1929 lines
125 KiB
Text
1929 lines
125 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# # Classifiers introduction\n",
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"\n",
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"In the following program we introduce the basic steps of classification of a dataset in a matrix"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the package for learning and modeling trees"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"from sklearn import tree"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Define the matrix containing the data (one example per row)\n",
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"and the vector containing the corresponding target value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = [[0, 0, 0], [1, 1, 1], [0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]\n",
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"Y = [1, 0, 0, 0, 1, 1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Declare the classification model you want to use and then fit the model to the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"clf = tree.DecisionTreeClassifier()\n",
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"clf = clf.fit(X, Y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Predict the target value (and print it) for the passed data, using the fitted model currently in clf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"text": [
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"[0]\n"
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]
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}
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],
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"source": [
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"print(clf.predict([[0, 1, 1]]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"text": [
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"[1 0]\n"
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]
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}
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],
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"source": [
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"print(clf.predict([[1, 0, 1],[0, 0, 1]]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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"text/plain": [
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"import os\n",
|
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"os.environ[\"PATH\"] += os.pathsep + 'C:/Users/galat/.conda/envs/aaut/Library/bin/graphviz'\n",
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"import graphviz\n",
|
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"dot_data = tree.export_graphviz(clf, out_file=None) \n",
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"graph = graphviz.Source(dot_data) \n",
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"graph"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the following we start using a dataset (from UCI Machine Learning repository)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_iris\n",
|
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"iris = load_iris()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Declare the type of prediction model and the working criteria for the model induction algorithm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:1,2:1})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Split the dataset in training and test set"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate a random permutation of the indices of examples that will be later used \n",
|
|
"# for the training and the test set\n",
|
|
"import numpy as np\n",
|
|
"np.random.seed(0)\n",
|
|
"indices = np.random.permutation(len(iris.data))\n",
|
|
"\n",
|
|
"# We now decide to keep the last 10 indices for test set, the remaining for the training set\n",
|
|
"indices_training=indices[:-10]\n",
|
|
"indices_test=indices[-10:]\n",
|
|
"\n",
|
|
"iris_X_train = iris.data[indices_training] # keep for training all the matrix elements with the exception of the last 10 \n",
|
|
"iris_y_train = iris.target[indices_training]\n",
|
|
"iris_X_test = iris.data[indices_test] # keep the last 10 elements for test set\n",
|
|
"iris_y_test = iris.target[indices_test]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Fit the learning model on training set"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fit the model to the training data\n",
|
|
"clf = clf.fit(iris_X_train, iris_y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Obtain predictions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Predictions:\n",
|
|
"[1 2 1 0 0 0 2 1 2 0]\n",
|
|
"True classes:\n",
|
|
"[1 1 1 0 0 0 2 1 2 0]\n",
|
|
"['setosa' 'versicolor' 'virginica']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# apply fitted model \"clf\" to the test set \n",
|
|
"predicted_y_test = clf.predict(iris_X_test)\n",
|
|
"\n",
|
|
"# print the predictions (class numbers associated to classes names in target names)\n",
|
|
"print(\"Predictions:\")\n",
|
|
"print(predicted_y_test)\n",
|
|
"print(\"True classes:\")\n",
|
|
"print(iris_y_test) \n",
|
|
"print(iris.target_names)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Print the index of the test instances and the corresponding predictions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Look at the specific examples"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Instance # 88: \n",
|
|
"sepal length (cm)=5.6, sepal width (cm)=3.0, petal length (cm)=4.1, petal width (cm)=1.3\n",
|
|
"Predicted: versicolor\t True: versicolor\n",
|
|
"\n",
|
|
"Instance # 70: \n",
|
|
"sepal length (cm)=5.9, sepal width (cm)=3.2, petal length (cm)=4.8, petal width (cm)=1.8\n",
|
|
"Predicted: virginica\t True: versicolor\n",
|
|
"\n",
|
|
"Instance # 87: \n",
|
|
"sepal length (cm)=6.3, sepal width (cm)=2.3, petal length (cm)=4.4, petal width (cm)=1.3\n",
|
|
"Predicted: versicolor\t True: versicolor\n",
|
|
"\n",
|
|
"Instance # 36: \n",
|
|
"sepal length (cm)=5.5, sepal width (cm)=3.5, petal length (cm)=1.3, petal width (cm)=0.2\n",
|
|
"Predicted: setosa\t True: setosa\n",
|
|
"\n",
|
|
"Instance # 21: \n",
|
|
"sepal length (cm)=5.1, sepal width (cm)=3.7, petal length (cm)=1.5, petal width (cm)=0.4\n",
|
|
"Predicted: setosa\t True: setosa\n",
|
|
"\n",
|
|
"Instance # 9: \n",
|
|
"sepal length (cm)=4.9, sepal width (cm)=3.1, petal length (cm)=1.5, petal width (cm)=0.1\n",
|
|
"Predicted: setosa\t True: setosa\n",
|
|
"\n",
|
|
"Instance # 103: \n",
|
|
"sepal length (cm)=6.3, sepal width (cm)=2.9, petal length (cm)=5.6, petal width (cm)=1.8\n",
|
|
"Predicted: virginica\t True: virginica\n",
|
|
"\n",
|
|
"Instance # 67: \n",
|
|
"sepal length (cm)=5.8, sepal width (cm)=2.7, petal length (cm)=4.1, petal width (cm)=1.0\n",
|
|
"Predicted: versicolor\t True: versicolor\n",
|
|
"\n",
|
|
"Instance # 117: \n",
|
|
"sepal length (cm)=7.7, sepal width (cm)=3.8, petal length (cm)=6.7, petal width (cm)=2.2\n",
|
|
"Predicted: virginica\t True: virginica\n",
|
|
"\n",
|
|
"Instance # 47: \n",
|
|
"sepal length (cm)=4.6, sepal width (cm)=3.2, petal length (cm)=1.4, petal width (cm)=0.2\n",
|
|
"Predicted: setosa\t True: setosa\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i in range(len(iris_y_test)): \n",
|
|
" print(\"Instance # \"+str(indices_test[i])+\": \")\n",
|
|
" s=\"\"\n",
|
|
" for j in range(len(iris.feature_names)):\n",
|
|
" s=s+iris.feature_names[j]+\"=\"+str(iris_X_test[i][j])\n",
|
|
" if (j<len(iris.feature_names)-1): s=s+\", \"\n",
|
|
" print(s)\n",
|
|
" print(\"Predicted: \"+iris.target_names[predicted_y_test[i]]+\"\\t True: \"+iris.target_names[iris_y_test[i]]+\"\\n\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Obtain model performance results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy score: 0.9\n",
|
|
"F1 score: 0.8857142857142858\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# print some metrics results\n",
|
|
"from sklearn.metrics import accuracy_score\n",
|
|
"from sklearn.metrics import f1_score\n",
|
|
"acc_score = accuracy_score(iris_y_test, predicted_y_test)\n",
|
|
"print(\"Accuracy score: \"+ str(acc_score))\n",
|
|
"f1=f1_score(iris_y_test, predicted_y_test, average='macro')\n",
|
|
"print(\"F1 score: \"+str(f1))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Use Cross Validation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0.96666667 1. 0.86666667 0.86666667 1. ]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.datasets import load_iris\n",
|
|
"from sklearn.model_selection import cross_val_score # will be used to separate training and test\n",
|
|
"iris = load_iris()\n",
|
|
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:1,2:1})\n",
|
|
"clf = clf.fit(iris.data, iris.target)\n",
|
|
"scores = cross_val_score(clf, iris.data, iris.target, cv=5) # score will be the accuracy\n",
|
|
"print(scores)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0.96658312 1. 0.86111111 0.86666667 1. ]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# computes F1- score\n",
|
|
"f1_scores = cross_val_score(clf, iris.data, iris.target, cv=5, scoring='f1_macro')\n",
|
|
"print(f1_scores)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Show the resulting tree "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 1. Print the picture in a PDF file"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'my_iris_predictions.pdf'"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import graphviz \n",
|
|
"dot_data = tree.export_graphviz(clf, out_file=None) \n",
|
|
"graph = graphviz.Source(dot_data) \n",
|
|
"graph.render(\"my_iris_predictions\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2. Generate a picture here"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
|
|
"['setosa', 'versicolor', 'virginica']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(list(iris.feature_names))\n",
|
|
"print(list(iris.target_names))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/svg+xml": [
|
|
"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
|
|
"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
|
|
" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
|
|
"<!-- Generated by graphviz version 2.42.3 (20191010.1750)\n",
|
|
" -->\n",
|
|
"<!-- Title: Tree Pages: 1 -->\n",
|
|
"<svg width=\"639pt\" height=\"552pt\"\n",
|
|
" viewBox=\"0.00 0.00 639.00 552.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
|
|
"<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 548)\">\n",
|
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"<title>Tree</title>\n",
|
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"<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-548 635,-548 635,4 -4,4\"/>\n",
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"<!-- 0 -->\n",
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"<g id=\"node1\" class=\"node\">\n",
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"<title>0</title>\n",
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"<path fill=\"#ffffff\" stroke=\"black\" d=\"M353.5,-544C353.5,-544 217.5,-544 217.5,-544 211.5,-544 205.5,-538 205.5,-532 205.5,-532 205.5,-473 205.5,-473 205.5,-467 211.5,-461 217.5,-461 217.5,-461 353.5,-461 353.5,-461 359.5,-461 365.5,-467 365.5,-473 365.5,-473 365.5,-532 365.5,-532 365.5,-538 359.5,-544 353.5,-544\"/>\n",
|
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"<text text-anchor=\"start\" x=\"213.5\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 2.45</text>\n",
|
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"<text text-anchor=\"start\" x=\"238\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.585</text>\n",
|
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"<text text-anchor=\"start\" x=\"240.5\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 150</text>\n",
|
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"<text text-anchor=\"start\" x=\"227.5\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 50, 50]</text>\n",
|
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"<text text-anchor=\"start\" x=\"242\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n",
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"</g>\n",
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"<!-- 1 -->\n",
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"<g id=\"node2\" class=\"node\">\n",
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"<title>1</title>\n",
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"<text text-anchor=\"start\" x=\"168.5\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
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"<text text-anchor=\"start\" x=\"165\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n",
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"</g>\n",
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"<!-- 0->1 -->\n",
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"<g id=\"edge1\" class=\"edge\">\n",
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"<title>0->1</title>\n",
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"</g>\n",
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"<!-- 2 -->\n",
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"<g id=\"node3\" class=\"node\">\n",
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"<text text-anchor=\"start\" x=\"293\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.75</text>\n",
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"</g>\n",
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"<!-- 0->2 -->\n",
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"<g id=\"edge2\" class=\"edge\">\n",
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"<title>0->2</title>\n",
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"<path fill=\"none\" stroke=\"black\" d=\"M312.21,-460.91C318.01,-452.1 324.2,-442.7 330.18,-433.61\"/>\n",
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"<!-- 3 -->\n",
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"<g id=\"node4\" class=\"node\">\n",
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"<title>3</title>\n",
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"</g>\n",
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"<!-- 2->3 -->\n",
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"<g id=\"edge3\" class=\"edge\">\n",
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"<title>2->3</title>\n",
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"<!-- 8 -->\n",
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"<g id=\"node9\" class=\"node\">\n",
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"<title>8</title>\n",
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
|
"dot_data = tree.export_graphviz(clf, out_file=None, \n",
|
|
" feature_names=iris.feature_names, \n",
|
|
" class_names=iris.target_names, \n",
|
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" filled=True, rounded=True, \n",
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" special_characters=True) \n",
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"graph = graphviz.Source(dot_data) \n",
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"graph"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
|
"# 1. Artificial inflation"
|
|
]
|
|
},
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|
{
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|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
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|
"scrolled": true
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|
},
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|
"outputs": [],
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|
"source": [
|
|
"# Generate a random permutation of the indices of examples that will be later used \n",
|
|
"# for the training and the test set\n",
|
|
"import numpy as np\n",
|
|
"np.random.seed(42)\n",
|
|
"indices = np.random.permutation(len(iris.data))\n",
|
|
"\n",
|
|
"# We now decide to keep the last 10 indices for test set, the remaining for the training set\n",
|
|
"indices_training=indices[:-10]\n",
|
|
"indices_test=indices[-10:]\n",
|
|
"\n",
|
|
"iris_X_train = iris.data[indices_training] # keep for training all the matrix elements with the exception of the last 10 \n",
|
|
"iris_y_train = iris.target[indices_training]\n",
|
|
"iris_X_test = iris.data[indices_test] # keep the last 10 elements for test set\n",
|
|
"iris_y_test = iris.target[indices_test]"
|
|
]
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|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"samples_x = []\n",
|
|
"samples_y = []\n",
|
|
"for i in range(0, len(iris_y_train)):\n",
|
|
" if iris_y_train[i] == 1:\n",
|
|
" for _ in range(9):\n",
|
|
" samples_x.append(iris_X_train[i])\n",
|
|
" samples_y.append(1)\n",
|
|
" elif iris_y_train[i] == 2:\n",
|
|
" for _ in range(9):\n",
|
|
" samples_x.append(iris_X_train[i])\n",
|
|
" samples_y.append(2)\n",
|
|
"\n",
|
|
"#Samples inflation\n",
|
|
"iris_X_train = np.append(iris_X_train, samples_x, axis = 0)\n",
|
|
"iris_y_train = np.append(iris_y_train, samples_y, axis = 0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
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|
"metadata": {},
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"outputs": [
|
|
{
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|
"name": "stdout",
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|
"output_type": "stream",
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"text": [
|
|
"Accuracy: 0.9\n",
|
|
"F1: 0.9153439153439153\n"
|
|
]
|
|
}
|
|
],
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|
"source": [
|
|
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=10,class_weight={0:1,1:1,2:1})\n",
|
|
"clf = clf.fit(iris_X_train, iris_y_train)\n",
|
|
"predicted_y_test = clf.predict(iris_X_test)\n",
|
|
"acc_score = accuracy_score(iris_y_test, predicted_y_test)\n",
|
|
"f1 = f1_score(iris_y_test, predicted_y_test, average='macro')\n",
|
|
"print(\"Accuracy: \", acc_score)\n",
|
|
"print(\"F1: \", f1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
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|
"metadata": {},
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"outputs": [
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{
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"<path fill=\"#9153e8\" stroke=\"black\" d=\"M435,-544C435,-544 304,-544 304,-544 298,-544 292,-538 292,-532 292,-532 292,-473 292,-473 292,-467 298,-461 304,-461 304,-461 435,-461 435,-461 441,-461 447,-467 447,-473 447,-473 447,-532 447,-532 447,-538 441,-544 435,-544\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"300\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.75</text>\n",
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"<text text-anchor=\"start\" x=\"322\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.516</text>\n",
|
|
"<text text-anchor=\"start\" x=\"324.5\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 520</text>\n",
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|
"<text text-anchor=\"start\" x=\"311.5\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 60, 460]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"321\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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|
"</g>\n",
|
|
"<!-- 0->4 -->\n",
|
|
"<g id=\"edge4\" class=\"edge\">\n",
|
|
"<title>0->4</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M312.03,-579.91C318.79,-570.92 326.02,-561.32 332.98,-552.05\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"335.81,-554.12 339.02,-544.02 330.21,-549.91 335.81,-554.12\"/>\n",
|
|
"<text text-anchor=\"middle\" x=\"342.52\" y=\"-565.07\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
|
|
"</g>\n",
|
|
"<!-- 2 -->\n",
|
|
"<g id=\"node3\" class=\"node\">\n",
|
|
"<title>2</title>\n",
|
|
"<path fill=\"#e58139\" stroke=\"black\" d=\"M105,-417.5C105,-417.5 12,-417.5 12,-417.5 6,-417.5 0,-411.5 0,-405.5 0,-405.5 0,-361.5 0,-361.5 0,-355.5 6,-349.5 12,-349.5 12,-349.5 105,-349.5 105,-349.5 111,-349.5 117,-355.5 117,-361.5 117,-361.5 117,-405.5 117,-405.5 117,-411.5 111,-417.5 105,-417.5\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"18.5\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
|
|
"<text text-anchor=\"start\" x=\"17.5\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
|
|
"<text text-anchor=\"start\" x=\"8\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [48, 0, 0]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"15\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n",
|
|
"</g>\n",
|
|
"<!-- 1->2 -->\n",
|
|
"<g id=\"edge2\" class=\"edge\">\n",
|
|
"<title>1->2</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M146.66,-460.91C133.04,-449.1 118.17,-436.22 104.6,-424.45\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"106.62,-421.57 96.77,-417.67 102.03,-426.86 106.62,-421.57\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 3 -->\n",
|
|
"<g id=\"node4\" class=\"node\">\n",
|
|
"<title>3</title>\n",
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M248,-417.5C248,-417.5 147,-417.5 147,-417.5 141,-417.5 135,-411.5 135,-405.5 135,-405.5 135,-361.5 135,-361.5 135,-355.5 141,-349.5 147,-349.5 147,-349.5 248,-349.5 248,-349.5 254,-349.5 260,-355.5 260,-361.5 260,-361.5 260,-405.5 260,-405.5 260,-411.5 254,-417.5 248,-417.5\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"157.5\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
|
|
"<text text-anchor=\"start\" x=\"152.5\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 400</text>\n",
|
|
"<text text-anchor=\"start\" x=\"143\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 400, 0]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"145\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
|
|
"</g>\n",
|
|
"<!-- 1->3 -->\n",
|
|
"<g id=\"edge3\" class=\"edge\">\n",
|
|
"<title>1->3</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M194.89,-460.91C195.25,-450.2 195.65,-438.62 196.02,-427.78\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"199.52,-427.78 196.37,-417.67 192.53,-427.54 199.52,-427.78\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 5 -->\n",
|
|
"<g id=\"node6\" class=\"node\">\n",
|
|
"<title>5</title>\n",
|
|
"<path fill=\"#d7fae6\" stroke=\"black\" d=\"M433.5,-425C433.5,-425 297.5,-425 297.5,-425 291.5,-425 285.5,-419 285.5,-413 285.5,-413 285.5,-354 285.5,-354 285.5,-348 291.5,-342 297.5,-342 297.5,-342 433.5,-342 433.5,-342 439.5,-342 445.5,-348 445.5,-354 445.5,-354 445.5,-413 445.5,-413 445.5,-419 439.5,-425 433.5,-425\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"293.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 4.95</text>\n",
|
|
"<text text-anchor=\"start\" x=\"318\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.991</text>\n",
|
|
"<text text-anchor=\"start\" x=\"324.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 90</text>\n",
|
|
"<text text-anchor=\"start\" x=\"311\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 50, 40]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"313\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
|
|
"</g>\n",
|
|
"<!-- 4->5 -->\n",
|
|
"<g id=\"edge5\" class=\"edge\">\n",
|
|
"<title>4->5</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M368.11,-460.91C367.83,-452.56 367.52,-443.67 367.23,-435.02\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"370.73,-434.9 366.89,-425.02 363.73,-435.13 370.73,-434.9\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 12 -->\n",
|
|
"<g id=\"node13\" class=\"node\">\n",
|
|
"<title>12</title>\n",
|
|
"<path fill=\"#843ee6\" stroke=\"black\" d=\"M611.5,-425C611.5,-425 475.5,-425 475.5,-425 469.5,-425 463.5,-419 463.5,-413 463.5,-413 463.5,-354 463.5,-354 463.5,-348 469.5,-342 475.5,-342 475.5,-342 611.5,-342 611.5,-342 617.5,-342 623.5,-348 623.5,-354 623.5,-354 623.5,-413 623.5,-413 623.5,-419 617.5,-425 611.5,-425\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"471.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 4.85</text>\n",
|
|
"<text text-anchor=\"start\" x=\"496\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.159</text>\n",
|
|
"<text text-anchor=\"start\" x=\"498.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 430</text>\n",
|
|
"<text text-anchor=\"start\" x=\"485.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 420]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"495\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
|
|
"</g>\n",
|
|
"<!-- 4->12 -->\n",
|
|
"<g id=\"edge12\" class=\"edge\">\n",
|
|
"<title>4->12</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M429.87,-460.91C444.31,-451.2 459.83,-440.76 474.63,-430.81\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"476.89,-433.51 483.24,-425.02 472.99,-427.7 476.89,-433.51\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 6 -->\n",
|
|
"<g id=\"node7\" class=\"node\">\n",
|
|
"<title>6</title>\n",
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M261,-298.5C261,-298.5 164,-298.5 164,-298.5 158,-298.5 152,-292.5 152,-286.5 152,-286.5 152,-242.5 152,-242.5 152,-236.5 158,-230.5 164,-230.5 164,-230.5 261,-230.5 261,-230.5 267,-230.5 273,-236.5 273,-242.5 273,-242.5 273,-286.5 273,-286.5 273,-292.5 267,-298.5 261,-298.5\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"172.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
|
|
"<text text-anchor=\"start\" x=\"171.5\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\n",
|
|
"<text text-anchor=\"start\" x=\"162\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 30, 0]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"160\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
|
|
"</g>\n",
|
|
"<!-- 5->6 -->\n",
|
|
"<g id=\"edge6\" class=\"edge\">\n",
|
|
"<title>5->6</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M312.42,-341.91C296.69,-329.88 279.5,-316.73 263.88,-304.79\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"265.94,-301.96 255.87,-298.67 261.69,-307.52 265.94,-301.96\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 7 -->\n",
|
|
"<g id=\"node8\" class=\"node\">\n",
|
|
"<title>7</title>\n",
|
|
"<path fill=\"#c09cf2\" stroke=\"black\" d=\"M434,-306C434,-306 303,-306 303,-306 297,-306 291,-300 291,-294 291,-294 291,-235 291,-235 291,-229 297,-223 303,-223 303,-223 434,-223 434,-223 440,-223 446,-229 446,-235 446,-235 446,-294 446,-294 446,-300 440,-306 434,-306\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"299\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.55</text>\n",
|
|
"<text text-anchor=\"start\" x=\"321\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n",
|
|
"<text text-anchor=\"start\" x=\"327.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 60</text>\n",
|
|
"<text text-anchor=\"start\" x=\"314\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 20, 40]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"320\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
|
|
"</g>\n",
|
|
"<!-- 5->7 -->\n",
|
|
"<g id=\"edge7\" class=\"edge\">\n",
|
|
"<title>5->7</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M366.54,-341.91C366.75,-333.56 366.98,-324.67 367.2,-316.02\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"370.7,-316.11 367.46,-306.02 363.71,-315.93 370.7,-316.11\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 8 -->\n",
|
|
"<g id=\"node9\" class=\"node\">\n",
|
|
"<title>8</title>\n",
|
|
"<path fill=\"#8139e5\" stroke=\"black\" d=\"M262,-179.5C262,-179.5 169,-179.5 169,-179.5 163,-179.5 157,-173.5 157,-167.5 157,-167.5 157,-123.5 157,-123.5 157,-117.5 163,-111.5 169,-111.5 169,-111.5 262,-111.5 262,-111.5 268,-111.5 274,-117.5 274,-123.5 274,-123.5 274,-167.5 274,-167.5 274,-173.5 268,-179.5 262,-179.5\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"175.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
|
|
"<text text-anchor=\"start\" x=\"174.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\n",
|
|
"<text text-anchor=\"start\" x=\"165\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 30]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"167\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
|
|
"</g>\n",
|
|
"<!-- 7->8 -->\n",
|
|
"<g id=\"edge8\" class=\"edge\">\n",
|
|
"<title>7->8</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M315.42,-222.91C299.69,-210.88 282.5,-197.73 266.88,-185.79\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"268.94,-182.96 258.87,-179.67 264.69,-188.52 268.94,-182.96\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 9 -->\n",
|
|
"<g id=\"node10\" class=\"node\">\n",
|
|
"<title>9</title>\n",
|
|
"<path fill=\"#9cf2c0\" stroke=\"black\" d=\"M440.5,-187C440.5,-187 304.5,-187 304.5,-187 298.5,-187 292.5,-181 292.5,-175 292.5,-175 292.5,-116 292.5,-116 292.5,-110 298.5,-104 304.5,-104 304.5,-104 440.5,-104 440.5,-104 446.5,-104 452.5,-110 452.5,-116 452.5,-116 452.5,-175 452.5,-175 452.5,-181 446.5,-187 440.5,-187\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"300.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 5.45</text>\n",
|
|
"<text text-anchor=\"start\" x=\"325\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n",
|
|
"<text text-anchor=\"start\" x=\"331.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\n",
|
|
"<text text-anchor=\"start\" x=\"318\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 20, 10]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"320\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
|
|
"</g>\n",
|
|
"<!-- 7->9 -->\n",
|
|
"<g id=\"edge9\" class=\"edge\">\n",
|
|
"<title>7->9</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M369.89,-222.91C370.17,-214.56 370.48,-205.67 370.77,-197.02\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"374.27,-197.13 371.11,-187.02 367.27,-196.9 374.27,-197.13\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 10 -->\n",
|
|
"<g id=\"node11\" class=\"node\">\n",
|
|
"<title>10</title>\n",
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M353,-68C353,-68 256,-68 256,-68 250,-68 244,-62 244,-56 244,-56 244,-12 244,-12 244,-6 250,0 256,0 256,0 353,0 353,0 359,0 365,-6 365,-12 365,-12 365,-56 365,-56 365,-62 359,-68 353,-68\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"264.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
|
|
"<text text-anchor=\"start\" x=\"263.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\n",
|
|
"<text text-anchor=\"start\" x=\"254\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 20, 0]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"252\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
|
|
"</g>\n",
|
|
"<!-- 9->10 -->\n",
|
|
"<g id=\"edge10\" class=\"edge\">\n",
|
|
"<title>9->10</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M347.18,-103.73C341.74,-94.97 335.99,-85.7 330.52,-76.91\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"333.43,-74.95 325.18,-68.3 327.48,-78.64 333.43,-74.95\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 11 -->\n",
|
|
"<g id=\"node12\" class=\"node\">\n",
|
|
"<title>11</title>\n",
|
|
"<path fill=\"#8139e5\" stroke=\"black\" d=\"M488,-68C488,-68 395,-68 395,-68 389,-68 383,-62 383,-56 383,-56 383,-12 383,-12 383,-6 389,0 395,0 395,0 488,0 488,0 494,0 500,-6 500,-12 500,-12 500,-56 500,-56 500,-62 494,-68 488,-68\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"401.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
|
|
"<text text-anchor=\"start\" x=\"400.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\n",
|
|
"<text text-anchor=\"start\" x=\"391\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 10]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"393\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
|
|
"</g>\n",
|
|
"<!-- 9->11 -->\n",
|
|
"<g id=\"edge11\" class=\"edge\">\n",
|
|
"<title>9->11</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M398.19,-103.73C403.71,-94.97 409.55,-85.7 415.09,-76.91\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"418.15,-78.63 420.52,-68.3 412.22,-74.89 418.15,-78.63\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 13 -->\n",
|
|
"<g id=\"node14\" class=\"node\">\n",
|
|
"<title>13</title>\n",
|
|
"<path fill=\"#c09cf2\" stroke=\"black\" d=\"M604.5,-306C604.5,-306 478.5,-306 478.5,-306 472.5,-306 466.5,-300 466.5,-294 466.5,-294 466.5,-235 466.5,-235 466.5,-229 472.5,-223 478.5,-223 478.5,-223 604.5,-223 604.5,-223 610.5,-223 616.5,-229 616.5,-235 616.5,-235 616.5,-294 616.5,-294 616.5,-300 610.5,-306 604.5,-306\"/>\n",
|
|
"<text text-anchor=\"start\" x=\"474.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">sepal width (cm) ≤ 3.1</text>\n",
|
|
"<text text-anchor=\"start\" x=\"494\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n",
|
|
"<text text-anchor=\"start\" x=\"500.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\n",
|
|
"<text text-anchor=\"start\" x=\"487\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 20]</text>\n",
|
|
"<text text-anchor=\"start\" x=\"493\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
|
|
"</g>\n",
|
|
"<!-- 12->13 -->\n",
|
|
"<g id=\"edge13\" class=\"edge\">\n",
|
|
"<title>12->13</title>\n",
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M542.81,-341.91C542.66,-333.56 542.51,-324.67 542.36,-316.02\"/>\n",
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"545.86,-315.96 542.19,-306.02 538.86,-316.08 545.86,-315.96\"/>\n",
|
|
"</g>\n",
|
|
"<!-- 16 -->\n",
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}
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],
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"source": [
|
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"dot_data = tree.export_graphviz(clf, out_file=None, \n",
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" feature_names=iris.feature_names, \n",
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" class_names=iris.target_names, \n",
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" filled=True, rounded=True, \n",
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" special_characters=True) \n",
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"graph = graphviz.Source(dot_data) \n",
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"graph"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"# 2. Class weights"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
|
|
"# Generate a random permutation of the indices of examples that will be later used \n",
|
|
"# for the training and the test set\n",
|
|
"import numpy as np\n",
|
|
"np.random.seed(1231)\n",
|
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"indices = np.random.permutation(len(iris.data))\n",
|
|
"\n",
|
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"# We now decide to keep the last 10 indices for test set, the remaining for the training set\n",
|
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"indices_training=indices[:-10]\n",
|
|
"indices_test=indices[-10:]\n",
|
|
"\n",
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"iris_X_train = iris.data[indices_training] # keep for training all the matrix elements with the exception of the last 10 \n",
|
|
"iris_y_train = iris.target[indices_training]\n",
|
|
"iris_X_test = iris.data[indices_test] # keep the last 10 elements for test set\n",
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"iris_y_test = iris.target[indices_test]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.8\n",
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"F1: 0.5\n"
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]
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}
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],
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"source": [
|
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"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:10,2:10})\n",
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"clf = clf.fit(iris_X_train, iris_y_train)\n",
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"predicted_y_test = clf.predict(iris_X_test)\n",
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"acc_score = accuracy_score(iris_y_test, predicted_y_test)\n",
|
|
"f1 = f1_score(iris_y_test, predicted_y_test, average='macro')\n",
|
|
"print(\"Accuracy: \", acc_score)\n",
|
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"print(\"F1: \", f1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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}
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],
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"source": [
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"dot_data = tree.export_graphviz(clf, out_file=None, \n",
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" feature_names=iris.feature_names, \n",
|
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" class_names=iris.target_names, \n",
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" filled=True, rounded=True, \n",
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" special_characters=True) \n",
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"graph = graphviz.Source(dot_data) \n",
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"graph"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"# 3. Avoid overfitting"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# Generate a random permutation of the indices of examples that will be later used \n",
|
|
"# for the training and the test set\n",
|
|
"import numpy as np\n",
|
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"np.random.seed(42)\n",
|
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"indices = np.random.permutation(len(iris.data))\n",
|
|
"\n",
|
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"# We now decide to keep the last 10 indices for test set, the remaining for the training set\n",
|
|
"indices_training=indices[:-10]\n",
|
|
"indices_test=indices[-10:]\n",
|
|
"\n",
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"iris_X_train = iris.data[indices_training] # keep for training all the matrix elements with the exception of the last 10 \n",
|
|
"iris_y_train = iris.target[indices_training]\n",
|
|
"iris_X_test = iris.data[indices_test] # keep the last 10 elements for test set\n",
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"iris_y_test = iris.target[indices_test]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.9\n",
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"F1: 0.9153439153439153\n"
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]
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}
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],
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"source": [
|
|
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=3,class_weight={0:1,1:10,2:10}, min_impurity_decrease = 0.005, max_depth = 4, max_leaf_nodes = 6)\n",
|
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"clf = clf.fit(iris_X_train, iris_y_train)\n",
|
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"predicted_y_test = clf.predict(iris_X_test)\n",
|
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"acc_score = accuracy_score(iris_y_test, predicted_y_test)\n",
|
|
"f1 = f1_score(iris_y_test, predicted_y_test, average='macro')\n",
|
|
"print(\"Accuracy: \", acc_score)\n",
|
|
"print(\"F1: \", f1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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"</g>\n",
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"<!-- 4->7 -->\n",
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"<g id=\"edge4\" class=\"edge\">\n",
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"<title>4->7</title>\n",
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"<path fill=\"none\" stroke=\"black\" d=\"M162.23,-103.73C150.54,-94.24 138.1,-84.16 126.46,-74.72\"/>\n",
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"</g>\n",
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"<!-- 8 -->\n",
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"<g id=\"node6\" class=\"node\">\n",
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"<title>8</title>\n",
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"<path fill=\"#c09cf2\" stroke=\"black\" d=\"M271,-68C271,-68 170,-68 170,-68 164,-68 158,-62 158,-56 158,-56 158,-12 158,-12 158,-6 164,0 170,0 170,0 271,0 271,0 277,0 283,-6 283,-12 283,-12 283,-56 283,-56 283,-62 277,-68 271,-68\"/>\n",
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"<text text-anchor=\"start\" x=\"173\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n",
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"<text text-anchor=\"start\" x=\"166\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 20]</text>\n",
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"<text text-anchor=\"start\" x=\"172\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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"</g>\n",
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"<!-- 4->8 -->\n",
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"<g id=\"edge5\" class=\"edge\">\n",
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"<title>4->8</title>\n",
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"<path fill=\"none\" stroke=\"black\" d=\"M215.48,-103.73C216.09,-95.43 216.73,-86.67 217.34,-78.28\"/>\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"220.83,-78.53 218.07,-68.3 213.85,-78.02 220.83,-78.53\"/>\n",
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"</g>\n",
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"<!-- 5 -->\n",
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"<g id=\"node8\" class=\"node\">\n",
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"<title>5</title>\n",
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"<path fill=\"#e0cef8\" stroke=\"black\" d=\"M456.5,-187C456.5,-187 320.5,-187 320.5,-187 314.5,-187 308.5,-181 308.5,-175 308.5,-175 308.5,-116 308.5,-116 308.5,-110 314.5,-104 320.5,-104 320.5,-104 456.5,-104 456.5,-104 462.5,-104 468.5,-110 468.5,-116 468.5,-116 468.5,-175 468.5,-175 468.5,-181 462.5,-187 456.5,-187\"/>\n",
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"<text text-anchor=\"start\" x=\"316.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 5.05</text>\n",
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"<text text-anchor=\"start\" x=\"341\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.985</text>\n",
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"<text text-anchor=\"start\" x=\"334\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 30, 40]</text>\n",
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"</g>\n",
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"<!-- 2->5 -->\n",
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"<g id=\"edge7\" class=\"edge\">\n",
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"<title>2->5</title>\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"392,-197.02 388.5,-187.02 385,-197.02 392,-197.02\"/>\n",
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"</g>\n",
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"<!-- 6 -->\n",
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"<g id=\"node11\" class=\"node\">\n",
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"<title>6</title>\n",
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"<path fill=\"#8139e5\" stroke=\"black\" d=\"M600,-179.5C600,-179.5 499,-179.5 499,-179.5 493,-179.5 487,-173.5 487,-167.5 487,-167.5 487,-123.5 487,-123.5 487,-117.5 493,-111.5 499,-111.5 499,-111.5 600,-111.5 600,-111.5 606,-111.5 612,-117.5 612,-123.5 612,-123.5 612,-167.5 612,-167.5 612,-173.5 606,-179.5 600,-179.5\"/>\n",
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"<text text-anchor=\"start\" x=\"509.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
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"<text text-anchor=\"start\" x=\"508.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 43</text>\n",
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"<text text-anchor=\"start\" x=\"495\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 430]</text>\n",
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"</g>\n",
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"<!-- 2->6 -->\n",
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"<g id=\"edge10\" class=\"edge\">\n",
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"<title>2->6</title>\n",
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"<path fill=\"none\" stroke=\"black\" d=\"M444.36,-222.91C460.91,-210.88 479,-197.73 495.43,-185.79\"/>\n",
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"</g>\n",
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"<!-- 9 -->\n",
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"<g id=\"node9\" class=\"node\">\n",
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"<title>9</title>\n",
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"<path fill=\"#9cf2c0\" stroke=\"black\" d=\"M430,-68C430,-68 329,-68 329,-68 323,-68 317,-62 317,-56 317,-56 317,-12 317,-12 317,-6 323,0 329,0 329,0 430,0 430,0 436,0 442,-6 442,-12 442,-12 442,-56 442,-56 442,-62 436,-68 430,-68\"/>\n",
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"<text text-anchor=\"start\" x=\"332\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n",
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"</g>\n",
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"<!-- 5->9 -->\n",
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"<g id=\"edge8\" class=\"edge\">\n",
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"<title>5->9</title>\n",
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"<!-- 10 -->\n",
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"<g id=\"node10\" class=\"node\">\n",
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"<title>10</title>\n",
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"<path fill=\"#ab7bee\" stroke=\"black\" d=\"M573,-68C573,-68 472,-68 472,-68 466,-68 460,-62 460,-56 460,-56 460,-12 460,-12 460,-6 466,0 472,0 472,0 573,0 573,0 579,0 585,-6 585,-12 585,-12 585,-56 585,-56 585,-62 579,-68 573,-68\"/>\n",
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"</g>\n",
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"<!-- 5->10 -->\n",
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"</g>\n",
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"</g>\n",
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"</svg>\n"
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],
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"text/plain": [
|
|
"<graphviz.files.Source at 0x7f360648aad0>"
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|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
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}
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],
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|
"source": [
|
|
"dot_data = tree.export_graphviz(clf, out_file=None, \n",
|
|
" feature_names=iris.feature_names, \n",
|
|
" class_names=iris.target_names, \n",
|
|
" filled=True, rounded=True, \n",
|
|
" special_characters=True) \n",
|
|
"graph = graphviz.Source(dot_data) \n",
|
|
"graph"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 4. Confusion Matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
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|
"text/plain": [
|
|
"array([[2, 0, 0],\n",
|
|
" [0, 4, 0],\n",
|
|
" [0, 1, 3]])"
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# initializes the confusion matrix\n",
|
|
"confusion = np.zeros([3, 3], dtype = int)\n",
|
|
"\n",
|
|
"# print the corresponding instances indexes and class names\n",
|
|
"for i in range(len(iris_y_test)): \n",
|
|
" #increments the indexed cell value\n",
|
|
" confusion[iris_y_test[i], predicted_y_test[i]]+=1\n",
|
|
"confusion"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 5. ROC Curves"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[(0.0, 48.0), (30.0, 0.0), (30.0, 0.0), (60.0, 0.0), (400.0, 0.0), (400.0, 0.0)], [(0.0, 400.0), (0.0, 30.0), (20.0, 10.0), (40.0, 20.0), (48.0, 0.0), (400.0, 0.0)], [(0.0, 400.0), (10.0, 20.0), (20.0, 40.0), (30.0, 0.0), (48.0, 0.0), (400.0, 0.0)]]\n"
|
|
]
|
|
},
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|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[[[0, 0.0, 30.0, 60.0, 120.0, 520.0, 920.0],\n",
|
|
" [0, 48.0, 48.0, 48.0, 48.0, 48.0, 48.0]],\n",
|
|
" [[0, 0.0, 0.0, 20.0, 60.0, 108.0, 508.0],\n",
|
|
" [0, 400.0, 430.0, 440.0, 460.0, 460.0, 460.0]],\n",
|
|
" [[0, 0.0, 10.0, 30.0, 60.0, 108.0, 508.0],\n",
|
|
" [0, 400.0, 420.0, 460.0, 460.0, 460.0, 460.0]]]"
|
|
]
|
|
},
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Calculates the ROC curves (x, y)\n",
|
|
"leafs = []\n",
|
|
"class_pairs = [[],[],[]]\n",
|
|
"roc_curves = [[[0], [0]], [[0], [0]], [[0], [0]]]\n",
|
|
"for i in range(clf.tree_.node_count):\n",
|
|
" if (clf.tree_.feature[i] == -2):\n",
|
|
" leafs.append(i)\n",
|
|
"\n",
|
|
"# c = class index\n",
|
|
"for leaf in leafs:\n",
|
|
" for c in range(3):\n",
|
|
" #pairs(neg, pos)\n",
|
|
" class_pairs[c].append((clf.tree_.value[leaf][0].sum() - clf.tree_.value[leaf][0][c], clf.tree_.value[leaf][0][c]))\n",
|
|
"\n",
|
|
"#pairs sorting\n",
|
|
"for c in range(3):\n",
|
|
" class_pairs[c] = sorted(class_pairs[c], key=lambda t: t[0]/max(1,t[1]))\n",
|
|
"print(class_pairs)\n",
|
|
"\n",
|
|
"for i in range(1, len(leafs) + 1):\n",
|
|
" for c in range(3):\n",
|
|
" roc_curves[c][0].append(class_pairs[c][i - 1][0] + roc_curves[c][0][i - 1])\n",
|
|
" roc_curves[c][1].append(class_pairs[c][i - 1][1] + roc_curves[c][1][i - 1])\n",
|
|
"\n",
|
|
"roc_curves"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPPUlEQVR4nO3dfYydZZnH8e9ly4u7ulTaWYJt42CsURIRSWWrmMhCVGDV8gcYjUKjTeofGEEwLrCJK3E1EhKLxA1KFqSsRl58g5AmyraY9SWWHaQi0FXGF6QN2BELrK9QvPaPc5ccastMZ86Zh7nm+0lOzvPc933Oc93T01+f3uc5cyIzkSTV8ryuC5AkDZ7hLkkFGe6SVJDhLkkFGe6SVNDCrgsAWLJkSY6OjnZdhiTNKXfeeedvMnNkX33PiXAfHR1lbGys6zIkaU6JiAf21+eyjCQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQV9Jy4zr1z27fDNdfA7t1dVyJpvnnb2+C1rx340xruTzzR++Fu3QoRXVcjab558YsN96H42Md6wf6Nb8Dq1V1XI0kDMb/X3L/7Xbj0Uli71mCXVMr8DffHH4ezzoLRUVi/vutqJGmg5u+yzHnnwa9+Bd/5DrzwhV1XI0kDNT/P3L/+dfjCF+Cii+D1r++6GkkauPkX7g8/DOvWwXHHwUc/2nU1kjQU8yvcM3tvnv7ud/DFL8LBB3ddkSQNxfxac//852HjRrjiCnjlK7uuRpKGZv6cuf/0p3DBBfDmN8M553RdjSQN1fwI9yef7F32eMghvV8z8Lz5MW1J89f8WJb55Cfhjjvgxhth6dKuq5Gkoat/CrtlC3z84/Ce98CZZ3ZdjSTNitrh/vvf95Zjli6Fz36262okadbUXpb58IdhfBw2b4bDDuu6GkmaNXXP3DduhM99Ds4/H048setqJGlW1Qz3iQl43/vgVa+CT3yi62okadbVW5bJhPe/H3btgm99q3f5oyTNM/XC/dpre78Y7LLL4Jhjuq5GkjpRa1nmF7+AD34Q3vhG+NCHuq5GkjpTJ9yfegrOPrv36dMNG2DBgq4rkqTO1FmWueyy3tfmXXcdvOQlXVcjSZ2qceZ+1129381+5pm9T6JK0jw35XCPiAURcVdE3Nr2j4qILRExHhE3RMTBrf2Qtj/e+keHU3rzxz/2An3JErjySogY6uEkaS44kDP3c4FtffuXAusz82XALmBta18L7Grt69u44bn4Yrjvvt7X5i1ePNRDSdJcMaVwj4hlwD8B/9H2AzgJ+EobsgE4vW2vbvu0/pPb+MH73vfg8svhAx+At7xlKIeQpLloqmfulwMfAf7S9hcDj2bm7ra/Hdjzu3SXAg8CtP7H2vhniIh1ETEWEWMTExPTq/773+/dX3LJ9B4vSUVNGu4R8VZgZ2beOcgDZ+ZVmbkyM1eOjIzM7Mn8FKokPcNULoU8AXh7RJwGHAr8HfAZYFFELGxn58uAHW38DmA5sD0iFgKHAY8MvHJJ0n5NeuaemRdl5rLMHAXeCWzOzHcDtwNntGFrgJvb9i1tn9a/OTNzoFVLkp7VTK5z/2fg/IgYp7emfnVrvxpY3NrPBy6cWYmSpAN1QJ9QzcxvA99u2z8Hjt/HmD8Bfp+dJHWoxidUJUnPYLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGThntEHBoRd0TEjyLi3oi4pLUfFRFbImI8Im6IiINb+yFtf7z1jw53CpKkvU3lzP3PwEmZ+WrgWOCUiFgFXAqsz8yXAbuAtW38WmBXa1/fxkmSZtGk4Z49v2u7B7VbAicBX2ntG4DT2/bqtk/rPzkiYmAVS5ImNaU194hYEBFbgZ3AbcDPgEczc3cbsh1Y2raXAg8CtP7HgMX7eM51ETEWEWMTExMzm4Uk6RmmFO6Z+VRmHgssA44HXjHTA2fmVZm5MjNXjoyMzPTpJEl9Duhqmcx8FLgdeB2wKCIWtq5lwI62vQNYDtD6DwMeGUi1kqQpmcrVMiMRsahtPx94E7CNXsif0YatAW5u27e0fVr/5szMQRYtSXp2CycfwpHAhohYQO8fgxsz89aIuA+4PiL+DbgLuLqNvxr4z4gYB34LvHMIdUuSnsWk4Z6ZdwOv2Uf7z+mtv+/d/ifgzIFUJ0maFj+hKkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVNCk4R4RyyPi9oi4LyLujYhzW/vhEXFbRNzf7l/U2iMiroiI8Yi4OyKOG/YkJEnPNJUz993ABZl5NLAKOCcijgYuBDZl5gpgU9sHOBVY0W7rgCsHXrUk6VlNGu6Z+VBm/rBt/x+wDVgKrAY2tGEbgNPb9mrguuz5AbAoIo4ceOWSpP06oDX3iBgFXgNsAY7IzIda18PAEW17KfBg38O2t7a9n2tdRIxFxNjExMQBli1JejZTDveIeAHwVeC8zHy8vy8zE8gDOXBmXpWZKzNz5cjIyIE8VJI0iSmFe0QcRC/Yv5SZX2vNv96z3NLud7b2HcDyvocva22SpFkylatlArga2JaZn+7rugVY07bXADf3tZ/drppZBTzWt3wjSZoFC6cw5gTgLODHEbG1tV0MfAq4MSLWAg8A72h9G4HTgHHgD8B7B1qxJGlSk4Z7Zn4XiP10n7yP8QmcM8O6JEkz4CdUJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCjLcJakgw12SCpo03CPimojYGRH39LUdHhG3RcT97f5FrT0i4oqIGI+IuyPiuGEWL0nat6mcuV8LnLJX24XApsxcAWxq+wCnAivabR1w5WDKlCQdiEnDPTP/G/jtXs2rgQ1tewNwel/7ddnzA2BRRBw5qGIlSVMz3TX3IzLzobb9MHBE214KPNg3bntr+ysRsS4ixiJibGJiYpplSJL2ZcZvqGZmAjmNx12VmSszc+XIyMhMy5Ak9ZluuP96z3JLu9/Z2ncAy/vGLWttkqRZNN1wvwVY07bXADf3tZ/drppZBTzWt3wjSZolCycbEBFfBk4ElkTEduBfgU8BN0bEWuAB4B1t+EbgNGAc+APw3iHULEmaxKThnpnv2k/XyfsYm8A5My1KkjQzfkJVkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpoKGEe0ScEhE/iYjxiLhwGMeQJO3fwMM9IhYA/w6cChwNvCsijh70cSRJ+zeMM/fjgfHM/HlmPgFcD6wewnEkSfsxjHBfCjzYt7+9tT1DRKyLiLGIGJuYmJjekV7+cjjjDFiwYHqPl6SiOntDNTOvysyVmblyZGRkek+yejXcdBMceuhgi5OkOW4Y4b4DWN63v6y1SZJmyTDC/X+AFRFxVEQcDLwTuGUIx5Ek7cfCQT9hZu6OiA8A3wQWANdk5r2DPo4kaf8GHu4AmbkR2DiM55YkTc5PqEpSQYa7JBVkuEtSQYa7JBUUmdl1DUTEBPDANB++BPjNAMt5rnO+tTnf2gY935dk5j4/BfqcCPeZiIixzFzZdR2zxfnW5nxrm835uiwjSQUZ7pJUUIVwv6rrAmaZ863N+dY2a/Od82vukqS/VuHMXZK0F8Ndkgqa0+Fe8Yu4I+KaiNgZEff0tR0eEbdFxP3t/kWtPSLiijb/uyPiuO4qP3ARsTwibo+I+yLi3og4t7VXne+hEXFHRPyozfeS1n5URGxp87qh/apsIuKQtj/e+ke7rH+6ImJBRNwVEbe2/bLzjYhfRsSPI2JrRIy1tk5ez3M23At/Efe1wCl7tV0IbMrMFcCmtg+9ua9ot3XAlbNU46DsBi7IzKOBVcA57c+w6nz/DJyUma8GjgVOiYhVwKXA+sx8GbALWNvGrwV2tfb1bdxcdC6wrW+/+nz/MTOP7buevZvXc2bOyRvwOuCbffsXARd1XdeA5jYK3NO3/xPgyLZ9JPCTtv154F37GjcXb8DNwJvmw3yBvwF+CPwDvU8sLmztT7+u6X0nwuva9sI2Lrqu/QDnuYxeoJ0E3ApE8fn+EliyV1snr+c5e+bOFL+Iu4gjMvOhtv0wcETbLvMzaP8Ffw2whcLzbUsUW4GdwG3Az4BHM3N3G9I/p6fn2/ofAxbPbsUzdjnwEeAvbX8xteebwLci4s6IWNfaOnk9D+XLOjQ8mZkRUer61Yh4AfBV4LzMfDwinu6rNt/MfAo4NiIWAV8HXtFxSUMTEW8FdmbmnRFxYtf1zJI3ZOaOiPh74LaI+N/+ztl8Pc/lM/f59EXcv46IIwHa/c7WPud/BhFxEL1g/1Jmfq01l53vHpn5KHA7vWWJRRGx50Srf05Pz7f1HwY8MsulzsQJwNsj4pfA9fSWZj5D3fmSmTva/U56/3gfT0ev57kc7vPpi7hvAda07TX01qb3tJ/d3nVfBTzW99+/57zonaJfDWzLzE/3dVWd70g7Yycink/v/YVt9EL+jDZs7/nu+TmcAWzOtjg7F2TmRZm5LDNH6f393JyZ76bofCPibyPihXu2gTcD99DV67nrNyBm+ObFacBP6a1b/kvX9QxoTl8GHgKepLcGt5beuuMm4H7gv4DD29igd8XQz4AfAyu7rv8A5/oGemuUdwNb2+20wvM9Brirzfce4KOt/aXAHcA4cBNwSGs/tO2Pt/6Xdj2HGcz9RODWyvNt8/pRu927J5O6ej376wckqaC5vCwjSdoPw12SCjLcJakgw12SCjLcJakgw12SCjLcJamg/weUVTeZzfbL2wAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"# Not ordered\n",
|
|
"for c in range(3):\n",
|
|
" plt.plot(roc_curves[c][0], roc_curves[c][1], color = \"red\")\n",
|
|
" plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"anaconda-cloud": {},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|