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evrenarslan/Decision_Tree_Algoritmasi.ipynb

Created May 26, 2020 13:47
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Decision Tree - Karar Ağacı Algoritması
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Decision_Tree_Algoritmasi.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Tree - Karar Ağacı Algoritması"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np \n",
"import pandas as pd\n",
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Yas</th>\n",
" <th>Cinsiyet</th>\n",
" <th>Tansiyon</th>\n",
" <th>Kolestrol</th>\n",
" <th>SoPo_Oran</th>\n",
" <th>İlaç</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>23</td>\n",
" <td>K</td>\n",
" <td>YÜKSEK</td>\n",
" <td>YÜKSEK</td>\n",
" <td>25.355</td>\n",
" <td>ilacY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>47</td>\n",
" <td>E</td>\n",
" <td>DÜŞÜK</td>\n",
" <td>YÜKSEK</td>\n",
" <td>13.093</td>\n",
" <td>ilacC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>47</td>\n",
" <td>E</td>\n",
" <td>DÜŞÜK</td>\n",
" <td>YÜKSEK</td>\n",
" <td>10.114</td>\n",
" <td>ilacC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>28</td>\n",
" <td>K</td>\n",
" <td>NORMAL</td>\n",
" <td>YÜKSEK</td>\n",
" <td>7.798</td>\n",
" <td>ilacX</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>61</td>\n",
" <td>K</td>\n",
" <td>DÜŞÜK</td>\n",
" <td>YÜKSEK</td>\n",
" <td>18.043</td>\n",
" <td>ilacY</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Yas Cinsiyet Tansiyon Kolestrol SoPo_Oran İlaç\n",
"0 23 K YÜKSEK YÜKSEK 25.355 ilacY\n",
"1 47 E DÜŞÜK YÜKSEK 13.093 ilacC\n",
"2 47 E DÜŞÜK YÜKSEK 10.114 ilacC\n",
"3 28 K NORMAL YÜKSEK 7.798 ilacX\n",
"4 61 K DÜŞÜK YÜKSEK 18.043 ilacY"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv(\"ilac_tahmin.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Verinin Hazırlanması"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scikit-Learn ile modelimizi eğitmeden önce tahmin edeceğimiz özellik \"ilaç\" ve diğer özellikleri iki ayrı değişkene atayacağız. X tahmin için kullanacağımız özelliklerden oluşan matris olacak. y ise tahmmin edeceğimiz veriden oluşan bir vektör olacak."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[23, 'K', 'YÜKSEK', 'YÜKSEK', 25.355],\n",
" [47, 'E', 'DÜŞÜK', 'YÜKSEK', 13.093],\n",
" [47, 'E', 'DÜŞÜK', 'YÜKSEK', 10.113999999999999],\n",
" [28, 'K', 'NORMAL', 'YÜKSEK', 7.797999999999999],\n",
" [61, 'K', 'DÜŞÜK', 'YÜKSEK', 18.043]], dtype=object)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X=df[[\"Yas\",\"Cinsiyet\",\"Tansiyon\",\"Kolestrol\",\"SoPo_Oran\"]].values\n",
"X[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sklearn Decision Tree Algoritması kategorik verileri analiz edemiyor. Bu sebeple Cinsiyet, Tansiyon,Kolestrol verilerini sayısal kategorik verilere çevirmemiz gerekiyor. Bunu sağlamak için ***pandas.get_dummies()*** medtodunu kullanırız."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import preprocessing\n",
"\n",
"label_cins=preprocessing.LabelEncoder()\n",
"label_cins.fit([\"K\",\"E\"])\n",
"X[:,1]=label_cins.transform(X[:,1])\n",
"\n",
"label_tans=preprocessing.LabelEncoder()\n",
"label_tans.fit([\"YÜKSEK\",\"DÜŞÜK\",\"NORMAL\"])\n",
"X[:,2]=label_tans.transform(X[:,2])\n",
"\n",
"label_kol=preprocessing.LabelEncoder()\n",
"label_kol.fit([\"YÜKSEK\",\"NORMAL\"])\n",
"X[:,3]=label_kol.transform(X[:,3])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[23, 1, 2, 1, 25.355],\n",
" [47, 0, 0, 1, 13.093],\n",
" [47, 0, 0, 1, 10.113999999999999],\n",
" [28, 1, 1, 1, 7.797999999999999],\n",
" [61, 1, 0, 1, 18.043]], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['ilacY'],\n",
" ['ilacC'],\n",
" ['ilacC'],\n",
" ['ilacX'],\n",
" ['ilacY']], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y=df[[\"İlaç\"]].values\n",
"y[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modelin Kurulması"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Öncelikli olarak verimizi train ve test olarak iki kısma ayıracağız. Yani elimizide X_train, X_test, y_train y_test olmak üzere 4 veri seti olacak."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oluşturduğumuz veri setlerinin boyutlarının analiz için birbiri ile uyumlu olması gerekiyor."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_train boyut: (140, 5) y_train boyut: (140, 1)\n",
"X_test boyut: (60, 5) y_test boyut: (60, 1)\n"
]
}
],
"source": [
"print(\"X_train boyut:\",X_train.shape,\" y_train boyut:\",y_train.shape)\n",
"print(\"X_test boyut:\",X_test.shape,\" y_test boyut:\",y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"İlk olarak ilacTree isminde bir ***DecisionTreeClassifier*** instance oluşturacağız. Bu instance içinde, her bir düğümün information gain değerini görebilmemiz için ***criterion=\"entropy\"*** olarak tanımlayacağız."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
" max_depth=4, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ilacTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
"ilacTree # Aşağıda default parametreleri göreceğiz."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Artık modeli eğitebiliriz."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
" max_depth=4, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ilacTree.fit(X_train,y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tahmin & Değerlendirme"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yukarıda oluşturmuş olduğumuz modeli kullanarak tahmin yapalım ve sonucları bir değişkende toplayalım."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"ilacTahmin=ilacTree.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['ilacY', 'ilacX', 'ilacX', 'ilacX', 'ilacX'], dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ilacTahmin[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['ilacY'],\n",
" ['ilacX'],\n",
" ['ilacX'],\n",
" ['ilacX'],\n",
" ['ilacX']], dtype=object)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"İlk beş değer için mükemmel çalıştı :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Değerlendirme***"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Karar Ağacı Modeli Accuracy: 0.9833333333333333\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"print (\"Karar Ağacı Modeli Accuracy: \",metrics.accuracy_score(y_test,ilacTahmin))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tüm test verisi üzerinden modelimizin başarımı %98 gibi gayet iyi bir değer verdi."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Karar ağacı modelinin görselleştirilmesi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Modelin oluşturdurğu karar ağacı modeline incelemek istersek onu bir resim olarak görüntüleyebiliriz."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.externals.six import StringIO\n",
"import pydotplus\n",
"import matplotlib.image as mpimg\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import tree\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Eğer gerekli olursa.\n",
"# !conda install -c conda-forge pydotplus -y\n",
"# !conda install -c conda-forge python-graphviz -y"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x216cb2b6d90>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
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"text/plain": [
"<Figure size 7200x14400 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dot_data = StringIO()\n",
"filename = \"ilactree.png\"\n",
"featureNames = df.columns[0:5]\n",
"targetNames = df[\"İlaç\"].unique().tolist()\n",
"out=tree.export_graphviz(ilacTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True, special_characters=True,rotate=False) \n",
"graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \n",
"graph.write_png(filename)\n",
"img = mpimg.imread(filename)\n",
"plt.figure(figsize=(100, 200))\n",
"plt.imshow(img,interpolation='nearest')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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