıdaki gibi bir tablo ile gösterilirler."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Not**: Bu gösterim şekli binary classficiation içindir ancak benzer mantıkla 3x3lük matris de bize 3 classlı bir veri seti için confusion matrixi oluşturacak ve ona uygun metrikler de hesaplanabilecektir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Uyarı:** Bazen bu tablo transpoze veya köşeler ters olacak şekilde gösterilebilir. Yani aşağıdaki gibi bir tablo da görebilirsiniz. Bu matrix, birçok yerde üstteki gibi gösterilmesine rağmen **sklearn'ün confusion matrix'i** aşağıdaki şekilde oluşur. Bu biraz kafa karıştırıcı ama maalesef durum böyle, bu konuda dikkatli olmanızı tavsiye ederim."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://towardsdatascience.com/decoding-the-confusion-matrix-bb4801decbb\">Şuradan</a> aldığım şu görsel daha da açıklayıcıdır. Bahsekonu Type I ve II errorları istatistik dünyasından gelmektedir. İlgilenen bunları da araştırabilir."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi sklearn ile bu matrix nasıl çıkarılır, ona bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:45.391908Z",
     "start_time": "2022-06-03T09:47:45.356004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28,  4],\n",
       "       [ 7, 22]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bunu görsel olarak da ifade edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:46.157857Z",
     "start_time": "2022-06-03T09:47:45.399888Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#classes sırasını doğru yazdığınızdan emin olun\n",
    "ml.plot_confusion_matrix(confusion_matrix(y_test,y_pred),classes=[\"No\",\"Yes\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:46.204733Z",
     "start_time": "2022-06-03T09:47:46.170822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819672131147541"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#manuel accuracy hesabı\n",
    "# (TP+TN)/Toplam\n",
    "(cm[1,1]+cm[0,0])/np.sum(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:47.289828Z",
     "start_time": "2022-06-03T09:47:46.213708Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vKuLkG8WWNWFWkzywPr3W8f1JHlY3MyuaypwuUZG1SX4zcIOkfdL9T0g6CbiKZBS9mVnRtOgmeURcJWlj4AmgPfAUycj5ayLi580Yn5m1MiIZWpRHmcdhRsT5ki4HdiSpmb4UEYubLTIza7Vy2iJv8sD1AJanX39Q5FjMzKBpE2uUVNZxmO0kXQe8QzJD8WTgHUnXp7N8mJkVTYvuwyS5sfMp4FQ+HF60F3AFyezEpxQ/NDNrjUR+75JnTZhHA5+LiCcKjk2T9DbwB5wwzayI8tokz5owlwCz6jg+C1hWx3Ezs/VSruZ2FlnHYf4MuFhSh5oD6dcXpufMzIomr8+SNzTj+p9rHRoKzJI0Od3fOX19p+YJzcxaq5xWMBtsks+vtf+HWvuvFzkWMzOgBfZhRsSXShmImRkkybKl3yU3MyuZnFYwsydMSV8CjgO2BNoWnouITxY5LjNrxfLaJM/6pM/3gJ8AE0lmXL8feBHoBtzSTLGZWSskkmfJs2yllnVY0WnA6RFxLrASuDEiPkOSRLdqruDMrHVS+jx5Y1upZU2YWwD/Sr9exoezrN8NHFXsoMysdVPGrdSyJszZQI/06+kkz5EDbEsyg5GZWVFIybPkWbZSy5ow/wp8Jv16NHCtpKeAe4D7miMwM2u98tokz3qX/HTS5BoRv5T0LrAPyWD2XzVTbGbWSuX0JnnmJSpWA6sL9u8hqV2amRWVKM9z4lk09Cz57lkLiYh/FyccM2v1cjxbUUM1zAkkN3QaCz2AyqJF9BHttP0W/Onxq8sdhjXRDt97qNwh2HqonrmwWcrN68D1hhLm1iWLwswsJaCypSXMiJheykDMzGrkdO4NT75hZvnjhGlmlkGyREU+M6YTppnljmuYZmYZ5bSCmfnRSAAk9ZA0WFK75grIzFo3AVVSpq3RsqQzJU2QtELSmILjvSWFpMUF24WNlZephimpC8kz5J8nGXe5Hcm65L8EZkfEyCzlmJllUcQaZjVwGXAI0KGO810jYlXWwrLWMK8EegG7s/Y65A8Cn836ZmZmjVHGJXazPD4ZEfdFxP2su6jjesmaMD8DfDsiJrH2dG7/Bbw8hZkVlZRtA3qkTe6a7fQmvtV0STMl3SqpR2MXZ73p8zHqztBdgA+aEp2ZWWOacJd8XkQMXI+3mAfsAUwCugM/B+4kabrXH1fGwsfz4XyY8GEt8yvAP5oSpZlZQ5I1fYrTJK9PRCyOiAkRsSoi5gBnAp9K79fUK2sN8zzgMUk7pa/5bvr1IGD/9Y7azKwOZRhWVFMJbLASmamGGRH/APYmWV73NWAYyd2nvTy1m5kVlZLJN7JsjRYlVUlqTzKjWqWk9umxwZL6SKqQ1B24ARgbEQ1Ov5R54HpEvACclPV6M7P1UbPMbpFcAFxcsP8F4BLgZeBHwCbAe8ATwHGNFZZ1HGa3hs5HxDtZyjEzy6JYCTMdIz6yntN3N7W8rDXMeTS8OmRuJhA2s5avpU++cUCt/TZAf+AMkiqvmVlRFLlJXlRZF0F7uo7DT0qaBpwK3FXUqMys9Wqha/pkMQkPKzKzIhJQldMq5nonTEmdgW8DM4oWjZkZLbyGKWkRa9/0EdARWAKc0AxxmVmrJSoaXay2PLLWMM+stb8amAs8FxHvFjckM2vNRAuuYUqqAjoB90dEdfOHZGatmvJ7l7zRRyPTyTWvJhlKZGbW7Jp78o31lbVJ/k9gAOC1ys2sWQmozGkVM2vCHAVcI2lLYCLJzZ41PAGHmRVTi+zDlHQLydChmoHp19ZxWeBHI82sSEQTV2csocZqmCcBPwC2LkEsZmbpkz75rGI2ljAFEBHuuzSzkslnuszWh9nQLEVmZkVVs0RFHmVJmLMbqx5HhPswzaxocnqTPFPCPB1Y0MxxmJml1GL7MAEeiIi3mz0SMzNa9l1y91+aWcm11BpmPqM2sw1aXhNPgwkzIvJaMzazDVULHodpZlZSgkxrjpeDE6aZ5U4+06UTppnlUE4rmE6YZpYvybCifGZMJ0wzyx3XMM3MMhFyDdPMLBvXMM3MMpA8rMjMLLOc5ksnTDPLH/dhmpllkEwgXO4o6uaEWQbVs2Zy9pmnMn/u20jimBNP4Uunf33N+V//4nquGHku4//7Jt2691jn9Scf8xkmTRzPwMF78es771vn/CXnncXv7/oNL7wxt1k/R2v0yY934mcn9V+z/4nuHfnpI6+wUYc2HLvnlryzZAUAVz/0MmP/u+73/5QhW3PMnp8gAl5+6z2+d/dk3l+1GoCzh/dh+K6b8kHAneOmM+bvb5TkM+WRa5i2RlVVJeddcgX9dunP4sWLOOKgfdh3yIFs12cHqmfN5Jmxf2HzLT5R7+tP+/p3WL5sKXf/ZvQ65yZPmsh7CxY0Y/St27S5S/j0Nc8ASS3onyOH8fgLc/j8oC245enXGTV2Wr2v7blxO07erzcHX/k0K1au5saT+nN4/835w/iZfH7QFmzWtT3Dfvw0EdC9c9tSfaRcymsfpmcjKoNNem5Gv12SWkrnzl3Ydvs+zHmrGoDLL/w+51x0WYOzteyz/wF06txlneMffPABP77kfM65+LLmCdzWss/2PZg+fymz3l2W+TWVFaJ9m8o1/7793nIAvrD3Vtzw+P+IdAba+Yvfb46QW4SayTeybKXmGmaZzXxzOlNeeJ5dB+zBE488QM/NNmeHfrusV1m/Gf1LDjrk02zSc7MiR2l1GdF/cx74d/Wa/S/utxWf26MXk2cs5PI/vcR7y1atdf2chSsYNXYa4y46kOUrP+DvL8/j7y/PA2DLHh0ZsdvmfGqXnryz+H0uuW8Kb8xbWtLPkx/5HbhekhqmpDsk3Vrr2BBJ8yW12t/uJYsX87VTjuPCS6+iqrKKm66/mu+cc+F6lTVndjWP/Pk+vnjqGUWO0urSplIctFNPHp70FpD0OQ657CmGX/N35r63gvOP2HGd12zUoYqD+/Vk/0ufYs+L/0LHtpUcOaAXAG2rKlix6gOOuHYcv312Blcdt2tJP0+uKGmSZ9kaLUo6U9IESSskjal1bpikqZKWSnpK0laNlVeqJvm3gMMkHQwgqT0wCjgrIt4qUQy5snLlSr5+yvEccdSxHDLiSN58Yxoz3pzOpw8YzP4D+jK7ehafOWhv5s6Znam8l154numvv8aBg/ux/4C+LFu2lAMG9WvmT9F6Dd1hE6bMWsi8tOk8b/H7rA6IgLuffZNdt+y6zmv23b4HM+Yv450l77NqdfDY5Nns3vtjAMxesJxHJyc/68demE2fzdbtcmlNlHHLoBq4DLhlrfKlHsB9wIVAN2ACcE9jhZUkYUbEfOAbwM2SOgEXA68BUyX9Q9ICSc9LGlrzGkknS5omaZGk1yWdUIpYSyEi+MG3z2Cb7fvw5TO+CUCfHfsx/qXp/G3iVP42cSqbbt6LPz/5Dz7ec9NMZR5w8GE8N+WNNa/v0KEjT/3rxeb8GK3a4f03588FzfGPb9RuzdeH7LIpr7y1aJ3XVL+7nP69u9K+TfJrt/f2PXjt7cUAPP7ibPbatjsAg7fpxutzlzRn+LlWsy55lq0xEXFfRNwPzK916nPAlIi4NyKWAyOBXSX1bai8kvVhRsS9ko4F7gb2AXYH/g2cCDwKDAP+kAa8FLgB2CMiXk6b7d3qK1vS6STLATd4dzkvJj73LPffexd9dujHiAMGA3DW+ZdwwEGH1nn95EkTufu2X3PFT28C4JjDD2Laq6+wZMli9tl1W6746U3sf+DBJYu/tevQtpJ9+/Tg/HtfWHPs3MP7ssPmGwEw851lnJee22Sjdvz4mF04ZdR4Jr25gEeef4sHz9qPVauDl2Yt5O5/vAnATU++xnUn9ueUIVuz9P0POPeeyaX/YDnShB7MHpImFOzfHBE3Z3jdTsDzNTsRsUTSa+nxqfXGFVG6hSEl9SSpWZ4PtAf6RcSJBecfA+4Cfg/MAr4MPBwRmW9D7rzb7vGnJ8YVNW5rfsN+9Ndyh2Drofqub7Nizv+Keodmh537x5j7x2a6ds9tu06MiIGNXSfpMmCLiDg53R8NzI2IHxRcMw4YFRFj6iunpMOKImIOMA+YAmwFHJ02xxdIWgDsC2wWEUuAY4CvAm9JeqixqrKZbTiKddOnAYuBjWod2whYty+lQDnHYc4Abo+IrgVbp4j4MUBEPBYRBwObkVSRR5UxVjMroSLe9KnPFGDNUIT03so26fF6lTNh3gEcLukQSZWS2ksaKmkLST0lHZF+iBUkfw1WlzFWMyulImVMSVXpqJxKoCbPVAF/BPpJOio9fxEwOSLq7b+EMibMiJgBHAGcB8wlqXF+L42pAvguyZCAd4AhgAcYmrUCSS7M9l8GFwDLgB8AX0i/viAi5gJHAZcD7wKDgWMbK6zkT/pERO+Cr58jSYZ1qe+4mW3IPnr/5BoRMZJkyFBd554EmnRvxI9Gmlnu5HXyDSdMM8uZ/D5L7oRpZrnjGqaZWQZFGDLUbJwwzSx/cpoxnTDNLHfch2lmlpEXQTMzyyLHnZhOmGaWO26Sm5llIDysyMwss5zmSydMM8uhnGZMJ0wzyx33YZqZZeRhRWZmWTlhmpk1rmYC4TxywjSzfCniBMLF5oRpZrmT03zphGlmOZTTjOmEaWY5Iypy2iZ3wjSzXMnx3BtOmGaWQznNmE6YZpY7HlZkZpZRTrswnTDNLH9ymi+dMM0sZwTKaRXTCdPMcsUTCJuZNUFO86UTppnlj2uYZmYZeViRmVlW+cyXTphmli+SZ1w3M8vMTXIzs6zymS+dMM0sf3KaL6kodwBmZrVJ2bZsZWmspOWSFqfby+sblxOmmeWMMv/XBGdGROd067O+kblJbma5kudHI13DNLPcKWaTPHWFpHmSxkkaur5xuYZpZrnThOZ2D0kTCvZvjoiba11zDvAS8D5wLPCApN0i4rWmxuWEaWb50rTa47yIGNjQBRHxXMHubZKOA4YDP2tqaE6YZpYrJVgELdb3LdyHaWb5o4xbY8VIXSUdIqm9pCpJJwD7A4+uT1iuYZpZ7hTx0cg2wGVAX+ADYCpwZES8sj6FOWGaWe4Ua/KNiJgL7FGc0pwwzSyPcjoO0wnTzHLHsxWZmWWQ5yd9FBHljqGoJM0Fppc7jmbSA5hX7iCsyTbkn9tWEfHxYhYo6VGS71kW8yLi0GK+f0M2uIS5IZM0obFBupY//rltODwO08wsIydMM7OMnDBbltqTCljL4J/bBsJ9mGZmGbmGaWaWkROmmVlGTphmZhk5YZqZZeSEadbMJFUVfO3fuRbMP7wWQlr76dra+5ZPkqoiYpWkCklXAvs7abZc/sG1AOkvXaRft5PUNjwerEWoSZbARGBbksW41vAfvpbF4zBzTlJlRHyQ/tL9mWTW6B2BS4GnI2JDnWhkgyHpeqBrRJyU7g8A2gIvRcRCSfIfwJbB07vlXJosK4GngBnARcAJwHHANpJuTGeVthxK+y87A/+T1A64hWS5hKXAKknDI2JZOWO07NwkzylJ50k6Md3tDqwATouI1yLih8BtwMFA7zKFaHUovMEDSZMc+B1wIXAXyc/yUyQthHkkNU1rIVzDzCFJ3YGNgeGSlpKscLc3MBR4GCAifivpeOBkYHx5IrXa0j7LSuCbwCbAWOAJ4BMkyfGttNWwE7A1uV2MwerihJlDETFf0i+BhcBZJM23C4DTJFVHxKT00leA2eWJ0gpJGgX8JyJ+ATwHzASmAecCxwIXR8SbkraTdCpwGjAsIhaUK2ZrOifMHJG0c0S8ABARr0u6nWSZ0B8A/wZeBG6S9DBQCZwEDClXvLaWRUAHSUcBMyPiSABJTwObAzPSPsyNge2BoRExuVzB2vpxH2ZOSBoNPC/pD5Jul7Qr8D7wE+ABYAdgJXA9MJjkl3BYRLxUX5lWUjOBzwI7A3MB0j943YARJDd+domICcDxTpYtkxNmftxCcmNnLsmNgUuBScDX0/1ngMOAGRExIiJO9y9deUnqWbD7KPAW8AegW9oK6AvsHhErgbOBMyS1913xlstN8pyIiHGSDgZ+AZxDMtB5T2AQSQ2lDUlT7lpJBwJLPXavfCRdCBwtaRwwB/gXMBy4H1gODAS+HBErJX0LOAM4MCKWlylkKwIPXM8ZSQcANwEXRsS96bF2wB7AXsDDETGljCEaIGlLYCuSP2pDSLpLDgIuBv5LMqLhcODV9LqTCm7WWQvlhJlDkoYAvya52fNoRCwpc0iWgaQjgWuA60jGXrYDVgMrImJDXWa3VXGTPIci4ul06MkvgLaS7ouIFeWOy9ZV81ijpIqIuF9SkCTNTYErI2JRmUO0InINM8ckHQRcQdL35V+8FiIdWnQRyc9tfrnjseJxwsw5SR0jYmm547BsCmqcnSNicbnjseJywjRrBp6BaMPkhGlmlpEHrpuZZeSEaWaWkROmmVlGTphmZhk5YZqZZeSEaWaWkROmZSLp8+ljfzX7J0sqy8BsSQ9KGtPA+aGSQlKPJpQ5VtKNHzGu3un7Dvwo5Vh+OWG2YJLGpL+gIWmlpGmSrpHUqQRvfw/wyawXS3pD0tnNGI9Zs/PkGy3fk8CJJPNl7kcyy1EnkvkX15KuaPhBMZ5ASSfB9US41qq4htnyrYiI2RExIyLuAu4EjgSQNFLSi2nz+TWSGd07SdpY0s2S3pa0SNLTtZuRkr4oabqkpZIeBHrWOr9Ok1zScEnPSVomab6kByS1lzSWZE7Iq2tqxAWv2Tt9/6WSZkm6SdJGBec7pjXpxZLmSDqvqd8gSd0l3S1pZhrbFElfquPSKknXS3o33a6WVFFQTltJV6blLJU0XtIhTY3HWi4nzA3PMpLaZo2tgeOBo4FdSZLmQ0Avkpnc+wN/A/4qaTMASYOBMcDNwG4kawr9sKE3lXQo8GeSJWUHAAcAT5P8P/Y5kjVvfghslm5I2hl4PH3drul1u5Es11HjGpL1148ChqXx7p/5u5FoT7KI3AhgJ5J1kX4laVit605I490L+ApwOvDtgvO3kkwWfDzQj2Rt+AfS9ZesNYgIby10I0lqDxbsDwLmAfek+yNJZgLvWXDNgcBioEOtsiYB30+/vgt4otb5Xyf/u6zZPxlYXLA/DvhtA7G+AZxd69hvgNG1ju0GBMma3p1JEvwJBec7AwuAMQ2819C0jB4NXPNb4NcF+2NJli1WwbELSFaABNiGZDLgLWuVcz/wi/Tr3un7Diz3/xvemmdzH2bLd2jaNK4iqVn+CfhGwfmZETGnYH8A0BGYK6mwnPYkSQGSFSofqPU+zwJfbiCO/iQJvCkGANtKOqbgWE1Q25Csx942fW8AImKxpBea8iaSKklmrz+GpGbdLi13bK1L/xlp5ks9C1yadhHsnsb2Uq3vWzvgr02Jx1ouJ8yW728kTceVQHUkKxQWqr28RQXJol371VHWe8UPr0EVJDXXn9ZxbhbJom/FcDZwFvAt4AWSGvaPSGqxWVWQ1B73IPleF/LNr1bCCbPlWxoRrzbh+n+T3MBZHRHT6rnmvySLexWqvV/bf0j6GEfVc/59oLKOWHaqL/70RtXK9L2npcc6kfQfvtZIPIX2BR6IiNvTMkSSjBfUum5wrXks9yT5I/SepP+Q1DA3jYinmvDetgHxTZ/W50mS/sY/STpM0taS9pJ0iaSaWucNwEGSzpW0naTTgM82Uu7lJMvOXiZpR0k7SfqOpI7p+TeA/ST1KhhQfiUwSNIvJfWXtK2kEZJ+BUnzGxgNXCnpYEk7kdwQqp14G/MKMEzSvpL6AjeS3AyrbXPgOkl9JH0e+B5p7TciXiEZgTBGySD+T0oaKOlsSZ9rYjzWQjlhtjJp7Wk4Sb/bKOBlkhUO+wDV6TX/JOmvPAOYTHL3emQj5T5MklQPI6ltPk1yp3x1eslFwCdIaoZz09dMJrnj3Tu9/nmSNYwK+1zPBp4C/pj++yJJN0RTXEaybvgj6WuXkCS/2u4kScbPkXxvRrN2d8GXSO6UXwVMBR5M45/exHishfKM62ZmGbmGaWaWkROmmVlGTphmZhk5YZqZZeSEaWaWkROmmVlGTphmZhk5YZqZZfT/a+FZ3TjxnHwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#normalized\n",
    "ml.plot_confusion_matrix(confusion_matrix(y_test,y_pred),classes=[\"No\",\"Yes\"],normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision(Speficifty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TP/(TP+FP)** şeklinde hesaplanır, ki bu aslında **pozitif tahminlerin accuracy'si** olarak da yorumlanabilir. Yani bunda **odak noktası pozitif tahminlerdir**. **Positive Prediction Value** olarak da bilinir. 1'den çıkarılmış haline False Positive Rate denir, ki bunu da ROC/AUC bölümünde göreceğiz.\n",
    "\n",
    "Bu metrik özellikle imbalanced veri setlerinde anlamlıdır, ama dengeli bir verisetinde de kullanılabilir. Pozitif tahmini yaptığımız zaman hata yapmış olmak çok sıkıntıya neden oluyorsa bu metriği takip etmek önemlidir. Birkaç örneği şöyle sayabiliriz:\n",
    "- Spam olmayan bir maile(actual=0) spam dersek(prediction=1), önemli bir mailin kaçmasına neden olabiliriz.\n",
    "- Churn etmeyecek bir müşteriye churn eder dersek, ona gereksiz promosyonlar verilebilir.(Gerçi bunun tersi daha maliyetlidir)\n",
    "- Kanser olmayan birine kanser teşhisi konması(Tersi durum daha tehlikeli olmakla birlikte, burda da kişinin hayat standartlarını gereksiz düşürebiliriz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:47.337700Z",
     "start_time": "2022-06-03T09:47:47.300801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8461538461538461"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "#y'leri labelencode etmediğimiz için pos_label değişkeninde pozitiflerin ne olduğunu belirtiriz\n",
    "precision_score(y_test,y_pred,pos_label=\"Yes\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pozitif tahminlerimizin %81i doğruymuş."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recall(Sensitivity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu da yine imbalanced datasetlerde çok anlamlıdır. **TP/(TP+FN)** oranıyla hesaplanır ve **gerçekte pozitif olanları tespit edebilme başarısı** olarak düşünülebilir. Yani bundaki **odak noktası pozitif classlardır**. **True Positive Rate(TPR)** olarak da bilinir. Burda da önemli olan gerçekte pozitif (actual=1) olanları doğru tahmin edebilmektir, yani yanlış(false) negatif tanıları minimize etmek isteriz. \n",
    "\n",
    "Yukarıda verdiğimiz örneklerden churn ve kanserli örneklerinde bu metrik çok daha önemlidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:47.385572Z",
     "start_time": "2022-06-03T09:47:47.345686Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7586206896551724"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "recall_score(y_test,y_pred,pos_label=\"Yes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu örnek de sağlıkla ilgili bir örnek olduğu için bence şuan en önemli metrik budur. Yani biz her ne kadar accuracy %80miş desek de asıl başarımız %76 civarındaymış."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### F1 ve F$\\beta$ score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision ve Recall'dan hangi durumda hangisinin kullanıldığını biliyoruz. Peki emin değilsek? İşte o zaman F1 oranını kullanabiliyoruz, bu oran bu iki skor arasında bi denge kurmaya çalışır. Şöyle bi örnek veriyorlar: \n",
    "Polisseniz ve suçluları yakalamak istiyorsanız, yakaladığınız kişinin suçlu olduğundan emin olmak ve mümkün olduğunca çok suçluyu yakalamak istersiniz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$f1 = \\frac{2 *\\text{Precision}*\\text{Recall}}{\\text{Precision}+\\text{Recall}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:47.431449Z",
     "start_time": "2022-06-03T09:47:47.395551Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test,y_pred,pos_label=\"Yes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f1 skoru, precision ve recall'u eşit öneme sahip gibi düşünür, ama siz FN'ler daha maliyetli diye düşünüyorsanız, yani recall daha önemli ise **F-2 skorunu**, FP'ler daha maliyetli diye düşünüyorsanız yani precision daha önemli ise **F-0.5 skorunu** kullanabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:47.479322Z",
     "start_time": "2022-06-03T09:47:47.443417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7746478873239436"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import fbeta_score\n",
    "fbeta_score(y_test,y_pred,pos_label=\"Yes\",beta=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### classification report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yukarıdaki metrikleri class/label bazında bir arada gösteren faydalı ve özet bir rapordur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:47.541157Z",
     "start_time": "2022-06-03T09:47:47.488304Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.80      0.88      0.84        32\n",
      "         Yes       0.85      0.76      0.80        29\n",
      "\n",
      "    accuracy                           0.82        61\n",
      "   macro avg       0.82      0.82      0.82        61\n",
      "weighted avg       0.82      0.82      0.82        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burda **support**, y_test içinde ilgili class'taki instance sayısını verir. **macro avg**, aritmetik ortalama verirken **weighted avg** ise adı üzerinde instance sayısıyla ağırlıklandırarak ortalama alır. accuracy'nin f1 altında göründüğüne bakmayın, tabloda bi yere oturtmak zorunda oldukları için ayrı bir kolon açmak yerine oraya koymuşlar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin\n",
    "- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision-Recall Dengesi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision ve Recall aynı anda 1 olamıyor. Biri artarken diğeri azalıyor demiştik. Problemin doğasına göre biri sizin için daha önemli olacaktır. Bunun için de positive demek için nasıl bir threshold belirlemek lazım, buna karar vermek gerekiyor.\n",
    "\n",
    "Thresholdlar özellikle %50 etrafındaki tahminler için önemli. %51'in 1, %49'ın 0 sayıldığı durumlar gibi. Burda ölçmek istediğiniz şeyde pozitif vakaları(spam mail, tümörlü hücre v.s) ne kadar doğru yapmaya heveslisiniz, bunun kararı kritik olmakta. Spam olmayan maile spam demekle, kanserli olmayan kişiye kanserli demek aynı düzeyde hata değildirler. İlkinde çok az hata yapmak istersiniz, aksi halde önemli bir maili kaçırmış olabilirsiniz, ikincisinde ise sağlıklı kişiye kanser dediğiniz için gereksiz yere kanser tedavi masrafları oluşur, belki bir de yan etkiler görülür. Tersten baktığımızda ise spam bir maile spam değil dersek, inboxımızda bu maili görmüş oluruz, bunun da çok yıkıcı etkisi olmaz, bu hata kabul edilebilir. Ama kanserli kişiye \"kanserli değil\" dersek kişiyi tedaviden mahrum bıraktığımız için ölümüne neden olabiliriz, ki bu çok büyük bir hatadır.\n",
    "\n",
    "Bu yüzden spam konusunda amacımız tüm pozitif(P) tahminlerimizi doğru(T) bilmiş olmak, yani %100 precision/specifity sağlamak isteriz. TP/(TP+FP). Burda, thresholdumuzun yüksek olmasına isteyeceğiz. Yani %51i spam saymayacağız, belki dieyceğiz ki %90 üzeri olasılık varsa bunu spam say. Varsın, %80 ihtimalle spam olan bir maile spam demeyelim ve inboxımza düşsün, çok da kritik değil.\n",
    "\n",
    "Kanser örneğinde ise gerçekte pozitif olan tüm caseleri doğru tahminlemek isteriz, yani kanserliye kanser değilsin(False Negative) demek istemeyiz. TP/(TP+FN), yani %100 recall isteriz. Bunun için de %20 bile bir ihtimal olsa buna kanserli diyelim diyeceğiz, varsın bazı sağlıklılara(%30 ihtimalle kanser) \"kansersin\" diyelim.\n",
    "\n",
    "Aynı anda ikisinin %100 olmasını bekleyemeyiz dedik, bi denge sağlamaya çalışacağız.\n",
    "\n",
    "Konuyu pekiştirmek için güncel bir örnek daha verelim(Yıl 2021). Bir kişiye yanlışlıkla \"corona pozitif\" demek(False positive), çok büyük bi sorun yaratmayacaktır, o kişi gereksiz yere karantinaya alınacak, hayat standardı düşecek, ancak yanlışlıkla \"negatifsin\" demek(False Negative), salgının artmasına neden olacaktır. O yüzden burdaki pozitiflik thresholdunun çok düşük tutulması gerektiği aşikardır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ROC Curve ve AUC score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Çeşitli thresholdlar için **TPR(y) ve FPR(x)** grafiği çizdiğimizde `ROC(Receiver Operating Characteristic)` eğrisini elde etmiş oluruz. ROC altında kalan alan ise `AUC(Area Under Curve)` metriği olarak karşımıza çıkar ve bu metrik farklı modellerin performanslarını karşılaştırmak için kullanılır. AUC değeri ne kadar büyükse o kadar iyidir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimal threshold bilgisi, farklı thresholdlar için oluşan confusion matrixlerden görülebilir ancak bu kadar çok confusion matrixi yorumlamak zor olmakta, o yüzden ROC gibi görsel bir araç yardımımıza koşmaktadır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Önemli:** AUC değeri, positive ve negativelerin sayılarının birbirine yakın olduğu yani **imbalance olmadığı** durumlarda anlamlıdır ve imbalanced bir verisetinde kullanıl**ma**malıdır. Ayrıca true negative ve true positiveler eşit öneme sahipse kullanılmalıdır. Bakınız **Özet ve Karşılaştırma**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bunun için hazırladığım fonksiyonu aşağıdaki gibi kullanabileceğimiz gibi, aşama aşama neler olduğunu görmek için bu fonksiyonu satır satır da çalıştırabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:48.331043Z",
     "start_time": "2022-06-03T09:47:47.553125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plotROC(y_test, X_test, gs4, pos_label=\"Yes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burdaki decision point için şuan standart thresholddaki(0.5) confusion matrix sonucuna göre TPR-FPR kesişimi gösterilmiştir. Yani 0.5 kullandığımızda değerler TPR ve FPR için sırayla ~0.75 ve ~0.15tir. Arzu edilen threshold için ilgili nokta bulunabilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burdan çıkan AUC değerini başka bir modelinkiyle de karşılaştırmak faydalı olacaktır. Şimdi mesela bir gridsearchümüzdeki DecisionTree'nin en iyi parametre bilgilerini alıp bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:48.440755Z",
     "start_time": "2022-06-03T09:47:48.342015Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([{'clf': DecisionTreeClassifier(random_state=42), 'clf__criterion': 'gini', 'clf__max_depth': 3, 'clf__min_samples_split': 4, 'ct__numerics__ouh': None, 'ct__numerics__scl': None}],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvres = gs4.cv_results_\n",
    "cv_results = pd.DataFrame(cvres)\n",
    "cv_results[\"param_clf\"]=cv_results[\"param_clf\"].apply(lambda x:str(x).split('(')[0])\n",
    "\n",
    "dtc=cv_results[cv_results[\"param_clf\"]==\"DecisionTreeClassifier\"]\n",
    "dtc.getRowOnAggregation(\"mean_test_score\",\"max\")[\"params\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu parametreleri aşağıda DecisionTreeClassifier constructorı içine verelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:48.753912Z",
     "start_time": "2022-06-03T09:47:48.450724Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(drop=\"first\",handle_unknown='ignore'))     \n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer))\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms),\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]) , #direkt burada işleme sokarak\n",
    "                                ('numerics',  num_pipe, nums)\n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\")\n",
    "\n",
    "\n",
    "pipe_dt = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), \n",
    "                       ('clf', DecisionTreeClassifier(random_state=40,criterion=\"gini\",max_depth=3,min_samples_split=2)) \n",
    "                       ])\n",
    "\n",
    "pipe_dt.fit(X_train,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:48.863622Z",
     "start_time": "2022-06-03T09:47:48.762889Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7868852459016393"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_dt.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:49.687416Z",
     "start_time": "2022-06-03T09:47:48.875590Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plotROC(y_test, X_test, pipe_dt, pos_label=\"Yes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bunun AUC değeri LogReg'den daha düşük çıktı."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision-Recall Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imbalanced data sözkonusu olduğunda Precision-Recall curve, ROC-AUC'dan çok daha yararlıdır. Zira, burda farklı modeller arasındaki sonuçlar çok daha çarpıcı olmakta ve model seçimi de daha kolay olmaktadır.\n",
    "\n",
    "ROC'da Recall ve 1-Precision bakılırken burda Recall ve Precision bakılır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burda yine hazır yazılmış bir fonksiyonum var, onu kullanacağız. Fakat bu sefer labellara Yes/No şeklinde değil de 1/0 şeklinde ulaşmak istedim, o yüzden şimdi bunları bi encode edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:49.735286Z",
     "start_time": "2022-06-03T09:47:49.699385Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "y_train_le=le.fit_transform(y_train)\n",
    "y_test_le=le.transform(y_test)\n",
    "y_test_le[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kontrol edelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:49.767203Z",
     "start_time": "2022-06-03T09:47:49.747253Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'Yes', 'Yes', 'No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.values[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotumuzu çizelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:51.233330Z",
     "start_time": "2022-06-03T09:47:49.779171Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plot_precision_recall_curve(y_test_le,X_test,gs4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Soldaki grafikte, Precision ve Recall'ün farklı thresholdlar için nasıl bir tradeoff içinde olduğunu görüyoruz. Sağdaki ise Precision-Recall eğrisi olup sağ üst köşeye doğru gittikçe mükemmel bir denge sözkonusu olmaya başlar. Mavi çizgi ideal halken, alttaki yeşil çizgi ise her instance'ın pozitif tahminlendiği, bi beceri gerektirmeyen durumu gösterir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Son durumda kendimize ideal bir threshold belirledikten sonra nihai tahminlememiz şöyle olacaktır:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:51.358945Z",
     "start_time": "2022-06-03T09:47:51.243260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., 0., 1., 0., 1., 0., 1., 1., 0., 0., 1., 1., 0., 1., 1.,\n",
       "       0., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0.,\n",
       "       1., 1., 0., 1., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 1., 1., 1.,\n",
       "       0., 1., 1., 0., 0., 0., 1., 1., 0., 0.])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import binarize\n",
    "pred_prob = gs4.predict_proba(X_test)    \n",
    "binarize(pred_prob, threshold=0.3)[:,1]\n",
    "#veya\n",
    "np.where(pred_prob[:,1]>0.3,1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cohen's Kappa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bunun da bazı durumlarda faydalı bir metrik olduğu söyleniyor. Ben çok inceleme fırsatı yakalayamadım açıkçası. Detaylarını şu sayfalarda bulabilirsiniz:\n",
    "- https://towardsdatascience.com/multi-class-metrics-made-simple-the-kappa-score-aka-cohens-kappa-coefficient-bdea137af09c\n",
    "- https://standardwisdom.com/softwarejournal/2011/12/confusion-matrix-another-single-value-metric-kappa-statistic/\n",
    "- https://stats.stackexchange.com/questions/82162/cohens-kappa-in-plain-english\n",
    "\n",
    "Ama <a href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222916 \">şurada</a> ise kaçınılması gerektiği yazıyor. O yüzden çok detaylarına girmiyorum, belki ilerleyen dönemlerde daha derin araştırmalar yaparsam burayı güncellerim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cumulative Gains ve Lift Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lift ve Gain analizi ile, model olmadan yapacağımız random tahminlere göre modelimizle ne kadar daha iyi **pozitif tahminlemesi** yaptığımızı ölçeriz. Görsel açıdan model performansını ölçmeye yardımcı olan bu analizlerin ikisinde bir baseline vardır, lift eğrisi ile bu baseline arasındaki alan ne kadar büyükse modelimiz o kadar iyi tahmin yapıyor demektir.\n",
    "\n",
    "Şu aşamalardan oluşur:\n",
    "\n",
    "- İlk olarak pozitif classtaki tahminlerin olasılık değerlerini alır ve datayı buna göre sıralarız.\n",
    "- Sonra bunları 10luk dilimlere(Decile) böleriz ve her bir Decile'da gerçek pozitiflerin sayısını buluruz->Number of Responses\n",
    "- Gain, bu Number of Responses'ların kümülatif değerinin datadaki toplam pozitiflere oranıdır. Yani ilgili decile seviyesinde gerçek pozitiflerin ne kadarını kapsama almışız, onu gösterir. Mesela aşağıdaki tablodan göreceğiniz üzere 5.decile'da gerçek pozitiflerin ~%80ini kapsama almışız. Yani datanın sadece %50siyle bile pozitiflerin %80lik kısmını doğru tahminleyebiliyoruz.\n",
    "- Lift ise, modelimiz sayesinde random guesse(modelsiz duruma) göre ne kadar iyi pozitif tahminlemesi yaptığımızı gösteriyor. Yine aşağıdaki tabloya baktığımızda 2.seviyede, modelimizin lifti 1.90 olup, datanın %20lik kısmını aldığımızda pozitifleri random seçeceğimiz bir duruma göre 1.90 kat daha doğru tespit edebildiğimizi gösterir, ki bu değer 37.93'ün 20ye oranı oluyor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:53.653801Z",
     "start_time": "2022-06-03T09:47:51.369914Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=cd0e05e7-d6b0-4145-82b6-c24c3c48717d style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('cd0e05e7-d6b0-4145-82b6-c24c3c48717d').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decile</th>\n",
       "      <th>Number of Cases</th>\n",
       "      <th>Number of Responses</th>\n",
       "      <th>Cumulative Responses</th>\n",
       "      <th>% of Events</th>\n",
       "      <th>Gain</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>20.69</td>\n",
       "      <td>20.69</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>17.24</td>\n",
       "      <td>37.93</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>20.69</td>\n",
       "      <td>58.62</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>10.34</td>\n",
       "      <td>68.96</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>13.79</td>\n",
       "      <td>82.75</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3.45</td>\n",
       "      <td>86.20</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>10.34</td>\n",
       "      <td>96.54</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.45</td>\n",
       "      <td>99.99</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.99</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.99</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "   Decile  Number of Cases  Number of Responses  Cumulative Responses  \\\n",
       "0       1                6                    6                     6   \n",
       "1       2                6                    5                    11   \n",
       "2       3                6                    6                    17   \n",
       "3       4                6                    3                    20   \n",
       "4       5                6                    4                    24   \n",
       "5       6                6                    1                    25   \n",
       "6       7                6                    3                    28   \n",
       "7       8                6                    1                    29   \n",
       "8       9                6                    0                    29   \n",
       "9      10                7                    0                    29   \n",
       "\n",
       "   % of Events   Gain  lift  \n",
       "0        20.69  20.69  2.07  \n",
       "1        17.24  37.93  1.90  \n",
       "2        20.69  58.62  1.95  \n",
       "3        10.34  68.96  1.72  \n",
       "4        13.79  82.75  1.66  \n",
       "5         3.45  86.20  1.44  \n",
       "6        10.34  96.54  1.38  \n",
       "7         3.45  99.99  1.25  \n",
       "8         0.00  99.99  1.11  \n",
       "9         0.00  99.99  1.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plot_gain_and_lift(gs4,X_test,y_test,pos_label=\"Yes\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Log loss(logistic loss or cross-entropy loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu metriğin detaylı açıklamasını <a href=\"Logistic%20Regression.ipynb\">Logistic Regression notebookunda</a> bulabilirsiniz.  Özetle, tahminlenen değerle gerçek değer arasındaki farkın negative logaritmasıdır. Algoritma çalışırken zaten bunu minimize etmeye çalışır.\n",
    "\n",
    "Bir modelde yapılan gerçek hatayı en iyi gösteren metric olup, modelin genel güvenilirliğini sorgulamak istediğinizde bu metrik kullanılabilir. Aşağıda gördüğünüz üzere gridsearch içinde scoring'e bunun negatif versiyonu(maximize edilsin diye) verilerek bu metric minimize edilmeye çalışılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:47:53.684719Z",
     "start_time": "2022-06-03T09:47:53.662781Z"
    }
   },
   "outputs": [],
   "source": [
    "gs5 = GridSearchCV(estimator = pipe, param_grid = params, cv = mycv, n_jobs=-1, verbose = 1, scoring = 'neg_log_loss',\n",
    "                  error_score='raise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eğitelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:27.069119Z",
     "start_time": "2022-06-03T09:47:53.694700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 6 folds for each of 72 candidates, totalling 432 fits\n",
      "Wall time: 41.1 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slo...\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                          &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                          &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                          &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "             scoring=&#x27;neg_log_loss&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-190\" type=\"checkbox\" ><label for=\"sk-estimator-id-190\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slo...\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {&#x27;clf&#x27;: [DecisionTreeClassifier(random_state=42)],\n",
       "                          &#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                          &#x27;clf__max_depth&#x27;: [2, 3],\n",
       "                          &#x27;clf__min_samples_split&#x27;: [2, 4],\n",
       "                          &#x27;ct__numerics__ouh&#x27;: [None],\n",
       "                          &#x27;ct__numerics__scl&#x27;: [None]}],\n",
       "             scoring=&#x27;neg_log_loss&#x27;, verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-191\" type=\"checkbox\" ><label for=\"sk-estimator-id-191\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;ChestPain&#x27;]),\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   DummyTransformer()),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   DummyTransformer())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, DummyEstimator())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-192\" type=\"checkbox\" ><label for=\"sk-estimator-id-192\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;, DummyTransformer()),\n",
       "                                                 (&#x27;scl&#x27;, DummyTransformer())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-193\" type=\"checkbox\" ><label for=\"sk-estimator-id-193\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-194\" type=\"checkbox\" ><label for=\"sk-estimator-id-194\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-195\" type=\"checkbox\" ><label for=\"sk-estimator-id-195\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-196\" type=\"checkbox\" ><label for=\"sk-estimator-id-196\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-197\" type=\"checkbox\" ><label for=\"sk-estimator-id-197\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-198\" type=\"checkbox\" ><label for=\"sk-estimator-id-198\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-199\" type=\"checkbox\" ><label for=\"sk-estimator-id-199\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-200\" type=\"checkbox\" ><label for=\"sk-estimator-id-200\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-201\" type=\"checkbox\" ><label for=\"sk-estimator-id-201\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyTransformer</label><div class=\"sk-toggleable__content\"><pre>DummyTransformer()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-202\" type=\"checkbox\" ><label for=\"sk-estimator-id-202\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-203\" type=\"checkbox\" ><label for=\"sk-estimator-id-203\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-204\" type=\"checkbox\" ><label for=\"sk-estimator-id-204\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-205\" type=\"checkbox\" ><label for=\"sk-estimator-id-205\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyEstimator</label><div class=\"sk-toggleable__content\"><pre>DummyEstimator()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=3, n_splits=2, random_state=1),\n",
       "             error_score='raise',\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slo...\n",
       "                          'clf__solver': ['newton-cg', 'lbfgs'],\n",
       "                          'ct__numerics__ouh': [OutlierHandler(featureindices=[1]),\n",
       "                                                None],\n",
       "                          'ct__numerics__scl': [StandardScaler(),\n",
       "                                                RobustScaler()]},\n",
       "                         {'clf': [DecisionTreeClassifier(random_state=42)],\n",
       "                          'clf__criterion': ['gini', 'entropy'],\n",
       "                          'clf__max_depth': [2, 3],\n",
       "                          'clf__min_samples_split': [2, 4],\n",
       "                          'ct__numerics__ouh': [None],\n",
       "                          'ct__numerics__scl': [None]}],\n",
       "             scoring='neg_log_loss', verbose=1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gs5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En iyi skora bakalım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:27.101039Z",
     "start_time": "2022-06-03T09:48:27.077097Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.41552355189800566"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs5.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçları dataframede görelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:27.178832Z",
     "start_time": "2022-06-03T09:48:27.110009Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=a16f64af-de02-4a6f-a4db-e7eb9d95b94d style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('a16f64af-de02-4a6f-a4db-e7eb9d95b94d').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_clf</th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>param_ct__numerics__ouh</th>\n",
       "      <th>param_ct__numerics__scl</th>\n",
       "      <th>param_clf__criterion</th>\n",
       "      <th>param_clf__max_depth</th>\n",
       "      <th>param_clf__min_samples_split</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>LogisticRegression(C=0.5, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.415524</td>\n",
       "      <td>0.042051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>LogisticRegression(C=0.5, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.416117</td>\n",
       "      <td>0.037367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>LogisticRegression(C=0.5, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>StandardScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.416770</td>\n",
       "      <td>0.036932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>LogisticRegression(C=0.5, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>OutlierHandler(featureindices=[1])</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.417036</td>\n",
       "      <td>0.034720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>LogisticRegression(C=0.5, max_iter=1000, random_state=42)</td>\n",
       "      <td>0.3</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>None</td>\n",
       "      <td>RobustScaler()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.418059</td>\n",
       "      <td>0.033395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "                                            param_clf param_clf__C  \\\n",
       "54  LogisticRegression(C=0.5, max_iter=1000, rando...          0.5   \n",
       "49  LogisticRegression(C=0.5, max_iter=1000, rando...          0.5   \n",
       "48  LogisticRegression(C=0.5, max_iter=1000, rando...          0.5   \n",
       "45  LogisticRegression(C=0.5, max_iter=1000, rando...          0.3   \n",
       "47  LogisticRegression(C=0.5, max_iter=1000, rando...          0.3   \n",
       "\n",
       "   param_clf__penalty param_clf__solver             param_ct__numerics__ouh  \\\n",
       "54                 l2             lbfgs                                None   \n",
       "49                 l2         newton-cg  OutlierHandler(featureindices=[1])   \n",
       "48                 l2         newton-cg  OutlierHandler(featureindices=[1])   \n",
       "45                 l2             lbfgs  OutlierHandler(featureindices=[1])   \n",
       "47                 l2             lbfgs                                None   \n",
       "\n",
       "   param_ct__numerics__scl param_clf__criterion param_clf__max_depth  \\\n",
       "54        StandardScaler()                  NaN                  NaN   \n",
       "49          RobustScaler()                  NaN                  NaN   \n",
       "48        StandardScaler()                  NaN                  NaN   \n",
       "45          RobustScaler()                  NaN                  NaN   \n",
       "47          RobustScaler()                  NaN                  NaN   \n",
       "\n",
       "   param_clf__min_samples_split  mean_test_score  std_test_score  \n",
       "54                          NaN        -0.415524        0.042051  \n",
       "49                          NaN        -0.416117        0.037367  \n",
       "48                          NaN        -0.416770        0.036932  \n",
       "45                          NaN        -0.417036        0.034720  \n",
       "47                          NaN        -0.418059        0.033395  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(gs5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu yeni modeli test seti üzerinden tahminleyip classification reportumuzu çıkaralım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:27.304497Z",
     "start_time": "2022-06-03T09:48:27.186810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.80      0.88      0.84        32\n",
      "         Yes       0.85      0.76      0.80        29\n",
      "\n",
      "    accuracy                           0.82        61\n",
      "   macro avg       0.82      0.82      0.82        61\n",
      "weighted avg       0.82      0.82      0.82        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred= gs5.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuçlar gs4'e göre bi miktar iyileşmiş oldu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Peki bu test setine göre generalization performance'ı ne oluyor, bir de ona bakalım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:27.398291Z",
     "start_time": "2022-06-03T09:48:27.312474Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4424973353803668"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs5.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buna şöyle de bakabiliriz, tabi bu sefer pozitif sonuç elde edeceğiz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:27.508951Z",
     "start_time": "2022-06-03T09:48:27.408212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4424973353803668"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "log_loss(y_test_le,gs5.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.415ten 0.449a yükselmiş oldu, ki generalization performansında beklediğimiz bir durumdur bu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Maliyet bazlı modelleme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi de farklı bir şekilde olaya yaklaşıp, yaptığımız hataların ölçülebilir maliyetleri olsaydı nasıl davranırdık, ona bakalım. Tabi ki hatayı minimize etmeye çalışırdık, şimdi bunları nasıl yapabileceğimizi görelim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Custom Cost function yazma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daha önce söylediğimiz gibi, iki tür hata vardır ve bunların maliyeti de farklı olabilmektedir. Ör:Spam örneğinde, spam maile \"spam değil\" demek büyük bir sorun yaratmaz, bu yüzden maliyet 1 olabilir. Ancak spam olmayana spam demek çok büyük sorun yaratabilir, bunun da maliyeti 10 seçilebilir. Buna göre en uygun trehsholdu bularak toplam maliyeti minimize etmek gerekebilir.\n",
    "\n",
    "Bu bilgiler ışığında kendi modelimiz için çeşitli maliyetler belirlemeye çalışalım. Unutmayın, amacımız, bir hastada kalp hastalığı var mı yok mu onu tahminlemeye çalışıyoruz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Cost of True Negative**: Bunun maliyeti 0'dır.\n",
    "- **Cost of True Positive**: Bu hastalara ilave tetkikler yapacağız diyelim,  maliyeti 5 olsun. (Ama bi hata yapmadığımız için 0 da diyebilirsiniz isterseniz)\n",
    "- **Cost of False Negative**: Yanlışlıkla \"iyisiniz\" diyoruz, halbuki kişi aslında hasta. Ölüm durumu sonunda ailesi tarafından tazminat davası açılabilir. Maliyeti 100 olsun.\n",
    "- **Cost of False Positive**: Yanlışlıkla hastasın diyoruz. Gerekisiz ilaç veriyoruz, yan etkiler nedeniyle dava açılabilir. Maliyeti 60 olsun.\n",
    "\n",
    "**Nihai maliyet fonksiyonumuz şöyle**:\n",
    "\n",
    "$$\\text{Cost} = 0.TN + 5.TP + 100.FN + 60.FP$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi yine hazır fonksiyonumuzu kullanarak çeşitli thresholdlarda(cutoff) maliyet ve diğer metrikler ne oluyormuş onlara bakalım ve bunların bi grafiğini çizdirelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:28.418265Z",
     "start_time": "2022-06-03T09:48:27.613663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cutoff\t Cost/Instance\t Accuracy\t FN\t FP\t TP\t TN\t Recall\t Precision F1-score\n",
      "0.00\t 33.85\t\t 0.475\t\t 0\t 32\t 29\t 0\t 1.000\t 0.475\t   0.644\n",
      "0.05\t 26.97\t\t 0.590\t\t 0\t 25\t 29\t 7\t 1.000\t 0.537\t   0.699\n",
      "0.10\t 16.72\t\t 0.770\t\t 1\t 13\t 28\t 19\t 0.966\t 0.683\t   0.800\n",
      "0.15\t 20.41\t\t 0.738\t\t 4\t 12\t 25\t 20\t 0.862\t 0.676\t   0.758\n",
      "0.21\t 17.46\t\t 0.787\t\t 4\t 9\t 25\t 23\t 0.862\t 0.735\t   0.794\n",
      "0.26\t 15.49\t\t 0.820\t\t 4\t 7\t 25\t 25\t 0.862\t 0.781\t   0.820\n",
      "0.31\t 14.51\t\t 0.836\t\t 4\t 6\t 25\t 26\t 0.862\t 0.806\t   0.833\n",
      "0.36\t 17.62\t\t 0.803\t\t 6\t 6\t 23\t 26\t 0.793\t 0.793\t   0.793\n",
      "0.41\t 16.64\t\t 0.820\t\t 6\t 5\t 23\t 27\t 0.793\t 0.821\t   0.807\n",
      "0.46\t 16.64\t\t 0.820\t\t 6\t 5\t 23\t 27\t 0.793\t 0.821\t   0.807\n",
      "0.52\t 18.77\t\t 0.803\t\t 8\t 4\t 21\t 28\t 0.724\t 0.840\t   0.778\n",
      "0.57\t 18.77\t\t 0.803\t\t 8\t 4\t 21\t 28\t 0.724\t 0.840\t   0.778\n",
      "0.62\t 21.89\t\t 0.770\t\t 10\t 4\t 19\t 28\t 0.655\t 0.826\t   0.731\n",
      "0.67\t 20.90\t\t 0.787\t\t 10\t 3\t 19\t 29\t 0.655\t 0.864\t   0.745\n",
      "0.72\t 19.92\t\t 0.803\t\t 10\t 2\t 19\t 30\t 0.655\t 0.905\t   0.760\n",
      "0.77\t 18.93\t\t 0.820\t\t 10\t 1\t 19\t 31\t 0.655\t 0.950\t   0.776\n",
      "0.83\t 22.05\t\t 0.787\t\t 12\t 1\t 17\t 31\t 0.586\t 0.944\t   0.723\n",
      "0.88\t 28.28\t\t 0.721\t\t 16\t 1\t 13\t 31\t 0.448\t 0.929\t   0.605\n",
      "0.93\t 36.07\t\t 0.639\t\t 21\t 1\t 8\t 31\t 0.276\t 0.889\t   0.421\n",
      "0.98\t 42.87\t\t 0.574\t\t 26\t 0\t 3\t 32\t 0.103\t 1.000\t   0.188\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.find_best_cutoff_for_classification(gs4, y_test_le, X_test,[0,5,100,60])    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Görüldüğü üzere thresholdun %21 olduğu yerde maliyetimiz en düşük(15.90) oluyor, ki bakarsanız bu aynı zamanda F1 skorunun da en yüksek olduğu yer. Demek ki maliyeti minimize etmek istiyorsak olasılığı default değer olan %50 değil de %21 üstündeki her şeyi pozitif saymalıyız.\n",
    "\n",
    "Şimdi diyelim ki, hiç model kullanmadık ve ihtiyatlı davranıp herkese hasta dedik, maliyet nolur, bi bakalım. Yani FP hatası yapıyoruz, threshold 0, yani 33.85 birim maliyetimiz olacak. Şimdi de, kimse hasta değil diyelim, yani FN hatası yapalım, böyle bi durumda da maliyetimiz yaklaşık 44 TL üzerinde olacak.\n",
    "\n",
    "Her iki durum da modelli yaklaşımımıza göre kötü olacaktır. **Sonuç: Modelimiz oldukça başarılıdır.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Class-weight "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bazen elimizde böyle maliyet değerleri olmaz, ama dersiniz ki, FN'deki hata FP'deki hatadan şu kadar kat daha önemli, o yüzden FN hatalarını şu kadar kat daha cezalandır.\n",
    "\n",
    "sklearn bunun için bize `class_weight` adına bir parametre verir. Bu şekilde classlar arasında bi denge yakalamaya çalışırız. Bunun default değeri **None** olup, her iki class(0-1, veya multiclass ise tüm sınıflar) için de eşit ağırlık kullanır, yani 1. Buna ayrıca bir dictionary veya **balanced** değeri de verilebilir. \"balanced\" verdiğinizde, classların toplam içindeki oranına göre yani prior probabilitysine göre ters mantıkla ağırlıklandırılır, ör: 1000 kaydın 50si \"Yes\" ise, yani 1'e 20lik bir oran varsa Yes'lerin ağırlığı 20 \"No\"'ların ağırlığı ise 1 olur.\n",
    "\n",
    "Burada ayrıca bir **dictionary** verebiliriz dedik,  {0:1, 1:10} şeklinde. Farkettiyseniz bu aynı zamanda bir hyperparametre, dolayısıyla şöyle de tune edilebilir.\n",
    "\n",
    "<pre>\n",
    "weights = np.arange(2,21,2)\n",
    "{'class_weight': [{0:1, 1:x} for x in weights]}\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:28.450180Z",
     "start_time": "2022-06-03T09:48:28.426246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': [{0: 1, 1: 2},\n",
       "  {0: 1, 1: 4},\n",
       "  {0: 1, 1: 6},\n",
       "  {0: 1, 1: 8},\n",
       "  {0: 1, 1: 10},\n",
       "  {0: 1, 1: 12},\n",
       "  {0: 1, 1: 14},\n",
       "  {0: 1, 1: 16},\n",
       "  {0: 1, 1: 18},\n",
       "  {0: 1, 1: 20}]}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.arange(2,21,2)\n",
    "{'class_weight': [{0:1, 1:x} for x in weights]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "class weight'leri atayarak modelimizin cost function'ını da değiştirmiş oluruz. Mesela Logistic Regression için normal log-loss denklemi aşağıdaki gibi olup,\n",
    "\n",
    "**The log-loss cost function**:\n",
    "\n",
    "$$\\text{Log-loss} = -\\sum_{1}^{N}[y_i log(y^*_i)+(1-y_i)log(1-y^*_i)]$$\n",
    "(Burda, $y_i$ gerçek değerler, $y^*_i$ ise targetın predict_proba değerleridir, N de isntance sayısı)\n",
    "\n",
    "class weight kullandığımızda şu şekle dönüşür:\n",
    "\n",
    "**Weighted Log-loss**: $$\\text{Log-loss} = -\\sum_{1}^{N}[w_0 y_i log(y^*_i)+w_1 (1-y_i)log(1-y^*_i)]$$\n",
    "\n",
    "Sonra bunları da f-beta skoruyla gridsearch içine sokabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:48:28.498051Z",
     "start_time": "2022-06-03T09:48:28.458158Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score, make_scorer\n",
    "\n",
    "weights = np.arange(2,11,2)\n",
    "params = [{'clf__C'           : [0.001, 0.01, 0.05, 0.1, 0.2, 0.5, 1],\n",
    "           'clf__penalty'     : ['l1'], \n",
    "           'clf__solver'      : ['liblinear', 'saga'],\n",
    "           'clf__class_weight': [{\"No\":1, \"Yes\":x} for x in weights] + ['balanced']\n",
    "          },\n",
    "          \n",
    "          {'clf__C'           : [0.001, 0.01, 0.05, 0.1, 0.2, 0.5, 1],\n",
    "           'clf__penalty'     : ['l2'], \n",
    "           'clf__solver'      : ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga'],\n",
    "           'clf__class_weight': [{\"No\":1, \"Yes\":x} for x in weights] + ['balanced']\n",
    "          }\n",
    "         ]\n",
    "\n",
    "pipe = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), \n",
    "                       ('clf', LogisticRegression(max_iter=1000,random_state=42)) \n",
    "                       ])\n",
    "\n",
    "grid_cw = GridSearchCV(pipe, param_grid = params, cv = 3, scoring=make_scorer(fbeta_score, beta=2, pos_label=\"Yes\"), \n",
    "                       verbose = 1, n_jobs = -1,error_score=\"raise\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eğitelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:45.166531Z",
     "start_time": "2022-06-03T09:48:28.507030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 294 candidates, totalling 882 fits\n",
      "Wall time: 1min 57s\n"
     ]
    },
    {
     "data": {
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       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordinals&#x27;,\n",
       "                                                                         OrdinalEncoder(categories=...\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;, &#x27;saga&#x27;]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.5, 1],\n",
       "                          &#x27;clf__class_weight&#x27;: [{&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 2},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 4},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 6},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 8},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 10},\n",
       "                                                &#x27;balanced&#x27;],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;,\n",
       "                                          &#x27;sag&#x27;, &#x27;saga&#x27;]}],\n",
       "             scoring=make_scorer(fbeta_score, beta=2, pos_label=Yes),\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-206\" type=\"checkbox\" ><label for=\"sk-estimator-id-206\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;nominals&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;ohe&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                        handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                                         [&#x27;Sex&#x27;,\n",
       "                                                                          &#x27;Fbs&#x27;,\n",
       "                                                                          &#x27;ExAng&#x27;,\n",
       "                                                                          &#x27;Slope&#x27;,\n",
       "                                                                          &#x27;Thal&#x27;]),\n",
       "                                                                        (&#x27;ordinals&#x27;,\n",
       "                                                                         OrdinalEncoder(categories=...\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l1&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;, &#x27;saga&#x27;]},\n",
       "                         {&#x27;clf__C&#x27;: [0.001, 0.01, 0.05, 0.1, 0.2, 0.5, 1],\n",
       "                          &#x27;clf__class_weight&#x27;: [{&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 2},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 4},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 6},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 8},\n",
       "                                                {&#x27;No&#x27;: 1, &#x27;Yes&#x27;: 10},\n",
       "                                                &#x27;balanced&#x27;],\n",
       "                          &#x27;clf__penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                          &#x27;clf__solver&#x27;: [&#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;,\n",
       "                                          &#x27;sag&#x27;, &#x27;saga&#x27;]}],\n",
       "             scoring=make_scorer(fbeta_score, beta=2, pos_label=Yes),\n",
       "             verbose=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-207\" type=\"checkbox\" ><label for=\"sk-estimator-id-207\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypical&#x27;]]),\n",
       "                                                  [&#x27;ChestPain&#x27;]),\n",
       "                                                 (&#x27;numerics&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;))]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression(max_iter=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-208\" type=\"checkbox\" ><label for=\"sk-estimator-id-208\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;))]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-209\" type=\"checkbox\" ><label for=\"sk-estimator-id-209\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-210\" type=\"checkbox\" ><label for=\"sk-estimator-id-210\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-211\" type=\"checkbox\" ><label for=\"sk-estimator-id-211\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-212\" type=\"checkbox\" ><label for=\"sk-estimator-id-212\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-213\" type=\"checkbox\" ><label for=\"sk-estimator-id-213\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-214\" type=\"checkbox\" ><label for=\"sk-estimator-id-214\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-215\" type=\"checkbox\" ><label for=\"sk-estimator-id-215\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028493040040&gt;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-216\" type=\"checkbox\" ><label for=\"sk-estimator-id-216\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;RestECG&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-217\" type=\"checkbox\" ><label for=\"sk-estimator-id-217\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-218\" type=\"checkbox\" ><label for=\"sk-estimator-id-218\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-219\" type=\"checkbox\" ><label for=\"sk-estimator-id-219\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise',\n",
       "             estimator=Pipeline(steps=[('ct',\n",
       "                                        ColumnTransformer(n_jobs=-1,\n",
       "                                                          remainder='passthrough',\n",
       "                                                          transformers=[('nominals',\n",
       "                                                                         Pipeline(steps=[('csi',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('ohe',\n",
       "                                                                                          OneHotEncoder(drop='first',\n",
       "                                                                                                        handle_unknown='ignore'))]),\n",
       "                                                                         ['Sex',\n",
       "                                                                          'Fbs',\n",
       "                                                                          'ExAng',\n",
       "                                                                          'Slope',\n",
       "                                                                          'Thal']),\n",
       "                                                                        ('ordinals',\n",
       "                                                                         OrdinalEncoder(categories=...\n",
       "                          'clf__penalty': ['l1'],\n",
       "                          'clf__solver': ['liblinear', 'saga']},\n",
       "                         {'clf__C': [0.001, 0.01, 0.05, 0.1, 0.2, 0.5, 1],\n",
       "                          'clf__class_weight': [{'No': 1, 'Yes': 2},\n",
       "                                                {'No': 1, 'Yes': 4},\n",
       "                                                {'No': 1, 'Yes': 6},\n",
       "                                                {'No': 1, 'Yes': 8},\n",
       "                                                {'No': 1, 'Yes': 10},\n",
       "                                                'balanced'],\n",
       "                          'clf__penalty': ['l2'],\n",
       "                          'clf__solver': ['liblinear', 'newton-cg', 'lbfgs',\n",
       "                                          'sag', 'saga']}],\n",
       "             scoring=make_scorer(fbeta_score, beta=2, pos_label=Yes),\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "grid_cw.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:45.261228Z",
     "start_time": "2022-06-03T09:49:45.176467Z"
    }
   },
   "source": [
    "Sonuçları görelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:45.325047Z",
     "start_time": "2022-06-03T09:49:45.269201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=773de678-59e5-4424-b9d4-7430990e658c style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('773de678-59e5-4424-b9d4-7430990e658c').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_clf__C</th>\n",
       "      <th>param_clf__class_weight</th>\n",
       "      <th>param_clf__penalty</th>\n",
       "      <th>param_clf__solver</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0.2</td>\n",
       "      <td>{'No': 1, 'Yes': 6}</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>0.875601</td>\n",
       "      <td>0.024817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>1</td>\n",
       "      <td>{'No': 1, 'Yes': 10}</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>0.871263</td>\n",
       "      <td>0.026545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.1</td>\n",
       "      <td>{'No': 1, 'Yes': 6}</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>0.869937</td>\n",
       "      <td>0.028474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.5</td>\n",
       "      <td>{'No': 1, 'Yes': 8}</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>0.869813</td>\n",
       "      <td>0.027058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0.2</td>\n",
       "      <td>{'No': 1, 'Yes': 8}</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>0.869701</td>\n",
       "      <td>0.018108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "    param_clf__C param_clf__class_weight param_clf__penalty param_clf__solver  \\\n",
       "215          0.2     {'No': 1, 'Yes': 6}                 l2         newton-cg   \n",
       "286            1    {'No': 1, 'Yes': 10}                 l2             lbfgs   \n",
       "186          0.1     {'No': 1, 'Yes': 6}                 l2             lbfgs   \n",
       "250          0.5     {'No': 1, 'Yes': 8}                 l2         newton-cg   \n",
       "219          0.2     {'No': 1, 'Yes': 8}                 l2         liblinear   \n",
       "\n",
       "     mean_test_score  std_test_score  \n",
       "215         0.875601        0.024817  \n",
       "286         0.871263        0.026545  \n",
       "186         0.869937        0.028474  \n",
       "250         0.869813        0.027058  \n",
       "219         0.869701        0.018108  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.gridsearch_to_df(grid_cw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Özet ve Karşılaştırma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Birçok metriğimiz oldu. Peki hangisini ne zaman kullanmak gerek?\n",
    "\n",
    "Ben mümkün olduğunda hangisinin ne zaman kullanmak gerektiğini anlatamaya çalıştım. Bununla birlikte <a href=\"https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc\">burada</a> ve <a href=\"https://stats.stackexchange.com/questions/210700/how-to-choose-between-roc-auc-and-f1-score\">şurada</a> güzel özetler yapılmış, bunlara bakmanızı tavsiye ederim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kaynaklar\n",
    "\n",
    "- https://scikit-learn.org/stable/modules/model_evaluation.html\n",
    "- https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics\n",
    "- https://towardsdatascience.com/the-5-classification-evaluation-metrics-you-must-know-aa97784ff226\n",
    "- https://towardsdatascience.com/understanding-the-roc-and-auc-curves-a05b68550b69\n",
    "- https://towardsdatascience.com/model-benefit-evaluation-with-lift-and-gain-analysis-4b69f9288ab3\n",
    "- https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c\n",
    "- https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62\n",
    "- https://medium.com/hugo-ferreiras-blog/confusion-matrix-and-other-metrics-in-machine-learning-894688cb1c0a\n",
    "- https://medium.com/hiredscore-engineering/7-things-you-should-know-about-roc-auc-b4389ea2b2e3\n",
    "- https://www.datasciencecentral.com/profiles/blogs/7-important-model-evaluation-error-metrics-everyone-should-know\n",
    "- https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/\n",
    "- https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/\n",
    "- https://jamesmccaffrey.wordpress.com/2013/11/05/why-you-should-use-cross-entropy-error-instead-of-classification-error-or-mean-squared-error-for-neural-network-classifier-training/\n",
    "- https://www.data4v.com/understanding-precision-and-recall/\n",
    "- https://www.youtube.com/watch?v=4jRBRDbJemM\n",
    "- https://machinelearningmastery.com/cost-sensitive-logistic-regression/\n",
    "- https://developpaper.com/using-class-weight-to-improve-class-imbalance/\n",
    "- https://stackoverflow.com/questions/30056331/how-to-list-all-scikit-learn-classifiers-that-support-predict-proba\n",
    "- https://hunch.net/?p=317\n",
    "- https://kiwidamien.github.io/are-you-sure-thats-a-probability.html\n",
    "\n",
    "\n",
    "- https://github.com/DistrictDataLabs/yellowbrick adresinde ise güzel bir paket var, henüz denemedim ama ilk fırsatta deneyeceğim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelimize en çok hangi featurelar katkıda bulunuyor, yani bunların modeli etkileme gücü ne, bunu ölçebiliyoruz. Bu bilgi DecisionTree/RandomForest/XGBoost gibi modellerin içinde embedded bir şekilde geliyor.(**feature_importance_** propertysi ile). Lineear algoritmalarda da(lineer/logistic regresyon, ridge, lasso, elasticnet gibi) **coefficientlar(katsayılar)** benzer bir görevi görür.(Ama <a href=\"Linear%20Regression.ipynb\">Lineer regresyon notebooknunda</a> görüleceği üzere multicollinearity problemi olmaması lazım, aksi halde bu katsayılar yanıltıcı çıkabilir, her ne kadar modelin tahminleme gücünü etkilemese de)\n",
    "\n",
    "Diğer algoritmalarda ise **permutation feature importance** diye bir yöntem var, onu kullanabiliyoruz. Bunlara tree-based algoritmalarda geleceğiz.\n",
    "\n",
    "Biz şimdi elimizdeki model için bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:45.356976Z",
     "start_time": "2022-06-03T09:49:45.333026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.13425309,  0.45592926, -0.98365906,  0.92228645, -0.36482979,\n",
       "        0.12347875, -0.46177857,  0.25214889,  0.95500333,  0.13976798])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs4.best_estimator_[\"clf\"].coef_[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hazır fonksiyonumuz yardımıyla feature isimlerini de içerecek şekilde grafiğe dökelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:48.053772Z",
     "start_time": "2022-06-03T09:49:46.482955Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wF3cfn92fup63md1P3OOt3P0FMzsI2Nzdd07v3swa37Ps2voBDwADiVUg/wr8yN3vqSPfQv6TgSWADYHB7v6WmS0GLOPujxfLWGc5mqGv9hQGAH8EjgXOBR4HdjSzfSFa8zXn/3PgF6kFMzilTQeG151/sXXq7u8DdwOHAVv3RI+hIAjnAKsARwLfAQ7NTFkVkpspCvd0R+C37r4+sAfwFNFq/0zFeQMdNvQkCP3d/T/AycBfgO2pscdgZscSPdHNUhmeAG4BRpjZ2qTff0E4K3nmhWv+GBH18/ZUIf4HeCUde79GQViw8N13AE+7+7rAZ4HlgAlmtlkdeaf8dwWWBrb04C0z+wBRz5xiZh+GnjHhNUOfFAV3fxu4HHjU3Z8DzgceI4Rh/+w8MxtdU/5PABekl+L9lPwi0arK8t7KzEZUmW/JbPM/ZraYu7/r7rcRwlC5KcnMFjazr5XSRgBjgC+4+2PAJ4B/Aj+uyt5sZksAD1os/TowpZ0HnEW656nFdinRQNjNzPaqIu9CGfq7+3vJXHUacIGZHQ0sS5hz/kgIw141CcP3CfG5PpVnWaKXMhq4DrjazCaa2UZmtmhVlVS65oHp/02Jd/tuone0tJl92cwONbPxZnaKma1TRb4AZnYKcJKZHWJmKwO/B/ZLh48FhgC/BU6uURhWBJ5y9zfSsx8OXAtMBt4EDjGzj9eUd8v0SVEAcPe33P3d1K1/jog78jjRYv5ssgceWGP+nv6+l5LeJbq4mQ34OFKrqgrSdb6f/r8cuAp43MwOM7PR7v4r4FBgCzM7u1jGFlmSsGP/qJD2HmDAa0kwDgc2dfe/A/uY2YYV5PsWMN7dn3P3d1PaicCfgFXNbAyAu/8ZuBB4HtjKKhwQzMYvCBPl6JT3J4BTgM2BbwFPEGuY71ZVvmZ2sJltma57DWBlM3sQuA843d3XA7YDJgGrEZXlAp19Xzfzznof2T3H3TchRP8UYAbh5DAWWJcYZ3isorxvAD4K3ATc5O5PAuellvqpwJruvipwPyHMX0mNlqp7xQsAgyDvoc4AvubuhwJfAdYknn/vxN21pQ0YSQwGzgD+DAzsgTyzcZ0jgR8CZxI/koHF4xXmdxXwEDAK2BN4mHCVXDEd35owqazaat5Av/R3VWLc4MeFY7cSFfHTwP8U8v4zsE7F1zwZ2DH9vxQxyH4FsELhnBWBJWp4vp8C7irsL0+Yyy4lGmWLEk4Gy1aU35h0bdcAG6e0gYRd/cVOPrNoRXn3T39XICq//bIypPSbCIGc7T5n70oLeX8feLhBevb7ugTYLf1/KHAqMKLC57whIXILAPsSprJVOrk/pwJfrfq3Xdm1tLsAvW0jurm/Awak/QE155e9tIcBM4F7CoJQad5EK+paYMG0fwQhgHcBE4AxKX3xCvPM7uNqKa+z0/7CRIvtXmBjYDzwMvDZiq95aKow/gZ8KqUtnYThUmDlmp/vrsAjpbSx6Vo3L74DFeb5EeBs4OqSMPwVuA1YJKX1rzDP7D1eA/g/4GfA7cQYxt6F8+4iesCLV3XtwIKE8GfPd2DhWH9iDPGW9LzPJcY5Vq3w2i8mnAaOKVzXz4FnSA2ewrkHEw2hFet871q6nnYXoDdtwFbAL+uqlOeS94bAr6sUI0qtL2K5vy3T/18G/kG0VL8KvE70VIZUdD2z9bJShTEDOCe7RsJ0c0H6sWY/6qYrikYVXRKGEwl30KIwPAWc16isTeY9uEHaSGK52KNL6dcBn2j1ejt73kSP4VLgRuDjKW0QMI3oHS5Uwzu8JOHN9dW0vwIhxn8gTHnZeWc2ek4t5DucMM19pnwfCudMSO/YRcBaFeZ9PdG4WbB4T4H/SXXJy8DXiTGkHxCCWGlPuPLn2O4C1Hpxs1eKc3wR04PNWjwtVcqNXsw5nGul/ZYrqeK1ph/Nwun/wUSr8Xpgi5S2J9HaGdtinoOANUpp2xL20zXS/mqEAJ3b6JqJsYamKkk6BLVfuqaDCXNN/1S2k5IwZMK4JPCBFq95GWZtCQ8Evk2MCW2f0vYGHgVOBz5ImAr/BSzX6nNO3/+jrKIDBqW/S6QK6H6ix7BJ4Rk9XFXepXKMAY7I3j/gEaJn+i3CLHhYZ+9oi/kOJXr3pxbS+tFhvhyVyrBsVXmm792ZGKcZVMjTCsct5XsdcCfhBr9a1fe98ufY7gLUdmEdL8RyzGrLbvhSUGGvgA7b4WjCHXCPuZxv5c9WWJbrCbe835NaKIRIPJUqqZ2I7v4Grd5v4DTgSmC9lDaZ8H65g5gXcHBKHwv8m/DAmu0etPi8+6cK4vaU943A/qkyHEC02N4gBrZbyc/SNX+FMDnul9LvIez3F6V89knpWxODyrcT3i9rV/iMf04M5K6Z9scQ4vc1wuPn7PRctqj43So3ugakircfcBlwVUrfjRhLOJsWRH8uZdkdeLvRb40YQ/glyWxWYZ4Hp+/tX6w/Gl0fJcHozVvbC1DLRXVUyssDBxC2+u+Vjzd6iIR3yAIVlGEN4AXC9fXx9MPdosEPqZj3l4Fvt5hv0YRwCGGSWgc4I1XMm6ZjuwBPpgpqjqLVjbw/ma73XOBo4MbCsV0JW+7n0/5ahIdQUy2nTn54/QkRvKaQ9hgxfnAQ0YofRNh+V2rhOovPbBWiV3AVYR74Yema3wf2T/sDiVbt0Irud9F2fjHw9/SO/Q04oXDso4Qp6SJi7kYVdvxiw2d3Yv7HSoXjlwC7pP/PIUwoWS+8DlFYgPBuejX9jpZPZfsa0WNas4Y8TwXuK+yXf9u7AFtXnW/dW9sLUNuFRWv0JWKi0C+IVuOFhePZS138gR9CCEhLXWvCDHUbHV3pgel7DyydV8z7IMJD50Mt5Fs0GS2UvnOLQtoJqSLOhGEEsFi5LC1e+waEMNwGXFS61/sRPuvLp/2Fm8yjeN8+R7TWP0aME5wADEvHzid6DRMIu/bhNLD7t5D3t1N+30l/f0vB0yids0e650fV9J4PKvw/Kb1nZzY4bz1gqYrzXp3oZV5O9JauJ1rl/YiB3buIntPjdJj2amstE84LhxC90L+m53E/1fbKJtExRrMuMS53UOF4scfwPeBzdV1vbfex3QWo4cUwoht7GfCdlDaUCEQ2lYItu/SDOoSYVbxuBWUYRnSXM0+EB4Er0/8rUzJVpbxbGoBi1h7CjYTd+O/AyaXzjk8Vx2YV3/NiZbkB4X0yFdiwcM6IVHksl6UV/3Y1r8L/N6QK5/Z0TdsDi9Fh1nksnbcM4QlyLtW5X95A2Ik3THkOSff2UeCA0rn7Eg2U4VVXioTr6YmF/XPSc1+t/F5U/LwXJCr+Y1LaaMKEdVzaX5IwrxxFhyBUahqdQ/mWBtYmBrqHV/i9NxKz4bP9xYge+J3AvqVzDyaEaUxPXHOl96/dBajxxTiLsJlntuZBdLh9/rh07kGpUm5KEBr90InBpYOJwbaLS+nbF/a/TLSemxajUkV5ZPqxbkx0p18k2bsL53yT5IVTwX3ubIzmY8QA54V0uF5uniqOFSrI9ybg9sL+DwlTUeZuOyGrnNPzPZeIAVTFNXfmE78U8A1CEMvCMKzq+02I3znM7tl0MdGCXauKPOdQlhuBUen/KcDl6f+x5XtND3ry1Xit9zZIX4YINPgEMW5zahKKShqYbbnWdhegogfWyAXtBMKEsVghbRzhDfEgcFJK2yMJRVOtdDpMI0sS9uUV0v5hhCtiseK6mPBWKH7mj8CHq7h2YkDvAeAjaX8R4IuEjXmfBp9tdXC3WEF9hxhIvIwOT6ePp/v9AtGy/i2wawXPextiVvRuhbTPESI0OO1PSM/1LMJ805JnVSGfhj7xdPR6Fk/CcCXJfFjD+26kcQli3GJyg3NuIMaMBlWUZ6NB5YcI8+w9wKWFYxcCO9Vx7e3YiAbF86W00YTZbKEkDNuke35FEoZV2lHWKrZ5fsGLQvTJlQlPmn8TL+UpRAV5tZmdTJgZDiQU/BdErJvFU/oa7j6tibzNI5TBWoQ99XXgv2b2iLsfmmL8rGRmDxHmi9GEl8/7KS7O/5nZOG8ylHDKP4tldCDhaTKGqCB/6+6vmtmVROX4A4tQwhdkn/f0djeLd4TNuIEY2LuSGGyeYmY7uPu9ZvYmIRivEYOfj1cQHXIqMbh7iJnNILybvgVM8IhrhbsfZWYvEAOQ6zXzfDthMDFDOwuHkcVR8vT3JTN7lWgQjDOz4R5B8KpkAvBpM/sLYSoxM1ufmAPyjLvPcPedzGyUu7/TambWEXJ8KeK6F3T3x8zsOCI8zAx33zudO4m4Pwe0mm9vIMUtWhS408w2c/fbzWwU0bD8SfrtvknMzr+5fSWtkHarUisbHaahMYQ72mWEGWgSsBLRvb6aGGz6PfBQOn8lYrLL6AryHkaEbBhP+IZvTAxqZl3p/yG8EDago4dQ6cQ0oiVzNWG++AoheocVjg8nTFmfqOEZHMWsdtYvESL0LMkbBdiIik0ZxNjM0YR57i3gyMKxOboHtpjv3HzilyYaJOtS0cxwmM33fRlgfWKc7EeEx811RMX0MNFircT1k44e0OpEg+p2QnxOIXreexIi/UvCw+5eOnpPPTKGUNdWuPYPEGa6iwmxexY4tnTuKuXPzatb2wtQwYNbjughHJL2xxD25suBD6a0pYhJQ9kP91DCBtpS7JP04/x5+kEsVUj/QPr+3Rt8pup5CIcTNu5F0/4ShBvez4AvlfNt9YVldjPCh0geFsRYxfOpDI8SvaNKutGEJ9GR6f9HUoU4inAv/QuFkBHlMtbwzs3NJ/5XVOd2WhS44Q2OL54qqU+k+7EiLYZQKPxOskpxCcIs8qW0vwFhCvwuYU5bknBJ/RgVNnzauTHruN9WhJPERGKs5qrSuYcRPeHh87oguM/jokC00O4kWqbFlvHoVFlfTPJ+SenZOrn/oaKp5kSr/E1idmP2gxhEtNYOr/n6VybGC6ZTaJUSoRW+QsEttqL8imMIW5HmcxD25bFEbywT4u8TNudPVpT3tkQk2adJvbCUvgwhgg/QQIRruu894hNfeJ/6EV5G2aS4zxbPIXqqW1WU59ZE1Nbs2Y4gBlJ/z6wNnzUIJ4a9G3xHraLcA893Y8Jp4XjCvHxDSl8u3YsLgO1S2oHpPoxrd7kru/52F6CFB5e1ZpYi/KEfLB1fPv2IjiukDSG6f6s3mWfe2mZW8811xHrLH6HD++VW4Pg6rrlYHsIl8jkKczDSsSWIkMiVvKyl651MiO7ahbQPpYrjA8RkqQdI0TBpLZZR0XTyHNEAyLyKsuexLBG/6U+kCVM98P7V7hOf3XeiZ3Q1sCnh+/5rCt5NxGD+hFbvdfr81wnHgF2Jxs1SRI/g38xuMjmDFidb9sYtPdv9CWeGh0vHxhA9hp8Sjc6XmUe9jDq9/nYXoJsP6rRGP/pUAf6B2ScOLVWuSFvIPxOh1dJLcQURZG2hlD6ZcEu7hQiy9jgVht5m1lb6RqkSznzRN0zX/9PSZyoP/U2Y5e4vlouYnLdoEopHCZ/82VqQTeQ1y4QnwjyxGdFjOKp07opEN350D7+XlfvEE95LWSjzLzLrDO2rCBPdrXSI435U6A+f7uOTdISaHkGYBn/FrIHtbid58c0PW+k3tjPRsLmTWAuh/K5dTphHe3Vwu6buQ7sL0I0Hti/RSnyd8DbZlUJcdsJkMg24o8FnW43VngnCyoSp5jQi7PSf0suRTca6jLA57k1HkKxKK2bCy+m3ROvxcZKJKgnD74k1f6vMr/hDWSRdY2YiOozoSt9L2LOzNWmz2EctRzslWso3EQPamavrLkkYsms/kDB5VOJ+2c6Njln12cI8q5N6e+leP0yYMe4heij7VJh3cTLnpanS2zPtL5WE4c9ET+XsVMaWQ8L0ho1Zgyl+Jl3vQMJl/V7g64VzlyNMppXOEO8tW9sL0I2HNja9hAcQg44PEEHd9iQNZhItmheAsyrIbwNg58K+pUpwQiFtFGG6Oq+QdhMhWmNrEISjgN+k/5cDdiCCrn0xpX2CmPfQlHmsQX5Fk9EpRFyd+wjX0wuJmbM7pQrqf6l+EL0f0fO4klJIjCQMM1O+71Gx2aZdG2F++zXR+HiCFMWVWK3rQTomi/2YmCMwquLnvC4xTnUa0fCYURCGJZIwPAz8qPCZlkKHtHujo9HXj2hYXVi47yOIBum9RJTd8USDZLF2lbf2+9HuAnTz4V0C/Dr9P4aYvTuTcIk7iejCj2i1ciLcDs8guujbFdJ/QGlWIxHY7RUKIaMJU9IjlMJIV3D9p5Di2tBhUtkjVSJLEK2XqsI4FG35RwMPpP/XIeYIHAgsk9L2JkxqVS8KdABpkC/t70YI8wZpf430I215hnRv2AhT3GBifGQ7YlD5H0ko1k3v+bqEE8HvKC3g0kR+65Jau6lCHEgMaB9fOOdrRHyjvdL+koTZ9GeZWMwvG9HAu6ywvzBpfRHgs0Tj51FST3h+3dpegLk8pPI6A2MI2/keaX8/wr56PNGy+knh3G4LQ6kiHEuEybiRjqUc1yHGDIozaYcSttZlmNXUciUp8FuT1z5bNNX0A72WWbv5HyLMSZUvJVm4x3cD63dyfCvCHW/HGp7354lB/K3T398TA64zmQfi0nfjuj9aenf2I0yDCxBzbp4iHCfuInrIz7VaMaX3dTJhkloypS2YKr4jSueeRSFcCjH35hvpvagkwm67N8Jc9L8FETgz3Z/fAp9Oaf2oqNHVm7e2F2AODylrCS9Lxxq+C6XK4WyitfRMVlmlY02PHdDRhVyAjhbwKoS3x42ES2R/whPjWqLrPib9aO8pfL6KiWnFCmI9OpbJHEPEDjqdNMeCCCUxhYrW+C2VYyTRO3od+G6xfEQr6jjCRXTX4jNrMq+iP342+WkE4fJ7PtFqzsZprqcTkZrXNjoim05M71T2Hp1Gx5jJzYTJZkyqzKuaFLcH0Xg5G1g6pWUNjw8WztuBGHi+tFC+5dO5LfVW2njfG63Qdz9hOruS6OlvSkwMPauVumVe27KKt9dhZkZUPFcQ6/remtLXI34grxCRPn+fwje8l4739xR+oRt5ZaEyhhI/wAeJsYN/mdmqRIt1FaKCfJgYxziIcEd7F9jB3d/NvqfF686/w8xuISqBFYgwDj8kBrtvJMwKrxE9hSPc/epW8k35zXbvUqiOLxEeT5e6+6TCseUJ2+rv0vPCm3ihCmEU+hGV4xJEJfS/7n53Osfc3c3scMK8sr67/6OZ6+xNmNnHiJ7mJYSZyIjJkEsTInhEOu8uwsNrXPauN5FX9p4X37FtCKeNN4l4YYsTpth/EjGV7jazC4gW8/nFzxd/d/MSWbnTO7su0cD6VXr/difcb3+Z3snTiHsyvtXf9rxCrxOFcsWaKsb73P0UMxtAtFInEkq/VzMi0Em+Q4kW9+1E5f93d383HRtLCMPKwBnufntKHw68miqrSn8gZvZxwrb7KTPbk7Dh30u0Jhch3DOHAE+4+0OtxhMq3sdU8Y4iTHUPEgPKxxDms+vd/ZIWLq2z/I3ocf2NaAisS4wpHOLuN5vZusSA325EMLpHqy5DuzCzDYgW6VGEA8FQwiS6GLF06G3pvGWbFcKC8A4hnuXSRIiW64lW/3Lp1K8R42R7pe1RQqTXrKrh0xtIAvAQYRZbkzDN3eTu16bjixK/+a8TS5k+3q6y9jS9ThQAzGwkYY+fYmanA/9x9+8Uju9DmBOWq6q1aGZnEnMOPl9IG0V4X/yX+IEeTXThr3H3KwvntRrgrVyWcwjvp7Pd/dyUtg0xGe1Owtvp71XlV8r7WqLCuI8Qnw8RA8mvEbbujYEr3P3CivNdn1j7YfO0fxVRWW1ADID2I+7/Ne7+pyrz7g2Y2ScIj6IT3P0GM1ub8Ie/BfhvKxVxoZc1jBigfpgYp1iRcHv9F+HpNIwI8HecRzDFNYlG2GPeEcSx5QZYu0mCcD7xe98tpf0TuM4jkOUQ4l3/EjF++Lv2lbYNtNt+lerSIcQDGE68mLcQ8VxuJGLM/AX4NB0rhi1DVBBVLfzdn/C6yJZNHEa0EqYRA37XEkH0liF6Kd+p+X5sTnh8XFtK34oUjI0mVy2bS76fpDAznAgJ8hQdiwUtRZixKp/Sn64tc7edRGHyH9FDWJj5IK7MXO7BJuldr3xWNuGZ9ksK7tMpfUdirs2N6Td1KfATSmNUzOM2dSJy6yg6Ig5cS3JjJmYnP57u0TCiAbgIFa2/Ma9t7S9APIR/FCtawu1tKPBhYjBzRqoMHyX88H9aOLcqYTieGFw6mZiR/G9iNunBhPvdD9J5Y3qiciJs+G8y+8pp2wIfrSiPsofT5kQXGsK+/AIdM2u3JVrrLfukN/oOwl7+J8JkVZwx/TXCy2W+9Qsv3YeN0zu+OxVOxkuC/htg5fIzIOaaPES4XW5FmFC/0u57UeG1X0q48z6SBCAbqzyR6DE8WhCL05kHl9Cs9H61+WENIwYUzyqlF10ulyCCy2Vuc+vW0WpJlf3JxLjC6RRc/ghPkCtK51cRlniO10FMRnsT+GbNz2GvVGmsRYwfXEJ4FWWTAncj3PWansFJ9LL2LuwPJNY4Po4UkZLwhnmamI+xJNFynU7NK4j1to0I5fEwFUVaTd/5gXRvP5S9e8V3mBhbuDr9v2kdv7E23csbiLGqpYnZ4o8ScY12Tf8/T0eP9JD0/q/c7nK39Z618WENIUxEE0rpWwCfomO8Ywjherpt6bxaXlpi8lD/UtoVwA8r/P5dC/tzE4ZNCJfFU6vIv8H3r0KYiDZL+18nXFC/kPa3JnpNTa2kRQoeSLgQ302Hr/s9dET9fIO0xm16/g8Qrsc3UlHE0XltI8XUqvD7RhDzG4oBIvOeH+F6XY6dNU8LA9EL+DuFyAKEp9fB6b08iVgn4Q7Cs+955sNYRt3d2rny2lrEJJgHswQz25moDDb09ATdfYaZPUtUpjnezYG3osvknDwoPK3cZWaDCbvi94j4M5/LvicrW5PsAexvZiPc/WwvuQg2KM/dZrYppetvlnL53f3PZnYxcKqZTSFMZ28Ax5nZ9kQP4gseg5/duvbC+W5mP0/XsKWZrQ5Mcfcj03m3AlckD67zzez29BWD3P2tKq57XsObXI1vDt/3spmdBJxhZn9z9yvSO/d2OmVlwrySP7fu/sZ6IZcSsbg2A24xs6UJk/QaxCJFRsTy2oLoUZzp7k+1qay9hraJgrvfZ2Z7AFeamRMePucR7ob3ZeeZ2UJEbP5bms2r4I43EHh3bi978k7YhAihsChhSnqvIu+LW4hJYVsmMTizC8JwV/lams08q9TNbBl3fz4lX0a4m67r7ncSFcdk4B2ilfWPTFS7SlFAzOzbxOpoCxL28m0Is1hWpqvNrD9woZkNc/cJ6XNvz/7NogWuJMyk55nZCoQb5r+J8bQVCWeO/B2Z13H3X5vZeOBcMxtNeO+dSfQgtiBC3U8kxit/5+5vtKusvYp2dE8oLBVItJxnpm3j0nnnEi562X4zoSuyaJvDCNe7TejCeABhg9yICleSKlzzSMJX/CZmXR1ttu46HdEbK4tGSXh63UHYULMZqj8G7qzhWd9AuNFuSHh1DCEqoUcprAmQzt2XCLs9vCvPSFtTz2Mhwt3yGWJOyK/TezhfLKHZyTVvRDirXNXg2IeZT6OdNru1bZ5CyZyzDRGLf1fvmDxyMdHFW93TJLIm8sh6CMOIGZm/cfcDy+XwBjeh1NLtD8xsdF43ypL5imd/lyIqwY8Bv3L3M9N5xdmm2czLEan8W3oT3dty78JiVvjqhHvp/YQN/xJiNvcP3H1ys9dZyvf7hNB/qJS+FDExbRwxa/m8wrFh7v5aFfmLzjGzTKDfAqand3KenKHcFdIEwfOJMbNfuXoFndKvpzJKJpmcQoVr7n4z4QFzpZntZmYTCXPG6h6zKPs3kZ8lQRhK2EpvLQqCmS1TLEe5rIXyrezu77coCP0Ln1/IzIa6+7+IEL33E6akL6XyzDSzfiVBeJjoUbQkCGa2nZltQUyGupiYmPYbwlf9QcJD5ZMWM8dbwswWJEwV30j7A9NfS9d+DuHp9UkzOyL7nAShZ3D3V9z9OXd/sdBYmS8FAcJcTbiYnwJsb2aD2lykXkvtopBVBt7AXl4ShisIYbiC6O6tlwRhgDdhQ89aPoS9fKanQc2U387EwNOSZVt5KkvWUj8c+IOZLdpdm3rh+/oVKuXziEkzj5rZdu7+IjGOch+wqZkdVSj+e6k19zBwsKfYT92lkPdkwuV2PHCPmX2ZaCGe4+6fJIJ+TSVivlRROQwmJgwNTfvvp/JkDgQvEWscPwuMswgZItpEK42eeQV3/zVwOBFOpBLHjfmSOm1TdNGOz6xjDOvTYUfvth2fWSOMLkQMLl0LfDWlbUMMrm0xl88eQswqrmqN42sJE9DGxKptL9Hh9rkYMVnsF6Rw28QA93TCZNRq3geSZgun/cnEDM7ywjXZymZVzMEYSpp9XUjrR8cYxtJEq21dKor6qU1bVzYqdved37Y6b/wwYobqTxscm63SScJQrJQHNFs5ETMWswlRixCtg+sJ74tXgI1K5+9U2j8onVfJgtyEeebOwv4XCM+eV4ADU9oIOkJkG9Fr+kST+ZXnWRxDmmdBzOJ8njDtDKOGkNuFfHcnXB5ni7kPHEr4jFc2QUubNm2tb/V8abQS/0JpwhdpnYJOPlOcXblSi/nvQHgz7ZP2M2GYBkwqnXs5YcLIWrBfTBV205NYymKWKvzPpP+PJcISDyA8ft6hsBh64TNNLeXJrEsrXpfEZV8i8uvJREiRlQrXei41rbNLrE1xCmEm+jIR4G40EbriFfroxDRt2nrzVv0XRmX3M+DPpfSdgceI8AXlSrMoCIen1uWi5fO6WY79iLV7P5/2h9LRY/hKSjufMHFk7ngLEKaWbgsCHeav4rUMoCOmykAiZMdddCxsfyQx5rFRd/PrQnk2JrwsIAaU/0CYzT6S0rYkzFNb1fqCRa/tkJT3XwkT2v3MJ2sqa9M2v22Vu6SmyWaHE7bih9z9e8nl9DLC5fRXpfOL3jGHELb1bdx9Shfzm8WlNA1sv+/hxbMdYT/fw92vNbNFiFbzhoQ75GvMOqD93pwmkc2hDIOJMA73e5poZmYXEcL2GuHmOTW5Yv6aWP/5j0QvZVN3f6Iz19hmsAi9vSEREfOMlLYNEWfouXTa+sDXPCaOVRr6u5MyLU00CF4HXnb3/9SZnxCiOSoTBTNbmKjgbipUvhsR5pEtiTV8f1M4fyd3v6GwfxAREnoz7+YCKqlSPgq42N1fMLNB7v6OxUI1vyE8Xw509wtT2Q4mwmzsXRSEJq45m3OwBhEO4ynCzXQXYpnMS4hZkxsRkRcfNLNjifUJBgMnuvtl3c23C+XajBDhB9x9p0L6akTYikHA8+7+eE8IghBiHqKqLgdtsOPTIWpbALcRU9hHpbTPkuzWwHaEKSkr20KFzzY1U5lZzUTrEF5NVwITgKuARdKxkYRg/I2O+O2jKAwq19EFpJPQ29q0adM2p63aL+shOz6Nw0FsBVwMfJfwxZ8ObF0q28xSWrPeTUVB+AmxdCfEKmFXE1Pq9yqcM5IIvz2ditZC6Ow+lI73SOhtbdq0zT9bJQHxCnb8i8zsJWCymb3uYceflE7b0MyeYXY7/ltmdp530Y5vs681O4oQn+sJF9gVCI+avdz9F8m09G4q24uEGyTQ9CLz5UBvnyLip+AR5O9NYiW3TczsaXe/392nm9kEYrB5ge7mWcp/MGGKu9q7EEjPzLYG7jSzge5+XCt5CyHmf5oeU2iHHb9gw2+01uzyxGSzxwkvn/8AZ7j786lCfLfwPS2NIaT/v0UEdtvN3a/Jrj8d+xjhh/9quj8PpPSB6dqbtuOb2X7EIiFXuPvZKW2Og+MW6/8O9iZnRQsh+g6tiMIWwFeBPwPfTsLwWaKVvglRSd9ArHt8cfJK+m+q1JsOvJVCV9wMPOfuBxTSdyTGERYiArytRni6fNvdX2jqIjsvw/eA7YkZyPsDn3X3W0uisQExCe59YKIXwoG3mPdSxNoOGwC3eYNAenP5/Hyx+LoQoh6aNh+5+68sAtXtBhxhZn8lvIf2cvfHgMfM7ADgIjOb7u6/gLy13UpsncWJuPzfS9832N3fdvcbU3yiY4lB3ceICVO7ExO3KsHM9iV6Pcu7+0tmNgO4IcUyuj0ThmRK6k+EqX61orzN3f+V3F37AVuYGT6HNRkKrrYLuPtbEgQhxJzoVk+hEzv+aMKOvz8hCFcU7Pgzk398VUHWMLMPEGsB7OruD1tEX/VCC/164D1339VixbK7ujpe0cX8F0n5vZZVwhYrWh0NzCIM6fzhXoFPfsF01uOht4UQfYcuR0lNdvyphP3+n4QnzxHARwlb/vnA2hYrer1NDLbi7jeniqmqVd5eI3o4W6TvnxnFsyzq4dPEeALufkfWgq4ob9z91UwQCmknEl5PPzezTbPKOx37T6t5WhtDbwsh+hZdqqhThX4tEdStkR1/dTrs+Mea2Wx2/Kp6Ct5L1pottMb7uftMdz/RzN4HbjOzLd39tirysdlDby8DrGRmR7n7z1MaROjtQR5LWbpXFHpbCNG36Grrva12/AbUutZsA1PNnNw+ZxaE4VsW7rndXhSoMwricy2wHGGmWocYqznGY6H7icQs5c3M7Hp3f9bMFiWcAPZy919WVR4hxPxNl8YU2m3H76RMCwG7EqGgnYjp8yrwaU+rtTUzqFoaD/i4u9/bxc/NIhytuJ02+O4dgcM8FsPBzL4AnE1MkjvW3X+azESLuPvTSaj3JEJZ3FVFGYQQfYOu9hSKdvyHC3brQWn84GkiNj/ufgd03UWyWdz9TaK1/DMqWmu2JAg/BPZJYyT/7UJ5Zpb2mxaEBoJyDzHxDYvYSYcRrrcTgJ+k0ycCL2d5m9nV3uTa1kKIvkuXBmDd/WXgJGK8YI+UNjMJAoQd/3mICi07Xn1xG5atkrVmS4LwPSKW0we7IgjpMy0NpGf3rVSOAWa2YLr/N5rZEoQwb5uu8W/ANcAT5e+TIAghmqE7FVmtdvyqaDb/QkV8OvAZYHV3n5HS+hGroN3R6LPJVPWemS0OfNzdb+xO3slz6itm1jD0tpllobf7Ee7AHzGzoUS47spDbwsh+i5dFgV3f9PMTiUWSjkR+Dwddvy1U6U4T8+WNbP1iEHyywuCMIhw+7yHGFcpfyZz/VyMWMhm/27kl1XkKxPrH4wys9eI0Nsr0hF6+1ozy0JvTyImxA0m1kN4AtovxkKI+YOmwlykCrASO35vwmJi2v7E3It7iQio9wN/dfe9GpxfngvwRS8tIjSHvIpmonWIHsBexByQUUTcqFfNbCThcbQLsZb0781sFLGE5tPqIQghqqSpSV1V2fF7C2Z2rJkd5O6vAhcA9wGbAi8CTxYFwcw+ZWZfh5h7kUxGDxJzAZoRhJ8AN7n7zcR6EMsA2xJrQODu04mJcVcTcyA+6u4vuPvT6bgEQQhRGZXMMp4PKqZXgbPM7E2P4H0XEYHsliJ6AACY2eZEkL8D0/5goidxhHdxclhJEHo89LYQQsyJytdonpcoeEq5me0PnEcsEDTJIqzH54mIr78gQnRfD4z3CJVthAltMXd/tqv5FQShLaG3hRBiTlQVj2iew8wWSoPnA9KcigtSRX+Omb3l7lelHsNMYB9inGGPTBBSpfx62rpEyeV1e+AM4Hwze80Lobfd/f5UloOAL6by3Ze5mUoQhBB10SdFIc0Q3tnMjvYILJetHrcPERriijQ/4CIzuwQw4HiPlcysxbz3pU2ht4UQYm70KfORdYS63p0I5PdP4Eh3f9vMHgWmufveZrYXEfX1MHefaB1LgObmphbK0JbQ20II0RXme1EoVbCLEGtJzzCzXYAtCfPQhsB9PmsE2AOBc4Dl3P0fNZSrH8wS8C4Thm3d/Q6NGwgh2sF8LQolQfg6MWi8AjEOcD1hPluBWKhm1dRjWAB4Ow0+L1uHIJTKWFwU5wTgm8RiOJWE3hZCiO4wX4tChpl9l1g29AAiNMe2RPjpQYTL6aJEj+E0d3++MCktM+90q9WenV/4O8fggCVhOJnotWj9AyFEjzPfi4KZrQ9MBLZ392cK6VsBewPvAA8RvYV3gK9ng89N5tfrQm8LIURXqWyZyl7M0sSqbP+0ILPl3wLcRojB3US00aHAiGYzKgnCD4GbzGzBrny23JOQIAgh2kFfEIUBhEvpO6miLa6ffFE6tpG730R4G01rJpOSIPR46G0hhKiCviAKU4nxg/HFRDMbnHoNz6UNd/9ns5kUBCELvb2mu7+Y0vpZrEjXECuE3k5zKIQQoi3M96Lg7k8CxwGnmNme3sHbwCeJENXPV5GXdYTevtJnDb39EDG43egzA9IciCz0thbHEUK0jb5isjiLiFN0qZltQixb+TLwDSJE9dSK8vkr8CPgo2Z2OBF6+zfAE+5+ZPlkmz309ue8i5FWhRCiDuZ776OMZLPfFPgCsZ70n4BbizGHWvjuY4H/uPs5aYLcfkQv5GPAL9z9c4VzPwWs6+7fTvuLAw8Ah8oNVQjRbvpKTwGP9R5+aWZ3ekQazecgVPD1PRZ6Wwgh6qTPiEKGdyxo72m/lR5C5sV0tpm9DVyUeh2TzOxiYl2ETdOxp4BrgX28I/T2IGBz72LobSGEqJs+JwoZrc4DsDaE3hZCiLrpM2MKVZKF3gbKobfvJCbArQns7xF6ezghCo95IfS2JqcJIXoj871LapVks6GBBYnK/4Q0LgDwW+AZd18b+BzRYxifwl6fKUEQQswLqKcwF3pr6G0hhKgDicIcmBdCbwshRJVIFLpAT4feFkKIdiFRmAs9HXpbCCHaiQaa506Phd4WQoh2I1GYOz0SelsIIXoDEoW50yOht4UQojcgUZgLPRl6Wwgh2k2fDXPRTXoq9LYQQrQVeR91kTpDbwshRG9BotBNzGxgo9DbEgUhxPyARKFJ1DsQQsyPSBSEEELkyPtICCFEjkRBCCFEjkRBCCFEjkRBCCFEjkRBCCFEjkRBCCFEjkRBCCFEzv8DPpy5nK84QmwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.linear_model_feature_importance(gs4,coltrans,\"fs\",\"clf\")   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buradaki yorumlama lineer regresyondan biraz farklı olacak, zira elimizde bir class label'ı ve bunun olasılık hesabı var, bunda da logit/sigmoid fonksiyonu kullanılıyor. \n",
    "\n",
    "Mesela Ca'daki 1 birim artış logit(p) değerinde 0.93 birimlik artışa denk deriz ama bu tam olarak ne anlama geliyor, buna bakalım. Burda log'un tersini alarak ilerlemek lazım. Yani np.exp(0.93)=2.53, bundan 1 çıkaralım, 1.53, yani Ca'daki 1 birimlik artış kalp hastalığı riskinde %153'lük bir artışa denk geliyormuş. Binary bir variable olarak Sex'e bakalım, bunda 1 birim artış lafı anlamsız, bunu 0'dan 1'e çıkmak olarak dile getirelim, 0 sanırım Kadındı, 0'dan 1e çıkmak, yani kadın yerine erkek olmak, kalp hastalık riskini np.exp(0.83)=2.29, yani %129 artırıyormuş.\n",
    "\n",
    "<a href=\"https://towardsdatascience.com/a-simple-interpretation-of-logistic-regression-coefficients-e3a40a62e8cf\">Şurada</a> ve <a href=\"https://quantifyinghealth.com/interpret-logistic-regression-coefficients/\">şurada</a> Logistic Regression katsayılarıyla ilgili daha açıklayıcı bir bilgi bulabilirsiniz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretability ve Explainabilty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature importance ile yakından ilişkili bir konu da modelin yorumlanabilirliği ve açıklanabilirliğidir. Aslında bunlarla karıştırma ihtimali olan bir de fature selection konusu var. Şimdi üçünü birden kısaca bi karşılaştıralım.\n",
    "\n",
    "- **Feature selection**, \"modele en çok hangi featurelar katkıda bulunur?\" sorusunu model oluşturmadan önce(filter ve wrapper yöntemler) veya model oluşturma sırasında(RFE veya embedded yöntemlerde olduğu gibi) belirlemeye çalışır.\n",
    "- **Feature importance** ise model oluştuktan sonra bir featureun model için ne kadar önemli olduğunu söyler.\n",
    "- **Interpretability/Explainability** konuları ise, belirli bir instance için neden o karar çıktı, onu gösterir. Ör: Bir müşteriye neden kredi çıkmadı, hangi özellikleri nedeniyle. Mesela finansal bir modelde en yüksek öneme sahip olan feature \"mevcut borç miktarı\" olsun. Bu ne kadar yüksekse kredi alma ihtimali o kadar düşüyor. Ama bi müşteriye, hiç borcu olmadığı halde kredi çıkmamış olabilir. Bunun nedenini bu iki kavram bize sağlamaya çalışır.\n",
    "\n",
    "Interpretability ve Explainabiltiy arasında da bazı farklar olmakla birlikte, özetle ilki, Desicison treelerde if'li söylemlerle ifade etmek veya lineer modellerde coefficintların önem derecesiyle \"dile getirilebilirken\", ikincisi o kadar kolay değildir ve ekstra yöntemlere ihtiyaç duyar, DeepLearning modellerinde olduğu gibi. İlk kavram white-box modeller için geçerliyken ikincisi black-box modeller için geçerlidir. Bu ikisi arasındaki farklar için kaynaklardaki ilk 3 linke bakmanızı öneririm.\n",
    "\n",
    "Şunu da söylemek gerekir ki, modelin performansı ile interpretability'si arasında bir trade-off vardır. Modelin karmaşıklığı ve yorumlanabilirliği azaldıkça başarısı da artar, ve tersi de doğrudur. Şimdi şunu düşünebilirsiniz, \"O zaman tüm kurumlar en kompleks, en black-box modelleri kullanıyordur\". Cevap hayır, çünkü mesela BDDK gibi regulatif kurumlar bankaların açıklanabilir modeller kullanmasını ister, buna zorlar. Ör:Bir müşteriye neden kredi vermediğini açıklayabilmesini ister.\n",
    "\n",
    "Bununla birlikte, bu bölümde göreceğimiz gibi `SHAP` ve `LIME` gibi toollar aracılığı ile modellerin açıklanabilirliği çok iyi şekilde yapılabilmektedir. Bunlar, satır bazında açıklanabilirlik veren yaklaşımlar olduğu için çok kıymetlidirler. Ama buna rağmen regulatif kurumlar neden hala white-box modellerde ısrar ederler, bu konuda bir fikrim yok açıkçası.\n",
    "\n",
    "Biz burada sadece Shap'den bahsedeceğiz. Lime ve başka tool'ları araştırmayı size bırakıyorum. Kaynaklarda bunlarla ilgili linkleri bulabilirsiniz. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDIT:** Bu konuyu şimdilik buraya değil de <a href=\"Ensemble_RandomForest.ipynb\">RandomForest notebook'una</a> almaya karar verdim. Siz yine de önden bakmak isterseniz diye kaynakları bırakıyorum. İleriki bir zamanda elimizdeki model için de buraya SHAP yaklaşımını koymaya çalışacağım."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaynaklar\n",
    "\n",
    "*Interpretability vs Explainabilty*\n",
    "- https://towardsdatascience.com/interperable-vs-explainable-machine-learning-1fa525e12f48\n",
    "- https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability\n",
    "- https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html\n",
    "\n",
    "*General concept*\n",
    "- https://christophm.github.io/interpretable-ml-book/ (çok kapsamlı)\n",
    "- https://medium.com/walmartlabs/interpretable-machine-learning-part-i-bd1829b42b3a\n",
    "- https://towardsdatascience.com/interpretable-machine-learning-1dec0f2f3e6b\n",
    "- https://www.kdnuggets.com/2019/05/interpretability-machine-learning-models.html\n",
    "- https://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf\n",
    "- https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476\n",
    "- https://towardsdatascience.com/three-model-explanability-methods-every-data-scientist-should-know-c332bdfd8df\n",
    "- https://medium.com/analytics-vidhya/an-explanation-for-explainable-ai-xai-d56ae3dacd13\n",
    "\n",
    "*SHAP*\n",
    "- https://github.com/slundberg/shap\n",
    "- https://towardsdatascience.com/how-to-explain-black-box-models-with-shap-the-ultimate-guide-539c152d3275\n",
    "- https://towardsdatascience.com/black-box-models-are-actually-more-explainable-than-a-logistic-regression-f263c22795d\n",
    "- https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27\n",
    "- https://medium.com/swlh/push-the-limits-of-explainability-an-ultimate-guide-to-shap-library-a110af566a02\n",
    "- https://towardsdatascience.com/you-are-underutilizing-shap-values-feature-groups-and-correlations-8df1b136e2c2\n",
    "- https://towardsdatascience.com/shap-shapley-additive-explanations-5a2a271ed9c3\n",
    "- https://towardsdatascience.com/explain-your-model-with-the-shap-values-bc36aac4de3d\n",
    "- https://towardsdatascience.com/four-custom-shap-plots-8605d73b4570\n",
    "- https://www.kaggle.com/code/dansbecker/shap-values\n",
    "\n",
    "*LIME ve diğerleri*\n",
    "- https://medium.com/analytics-vidhya/explain-your-model-with-lime-5a1a5867b423\n",
    "- https://towardsdatascience.com/idea-behind-lime-and-shap-b603d35d34eb\n",
    "- https://medium.com/analytics-vidhya/explain-your-model-with-microsofts-interpretml-5daab1d693b4\n",
    "- https://towardsdatascience.com/python-libraries-for-interpretable-machine-learning-c476a08ed2c7\n",
    "- https://towardsdatascience.com/interpretml-analysis-of-svm-and-xgboost-models-e68062f7299f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tüm verisetiyle Final Model eğitimi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyperparametrelerimizi tune ettikten sonra nihai modelimize karar vermiş olduk. Şimdi bu modeli tüm verisetiyle, yani daha fazla miktarda data ile eğiterek öğrenim gücünü artırmaya çalışalım ve bu haliyle, ilave teste tabi tutmadan deploy edelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:50.227941Z",
     "start_time": "2022-06-03T09:49:49.150821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf': LogisticRegression(C=1, max_iter=1000, random_state=42, solver='newton-cg'),\n",
       " 'clf__C': 1,\n",
       " 'clf__penalty': 'l2',\n",
       " 'clf__solver': 'newton-cg',\n",
       " 'ct__numerics__ouh': None,\n",
       " 'ct__numerics__scl': StandardScaler()}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#en iyi parametre kombinasyonuna tekrar bakalım\n",
    "gs4.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En iyi parametre setiyle yeni bir model objesi yaratalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:51.365887Z",
     "start_time": "2022-06-03T09:49:50.236918Z"
    }
   },
   "outputs": [],
   "source": [
    "finalmodel=LogisticRegression(C=0.2, max_iter=1000, random_state=42, solver='newton-cg' ,penalty=\"l2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:52.472925Z",
     "start_time": "2022-06-03T09:49:51.398802Z"
    }
   },
   "outputs": [],
   "source": [
    "#pickle hatası almamak için custom fonksiyon ve classımızı ayrı bir dosyaya(custom_func_and_classes.py) koyduk\n",
    "#custom fonksiyon/class'ınız yoksa buna gerek yoktur\n",
    "from custom_func_and_classes import numericImputer, OutlierHandler\n",
    "\n",
    "cat_pipe=Pipeline([ \n",
    "                   (\"csi\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "                   (\"ohe\", OneHotEncoder(drop=\"first\",handle_unknown='ignore'))     \n",
    "                  ])\n",
    "\n",
    "num_pipe=Pipeline([  \n",
    "                   (\"nsi\", FunctionTransformer(numericImputer)), \n",
    "                   (\"ouh\", OutlierHandler(featureindices=[1])), \n",
    "                   (\"scl\", RobustScaler())\n",
    "                  ])\n",
    "\n",
    "coltrans = ColumnTransformer([\n",
    "                                ('nominals',  cat_pipe, noms),\n",
    "                                ('ordinals',  OrdinalEncoder(categories=[['typical', 'asymptomatic', 'nonanginal', 'nontypical']]), [\"ChestPain\"]),\n",
    "                                ('numerics',  num_pipe, nums)\n",
    "                                 ],n_jobs=-1,remainder=\"passthrough\")\n",
    "\n",
    "finalpipe = Pipeline(steps=[('ct', coltrans),\n",
    "                       ('fs', SelectKBest(score_func=mutual_info_classif,k=10)), \n",
    "                       ('clf', finalmodel) \n",
    "                       ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi modelimizi tüm data ile eğitelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:54.106565Z",
     "start_time": "2022-06-03T09:49:52.482910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028496CC60D0&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   RobustScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 LogisticRegression(C=0.2, max_iter=1000, random_state=42,\n",
       "                                    solver=&#x27;newton-cg&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-220\" type=\"checkbox\" ><label for=\"sk-estimator-id-220\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ct&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;nominals&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;,\n",
       "                                                   &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                                 (&#x27;ordinals&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                                              &#x27;asymptomatic&#x27;,\n",
       "                                                                              &#x27;nonanginal&#x27;,\n",
       "                                                                              &#x27;nontypi...\n",
       "                                                                   FunctionTransformer(func=&lt;function numericImputer at 0x0000028496CC60D0&gt;)),\n",
       "                                                                  (&#x27;ouh&#x27;,\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  (&#x27;scl&#x27;,\n",
       "                                                                   RobustScaler())]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;,\n",
       "                                                   &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                                   &#x27;Ca&#x27;])])),\n",
       "                (&#x27;fs&#x27;,\n",
       "                 SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 LogisticRegression(C=0.2, max_iter=1000, random_state=42,\n",
       "                                    solver=&#x27;newton-cg&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-221\" type=\"checkbox\" ><label for=\"sk-estimator-id-221\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ct: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;nominals&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;csi&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#x27;]),\n",
       "                                (&#x27;ordinals&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;typical&#x27;,\n",
       "                                                             &#x27;asymptomatic&#x27;,\n",
       "                                                             &#x27;nonanginal&#x27;,\n",
       "                                                             &#x27;nontypical&#x27;]]),\n",
       "                                 [&#x27;ChestPain&#x27;]),\n",
       "                                (&#x27;numerics&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;nsi&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function numericImputer at 0x0000028496CC60D0&gt;)),\n",
       "                                                 (&#x27;ouh&#x27;,\n",
       "                                                  OutlierHandler(featureindices=[1])),\n",
       "                                                 (&#x27;scl&#x27;, RobustScaler())]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;,\n",
       "                                  &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-222\" type=\"checkbox\" ><label for=\"sk-estimator-id-222\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nominals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Fbs&#x27;, &#x27;ExAng&#x27;, &#x27;Slope&#x27;, &#x27;Thal&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-223\" type=\"checkbox\" ><label for=\"sk-estimator-id-223\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-224\" type=\"checkbox\" ><label for=\"sk-estimator-id-224\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-225\" type=\"checkbox\" ><label for=\"sk-estimator-id-225\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinals</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ChestPain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-226\" type=\"checkbox\" ><label for=\"sk-estimator-id-226\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;typical&#x27;, &#x27;asymptomatic&#x27;, &#x27;nonanginal&#x27;,\n",
       "                            &#x27;nontypical&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-227\" type=\"checkbox\" ><label for=\"sk-estimator-id-227\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerics</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;RestBP&#x27;, &#x27;Chol&#x27;, &#x27;MaxHR&#x27;, &#x27;Oldpeak&#x27;, &#x27;Ca&#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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-228\" type=\"checkbox\" ><label for=\"sk-estimator-id-228\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function numericImputer at 0x0000028496CC60D0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-229\" type=\"checkbox\" ><label for=\"sk-estimator-id-229\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierHandler</label><div class=\"sk-toggleable__content\"><pre>OutlierHandler(featureindices=[1])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-230\" type=\"checkbox\" ><label for=\"sk-estimator-id-230\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RobustScaler</label><div class=\"sk-toggleable__content\"><pre>RobustScaler()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-231\" type=\"checkbox\" ><label for=\"sk-estimator-id-231\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;RestECG&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-232\" type=\"checkbox\" ><label for=\"sk-estimator-id-232\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-233\" type=\"checkbox\" ><label for=\"sk-estimator-id-233\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function mutual_info_classif at 0x00000284930AE790&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-234\" type=\"checkbox\" ><label for=\"sk-estimator-id-234\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.2, max_iter=1000, random_state=42, solver=&#x27;newton-cg&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ct',\n",
       "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
       "                                   transformers=[('nominals',\n",
       "                                                  Pipeline(steps=[('csi',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('ohe',\n",
       "                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                 handle_unknown='ignore'))]),\n",
       "                                                  ['Sex', 'Fbs', 'ExAng',\n",
       "                                                   'Slope', 'Thal']),\n",
       "                                                 ('ordinals',\n",
       "                                                  OrdinalEncoder(categories=[['typical',\n",
       "                                                                              'asymptomatic',\n",
       "                                                                              'nonanginal',\n",
       "                                                                              'nontypi...\n",
       "                                                                   FunctionTransformer(func=<function numericImputer at 0x0000028496CC60D0>)),\n",
       "                                                                  ('ouh',\n",
       "                                                                   OutlierHandler(featureindices=[1])),\n",
       "                                                                  ('scl',\n",
       "                                                                   RobustScaler())]),\n",
       "                                                  ['Age', 'RestBP', 'Chol',\n",
       "                                                   'MaxHR', 'Oldpeak',\n",
       "                                                   'Ca'])])),\n",
       "                ('fs',\n",
       "                 SelectKBest(score_func=<function mutual_info_classif at 0x00000284930AE790>)),\n",
       "                ('clf',\n",
       "                 LogisticRegression(C=0.2, max_iter=1000, random_state=42,\n",
       "                                    solver='newton-cg'))])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalpipe.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local'e Kaydetme ve Yükleme(Çağırma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pickle yöntemi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mevcut **pickle** modülü yerine bunun sklearn replacementı olan **joblib**'teki dump ve load fonksiyonlarını kullanacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:55.144819Z",
     "start_time": "2022-06-03T09:49:54.114543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['finalmodel.pkl']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sadece modeli kaydetme\n",
    "from joblib import dump, load\n",
    "dump(finalmodel, 'finalmodel.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ama bizim modeli değil pipeline'ı kaydetmemiz lazım, zira production ortamında bu pipeline'ı çağıracağız, tüm preprocessingler yapılsın diye."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:56.235859Z",
     "start_time": "2022-06-03T09:49:55.151803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['finalpipe.pkl']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(finalpipe, 'finalpipe.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:57.324948Z",
     "start_time": "2022-06-03T09:49:56.246830Z"
    }
   },
   "outputs": [],
   "source": [
    "#geri yüklemek için\n",
    "loaded_model = load('finalpipe.pkl') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şimdi de yeni bir input verip bunu tahminleyelim. Ama önce inputun shape'i nasıl olmalı, ona bakalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:58.377438Z",
     "start_time": "2022-06-03T09:49:57.332933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 13)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.iloc[[0],:].shape #olması gereken input shape böyle\n",
    "X_test.iloc[0,:].shape #bu değil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sahadan yeni gelen bir veriyi temsilen X_test'teki ilk satırın aynısını alalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-03T09:49:59.570257Z",
     "start_time": "2022-06-03T09:49:58.387413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes'], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(X_test.iloc[[0],:]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pickling alternatifleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://faoel.medium.com/stop-using-pickle-to-save-machine-learning-models-eaa46e8e561a\">Şurada</a> belirtildiği gibi pickle yönteminin bazı dezavantajları var. Ona alternatif olarak şu yöntemlerle locale kaydetme yapılabilir.\n",
    "\n",
    "- json tercih edilebilir\n",
    "- keras(veya tensorflow) kullanıyorsak, bunun kendi save metodu kullanılabilir\n",
    "- pmml kullanmak tercih edilebilir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Web Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelleri locale kaydedip sahadan gelen gerçek verileri localde yaptığımız gibi sonra tekrar load ederek tahminleme yapmak çok amatörce olacaktır. Bunun için kurumunuzun yazılım ekipleriyle görüşüp modelinizin production ortamına alınması gerekecektir. Biz burada bu kadar profesyonel bir iş yapmayacağız ama onun yerine daha orta seviyede bir iş yapıp, projemizi web ortamında nasıl çalıştırırız, ona bakacağız.\n",
    "\n",
    "Böylece, yaptığınız bir projeyi başkalarıyla(belki de iş görüşmesi yaptığınız kişilerle) paylaşma imkanınız da olacaktır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOT**:<br>\n",
    "Bu kısım biraz daha programcı ve veri mühendisliği yeteneklerini gerektiriyor. O yüzden isterseniz şimdilik es geçin, ML konusunda kendinizi geliştirdikten sonra tekrar ziyaret edin. Ben de şuan bu kısma çok eğilmemeye karar verdim, sadece birkaç kaynak koyacağım, ilerleyen dönemlerde burayı açıklama ve kod örnekleriyle zenginleştirmeye çalışacağım."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flask ile deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-01T16:31:53.708023Z",
     "start_time": "2022-06-01T16:31:53.418796Z"
    }
   },
   "source": [
    "- https://towardsdatascience.com/model-deployment-using-flask-c5dcbb6499c9\n",
    "- https://towardsdatascience.com/lets-deploy-a-machine-learning-model-be6057f2d304\n",
    "- https://towardsdatascience.com/deploying-models-to-flask-fb62155ca2c4\n",
    "- https://medium.com/analytics-vidhya/deploying-a-machine-learning-model-on-web-using-flask-and-python-54b86c44e14a\n",
    "- https://towardsdatascience.com/deploy-a-machine-learning-model-using-flask-da580f84e60c\n",
    "- https://towardsdatascience.com/deploying-a-machine-learning-model-as-a-rest-api-4a03b865c166"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Streamlit ile deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://towardsdatascience.com/how-to-deploy-machine-learning-models-601f8c13ff45\n",
    "- https://www.datacamp.com/tutorial/streamlit\n",
    "- https://towardsdatascience.com/streamlit-hands-on-from-zero-to-your-first-awesome-web-app-2c28f9f4e214\n",
    "- https://towardsdatascience.com/how-to-deploy-machine-learning-models-601f8c13ff45"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Docker Container ile deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://blog.dataminded.com/from-notebook-hell-to-container-heaven-20cbe05100a1\n",
    "- https://towardsdatascience.com/the-nice-way-to-deploy-an-ml-model-using-docker-91995f072fe8\n",
    "- https://medium.com/analytics-vidhya/deploying-machine-learning-model-on-the-top-of-docker-container-ce2c1234a551\n",
    "- https://towardsdatascience.com/build-and-run-a-docker-container-for-your-machine-learning-model-60209c2d7a7f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exe dosyası yaratma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir diğer alternatif de projenizi webde değil de masaüstü bir program olarak çalıştırmak olabilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://python.plainenglish.io/convert-a-python-project-to-an-executable-exe-file-175080da4485\n",
    "- https://towardsdatascience.com/how-to-easily-convert-a-python-script-to-an-executable-file-exe-4966e253c7e9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaynaklar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Genel Concepts*\n",
    "- https://stackoverflow.blog/2020/10/12/how-to-put-machine-learning-models-into-production/\n",
    "- https://towardsdatascience.com/3-ways-to-deploy-machine-learning-models-in-production-cdba15b00e\n",
    "- https://machinelearningmastery.com/deploy-machine-learning-model-to-production/\n",
    "- https://anu-ganesan.medium.com/mlops-in-2021-the-pillar-for-seamless-machine-learning-lifecycle-3a12300d7785\n",
    "- https://towardsdatascience.com/from-jupyter-notebooks-to-real-life-mlops-9f590a7b5faa\n",
    "\n",
    "*3rd party webs/apps*\n",
    "- https://www.kdnuggets.com/2021/04/deploy-machine-learning-models-to-web.html\n",
    "- https://www.freecodecamp.org/news/deploy-your-machine-learning-models-for-free/\n",
    "- https://towardsdatascience.com/there-are-two-very-different-ways-to-deploy-ml-models-heres-both-ce2e97c7b9b1\n",
    "- https://towardsdatascience.com/simple-deployment-of-web-app-ml-model-apis-tutorial-2ece8e66d98c\n",
    "\n",
    "*Deploying on other environments(ör:asp.net)*\n",
    "- https://www.activestate.com/blog/how-deploy-machine-learning-models-net-environment\n",
    "- https://towardsdatascience.com/deploy-sci-kit-learn-models-in-net-core-applications-90e24e572f64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Faydalı Kaynaklar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kitaplar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow (Aurelien Geron)\n",
    "- The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)\n",
    "- An Introduction to Statistical Learning (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Online"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://scikit-learn.org/stable/\n",
    "- https://ml-cheatsheet.readthedocs.io/en/latest/index.html\n",
    "- https://machinelearningmastery.com/\n",
    "- https://hdsr.mitpress.mit.edu/\n",
    "- https://data-flair.training/blogs/\n",
    "- https://www.machinelearningplus.com/\n",
    "- https://www.geeksforgeeks.org/machine-learning/\n",
    "- https://developers.google.com/machine-learning/crash-course\n",
    "- https://www.tensorflow.org/resources/learn-ml"
   ]
  }
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