1811 lines
126 KiB
Text
1811 lines
126 KiB
Text
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"cells": [
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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": 3,
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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": 4,
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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": 5,
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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": 6,
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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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"[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": 7,
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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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"[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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"<!-- 8->9 -->\r\n",
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"<g id=\"edge9\" class=\"edge\"><title>8->9</title>\r\n",
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"<path fill=\"none\" stroke=\"black\" d=\"M353.264,-88.9485C348.206,-80.2579 342.736,-70.8608 337.633,-62.0917\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"340.534,-60.1189 332.479,-53.2367 334.484,-63.6401 340.534,-60.1189\"/>\r\n",
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"</g>\r\n",
|
||
|
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"<!-- 10 -->\r\n",
|
||
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"<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
|
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"<polygon fill=\"none\" stroke=\"black\" points=\"472,-53 381,-53 381,-0 472,-0 472,-53\"/>\r\n",
|
||
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"<text text-anchor=\"middle\" x=\"426.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
|
||
|
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"<text text-anchor=\"middle\" x=\"426.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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||
|
|
"<text text-anchor=\"middle\" x=\"426.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 0]</text>\r\n",
|
||
|
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"</g>\r\n",
|
||
|
|
"<!-- 8->10 -->\r\n",
|
||
|
|
"<g id=\"edge10\" class=\"edge\"><title>8->10</title>\r\n",
|
||
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|
"<path fill=\"none\" stroke=\"black\" d=\"M391.387,-88.9485C396.353,-80.2579 401.722,-70.8608 406.733,-62.0917\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"409.871,-63.6557 411.793,-53.2367 403.793,-60.1826 409.871,-63.6557\"/>\r\n",
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"</g>\r\n",
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|
"</g>\r\n",
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|
|
"</svg>\r\n"
|
||
|
|
],
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||
|
|
"text/plain": [
|
||
|
|
"<graphviz.files.Source at 0x1b867311fc8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import os\n",
|
||
|
|
"os.environ[\"PATH\"] += os.pathsep + 'C:/Users/galat/.conda/envs/aaut/Library/bin/graphviz'\n",
|
||
|
|
"import graphviz\n",
|
||
|
|
"dot_data = tree.export_graphviz(clf, out_file=None) \n",
|
||
|
|
"graph = graphviz.Source(dot_data) \n",
|
||
|
|
"graph"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"In the following we start using a dataset (from UCI Machine Learning repository)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.datasets import load_iris\n",
|
||
|
|
"iris = load_iris()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Declare the type of prediction model and the working criteria for the model induction algorithm"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:1,2:1})"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Split the dataset in training and test set"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"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",
|
||
|
|
"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": 12,
|
||
|
|
"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": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Predictions:\n",
|
||
|
|
"[0 0 0 1 0 0 1 2 0 0]\n",
|
||
|
|
"True classes:\n",
|
||
|
|
"[0 0 0 2 0 0 1 1 0 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": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Instance # 33: \n",
|
||
|
|
"sepal length (cm)=5.5, sepal width (cm)=4.2, petal length (cm)=1.4, petal width (cm)=0.2\n",
|
||
|
|
"Predicted: setosa\t True: setosa\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 2: \n",
|
||
|
|
"sepal length (cm)=4.7, sepal width (cm)=3.2, petal length (cm)=1.3, petal width (cm)=0.2\n",
|
||
|
|
"Predicted: setosa\t True: setosa\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 11: \n",
|
||
|
|
"sepal length (cm)=4.8, sepal width (cm)=3.4, petal length (cm)=1.6, petal width (cm)=0.2\n",
|
||
|
|
"Predicted: setosa\t True: setosa\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 126: \n",
|
||
|
|
"sepal length (cm)=6.2, sepal width (cm)=2.8, petal length (cm)=4.8, petal width (cm)=1.8\n",
|
||
|
|
"Predicted: versicolor\t True: virginica\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 49: \n",
|
||
|
|
"sepal length (cm)=5.0, sepal width (cm)=3.3, petal length (cm)=1.4, petal width (cm)=0.2\n",
|
||
|
|
"Predicted: setosa\t True: setosa\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 10: \n",
|
||
|
|
"sepal length (cm)=5.4, sepal width (cm)=3.7, petal length (cm)=1.5, petal width (cm)=0.2\n",
|
||
|
|
"Predicted: setosa\t True: setosa\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 85: \n",
|
||
|
|
"sepal length (cm)=6.0, sepal width (cm)=3.4, petal length (cm)=4.5, petal width (cm)=1.6\n",
|
||
|
|
"Predicted: versicolor\t True: versicolor\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 52: \n",
|
||
|
|
"sepal length (cm)=6.9, sepal width (cm)=3.1, petal length (cm)=4.9, petal width (cm)=1.5\n",
|
||
|
|
"Predicted: virginica\t True: versicolor\n",
|
||
|
|
"\n",
|
||
|
|
"Instance # 5: \n",
|
||
|
|
"sepal length (cm)=5.4, sepal width (cm)=3.9, petal length (cm)=1.7, petal width (cm)=0.4\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"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Accuracy score: 0.8\n",
|
||
|
|
"F1 score: 0.5\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": 16,
|
||
|
|
"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": 17,
|
||
|
|
"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": 18,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"'my_iris_predictions.pdf'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"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": 19,
|
||
|
|
"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": 20,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/svg+xml": [
|
||
|
|
"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
|
||
|
|
"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
|
||
|
|
" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
|
||
|
|
"<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n",
|
||
|
|
" -->\r\n",
|
||
|
|
"<!-- Title: Tree Pages: 1 -->\r\n",
|
||
|
|
"<svg width=\"660pt\" height=\"552pt\"\r\n",
|
||
|
|
" viewBox=\"0.00 0.00 660.00 552.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
|
||
|
|
"<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 548)\">\r\n",
|
||
|
|
"<title>Tree</title>\r\n",
|
||
|
|
"<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-548 656,-548 656,4 -4,4\"/>\r\n",
|
||
|
|
"<!-- 0 -->\r\n",
|
||
|
|
"<g id=\"node1\" class=\"node\"><title>0</title>\r\n",
|
||
|
|
"<path fill=\"#ffffff\" stroke=\"black\" d=\"M366,-544C366,-544 225,-544 225,-544 219,-544 213,-538 213,-532 213,-532 213,-473 213,-473 213,-467 219,-461 225,-461 225,-461 366,-461 366,-461 372,-461 378,-467 378,-473 378,-473 378,-532 378,-532 378,-538 372,-544 366,-544\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"221\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 2.45</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"245.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.585</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"248\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 150</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"235\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 50, 50]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"249.5\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 1 -->\r\n",
|
||
|
|
"<g id=\"node2\" class=\"node\"><title>1</title>\r\n",
|
||
|
|
"<path fill=\"#e58139\" stroke=\"black\" d=\"M265,-417.5C265,-417.5 168,-417.5 168,-417.5 162,-417.5 156,-411.5 156,-405.5 156,-405.5 156,-361.5 156,-361.5 156,-355.5 162,-349.5 168,-349.5 168,-349.5 265,-349.5 265,-349.5 271,-349.5 277,-355.5 277,-361.5 277,-361.5 277,-405.5 277,-405.5 277,-411.5 271,-417.5 265,-417.5\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"174.5\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"173\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"164\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 0, 0]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"170.5\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 0->1 -->\r\n",
|
||
|
|
"<g id=\"edge1\" class=\"edge\"><title>0->1</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M268.091,-460.907C260.492,-449.652 252.231,-437.418 244.593,-426.106\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"247.391,-423.996 238.895,-417.667 241.59,-427.913 247.391,-423.996\"/>\r\n",
|
||
|
|
"<text text-anchor=\"middle\" x=\"234.136\" y=\"-438.51\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 2 -->\r\n",
|
||
|
|
"<g id=\"node3\" class=\"node\"><title>2</title>\r\n",
|
||
|
|
"<path fill=\"#ffffff\" stroke=\"black\" d=\"M442,-425C442,-425 307,-425 307,-425 301,-425 295,-419 295,-413 295,-413 295,-354 295,-354 295,-348 301,-342 307,-342 307,-342 442,-342 442,-342 448,-342 454,-348 454,-354 454,-354 454,-413 454,-413 454,-419 448,-425 442,-425\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"303\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.75</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"332.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.0</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"327\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 100</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"318\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 50, 50]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"319\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 0->2 -->\r\n",
|
||
|
|
"<g id=\"edge2\" class=\"edge\"><title>0->2</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M322.909,-460.907C328.914,-452.014 335.331,-442.509 341.529,-433.331\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"344.444,-435.267 347.14,-425.021 338.643,-431.35 344.444,-435.267\"/>\r\n",
|
||
|
|
"<text text-anchor=\"middle\" x=\"351.898\" y=\"-445.864\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 3 -->\r\n",
|
||
|
|
"<g id=\"node4\" class=\"node\"><title>3</title>\r\n",
|
||
|
|
"<path fill=\"#4de88e\" stroke=\"black\" d=\"M354,-306C354,-306 213,-306 213,-306 207,-306 201,-300 201,-294 201,-294 201,-235 201,-235 201,-229 207,-223 213,-223 213,-223 354,-223 354,-223 360,-223 366,-229 366,-235 366,-235 366,-294 366,-294 366,-300 360,-306 354,-306\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"209\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 4.95</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"233.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.445</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"240\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 54</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"231\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 49, 5]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"228\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 2->3 -->\r\n",
|
||
|
|
"<g id=\"edge3\" class=\"edge\"><title>2->3</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M342.928,-341.907C335.94,-332.923 328.467,-323.315 321.261,-314.05\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"323.918,-311.766 315.016,-306.021 318.393,-316.063 323.918,-311.766\"/>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 8 -->\r\n",
|
||
|
|
"<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
|
||
|
|
"<path fill=\"#843de6\" stroke=\"black\" d=\"M537,-306C537,-306 396,-306 396,-306 390,-306 384,-300 384,-294 384,-294 384,-235 384,-235 384,-229 390,-223 396,-223 396,-223 537,-223 537,-223 543,-223 549,-229 549,-235 549,-235 549,-294 549,-294 549,-300 543,-306 537,-306\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"392\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 4.95</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"416.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.151</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"423\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"414\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 45]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"416.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 2->8 -->\r\n",
|
||
|
|
"<g id=\"edge8\" class=\"edge\"><title>2->8</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M406.419,-341.907C413.484,-332.923 421.039,-323.315 428.324,-314.05\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"431.208,-316.045 434.637,-306.021 425.705,-311.718 431.208,-316.045\"/>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 4 -->\r\n",
|
||
|
|
"<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
|
||
|
|
"<path fill=\"#3de684\" stroke=\"black\" d=\"M208,-187C208,-187 63,-187 63,-187 57,-187 51,-181 51,-175 51,-175 51,-116 51,-116 51,-110 57,-104 63,-104 63,-104 208,-104 208,-104 214,-104 220,-110 220,-116 220,-116 220,-175 220,-175 220,-181 214,-187 208,-187\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"59\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">sepal length (cm) ≤ 5.15</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"85.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.146</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"92\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"83\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 47, 1]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"80\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 3->4 -->\r\n",
|
||
|
|
"<g id=\"edge4\" class=\"edge\"><title>3->4</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M232.152,-222.907C220.099,-213.379 207.158,-203.148 194.788,-193.37\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"196.772,-190.477 186.757,-187.021 192.431,-195.968 196.772,-190.477\"/>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 7 -->\r\n",
|
||
|
|
"<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
|
||
|
|
"<path fill=\"#c09cf2\" stroke=\"black\" d=\"M342.5,-179.5C342.5,-179.5 250.5,-179.5 250.5,-179.5 244.5,-179.5 238.5,-173.5 238.5,-167.5 238.5,-167.5 238.5,-123.5 238.5,-123.5 238.5,-117.5 244.5,-111.5 250.5,-111.5 250.5,-111.5 342.5,-111.5 342.5,-111.5 348.5,-111.5 354.5,-117.5 354.5,-123.5 354.5,-123.5 354.5,-167.5 354.5,-167.5 354.5,-173.5 348.5,-179.5 342.5,-179.5\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"246.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"257\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"248\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 4]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"246.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 3->7 -->\r\n",
|
||
|
|
"<g id=\"edge7\" class=\"edge\"><title>3->7</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M288.01,-222.907C289.2,-212.204 290.487,-200.615 291.692,-189.776\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"295.189,-189.992 292.815,-179.667 288.232,-189.219 295.189,-189.992\"/>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 5 -->\r\n",
|
||
|
|
"<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
|
||
|
|
"<path fill=\"#6aeca0\" stroke=\"black\" d=\"M115,-68C115,-68 12,-68 12,-68 6,-68 -7.10543e-015,-62 -7.10543e-015,-56 -7.10543e-015,-56 -7.10543e-015,-12 -7.10543e-015,-12 -7.10543e-015,-6 6,-0 12,-0 12,-0 115,-0 115,-0 121,-0 127,-6 127,-12 127,-12 127,-56 127,-56 127,-62 121,-68 115,-68\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"13.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.722</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"24\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"15\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4, 1]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"8\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 4->5 -->\r\n",
|
||
|
|
"<g id=\"edge5\" class=\"edge\"><title>4->5</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M108.69,-103.726C102.932,-94.9703 96.8391,-85.7032 91.054,-76.9051\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"93.8142,-74.7322 85.3956,-68.2996 87.9653,-78.5781 93.8142,-74.7322\"/>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 6 -->\r\n",
|
||
|
|
"<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
|
||
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M260,-68C260,-68 157,-68 157,-68 151,-68 145,-62 145,-56 145,-56 145,-12 145,-12 145,-6 151,-0 157,-0 157,-0 260,-0 260,-0 266,-0 272,-6 272,-12 272,-12 272,-56 272,-56 272,-62 266,-68 260,-68\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"166.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"165\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 43</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"156\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 43, 0]</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"153\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 4->6 -->\r\n",
|
||
|
|
"<g id=\"edge6\" class=\"edge\"><title>4->6</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M162.683,-103.726C168.581,-94.879 174.827,-85.51 180.746,-76.6303\"/>\r\n",
|
||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"183.665,-78.5616 186.3,-68.2996 177.841,-74.6787 183.665,-78.5616\"/>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 9 -->\r\n",
|
||
|
|
"<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
|
||
|
|
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"metadata": {},
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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",
|
||
|
|
" 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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||
|
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"# 1. Artificial inflation"
|
||
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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": 21,
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||
|
|
"metadata": {
|
||
|
|
"scrolled": true
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||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"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",
|
||
|
|
"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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|
|
{
|
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|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
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||
|
|
"metadata": {},
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|
"outputs": [],
|
||
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|
"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)"
|
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|
|
]
|
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|
|
},
|
||
|
|
{
|
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|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Accuracy: 1.0\n",
|
||
|
|
"F1: 1.0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
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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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"data": {
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"<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
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|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M117,-298.5C117,-298.5 12,-298.5 12,-298.5 6,-298.5 0,-292.5 0,-286.5 0,-286.5 0,-242.5 0,-242.5 0,-236.5 6,-230.5 12,-230.5 12,-230.5 117,-230.5 117,-230.5 123,-230.5 129,-236.5 129,-242.5 129,-242.5 129,-286.5 129,-286.5 129,-292.5 123,-298.5 117,-298.5\"/>\r\n",
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"<text text-anchor=\"start\" x=\"22.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
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"<text text-anchor=\"start\" x=\"17\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 440</text>\r\n",
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"<text text-anchor=\"start\" x=\"8\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 440, 0]</text>\r\n",
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"<text text-anchor=\"start\" x=\"9\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
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"</g>\r\n",
|
||
|
|
"<!-- 3->4 -->\r\n",
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|
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"<g id=\"edge4\" class=\"edge\"><title>3->4</title>\r\n",
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|
"<path fill=\"none\" stroke=\"black\" d=\"M168.336,-341.907C151.991,-329.88 134.126,-316.735 117.895,-304.791\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"119.702,-301.775 109.573,-298.667 115.553,-307.413 119.702,-301.775\"/>\r\n",
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"</g>\r\n",
|
||
|
|
"<!-- 5 -->\r\n",
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|
|
"<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
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|
|
"<path fill=\"#c09cf2\" stroke=\"black\" d=\"M290,-306C290,-306 159,-306 159,-306 153,-306 147,-300 147,-294 147,-294 147,-235 147,-235 147,-229 153,-223 159,-223 159,-223 290,-223 290,-223 296,-223 302,-229 302,-235 302,-235 302,-294 302,-294 302,-300 296,-306 290,-306\"/>\r\n",
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|
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"<text text-anchor=\"start\" x=\"155\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">sepal width (cm) ≤ 3.1</text>\r\n",
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"<text text-anchor=\"start\" x=\"174.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\r\n",
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"<text text-anchor=\"start\" x=\"181\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\r\n",
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"<text text-anchor=\"start\" x=\"168\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 20]</text>\r\n",
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"<text text-anchor=\"start\" x=\"174.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
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"</g>\r\n",
|
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|
|
"<!-- 3->5 -->\r\n",
|
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|
|
"<g id=\"edge5\" class=\"edge\"><title>3->5</title>\r\n",
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"<path fill=\"none\" stroke=\"black\" d=\"M223.847,-341.907C223.918,-333.649 223.993,-324.864 224.066,-316.302\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"227.568,-316.05 224.154,-306.021 220.568,-315.99 227.568,-316.05\"/>\r\n",
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"</g>\r\n",
|
||
|
|
"<!-- 6 -->\r\n",
|
||
|
|
"<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
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|
|
"<path fill=\"#8139e5\" stroke=\"black\" d=\"M134,-179.5C134,-179.5 37,-179.5 37,-179.5 31,-179.5 25,-173.5 25,-167.5 25,-167.5 25,-123.5 25,-123.5 25,-117.5 31,-111.5 37,-111.5 37,-111.5 134,-111.5 134,-111.5 140,-111.5 146,-117.5 146,-123.5 146,-123.5 146,-167.5 146,-167.5 146,-173.5 140,-179.5 134,-179.5\"/>\r\n",
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"<text text-anchor=\"start\" x=\"43.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
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"<text text-anchor=\"start\" x=\"42\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\r\n",
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"<text text-anchor=\"start\" x=\"33\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 20]</text>\r\n",
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"<text text-anchor=\"start\" x=\"35.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
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"</g>\r\n",
|
||
|
|
"<!-- 5->6 -->\r\n",
|
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|
|
"<g id=\"edge6\" class=\"edge\"><title>5->6</title>\r\n",
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|
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"<path fill=\"none\" stroke=\"black\" d=\"M176.275,-222.907C162.117,-210.99 146.655,-197.976 132.57,-186.12\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"134.808,-183.429 124.903,-179.667 130.3,-188.784 134.808,-183.429\"/>\r\n",
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||
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|
"</g>\r\n",
|
||
|
|
"<!-- 7 -->\r\n",
|
||
|
|
"<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
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||
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M279,-179.5C279,-179.5 176,-179.5 176,-179.5 170,-179.5 164,-173.5 164,-167.5 164,-167.5 164,-123.5 164,-123.5 164,-117.5 170,-111.5 176,-111.5 176,-111.5 279,-111.5 279,-111.5 285,-111.5 291,-117.5 291,-123.5 291,-123.5 291,-167.5 291,-167.5 291,-173.5 285,-179.5 279,-179.5\"/>\r\n",
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|
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"<text text-anchor=\"start\" x=\"185.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
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"<text text-anchor=\"start\" x=\"184\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n",
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"<text text-anchor=\"start\" x=\"175\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 0]</text>\r\n",
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"<text text-anchor=\"start\" x=\"172\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
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|
|
"</g>\r\n",
|
||
|
|
"<!-- 5->7 -->\r\n",
|
||
|
|
"<g id=\"edge7\" class=\"edge\"><title>5->7</title>\r\n",
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|
|
"<path fill=\"none\" stroke=\"black\" d=\"M225.541,-222.907C225.815,-212.204 226.112,-200.615 226.39,-189.776\"/>\r\n",
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|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"229.892,-189.753 226.65,-179.667 222.894,-189.574 229.892,-189.753\"/>\r\n",
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||
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|
"</g>\r\n",
|
||
|
|
"<!-- 9 -->\r\n",
|
||
|
|
"<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
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||
|
|
"<path fill=\"#e0cef8\" stroke=\"black\" d=\"M474,-425C474,-425 333,-425 333,-425 327,-425 321,-419 321,-413 321,-413 321,-354 321,-354 321,-348 327,-342 333,-342 333,-342 474,-342 474,-342 480,-342 486,-348 486,-354 486,-354 486,-413 486,-413 486,-419 480,-425 474,-425\"/>\r\n",
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||
|
|
"<text text-anchor=\"start\" x=\"329\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 5.35</text>\r\n",
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|
|
"<text text-anchor=\"start\" x=\"353.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.985</text>\r\n",
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|
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"<text text-anchor=\"start\" x=\"360\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 70</text>\r\n",
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"<text text-anchor=\"start\" x=\"347\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 30, 40]</text>\r\n",
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"<text text-anchor=\"start\" x=\"353.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
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|
"</g>\r\n",
|
||
|
|
"<!-- 8->9 -->\r\n",
|
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|
|
"<g id=\"edge9\" class=\"edge\"><title>8->9</title>\r\n",
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|
|
"<path fill=\"none\" stroke=\"black\" d=\"M403.5,-460.907C403.5,-452.649 403.5,-443.864 403.5,-435.302\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"407,-435.021 403.5,-425.021 400,-435.021 407,-435.021\"/>\r\n",
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"</g>\r\n",
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||
|
|
"<!-- 16 -->\r\n",
|
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|
|
"<g id=\"node17\" class=\"node\"><title>16</title>\r\n",
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|
|
"<path fill=\"#8139e5\" stroke=\"black\" d=\"M621,-417.5C621,-417.5 516,-417.5 516,-417.5 510,-417.5 504,-411.5 504,-405.5 504,-405.5 504,-361.5 504,-361.5 504,-355.5 510,-349.5 516,-349.5 516,-349.5 621,-349.5 621,-349.5 627,-349.5 633,-355.5 633,-361.5 633,-361.5 633,-405.5 633,-405.5 633,-411.5 627,-417.5 621,-417.5\"/>\r\n",
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"<text text-anchor=\"start\" x=\"526.5\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
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|
|
"<text text-anchor=\"start\" x=\"521\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 430</text>\r\n",
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"<text text-anchor=\"start\" x=\"512\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 430]</text>\r\n",
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"<text text-anchor=\"start\" x=\"518.5\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
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||
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|
"</g>\r\n",
|
||
|
|
"<!-- 8->16 -->\r\n",
|
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|
|
"<g id=\"edge16\" class=\"edge\"><title>8->16</title>\r\n",
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|
|
"<path fill=\"none\" stroke=\"black\" d=\"M460.746,-460.907C477.864,-448.769 496.586,-435.493 513.553,-423.462\"/>\r\n",
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|
"<polygon fill=\"black\" stroke=\"black\" points=\"515.593,-426.306 521.726,-417.667 511.544,-420.596 515.593,-426.306\"/>\r\n",
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||
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|
"</g>\r\n",
|
||
|
|
"<!-- 10 -->\r\n",
|
||
|
|
"<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
|
||
|
|
"<path fill=\"#bdf6d5\" stroke=\"black\" d=\"M469,-306C469,-306 334,-306 334,-306 328,-306 322,-300 322,-294 322,-294 322,-235 322,-235 322,-229 328,-223 334,-223 334,-223 469,-223 469,-223 475,-223 481,-229 481,-235 481,-235 481,-294 481,-294 481,-300 475,-306 469,-306\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"330\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.55</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"351.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.971</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"358\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\r\n",
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||
|
|
"<text text-anchor=\"start\" x=\"345\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 30, 20]</text>\r\n",
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||
|
|
"<text text-anchor=\"start\" x=\"346\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
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||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 9->10 -->\r\n",
|
||
|
|
"<g id=\"edge10\" class=\"edge\"><title>9->10</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M402.806,-341.907C402.663,-333.558 402.511,-324.671 402.364,-316.02\"/>\r\n",
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||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"405.863,-315.959 402.193,-306.021 398.864,-316.079 405.863,-315.959\"/>\r\n",
|
||
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|
"</g>\r\n",
|
||
|
|
"<!-- 15 -->\r\n",
|
||
|
|
"<g id=\"node16\" class=\"node\"><title>15</title>\r\n",
|
||
|
|
"<path fill=\"#8139e5\" stroke=\"black\" d=\"M608,-298.5C608,-298.5 511,-298.5 511,-298.5 505,-298.5 499,-292.5 499,-286.5 499,-286.5 499,-242.5 499,-242.5 499,-236.5 505,-230.5 511,-230.5 511,-230.5 608,-230.5 608,-230.5 614,-230.5 620,-236.5 620,-242.5 620,-242.5 620,-286.5 620,-286.5 620,-292.5 614,-298.5 608,-298.5\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"517.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"516\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"507\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 20]</text>\r\n",
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||
|
|
"<text text-anchor=\"start\" x=\"509.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
|
||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 9->15 -->\r\n",
|
||
|
|
"<g id=\"edge15\" class=\"edge\"><title>9->15</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M457.623,-341.907C473.66,-329.88 491.187,-316.735 507.112,-304.791\"/>\r\n",
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||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"509.377,-307.467 515.277,-298.667 505.177,-301.867 509.377,-307.467\"/>\r\n",
|
||
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|
"</g>\r\n",
|
||
|
|
"<!-- 11 -->\r\n",
|
||
|
|
"<g id=\"node12\" class=\"node\"><title>11</title>\r\n",
|
||
|
|
"<path fill=\"#c09cf2\" stroke=\"black\" d=\"M467,-187C467,-187 326,-187 326,-187 320,-187 314,-181 314,-175 314,-175 314,-116 314,-116 314,-110 320,-104 326,-104 326,-104 467,-104 467,-104 473,-104 479,-110 479,-116 479,-116 479,-175 479,-175 479,-181 473,-187 467,-187\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"322\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 4.95</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"346.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"353\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"340\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 20]</text>\r\n",
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|
|
"<text text-anchor=\"start\" x=\"346.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\r\n",
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||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 10->11 -->\r\n",
|
||
|
|
"<g id=\"edge11\" class=\"edge\"><title>10->11</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M399.765,-222.907C399.408,-214.558 399.029,-205.671 398.659,-197.02\"/>\r\n",
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||
|
|
"<polygon fill=\"black\" stroke=\"black\" points=\"402.156,-196.862 398.232,-187.021 395.162,-197.161 402.156,-196.862\"/>\r\n",
|
||
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|
"</g>\r\n",
|
||
|
|
"<!-- 14 -->\r\n",
|
||
|
|
"<g id=\"node15\" class=\"node\"><title>14</title>\r\n",
|
||
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M612,-179.5C612,-179.5 509,-179.5 509,-179.5 503,-179.5 497,-173.5 497,-167.5 497,-167.5 497,-123.5 497,-123.5 497,-117.5 503,-111.5 509,-111.5 509,-111.5 612,-111.5 612,-111.5 618,-111.5 624,-117.5 624,-123.5 624,-123.5 624,-167.5 624,-167.5 624,-173.5 618,-179.5 612,-179.5\"/>\r\n",
|
||
|
|
"<text text-anchor=\"start\" x=\"518.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
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||
|
|
"<text text-anchor=\"start\" x=\"517\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\r\n",
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||
|
|
"<text text-anchor=\"start\" x=\"508\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 20, 0]</text>\r\n",
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"<text text-anchor=\"start\" x=\"505\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
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||
|
|
"</g>\r\n",
|
||
|
|
"<!-- 10->14 -->\r\n",
|
||
|
|
"<g id=\"edge14\" class=\"edge\"><title>10->14</title>\r\n",
|
||
|
|
"<path fill=\"none\" stroke=\"black\" d=\"M456.664,-222.907C473.009,-210.88 490.874,-197.735 507.105,-185.791\"/>\r\n",
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|
"<polygon fill=\"black\" stroke=\"black\" points=\"509.447,-188.413 515.427,-179.667 505.298,-182.775 509.447,-188.413\"/>\r\n",
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||
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|
"</g>\r\n",
|
||
|
|
"<!-- 12 -->\r\n",
|
||
|
|
"<g id=\"node13\" class=\"node\"><title>12</title>\r\n",
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||
|
|
"<path fill=\"#39e581\" stroke=\"black\" d=\"M377,-68C377,-68 274,-68 274,-68 268,-68 262,-62 262,-56 262,-56 262,-12 262,-12 262,-6 268,-0 274,-0 274,-0 377,-0 377,-0 383,-0 389,-6 389,-12 389,-12 389,-56 389,-56 389,-62 383,-68 377,-68\"/>\r\n",
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"<text text-anchor=\"start\" x=\"283.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
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|
"<text text-anchor=\"start\" x=\"282\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n",
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|
|
"<text text-anchor=\"start\" x=\"273\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 10, 0]</text>\r\n",
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"<text text-anchor=\"start\" x=\"270\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\r\n",
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|
|
"</g>\r\n",
|
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|
|
"<!-- 11->12 -->\r\n",
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|
|
"<g id=\"edge12\" class=\"edge\"><title>11->12</title>\r\n",
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|
|
"<path fill=\"none\" stroke=\"black\" d=\"M370.062,-103.726C364.385,-94.9703 358.376,-85.7032 352.671,-76.9051\"/>\r\n",
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"<polygon fill=\"black\" stroke=\"black\" points=\"355.469,-74.786 347.092,-68.2996 349.595,-78.5943 355.469,-74.786\"/>\r\n",
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"</g>\r\n",
|
||
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}
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],
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"source": [
|
||
|
|
"dot_data = tree.export_graphviz(clf, out_file=None, \n",
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|
|
" 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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||
|
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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": 25,
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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",
|
||
|
|
"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",
|
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|
|
"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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},
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{
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"cell_type": "code",
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"execution_count": 26,
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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": [
|
||
|
|
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:10,2:10})\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)"
|
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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": 27,
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"metadata": {},
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"outputs": [
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"metadata": {},
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}
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],
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"source": [
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|
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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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"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": 28,
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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(1231)\n",
|
||
|
|
"indices = np.random.permutation(len(iris.data))\n",
|
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|
|
"\n",
|
||
|
|
"# 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",
|
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|
|
"\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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},
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{
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"cell_type": "code",
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"execution_count": 29,
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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: 1.0\n",
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|
|
"F1: 1.0\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",
|
||
|
|
"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)"
|
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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": 30,
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"metadata": {},
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"outputs": [
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"<!-- 5 -->\r\n",
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"</g>\r\n",
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"<!-- 6 -->\r\n",
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"<!-- 9 -->\r\n",
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"<!-- 5->10 -->\r\n",
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"</g>\r\n",
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"</svg>\r\n"
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],
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"text/plain": [
|
||
|
|
"<graphviz.files.Source at 0x1b86742c348>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
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|
|
"output_type": "execute_result"
|
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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": 31,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
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|
|
},
|
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|
|
"outputs": [
|
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|
|
{
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|
|
"data": {
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|
"text/plain": [
|
||
|
|
"array([[7, 0, 0],\n",
|
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|
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" [0, 2, 0],\n",
|
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|
|
" [0, 0, 1]])"
|
||
|
|
]
|
||
|
|
},
|
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|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
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|
|
"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"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
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|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
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|
|
"source": [
|
||
|
|
"# 5. ROC Curves"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
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|
|
"text": [
|
||
|
|
"[[(0.0, 43.0), (30.0, 0.0), (30.0, 0.0), (40.0, 0.0), (430.0, 0.0), (440.0, 0.0)], [(0.0, 440.0), (10.0, 20.0), (20.0, 10.0), (30.0, 10.0), (43.0, 0.0), (430.0, 0.0)], [(0.0, 430.0), (10.0, 30.0), (10.0, 20.0), (20.0, 10.0), (43.0, 0.0), (440.0, 0.0)]]\n"
|
||
|
|
]
|
||
|
|
},
|
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|
|
{
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|
"data": {
|
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|
|
"text/plain": [
|
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|
|
"[[[0, 0.0, 30.0, 60.0, 100.0, 530.0, 970.0],\n",
|
||
|
|
" [0, 43.0, 43.0, 43.0, 43.0, 43.0, 43.0]],\n",
|
||
|
|
" [[0, 0.0, 10.0, 30.0, 60.0, 103.0, 533.0],\n",
|
||
|
|
" [0, 440.0, 460.0, 470.0, 480.0, 480.0, 480.0]],\n",
|
||
|
|
" [[0, 0.0, 10.0, 20.0, 40.0, 83.0, 523.0],\n",
|
||
|
|
" [0, 430.0, 460.0, 480.0, 490.0, 490.0, 490.0]]]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"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": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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": "3.7.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|