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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for **classification** or for **regression**. SVMs establish a hyperplane that separates the dataset by maximizing the margin."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits import mplot3d\n",
"\n",
"import numpy as np\n",
"import seaborn; \n",
"\n",
"import sklearn\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.datasets import make_blobs\n",
"from sklearn.datasets import make_circles\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import f1_score\n",
"\n",
"from scipy import stats\n",
"import pylab as pl\n",
"import random\n",
"import pandas as pd\n",
"#from IPython.html.widgets.interaction import interact\n",
"import ipywidgets as widgets\n",
"seaborn.set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before starting with the exercises, watch the video from Josh Starmer on youtube."
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
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\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/efR1C6CvhmE\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7fa6fd99a820>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('efR1C6CvhmE')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1 - Simple Example"
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X, y = make_blobs(n_samples=60, centers=2,\n",
" random_state=0, cluster_std=0.60)\n",
"xfit = np.linspace(-1, 3.5)\n",
"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n",
"plt.xlim(-1, 3.5);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Now fit the model by using the [SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) class from scikit-learn. Use a `linear` kernel."
]
},
{
"cell_type": "code",
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"# clf = ...\n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Click on the dots to display the solution*"
]
},
"jupyter": {
"source_hidden": true
},
"solution2": "hidden",
"tags": []
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel='linear')</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel='linear')</pre></div></div></div></div></div>"
],
"text/plain": [
"SVC(kernel='linear')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = SVC(kernel='linear')\n",
"clf.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We plot de decision boundary. In the following plot the dashed lines touch a couple of the points known as *support vectors*, which are stored in the ``support_vectors_`` attribute of the classifier."
]
},
{
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.19259775 -0.66829007]\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"def plot_svc_decision_function(clf, ax=None):\n",
" \"\"\"Plot the decision function for a 2D SVC\"\"\"\n",
" if ax is None:\n",
" ax = plt.gca()\n",
" x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)\n",
" y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)\n",
" Y, X = np.meshgrid(y, x)\n",
" P = np.zeros_like(X)\n",
" for i, xi in enumerate(x):\n",
" for j, yj in enumerate(y):\n",
" P[i, j] = clf.decision_function([[xi, yj]])\n",
" # plot the margins\n",
" ax.contour(X, Y, P, colors='k',\n",
" levels=[-1, 0, 1], alpha=0.5,\n",
" linestyles=['--', '-', '--'])\n",
" \n",
"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n",
"print(clf.decision_function([[2,2],[1,3]]))\n",
"plot_svc_decision_function(clf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we want to indicate the support vectors by sourrounding circles."
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n",
"plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
" s=200, facecolors='none',edgecolors=\"black\");\n",
"plot_svc_decision_function(clf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dataset above was non-overlapping, which means we could come up with a hyperplane that separated the dataset perfectly. Let us now consider a dataset where no perfect separation is possible. In this case the SVM tries to minimize the datapoints lying on the wrong side of the hyperplane. These datapoints are considered support vectors as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At first, we generate the datapoints of the first class by sampling from a normal distribution with standard deviation 1.3 and mean (2,4)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"num_entries=100\n",
"X=np.zeros((2*num_entries,2))\n",
"for i in range(0,num_entries):\n",
" X[i,0]=np.random.normal()*1.3+2\n",
" X[i,1]=np.random.normal()*1.3+4\n",
"y = num_entries*[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Now we sample the data points from the second class with standard deviation 1.0 and mean (1,0). "
]
},
{
"cell_type": "code",
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"# y2 = \n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Click on the dots to display the solution*"
]
},
"jupyter": {
"source_hidden": true
},
"solution2": "hidden",
"tags": []
},
"outputs": [],
"source": [
"for i in range(num_entries,2*num_entries):\n",
" X[i,0]=np.random.normal()+1\n",
" X[i,1]=np.random.normal()\n",
"y2 = num_entries*[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us combine the class vectors `y` and `y2`"
]
},
{
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"len X: 200\n",
"len y: 200\n"
]
}
],
"source": [
"y.extend(y2)\n",
"\n",
"print (\"len X: \",len(X))\n",
"print (\"len y: \",len(y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us visualize the generated data samples. "
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": [
"(-1.0, 3.5)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n",
"plt.xlim(-1, 3.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Now we train a linear SVM to find the best separating hyperplane."
]
},
{
"cell_type": "code",
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"# clf = ...\n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Click on the dots to display the solution*"
]
},
"jupyter": {
"source_hidden": true
},
"solution2": "hidden",
"tags": []
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel='linear')</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel='linear')</pre></div></div></div></div></div>"
],
"text/plain": [
"SVC(kernel='linear')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = SVC(kernel='linear')\n",
"clf.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us again visualize the hyperplane and the support vectors."
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": [
"(-1.0, 3.5)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring')\n",
"plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
" s=200, facecolors='none',edgecolors=\"black\");\n",
"plot_svc_decision_function(clf)\n",
"plt.xlim(-1, 3.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 2 - Support Vector Machine with Kernels Classifier\n",
"\n",
"Kernels are useful when the decision boundary is not linear. A Kernel is some functional transformation of the input data. SVMs have clever tricks to ensure kernel calculations are efficient. In the example below, a linear boundary is not useful in separating the groups of points."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"X_circles, y_circles = make_circles(100, factor=.1, noise=.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Create a linear SVM and fit it to X and y"
]
},
{
"cell_type": "code",
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"# clf = \n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Click on the dots to display the solution*"
]
},
"jupyter": {
"source_hidden": true
},
"solution2": "hidden",
"tags": []
},
"outputs": [],
"source": [
"clf = SVC(kernel='linear').fit(X_circles, y_circles)"
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_circles[:, 0], X_circles[:, 1], c=y_circles, s=50, cmap='spring')\n",
"plot_svc_decision_function(clf);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A simple model that could be useful is a **radial basis function (rbf)**:"
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "333c57c56b9f4fa4bd19ca6f5df690da",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='elev', options=(-90, 90), value=-90), IntSlider(value=30, descript…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r = np.exp(-(X_circles[:, 0] ** 2 + X_circles[:, 1] ** 2))\n",
"\n",
"@widgets.interact(elev=[-90, 90], azip=(-180, 180))\n",
"def plot_3D(elev=30, azim=30):\n",
" #fig = plt.figure(figsize=(12,12))\n",
" #ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
" ax = plt.subplot(projection='3d')\n",
" ax.scatter3D(X_circles[:, 0], X_circles[:, 1], r, c=y_circles, s=50, cmap='spring')\n",
" ax.view_init(elev=elev, azim=azim)\n",
" ax.set_xlabel('x')\n",
" ax.set_ylabel('y')\n",
" ax.set_zlabel('r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In three dimensions, there is a clear separation between the data. \n",
"> Run the SVM with the `rbf` kernel."
]
},
{
"cell_type": "code",
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"# clf = ... # create an SVM with kernel=\"rbf\" (abbreviation for Radial Basis Function)\n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Click on the dots to display the solution*"
]
},
"jupyter": {
"source_hidden": true
},
"solution2": "hidden",
"tags": []
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
],
"text/plain": [
"SVC()"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = SVC(kernel='rbf')\n",
"clf.fit(X_circles, y_circles)"
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_circles[:, 0], X_circles[:, 1], c=y_circles, s=50, cmap='spring')\n",
"plot_svc_decision_function(clf)\n",
"plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
" s=200, facecolors='none');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 3 - Skin disease dataset\n",
"Now we want to apply the SVM on our skin disease data. Load this dataset using pandas."
]
},
{
"cell_type": "code",
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"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>class</th>\n",
" <th>t0</th>\n",
" <th>t1</th>\n",
" <th>t2</th>\n",
" <th>t3</th>\n",
" <th>t4</th>\n",
" <th>t5</th>\n",
" <th>t6</th>\n",
" <th>t7</th>\n",
" <th>t8</th>\n",
" <th>t9</th>\n",
" <th>t10</th>\n",
" <th>t11</th>\n",
" <th>t12</th>\n",
" <th>t13</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>145.589218</td>\n",
" <td>136.262619</td>\n",
" <td>89.286194</td>\n",
" <td>1.444699</td>\n",
" <td>1.121440</td>\n",
" <td>6.850170</td>\n",
" <td>-0.416775</td>\n",
" <td>-0.961082</td>\n",
" <td>-3.694071</td>\n",
" <td>1.782547</td>\n",
" <td>1.507777</td>\n",
" <td>8.454105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>145.745285</td>\n",
" <td>136.358490</td>\n",
" <td>89.779877</td>\n",
" <td>1.527904</td>\n",
" <td>1.150789</td>\n",
" <td>6.972225</td>\n",
" <td>0.259111</td>\n",
" <td>-0.973702</td>\n",
" <td>-4.002241</td>\n",
" <td>1.891008</td>\n",
" <td>1.542660</td>\n",
" <td>8.602075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>145.902649</td>\n",
" <td>136.442474</td>\n",
" <td>90.168144</td>\n",
" <td>1.612806</td>\n",
" <td>1.174493</td>\n",
" <td>6.978986</td>\n",
" <td>0.429408</td>\n",
" <td>-0.974900</td>\n",
" <td>-4.450916</td>\n",
" <td>1.988215</td>\n",
" <td>1.577558</td>\n",
" <td>8.610969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>146.033340</td>\n",
" <td>136.516663</td>\n",
" <td>90.452774</td>\n",
" <td>1.659652</td>\n",
" <td>1.190121</td>\n",
" <td>6.952057</td>\n",
" <td>-0.541607</td>\n",
" <td>-0.988754</td>\n",
" <td>-4.659405</td>\n",
" <td>2.026301</td>\n",
" <td>1.597203</td>\n",
" <td>8.615275</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>146.152237</td>\n",
" <td>136.569550</td>\n",
" <td>90.808395</td>\n",
" <td>1.696972</td>\n",
" <td>1.185440</td>\n",
" <td>6.971071</td>\n",
" <td>-0.723738</td>\n",
" <td>-1.007998</td>\n",
" <td>-4.903327</td>\n",
" <td>2.068065</td>\n",
" <td>1.599503</td>\n",
" <td>8.667152</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" class t0 t1 t2 t3 t4 t5 t6 \\\n",
"0 0 0.0 1.0 145.589218 136.262619 89.286194 1.444699 1.121440 \n",
"1 0 0.0 1.0 145.745285 136.358490 89.779877 1.527904 1.150789 \n",
"2 0 0.0 1.0 145.902649 136.442474 90.168144 1.612806 1.174493 \n",
"3 0 0.0 1.0 146.033340 136.516663 90.452774 1.659652 1.190121 \n",
"4 0 0.0 1.0 146.152237 136.569550 90.808395 1.696972 1.185440 \n",
"\n",
" t7 t8 t9 t10 t11 t12 t13 \n",
"0 6.850170 -0.416775 -0.961082 -3.694071 1.782547 1.507777 8.454105 \n",
"1 6.972225 0.259111 -0.973702 -4.002241 1.891008 1.542660 8.602075 \n",
"2 6.978986 0.429408 -0.974900 -4.450916 1.988215 1.577558 8.610969 \n",
"3 6.952057 -0.541607 -0.988754 -4.659405 2.026301 1.597203 8.615275 \n",
"4 6.971071 -0.723738 -1.007998 -4.903327 2.068065 1.599503 8.667152 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"skin_disease.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to save time, we only use 100000 entries for training / testing"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"df = df.sample(frac=1) # shuffling the data\n",
"df = df.iloc[0:100000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us split this dataset into training and test set"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"train, test = train_test_split(df, test_size=0.5)\n",
"X_train = train.drop('class', axis=1)\n",
"X_test = test.drop('class', axis=1)\n",
"y_train = train[\"class\"]\n",
"y_test = test[\"class\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Now train an SVM classifier on this dataset, which can take some minutes. Use the `rbf` kernel and a `gamma` value of 0.1."
]
},
{
"cell_type": "code",
"metadata": {
"solution2": "hidden",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"\n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Click on the dots to display the solution*"
]
},
"execution_count": null,
"jupyter": {
"source_hidden": true
},
"solution2": "hidden",
"tags": []
"source": [
"clf = SVC(kernel='rbf', gamma=0.1)\n",
"clf.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Next, determine f-score and accuracy on the testset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"solution2": "shown",
"solution2_first": true
},
"outputs": [],
"source": [
"# START YOUR CODE\n",
"# print (\"f1 SVM:\", f1)\n",
"# print (\"accuracy SVM:\", accuracy)\n",
"# END YOUR CODE"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"*Click on the dots to display the solution*"
]
},
"execution_count": null,
"jupyter": {
"source_hidden": true
},
"solution2": "shown",
"tags": []