diff --git a/anno3/apprendimento_automatico/esercizi/1/coverage_plots.ipynb b/anno3/apprendimento_automatico/esercizi/1/coverage_plots.ipynb new file mode 100644 index 0000000..48c0a49 --- /dev/null +++ b/anno3/apprendimento_automatico/esercizi/1/coverage_plots.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Coverage plots" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us consider the following function which applies a linear model to the given data. \n", + "Specifically, given a \"model\" vector containing the model coefficients $(a,b)$ and a $n \\times 2$ \"data\" matrix containing the data points to be classified, the function outputs a vector $\\mathbf{z}$, $|\\mathbf{z}| = n$ of booleans where $z_i$ is `True` if $a \\cdot x_{i,1} + b \\cdot x_{i,2} \\geq 0$, it is `False` otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def apply_linear_model(model, data):\n", + " return np.dot(data, np.transpose(model)) > 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us define `data` by generating $1000$ points drawn uniformly from $\\mathcal{X} = [-100,100]^2$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-49, 30],\n", + " [-72, 67],\n", + " [-19, 37],\n", + " ..., \n", + " [ 51, 68],\n", + " [ 18, -98],\n", + " [ 35, -65]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = np.random.random_integers(-100,100,[1000,2])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and let target_labels be the labeling output by applying `apply_linear_model` with our target model: $4x -y > 0$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "target_model = [4.,-1.]\n", + "target_labels = apply_linear_model(target_model, data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By using matplotlib.pyplot module it is easy to plot the generated points onto a 2D plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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jjN1/v/0afD7sCSspgc1OVbE5depAKohEGBsxAna41q3tCX6s48ILE5/s6NHxx2vSBOfO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\nwTljfj7MQ7wrT506OnFOxoG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cbP6sRw+ivXuJ8vPx3dixRO+9h/kMGkQ0caJ5PkREimIe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['r' if l else 'b' for l in target_labels]\n", + "plt.scatter(data[:,0], data[:,1], color=colors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally let us now generate at random 100 linear models with coefficients in $[-5,5]$:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.42065006, -1.51408433],\n", + " [-4.65413701, -4.75333564],\n", + " [-0.19447508, 3.05241949],\n", + " [-2.48775025, 2.85338914],\n", + " [ 2.1362176 , 1.08304026],\n", + " [ 2.00429234, 3.42847837],\n", + " [ 0.88481654, 3.8669342 ],\n", + " [ 4.44211929, -3.83888206],\n", + " [-3.66822357, -4.28716487],\n", + " [-3.91824496, 3.06436092],\n", + " [-0.3920509 , 0.5227957 ],\n", + " [-0.94013027, 0.46786464],\n", + " [-4.57795069, 1.779259 ],\n", + " [ 0.36808181, -0.5344856 ],\n", + " [-4.93262404, -1.90698095],\n", + " [-0.33777705, -1.63020961],\n", + " [ 4.47799157, -1.31602341],\n", + " [-2.65965489, -3.18664523],\n", + " [ 3.1002499 , 3.88230724],\n", + " [ 1.2543389 , -3.92972403],\n", + " [-2.08471236, 0.19273355],\n", + " [ 4.04972983, -1.80477782],\n", + " [-3.04213845, -3.86273346],\n", + " [ 3.23897921, 2.35710368],\n", + " [ 0.82709134, 1.97296822],\n", + " [-2.41348448, 3.52387581],\n", + " [ 3.272664 , -4.01131236],\n", + " [-2.78192199, 1.20800436],\n", + " [ 2.15143713, 0.46205732],\n", + " [-0.27395696, -3.71015376],\n", + " [-3.49595585, 3.35236591],\n", + " [ 3.87269394, -2.38906937],\n", + " [ 3.54731272, 3.61539208],\n", + " [-4.24188741, 2.32108886],\n", + " [ 0.64768018, 2.67719383],\n", + " [-1.46035943, 3.40867468],\n", + " [ 2.09576921, 3.21825488],\n", + " [-2.61612374, -0.37691585],\n", + " [-3.44960902, -4.20342928],\n", + " [-4.74685223, 3.45572264],\n", + " [ 0.47240822, -4.01583302],\n", + " [ 2.999604 , 4.75639895],\n", + " [ 4.92750221, 1.78622421],\n", + " [-4.77167292, -2.26895667],\n", + " [ 4.33833673, 3.21539957],\n", + " [ 4.30829825, 1.92057033],\n", + " [ 1.14997347, -0.40697769],\n", + " [-0.52503915, -2.00002841],\n", + " [-0.80040233, 1.70290673],\n", + " [-2.97302617, -2.98759477],\n", + " [ 3.38727939, -4.75730242],\n", + " [ 4.30885635, -2.52883579],\n", + " [-3.23360834, 2.7252995 ],\n", + " [-4.98056317, -3.07687503],\n", + " [-0.62752698, -1.84668032],\n", + " [-4.46693483, 2.39512863],\n", + " [-4.53139418, 4.8182652 ],\n", + " [ 2.90990641, -0.81200988],\n", + " [ 0.81473891, -0.51190201],\n", + " [-1.70213346, 1.50323875],\n", + " [-3.42105141, -2.30779652],\n", + " [ 2.17377044, 3.57951352],\n", + " [ 2.46715515, -2.61123393],\n", + " [ 2.6401855 , -3.66495187],\n", + " [-3.62372777, 1.95002019],\n", + " [ 1.57178844, -1.57386143],\n", + " [ 0.97628317, -3.22374322],\n", + " [-2.62901857, -2.3521806 ],\n", + " [-2.07241213, -0.06618532],\n", + " [-1.35874934, 3.67684431],\n", + " [ 0.72182233, 1.16568076],\n", + " [ 4.18505486, -3.53601869],\n", + " [-1.37070263, -0.16277551],\n", + " [ 0.08486058, -2.78638763],\n", + " [ 2.32323512, 4.79326219],\n", + " [-3.17893632, -1.62899238],\n", + " [-0.16249256, -1.38160519],\n", + " [ 1.63573918, -2.67334439],\n", + " [ 3.89370811, 4.07947346],\n", + " [ 1.54299994, 2.40618785],\n", + " [-1.30578765, 1.82646402],\n", + " [ 2.61456717, -0.23854833],\n", + " [ 1.32655801, -1.09311069],\n", + " [-3.64407544, 4.17780474],\n", + " [-0.48365564, -0.31774477],\n", + " [-1.57456997, -2.64578667],\n", + " [-4.49431739, -4.50338848],\n", + " [ 1.21613128, -4.34637332],\n", + " [ 4.31502574, 0.22312125],\n", + " [ 0.85531139, 1.77814044],\n", + " [ 1.36949581, -1.56175179],\n", + " [ 0.07691984, 3.0667515 ],\n", + " [ 1.38754742, -3.26294532],\n", + " [-3.14312877, -3.88441664],\n", + " [ 3.08398027, -0.02962741],\n", + " [ 1.68912828, 2.02588202],\n", + " [-1.69812072, 4.41390119],\n", + " [ 4.47082376, -3.15265638],\n", + " [ 2.23288076, -3.36156815],\n", + " [-4.44213493, 3.2528898 ]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models = (np.random.rand(100,2) - 0.5) * 10\n", + "models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Write a function that, taken two list of labellings build the corresponding confusion matrix [[1](#hint1)];\n", + "1. For each model in `models` plot the [FP,TP] pairs on a scatter plot;\n", + "1. Just looking at the plot: which is the best model in the pool?\n", + "1. Find the model with the best accuracy [[2](#hint2)] and compare it with the target model, is it close? Is it the model you would have picked up visually from the scatter plot?\n", + "1. If everything is ok, you should have found a pretty good model for our data. It fits the data quite well and it is quite close to the target model. Did you expect this? If so, why? If not so, why not?\n", + "\n", + "Hint 1: it may be helpful to have a way to map TRUE to 0, FALSE to 1 and to use these values as indices in the confusion matrix. \n", + "\n", + "Hint 2: one way to proceed is to build a function `accuracy`, use the `map` function to calculate the accuracies of all the models, and then apply the `numpy.argmax` to retrieve the index of the best model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [aaaid]", + "language": "python", + "name": "Python [aaaid]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/anno3/apprendimento_automatico/esercizi/1/one_vs_rest.ipynb b/anno3/apprendimento_automatico/esercizi/1/one_vs_rest.ipynb new file mode 100644 index 0000000..79846d3 --- /dev/null +++ b/anno3/apprendimento_automatico/esercizi/1/one_vs_rest.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiments with the one vs rest multiclass classification scheme" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from sklearn import datasets\n", + "from sklearn.multiclass import OneVsRestClassifier as OvR\n", + "from sklearn.svm import LinearSVC\n", + "import numpy as np\n", + "import copy\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "digits = datasets.load_digits()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the \"Optical Recognition of Handwritten Digits Data Set\" from UCI (included in scikit learn and already loaded on the previous line). Let us plot the first 10 images in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for index, image in enumerate(digits.images[:10]):\n", + " plt.subplot(2, 5, index + 1)\n", + " plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us create a training set using the first 1000 images and a test set using the rest of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5.\n", + " 0. 0. 3. 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8.\n", + " 8. 0. 0. 5. 8. 0. 0. 9. 8. 0. 0. 4. 11. 0. 1.\n", + " 12. 7. 0. 0. 2. 14. 5. 10. 12. 0. 0. 0. 0. 6. 13.\n", + " 10. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "X,y = digits.data[0:1000], digits.target[0:1000]\n", + "X_test, y_test = digits.data[1000:], digits.target[1000:]\n", + "print(X[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "scikit-learn provide us with an One-Vs-Rest classifier that we already imported with name `OvR`. Let us use that classifier to fit the training set and to make predictions over the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "binaryLearner = LinearSVC(random_state=0)\n", + "\n", + "oneVrestLearningAlgorithm = OvR(binaryLearner)\n", + "oneVrestLearningAlgorithm.fit(X,y)\n", + "predicted_labels = oneVrestLearningAlgorithm.predict(X_test)\n", + "\n", + "# n.b.: the above is equivalent to:\n", + "# predicted_labels = OvR(LinearSVC(random_state=0)).fit(X,y).predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print \"Accuracy: %s\" % (1.0 - np.count_nonzero(y_test - predicted_labels) / float(len(predicted_labels)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reimplement the OvR classifier by completing the methods in the following class [[1](#hint1)]:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class OneVsRestClassifier:\n", + " def __init__(self, learner):\n", + " #... to be done ...\n", + " return 1\n", + "\n", + " def fit(self, data, labels):\n", + " #... to be done ...\n", + "\n", + " return self\n", + "\n", + " def predict(self, data):\n", + " #... to be done ...\n", + " return 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the accuracy of your solution using the following code [[2](#hint2)]:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ovr = OneVsRestClassifier(LinearSVC(random_state=0))\n", + "predicted_labels = ovr.fit(X,y).predict(X_test)\n", + "print \"Accuracy: %s\" % (1.0-np.count_nonzero(predicted_labels-y_test)/float(len(y_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hint 1: Feel free to organize your code as you like (add as many methods as you believe are necessary).\n", + "\n", + "Hint 2: The scheme provided by scikit-learn is a little different from the one we have seen in the lessons. It is normal if your accuracy is not as good as the one obtained above (expect the accuracy to be between 0.8 and 0.9)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [aaaid]", + "language": "python", + "name": "Python [aaaid]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/anno3/apprendimento_automatico/esercizi/3/least_squares.ipynb b/anno3/apprendimento_automatico/esercizi/3/least_squares.ipynb new file mode 100644 index 0000000..a23b382 --- /dev/null +++ b/anno3/apprendimento_automatico/esercizi/3/least_squares.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experimenting with least squares and its variants" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from sklearn import datasets\n", + "from scipy.optimize import fmin_bfgs\n", + "import numpy as np\n", + "from numpy.linalg import norm\n", + "from numpy.linalg import inv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 6.32000000e-03, 1.80000000e+01, 2.31000000e+00, ...,\n", + " 1.53000000e+01, 3.96900000e+02, 4.98000000e+00],\n", + " [ 2.73100000e-02, 0.00000000e+00, 7.07000000e+00, ...,\n", + " 1.78000000e+01, 3.96900000e+02, 9.14000000e+00],\n", + " [ 2.72900000e-02, 0.00000000e+00, 7.07000000e+00, ...,\n", + " 1.78000000e+01, 3.92830000e+02, 4.03000000e+00],\n", + " ..., \n", + " [ 6.07600000e-02, 0.00000000e+00, 1.19300000e+01, ...,\n", + " 2.10000000e+01, 3.96900000e+02, 5.64000000e+00],\n", + " [ 1.09590000e-01, 0.00000000e+00, 1.19300000e+01, ...,\n", + " 2.10000000e+01, 3.93450000e+02, 6.48000000e+00],\n", + " [ 4.74100000e-02, 0.00000000e+00, 1.19300000e+01, ...,\n", + " 2.10000000e+01, 3.96900000e+02, 7.88000000e+00]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "boston = datasets.load_boston()\n", + "data = np.array(boston.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The boston dataset is one of the standard regression problems used to experiment with learning algorithms. Below you can find the dataset description" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Boston House Prices dataset\n", + "\n", + "Notes\n", + "------\n", + "Data Set Characteristics: \n", + "\n", + " :Number of Instances: 506 \n", + "\n", + " :Number of Attributes: 13 numeric/categorical predictive\n", + " \n", + " :Median Value (attribute 14) is usually the target\n", + "\n", + " :Attribute Information (in order):\n", + " - CRIM per capita crime rate by town\n", + " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", + " - INDUS proportion of non-retail business acres per town\n", + " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", + " - NOX nitric oxides concentration (parts per 10 million)\n", + " - RM average number of rooms per dwelling\n", + " - AGE proportion of owner-occupied units built prior to 1940\n", + " - DIS weighted distances to five Boston employment centres\n", + " - RAD index of accessibility to radial highways\n", + " - TAX full-value property-tax rate per $10,000\n", + " - PTRATIO pupil-teacher ratio by town\n", + " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", + " - LSTAT % lower status of the population\n", + " - MEDV Median value of owner-occupied homes in $1000's\n", + "\n", + " :Missing Attribute Values: None\n", + "\n", + " :Creator: Harrison, D. and Rubinfeld, D.L.\n", + "\n", + "This is a copy of UCI ML housing dataset.\n", + "http://archive.ics.uci.edu/ml/datasets/Housing\n", + "\n", + "\n", + "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", + "\n", + "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", + "prices and the demand for clean air', J. Environ. Economics & Management,\n", + "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", + "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", + "pages 244-261 of the latter.\n", + "\n", + "The Boston house-price data has been used in many machine learning papers that address regression\n", + "problems. \n", + " \n", + "**References**\n", + "\n", + " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", + " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", + " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", + "\n" + ] + } + ], + "source": [ + "print(boston.DESCR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First step to apply the formulae we learnt during the lectures is to rewrite the dataset in homogeneous coordinates (i.e., we append a column of 1 to the matrix containing the examples):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "t = np.ones(len(data)).reshape(len(data),1)\n", + "data = np.append(data, t, 1)\n", + "target = np.array(boston.target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now divide the data into a training set $X$ and a test set $X_\\textrm{test}$." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X,y = data[0:400,:], target[0:400]\n", + "X_test, y_test = data[400:,:], target[400:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Calculate the least square solution (to the regression problem outlined above) and evaluate its performances on the training set and on the test set.\n", + "1. Calculate the ridge regression solution (set lambda to 0.01) and evaluate its performances on the training set and on test set.\n", + "1. Calculate the lasso regression solution and evaluate its performances on the training set and on the test set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Here it follows a list of functions you may want to use (the required packages are already imported at the beginning of this notebook) along with a very brief explanation of their purpose (`help(nomefun)` will provide you more information about function `nomefun`):\n", + " - `transpose`: matrix transposition (e.g., `transpose(X)`)\n", + " - `dot`: matrix multiplication (e.g., `X.dot(X2)`) \n", + " - `inv`: matrix inversion (e.g., `inv(X)`)\n", + "- to solve the lasso problem you will need to perform a numerical minimization of the associated loss function (as you know, a closed form solution does not exist). There are many numerical optimization algorithms available in the scipy package. My suggestion is to use `fmin_bfgs`. Here it follows an example of how to use it:\n", + " ```python\n", + " def f(w):\n", + " return w[0]**2 + w[1]**2 + w[0] + w[1]\n", + " \n", + " w = fmin_bfgs(f, [0,0])\n", + " ```\n", + " note that the function may (and should) reference your data variables (i.e., $X$ and $y$).\n", + "- to evaluate the performances of your solutions use the $S$ statistic:\n", + " $$\n", + " S = \\sqrt{ \\frac{1}{n} \\sum_{i=1}^n (y_i' - y_i)^2 }\n", + " $$\n", + " where $y'_i$ is your model prediction for the i-th example, and $n$ is the number of examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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 +} diff --git a/anno3/apprendimento_automatico/esercizi/3/svm.ipynb b/anno3/apprendimento_automatico/esercizi/3/svm.ipynb new file mode 100644 index 0000000..d00b7ae --- /dev/null +++ b/anno3/apprendimento_automatico/esercizi/3/svm.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Support Vector Machines" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.array([[ 0.46613554, 0.92048757],\n", + " [-0.92129195, 0.06723639],\n", + " [-0.15836636, 0.00430243],\n", + " [-0.24055905, -0.87032292],\n", + " [ 0.06245105, -0.53698416],\n", + " [-0.2265037 , -0.43835751],\n", + " [-0.00480479, -0.17372081],\n", + " [-0.1525277 , -0.34399658],\n", + " [-0.27360329, 0.35339202],\n", + " [-0.77464508, -0.48715511],\n", + " [-0.58724291, 0.74419972],\n", + " [-0.97596949, -0.72172963],\n", + " [ 0.42376225, -0.72655597],\n", + " [ 0.96383922, -0.23371331],\n", + " [ 0.16264643, -0.46949742],\n", + " [-0.74294705, -0.42576417],\n", + " [ 0.05089437, -0.20522071],\n", + " [-0.19442744, 0.09617478],\n", + " [-0.97102743, 0.79663992],\n", + " [ 0.0596995 , -0.70129219],\n", + " [-0.83934851, -0.95616033],\n", + " [-0.38249705, 0.4973605 ],\n", + " [ 0.3474666 , 0.70664397],\n", + " [ 0.35871444, 0.88679345],\n", + " [-0.05914582, 0.23124686],\n", + " [-0.52156643, 0.32986941],\n", + " [-0.53579646, 0.67530208],\n", + " [ 0.13683914, -0.96158184],\n", + " [ 0.65904541, -0.12015303],\n", + " [-0.69078363, 0.5615536 ],\n", + " [ 0.47738323, -0.70919275],\n", + " [ 0.93069669, 0.44019132],\n", + " [ 0.19750088, -0.68869404],\n", + " [-0.75048675, -0.18170522],\n", + " [-0.45288395, -0.25894991],\n", + " [-0.74644547, 0.87781953],\n", + " [ 0.14620452, 0.56864508],\n", + " [ 0.25719272, -0.58405476],\n", + " [ 0.87149524, 0.01384224],\n", + " [-0.71473576, 0.31568314],\n", + " [-0.252637 , -0.67418371],\n", + " [ 0.24718308, 0.95191416],\n", + " [-0.38149953, -0.64066291],\n", + " [-0.23112698, 0.04678807],\n", + " [ 0.72631766, 0.7390158 ],\n", + " [-0.91748062, -0.15131021],\n", + " [ 0.74957917, 0.66966866],\n", + " [ 0.76771849, 0.06662777],\n", + " [-0.04233756, -0.91320835],\n", + " [ 0.63840333, 0.06277738],\n", + " [-0.78887281, -0.90311183],\n", + " [-0.73099834, -0.69587363],\n", + " [-0.50947652, -0.99144951],\n", + " [ 0.14294609, 0.5474932 ],\n", + " [ 0.4367906 , 0.31953258],\n", + " [-0.13970851, 0.81817884],\n", + " [ 0.6440873 , 0.79118775],\n", + " [ 0.41714043, -0.66672029],\n", + " [ 0.59283022, -0.71836746],\n", + " [ 0.55379696, 0.98846202],\n", + " [-0.91819517, 0.34203895],\n", + " [ 0.02020188, 0.83696694],\n", + " [ 0.6182918 , 0.04254014],\n", + " [-0.09354765, -0.30050483],\n", + " [-0.08489545, 0.06431463],\n", + " [-0.11886358, -0.68738895],\n", + " [ 0.44428375, 0.18273761],\n", + " [ 0.26486362, -0.98398013],\n", + " [ 0.13222452, 0.91495035],\n", + " [-0.11101656, 0.00541343],\n", + " [-0.07696178, -0.92720555],\n", + " [ 0.22602214, 0.56040092],\n", + " [ 0.74227542, 0.32930104],\n", + " [ 0.43524657, 0.35332933],\n", + " [-0.89277607, -0.59996171],\n", + " [-0.94836212, 0.78777302],\n", + " [ 0.1783319 , -0.2142071 ],\n", + " [-0.07832238, -0.25046584],\n", + " [ 0.17611799, -0.96927832],\n", + " [-0.95938454, -0.26504646],\n", + " [ 0.58666766, -0.94620881],\n", + " [-0.77336565, 0.46735057],\n", + " [-0.94414054, 0.39044333],\n", + " [ 0.61524645, 0.15907662],\n", + " [-0.09855302, 0.9816656 ],\n", + " [ 0.53937097, 0.34487634]])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "y = [\"red\" if x + y > 0.3 else \"green\" for [x,y] in X]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WLbRr187saOIh5MyEYPqs6TiCHHfeqFsWLEUszJkzR36RhRCpJjg4WG4FTYfk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\nL774Yp73+ey/n8VTyQM3AVWBOuDc4nDk+BHGjx8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:,0], X[:,1], c=y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "- import the support vector machine classifier from scikit-learn (the SVC class) and train a classifier for the examples above using a linear kernel;\n", + "- read the documentation to find out how to obtain the support vectors and the associated (dual) weights; use this information to analyze the learnt model: \n", + " - how many support vectors have been learnt? \n", + " - are them in the position you would have expected [[1](#note1)]? \n", + " - is there any margin error?\n", + " - is there any classification error (check it using the classifier predictions)?\n", + "- learn a new SVC model using custom C values:\n", + " - how the answers to the questions above change when you use a very high C value (e.g., 1000)?\n", + " - how the answers to the questions above change when you use a very low C value (e.g., 0.3)?\n", + "- learn a new SVC model using a rbf kernel:\n", + " - is the new kernel able to capture the linear model?\n", + " - are you surprised by the above answer? Regarless to whether you are surprised or not: why?\n", + " \n", + "[1] If you make two plots one after the other (in the same cell), the plots will be merged into a single one. You may want to use this feature to plot the support vectors on top of the scatter plot for the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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 +} diff --git a/anno3/apprendimento_automatico/esercizi/all_m/Datasets for Naive Bayes Classification/joy_sadness6000.txt b/anno3/apprendimento_automatico/esercizi/all_m/Datasets for Naive Bayes Classification/joy_sadness6000.txt new file mode 100644 index 0000000..4aa3a6a --- /dev/null +++ b/anno3/apprendimento_automatico/esercizi/all_m/Datasets for Naive Bayes Classification/joy_sadness6000.txt @@ -0,0 +1,11981 @@ +1,1,2,1,3,1,4,1,Joy +5,1,6,1,7,1,8,1,9,1,10,1,11,1,12,1,Joy +13,1,14,1,15,1,16,1,17,1,18,1,19,1,20,1,21,1,22,1,Joy +20,1,23,1,24,1,25,1,26,1,27,1,28,1,29,1,30,1,31,1,32,1,33,1,Joy +23,1,34,1,35,1,36,1,37,2,Joy +31,1,38,5,39,1,40,1,41,1,42,1,43,1,44,1,45,1,Joy +23,1,46,1,47,1,Joy +23,1,48,1,49,1,Joy +50,1,51,1,52,1,53,1,54,1,55,1,56,1,Joy +23,1,57,1,58,2,59,1,60,1,61,1,62,1,Joy +24,2,63,1,64,1,65,1,66,1,67,1,68,1,Joy +69,1,70,1,71,1,72,1,Joy +54,1,73,1,74,1,75,1,76,1,77,1,78,1,Joy +23,1,79,1,80,1,Joy +81,1,82,1,83,1,84,1,85,1,86,1,Joy +62,1,87,1,88,1,89,1,Joy +90,1,91,1,92,1,93,1,94,1,95,1,96,1,Joy +13,1,97,1,Joy +23,1,98,1,99,1,100,1,Joy +38,5,101,1,102,1,103,1,104,1,Joy +23,1,47,1,99,1,105,1,106,1,Joy +105,1,107,2,108,1,109,1,110,1,111,1,112,1,113,1,114,1,115,1,Joy +2,1,71,1,72,1,116,1,117,1,Joy +118,1,119,1,120,1,121,1,122,1,Joy 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"joy_sadness6000.txt" contains the description of the content of a collection of 11981 microblogs (messages) from Twitter. +Each message belongs to a class (either Joy or Sadness) representing the sentiment that is supposed to be expressed by the words in that message. +First, in the file, we have the description of the messages of the first class (Joy) and then Sadness. +There are 5988 messages of Joy and 5994 of Sadness. +Each line of the file represents a single message. +The format of each line is a sequence of pairs followed by the class label, separated by commas. +An example of a line is: +38,3,264,1,635,1,2780,1,Joy + +where 38 is the identifier of the first word occurring in that message, and 3 is the number of times (frequency count) in +which that word is present in that message. +38, 264, 635, 2780 are the identifiers of the words and 3, 1, 1, 1 are the respective frequencies in that message. + +classLabel can be either the string Joy or Sadness. + diff --git 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the package for learning and modeling trees" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "from sklearn import tree " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the matrix containing the data (one example per row)\n", + "and the vector containing the corresponding target value" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X = [[0, 0, 0], [1, 1, 1], [0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]\n", + "Y = [1, 0, 0, 0, 1, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Declare the classification model you want to use and then fit the model to the data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "clf = tree.DecisionTreeClassifier()\n", + "clf = clf.fit(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predict the target value (and print it) for the passed data, using the fitted model currently in clf" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0]\n" + ] + } + ], + "source": [ + "print(clf.predict([[0, 1, 1]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0]\n" + ] + } + ], + "source": [ + "print(clf.predict([[1, 0, 1],[0, 0, 1]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "0\n", + "\n", + "X[1] <= 0.5\n", + "gini = 0.5\n", + "samples = 6\n", + "value = [3, 3]\n", + "\n", + "\n", + "1\n", + "\n", + "X[0] <= 0.5\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [1, 2]\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "6\n", + "\n", + "X[2] <= 0.5\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [2, 1]\n", + "\n", + "\n", + "0->6\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "2\n", + "\n", + "X[2] <= 0.5\n", + "gini = 0.5\n", + "samples = 2\n", + "value = [1, 1]\n", + "\n", + "\n", + "1->2\n", + "\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", + "\n", + "\n", + "1->5\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", + "\n", + "\n", + "2->4\n", + "\n", + "\n", + "\n", + "\n", + "7\n", + "\n", + "X[0] <= 0.5\n", + "gini = 0.5\n", + "samples = 2\n", + "value = [1, 1]\n", + "\n", + "\n", + "6->7\n", + "\n", + "\n", + "\n", + "\n", + "10\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", + "\n", + "\n", + "6->10\n", + "\n", + "\n", + "\n", + "\n", + "8\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", + "\n", + "\n", + "7->8\n", + "\n", + "\n", + "\n", + "\n", + "9\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", + "\n", + "\n", + "7->9\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import graphviz \n", + "dot_data = tree.export_graphviz(clf, out_file=None) \n", + "graph = graphviz.Source(dot_data) \n", + "graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following we start using a dataset (from UCI Machine Learning repository)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "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": 18, + "metadata": { + "collapsed": true + }, + "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": 19, + "metadata": { + "collapsed": false + }, + "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(0)\n", + "indices = np.random.permutation(len(iris.data))\n", + "\n", + "# We now decide to keep the last 10 indices for test set, the remaining for the training set\n", + "indices_training=indices[:-10]\n", + "indices_test=indices[-10:]\n", + "\n", + "iris_X_train = iris.data[indices_training] # keep for training all the matrix elements with the exception of the last 10 \n", + "iris_y_train = iris.target[indices_training]\n", + "iris_X_test = iris.data[indices_test] # keep the last 10 elements for test set\n", + "iris_y_test = iris.target[indices_test]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fit the learning model on training set" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "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": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions:\n", + "[1 2 1 0 0 0 2 1 2 0]\n", + "True classes:\n", + "[1 1 1 0 0 0 2 1 2 0]\n", + "['setosa' 'versicolor' 'virginica']\n" + ] + } + ], + "source": [ + "# apply fitted model \"clf\" to the test set \n", + "predicted_y_test = clf.predict(iris_X_test)\n", + "\n", + "# print the predictions (class numbers associated to classes names in target names)\n", + "print(\"Predictions:\")\n", + "print(predicted_y_test)\n", + "print(\"True classes:\")\n", + "print(iris_y_test) \n", + "print(iris.target_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the index of the test instances and the corresponding predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instance # 88: \n", + "Predicted: versicolor\t True: versicolor\n", + "\n", + "Instance # 70: \n", + "Predicted: virginica\t True: versicolor\n", + "\n", + "Instance # 87: \n", + "Predicted: versicolor\t True: versicolor\n", + "\n", + "Instance # 36: \n", + "Predicted: setosa\t True: setosa\n", + "\n", + "Instance # 21: \n", + "Predicted: setosa\t True: setosa\n", + "\n", + "Instance # 9: \n", + "Predicted: setosa\t True: setosa\n", + "\n", + "Instance # 103: \n", + "Predicted: virginica\t True: virginica\n", + "\n", + "Instance # 67: \n", + "Predicted: versicolor\t True: versicolor\n", + "\n", + "Instance # 117: \n", + "Predicted: virginica\t True: virginica\n", + "\n", + "Instance # 47: \n", + "Predicted: setosa\t True: setosa\n", + "\n" + ] + } + ], + "source": [ + "# print the corresponding instances indexes and class names \n", + "for i in range(len(iris_y_test)): \n", + " print(\"Instance # \"+str(indices_test[i])+\": \")\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": [ + "# Look at the specific examples" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=5.6, sepal width (cm)=3.0, petal length (cm)=4.1, petal width (cm)=1.3\n", + "Predicted: versicolor\t True: versicolor\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=5.9, sepal width (cm)=3.2, petal length (cm)=4.8, petal width (cm)=1.8\n", + "Predicted: virginica\t True: versicolor\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=6.3, sepal width (cm)=2.3, petal length (cm)=4.4, petal width (cm)=1.3\n", + "Predicted: versicolor\t True: versicolor\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=5.5, sepal width (cm)=3.5, petal length (cm)=1.3, petal width (cm)=0.2\n", + "Predicted: setosa\t True: setosa\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=5.1, sepal width (cm)=3.7, petal length (cm)=1.5, petal width (cm)=0.4\n", + "Predicted: setosa\t True: setosa\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=4.9, sepal width (cm)=3.1, petal length (cm)=1.5, petal width (cm)=0.1\n", + "Predicted: setosa\t True: setosa\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=6.3, sepal width (cm)=2.9, petal length (cm)=5.6, petal width (cm)=1.8\n", + "Predicted: virginica\t True: virginica\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=5.8, sepal width (cm)=2.7, petal length (cm)=4.1, petal width (cm)=1.0\n", + "Predicted: versicolor\t True: versicolor\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=7.7, sepal width (cm)=3.8, petal length (cm)=6.7, petal width (cm)=2.2\n", + "Predicted: virginica\t True: virginica\n", + "\n", + "Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n", + "sepal length (cm)=4.6, sepal width (cm)=3.2, petal length (cm)=1.4, petal width (cm)=0.2\n", + "Predicted: setosa\t True: setosa\n", + "\n" + ] + } + ], + "source": [ + "for i in range(len(iris_y_test)): \n", + " print(\"Instance # \"+str(indices_test)+\": \")\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\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "0\n", + "\n", + "petal length (cm) ≤ 2.45\n", + "entropy = 1.585\n", + "samples = 150\n", + "value = [50, 50, 50]\n", + "class = setosa\n", + "\n", + "\n", + "1\n", + "\n", + "entropy = 0.0\n", + "samples = 50\n", + "value = [50, 0, 0]\n", + "class = setosa\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "2\n", + "\n", + "petal width (cm) ≤ 1.75\n", + "entropy = 1.0\n", + "samples = 100\n", + "value = [0, 50, 50]\n", + "class = versicolor\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "3\n", + "\n", + "petal length (cm) ≤ 4.95\n", + "entropy = 0.445\n", + "samples = 54\n", + "value = [0, 49, 5]\n", + "class = versicolor\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "\n", + "\n", + "8\n", + "\n", + "petal length (cm) ≤ 4.95\n", + "entropy = 0.151\n", + "samples = 46\n", + "value = [0, 1, 45]\n", + "class = virginica\n", + "\n", + "\n", + "2->8\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "sepal length (cm) ≤ 5.15\n", + "entropy = 0.146\n", + "samples = 48\n", + "value = [0, 47, 1]\n", + "class = versicolor\n", + "\n", + "\n", + "3->4\n", + "\n", + "\n", + "\n", + "\n", + "7\n", + "\n", + "entropy = 0.918\n", + "samples = 6\n", + "value = [0, 2, 4]\n", + "class = virginica\n", + "\n", + "\n", + "3->7\n", + "\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "entropy = 0.722\n", + "samples = 5\n", + "value = [0, 4, 1]\n", + "class = versicolor\n", + "\n", + "\n", + "4->5\n", + "\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "entropy = 0.0\n", + "samples = 43\n", + "value = [0, 43, 0]\n", + "class = versicolor\n", + "\n", + "\n", + "4->6\n", + "\n", + "\n", + "\n", + "\n", + "9\n", + "\n", + "entropy = 0.65\n", + "samples = 6\n", + "value = [0, 1, 5]\n", + "class = virginica\n", + "\n", + "\n", + "8->9\n", + "\n", + "\n", + "\n", + "\n", + "10\n", + "\n", + "entropy = 0.0\n", + "samples = 40\n", + "value = [0, 0, 40]\n", + "class = virginica\n", + "\n", + "\n", + "8->10\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:aaaid]", + "language": "python", + "name": "conda-env-aaaid-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/anno3/apprendimento_automatico/esercizi/all_m/classification_iris_knn_aa_19_20.ipynb b/anno3/apprendimento_automatico/esercizi/all_m/classification_iris_knn_aa_19_20.ipynb new file mode 100644 index 0000000..47dce87 --- /dev/null +++ b/anno3/apprendimento_automatico/esercizi/all_m/classification_iris_knn_aa_19_20.ipynb @@ -0,0 +1,1659 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# # Classifiers comparison: decision trees and k-nearest neighbors on the dataset Iris\n", + "\n", + "\n", + "In the following program we compare the prediction results obtained by decision trees and k-nearest neighbors on the dataset Iris" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The following cell shows the program training a decision tree and its results in preciction " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.96666667 1. 0.9 0.86666667 1. ]\n" + ] + } + ], + "source": [ + "from sklearn import tree \n", + "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)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell shows the training of k-nearest neighbors and its prediction results.\n", + "Here we use a uniform weighting setting (weights='uniform'): any neighbors weights the same in the majority voting aggregation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.93333333 1. 1. 0.96666667 1. ]\n", + "0 ok setosa\n", + "1 ok setosa\n", + "2 ok setosa\n", + "3 ok setosa\n", + "4 ok setosa\n", + "5 ok setosa\n", + "6 ok setosa\n", + "7 ok setosa\n", + "8 ok setosa\n", + "9 ok setosa\n", + "10 ok setosa\n", + "11 ok setosa\n", + "12 ok setosa\n", + "13 ok setosa\n", + "14 ok setosa\n", + "15 ok setosa\n", + "16 ok setosa\n", + "17 ok setosa\n", + "18 ok setosa\n", + "19 ok setosa\n", + "20 ok setosa\n", + "21 ok setosa\n", + "22 ok setosa\n", + "23 ok setosa\n", + "24 ok setosa\n", + "25 ok setosa\n", + "26 ok setosa\n", + "27 ok setosa\n", + "28 ok setosa\n", + "29 ok setosa\n", + "30 ok setosa\n", + "31 ok setosa\n", + "32 ok setosa\n", + "33 ok setosa\n", + "34 ok setosa\n", + "35 ok setosa\n", + "36 ok setosa\n", + "37 ok setosa\n", + "38 ok setosa\n", + "39 ok setosa\n", + "40 ok setosa\n", + "41 ok setosa\n", + "42 ok setosa\n", + "43 ok setosa\n", + "44 ok setosa\n", + "45 ok setosa\n", + "46 ok setosa\n", + "47 ok setosa\n", + "48 ok setosa\n", + "49 ok setosa\n", + "50 ok versicolor\n", + "51 ok versicolor\n", + "52 ok versicolor\n", + "53 ok versicolor\n", + "54 ok versicolor\n", + "55 ok versicolor\n", + "56 ok versicolor\n", + "57 ok versicolor\n", + "58 ok versicolor\n", + "59 ok versicolor\n", + "60 ok versicolor\n", + "61 ok versicolor\n", + "62 ok versicolor\n", + "63 ok versicolor\n", + "64 ok versicolor\n", + "65 ok versicolor\n", + "66 ok versicolor\n", + "67 ok versicolor\n", + "68 ok versicolor\n", + "69 ok versicolor\n", + "70 ok versicolor\n", + "71 ok versicolor\n", + "72 ok versicolor\n", + "73 ok versicolor\n", + "74 ok versicolor\n", + "75 ok versicolor\n", + "76 ok versicolor\n", + "77 nok true class: versicolor; predicted: virginica\n", + "78 ok versicolor\n", + "79 ok versicolor\n", + "80 ok versicolor\n", + "81 ok versicolor\n", + "82 ok versicolor\n", + "83 nok true class: versicolor; predicted: virginica\n", + "84 ok versicolor\n", + "85 ok versicolor\n", + "86 ok versicolor\n", + "87 ok versicolor\n", + "88 ok versicolor\n", + "89 ok versicolor\n", + "90 ok versicolor\n", + "91 ok versicolor\n", + "92 ok versicolor\n", + "93 ok versicolor\n", + "94 ok versicolor\n", + "95 ok versicolor\n", + "96 ok versicolor\n", + "97 ok versicolor\n", + "98 ok versicolor\n", + "99 ok versicolor\n", + "100 ok virginica\n", + "101 ok virginica\n", + "102 ok virginica\n", + "103 ok virginica\n", + "104 ok virginica\n", + "105 ok virginica\n", + "106 nok true class: virginica; predicted: versicolor\n", + "107 ok virginica\n", + "108 ok virginica\n", + "109 ok virginica\n", + "110 ok virginica\n", + "111 ok virginica\n", + "112 ok virginica\n", + "113 ok virginica\n", + "114 ok virginica\n", + "115 ok virginica\n", + "116 ok virginica\n", + "117 ok virginica\n", + "118 ok virginica\n", + "119 ok virginica\n", + "120 ok virginica\n", + "121 ok virginica\n", + "122 ok virginica\n", + "123 ok virginica\n", + "124 ok virginica\n", + "125 ok virginica\n", + "126 ok virginica\n", + "127 ok virginica\n", + "128 ok virginica\n", + "129 ok virginica\n", + "130 ok virginica\n", + "131 ok virginica\n", + "132 ok virginica\n", + "133 ok virginica\n", + "134 ok virginica\n", + "135 ok virginica\n", + "136 ok virginica\n", + "137 ok virginica\n", + "138 nok true class: virginica; predicted: versicolor\n", + "139 ok virginica\n", + "140 ok virginica\n", + "141 ok virginica\n", + "142 ok virginica\n", + "143 ok virginica\n", + "144 ok virginica\n", + "145 ok virginica\n", + "146 ok virginica\n", + "147 ok virginica\n", + "148 ok virginica\n", + "149 ok virginica\n" + ] + } + ], + "source": [ + "from sklearn import neighbors\n", + "n_neighbors = 11\n", + "clf_knn = neighbors.KNeighborsClassifier(n_neighbors, weights='uniform')\n", + "clf_knn = clf_knn.fit(iris.data, iris.target)\n", + "scores = cross_val_score(clf_knn, iris.data, iris.target, cv=5) # score will be the accuracy\n", + "print(scores)\n", + "# shows the model predictions \n", + "for i in range(len(iris.target)):\n", + " instance=(iris.data[i,:]).reshape(1, -1)\n", + " predicted=clf_knn.predict(instance)[0]\n", + " if iris.target[i]==predicted:\n", + " print(str(i)+\" ok \"+str(iris.target_names[iris.target[i]]))\n", + " else:\n", + " print(str(i)+\" nok \"+\"true class: \"+str(iris.target_names[iris.target[i]])+\"; predicted: \"+str(iris.target_names[predicted]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following cell we use a varying weighting setting (weights='distance'): any neighbors weights inversely with its distance to the test instance in the majority voting aggregation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 ok setosa\n", + "1 ok setosa\n", + "2 ok setosa\n", + "3 ok setosa\n", + "4 ok setosa\n", + "5 ok setosa\n", + "6 ok setosa\n", + "7 ok setosa\n", + "8 ok setosa\n", + "9 ok setosa\n", + "10 ok setosa\n", + "11 ok setosa\n", + "12 ok setosa\n", + "13 ok setosa\n", + "14 ok setosa\n", + "15 ok setosa\n", + "16 ok setosa\n", + "17 ok setosa\n", + "18 ok setosa\n", + "19 ok setosa\n", + "20 ok setosa\n", + "21 ok setosa\n", + "22 ok setosa\n", + "23 ok setosa\n", + "24 ok setosa\n", + "25 ok setosa\n", + "26 ok setosa\n", + "27 ok setosa\n", + "28 ok setosa\n", + "29 ok setosa\n", + "30 ok setosa\n", + "31 ok setosa\n", + "32 ok setosa\n", + "33 ok setosa\n", + "34 ok setosa\n", + "35 ok setosa\n", + "36 ok setosa\n", + "37 ok setosa\n", + "38 ok setosa\n", + "39 ok setosa\n", + "40 ok setosa\n", + "41 ok setosa\n", + "42 ok setosa\n", + "43 ok setosa\n", + "44 ok setosa\n", + "45 ok setosa\n", + "46 ok setosa\n", + "47 ok setosa\n", + "48 ok setosa\n", + "49 ok setosa\n", + "50 ok versicolor\n", + "51 ok versicolor\n", + "52 ok versicolor\n", + "53 ok versicolor\n", + "54 ok versicolor\n", + "55 ok versicolor\n", + "56 ok versicolor\n", + "57 ok versicolor\n", + "58 ok versicolor\n", + "59 ok versicolor\n", + "60 ok versicolor\n", + "61 ok versicolor\n", + "62 ok versicolor\n", + "63 ok versicolor\n", + "64 ok versicolor\n", + "65 ok versicolor\n", + "66 ok versicolor\n", + "67 ok versicolor\n", + "68 ok versicolor\n", + "69 ok versicolor\n", + "70 ok versicolor\n", + "71 ok versicolor\n", + "72 ok versicolor\n", + "73 ok versicolor\n", + "74 ok versicolor\n", + "75 ok versicolor\n", + "76 ok versicolor\n", + "77 ok versicolor\n", + "78 ok versicolor\n", + "79 ok versicolor\n", + "80 ok versicolor\n", + "81 ok versicolor\n", + "82 ok versicolor\n", + "83 ok versicolor\n", + "84 ok versicolor\n", + "85 ok versicolor\n", + "86 ok versicolor\n", + "87 ok versicolor\n", + "88 ok versicolor\n", + "89 ok versicolor\n", + "90 ok versicolor\n", + "91 ok versicolor\n", + "92 ok versicolor\n", + "93 ok versicolor\n", + "94 ok versicolor\n", + "95 ok versicolor\n", + "96 ok versicolor\n", + "97 ok versicolor\n", + "98 ok versicolor\n", + "99 ok versicolor\n", + "100 ok virginica\n", + "101 ok virginica\n", + "102 ok virginica\n", + "103 ok virginica\n", + "104 ok virginica\n", + "105 ok virginica\n", + "106 ok virginica\n", + "107 ok virginica\n", + "108 ok virginica\n", + "109 ok virginica\n", + "110 ok virginica\n", + "111 ok virginica\n", + "112 ok virginica\n", + "113 ok virginica\n", + "114 ok virginica\n", + "115 ok virginica\n", + "116 ok virginica\n", + "117 ok virginica\n", + "118 ok virginica\n", + "119 ok virginica\n", + "120 ok virginica\n", + "121 ok virginica\n", + "122 ok virginica\n", + "123 ok virginica\n", + "124 ok virginica\n", + "125 ok virginica\n", + "126 ok virginica\n", + "127 ok virginica\n", + "128 ok virginica\n", + "129 ok virginica\n", + "130 ok virginica\n", + "131 ok virginica\n", + "132 ok virginica\n", + "133 ok virginica\n", + "134 ok virginica\n", + "135 ok virginica\n", + "136 ok virginica\n", + "137 ok virginica\n", + "138 ok virginica\n", + "139 ok virginica\n", + "140 ok virginica\n", + "141 ok virginica\n", + "142 ok virginica\n", + "143 ok virginica\n", + "144 ok virginica\n", + "145 ok virginica\n", + "146 ok virginica\n", + "147 ok virginica\n", + "148 ok virginica\n", + "149 ok virginica\n", + "Classification score of k-nn with distance weighting\n", + "[ 0.96666667 1. 1. 0.96666667 1. ]\n" + ] + } + ], + "source": [ + "n_neighbors = 11\n", + "clf_knn2 = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')\n", + "clf_knn2.fit(iris.data, iris.target)\n", + "\n", + "for i in range(len(iris.target)):\n", + " instance=(iris.data[i,:]).reshape(1, -1)\n", + " predicted2=clf_knn2.predict(instance)[0]\n", + " if iris.target[i]==predicted2:\n", + " print(str(i)+\" ok \"+str(iris.target_names[iris.target[i]]))\n", + " else:\n", + " print(str(i)+\" nok \"+\"true class: \"+str(iris.target_names[iris.target[i]])+\"; predicted: \"+str(iris.target_names[predicted]))\n", + "print(\"Classification score of k-nn with distance weighting\")\n", + "scores2 = cross_val_score(clf_knn2, iris.data, iris.target, cv=5,scoring='accuracy') # score will be the accuracy\n", + "print(scores2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell shows the tuning of the k-nn models with a varying value of k (number of nearest neighbors) and finds the best value of k (giving the maximum accuracy)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k neighbors=1\n", + "accuracy=0.916666666667\n", + "k neighbors=2\n", + "accuracy=0.916666666667\n", + "k neighbors=3\n", + "accuracy=0.933333333333\n", + "k neighbors=4\n", + "accuracy=0.916666666667\n", + "k neighbors=5\n", + "accuracy=0.95\n", + "k neighbors=6\n", + "accuracy=0.933333333333\n", + "k neighbors=7\n", + "accuracy=0.966666666667\n", + "k neighbors=8\n", + "accuracy=0.933333333333\n", + "k neighbors=9\n", + "accuracy=0.966666666667\n", + "k neighbors=10\n", + "accuracy=0.966666666667\n", + "k neighbors=11\n", + "accuracy=0.966666666667\n", + "k neighbors=12\n", + "accuracy=0.966666666667\n", + "k neighbors=13\n", + "accuracy=0.966666666667\n", + "k neighbors=14\n", + "accuracy=0.95\n", + "k neighbors=15\n", + "accuracy=0.95\n", + "k neighbors=16\n", + "accuracy=0.916666666667\n", + "k neighbors=17\n", + "accuracy=0.933333333333\n", + "k neighbors=18\n", + "accuracy=0.916666666667\n", + "k neighbors=19\n", + "accuracy=0.916666666667\n", + "k neighbors=20\n", + "accuracy=0.916666666667\n", + "k neighbors=21\n", + "accuracy=0.916666666667\n", + "k neighbors=22\n", + "accuracy=0.916666666667\n", + "k neighbors=23\n", + "accuracy=0.916666666667\n", + "k neighbors=24\n", + "accuracy=0.9\n", + "k neighbors=25\n", + "accuracy=0.916666666667\n", + "k neighbors=26\n", + "accuracy=0.916666666667\n", + "k neighbors=27\n", + "accuracy=0.933333333333\n", + "k neighbors=28\n", + "accuracy=0.933333333333\n", + "k neighbors=29\n", + "accuracy=0.933333333333\n", + "k neighbors=30\n", + "accuracy=0.916666666667\n", + "k neighbors=31\n", + "accuracy=0.933333333333\n", + "k neighbors=32\n", + "accuracy=0.916666666667\n", + "k neighbors=33\n", + "accuracy=0.933333333333\n", + "k neighbors=34\n", + "accuracy=0.916666666667\n", + "k neighbors=35\n", + "accuracy=0.933333333333\n", + "k neighbors=36\n", + "accuracy=0.916666666667\n", + "k neighbors=37\n", + "accuracy=0.933333333333\n", + "k neighbors=38\n", + "accuracy=0.9\n", + "k neighbors=39\n", + "accuracy=0.916666666667\n", + "k neighbors=40\n", + "accuracy=0.916666666667\n", + "k neighbors=41\n", + "accuracy=0.933333333333\n", + "k neighbors=42\n", + "accuracy=0.933333333333\n", + "k neighbors=43\n", + "accuracy=0.933333333333\n", + "k neighbors=44\n", + "accuracy=0.916666666667\n", + "k neighbors=45\n", + "accuracy=0.933333333333\n", + "k neighbors=46\n", + "accuracy=0.916666666667\n", + "k neighbors=47\n", + "accuracy=0.916666666667\n", + "k neighbors=48\n", + "accuracy=0.916666666667\n", + "k neighbors=49\n", + "accuracy=0.916666666667\n", + "k neighbors=50\n", + "accuracy=0.916666666667\n", + "k neighbors=51\n", + "accuracy=0.933333333333\n", + "k neighbors=52\n", + "accuracy=0.933333333333\n", + "k neighbors=53\n", + "accuracy=0.933333333333\n", + "k neighbors=54\n", + "accuracy=0.933333333333\n", + "k neighbors=55\n", + "accuracy=0.933333333333\n", + "k neighbors=56\n", + "accuracy=0.95\n", + "k neighbors=57\n", + "accuracy=0.95\n", + "k neighbors=58\n", + "accuracy=0.95\n", + "k neighbors=59\n", + "accuracy=0.95\n", + "k neighbors=60\n", + "accuracy=0.95\n", + "k neighbors=61\n", + "accuracy=0.95\n", + "k neighbors=62\n", + "accuracy=0.95\n", + "k neighbors=63\n", + "accuracy=0.95\n", + "k neighbors=64\n", + "accuracy=0.95\n", + "k neighbors=65\n", + "accuracy=0.95\n", + "k neighbors=66\n", + "accuracy=0.95\n", + "k neighbors=67\n", + "accuracy=0.95\n", + "k neighbors=68\n", + "accuracy=0.95\n", + "k neighbors=69\n", + "accuracy=0.95\n", + "k neighbors=70\n", + "accuracy=0.95\n", + "k neighbors=71\n", + "accuracy=0.95\n", + "k neighbors=72\n", + "accuracy=0.95\n", + "k neighbors=73\n", + "accuracy=0.95\n", + "k neighbors=74\n", + "accuracy=0.95\n", + "k neighbors=75\n", + "accuracy=0.95\n", + "k neighbors=76\n", + "accuracy=0.95\n", + "k neighbors=77\n", + "accuracy=0.95\n", + "k neighbors=78\n", + "accuracy=0.95\n", + "k neighbors=79\n", + "accuracy=0.95\n", + "k neighbors=80\n", + "accuracy=0.95\n", + "k neighbors=81\n", + "accuracy=0.95\n", + "k neighbors=82\n", + "accuracy=0.95\n", + "k neighbors=83\n", + "accuracy=0.95\n", + "k neighbors=84\n", + "accuracy=0.95\n", + "k neighbors=85\n", + "accuracy=0.95\n", + "k neighbors=86\n", + "accuracy=0.95\n", + "k neighbors=87\n", + "accuracy=0.95\n", + "k neighbors=88\n", + "accuracy=0.95\n", + "k neighbors=89\n", + "accuracy=0.95\n", + "k neighbors=90\n", + "accuracy=0.95\n", + "\n", + "\n", + "best k=7\n", + "best accuracy=0.966666666667\n" + ] + }, + { + "data": { + "image/png": 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eXV6tU17KdVI9V7t35IbHort1w5F2p+yGd8wuX/iTFmobVJnT0anQ2X0BKofv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\nKCUaDGPRMOFfUMpBzZlu4R+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from numpy import zeros\n", + "from sklearn import neighbors\n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)\n", + "\n", + "best_accuracy=0\n", + "best_k=1\n", + "A=np.zeros(len(y_train), dtype=np.float) # for storing accuracies\n", + "for n_neighbors in np.arange(1,len(y_train)+1):\n", + " clf_knn3 = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')\n", + " # (n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs)\n", + " clf_knn3.fit(X_train, y_train)\n", + " index=n_neighbors-1\n", + " A[index]=clf_knn3.score(X_test, y_test)\n", + " if best_accuracy" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from numpy import zeros\n", + "from sklearn import neighbors\n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)\n", + "\n", + "i=0 #parameter in the control of the subplot to draw on'\n", + "f,(ax1, ax2) = plt.subplots(1, 2, sharey=True)\n", + "for weight_type in ['uniform','distance']:\n", + " print(\"weighting:\"+str(weight_type))\n", + " A=np.zeros(len(y_train), dtype=np.float) # for storing accuracies\n", + " best_accuracy=0\n", + " best_k=1\n", + " for n_neighbors in np.arange(1,len(y_train)+1):\n", + " clf_knn2 = neighbors.KNeighborsClassifier(n_neighbors, weights=weight_type)\n", + " # (n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs)\n", + " clf_knn2.fit(X_train, y_train)\n", + " index=n_neighbors-1\n", + " A[index]=clf_knn2.score(X_test, y_test)\n", + " if best_accuracy" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from numpy import zeros\n", + "from sklearn import neighbors\n", + "from sklearn.datasets import load_iris\n", + "iris = load_iris()\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)\n", + "\n", + "fig = plt.figure()\n", + "fig.suptitle('Accuracy in k-nn with number of neighbors and types of weighting', fontsize=14, fontweight='bold')\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel('n. neighbors')\n", + "ax.set_ylabel('accuracy')\n", + "\n", + "A=np.zeros((len(y_train),2), dtype=np.float) # 2 arrays for storing accuracies for each type of weigthing\n", + "i=0 #parameter in the control of the different diagram (=matrix A column index)\n", + "best_accuracy=0\n", + "for weight_type in ['uniform','distance']:\n", + " print(\"\\n weighting:\"+str(weight_type))\n", + " best_accuracy=0\n", + " best_k=1\n", + " for n_neighbors in np.arange(1,len(y_train)+1):\n", + " clf_knn2 = neighbors.KNeighborsClassifier(n_neighbors, weights=weight_type)\n", + " # (n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs)\n", + " clf_knn2.fit(X_train, y_train)\n", + " index=n_neighbors-1 # computes the matrix row index\n", + " A[index,i]=clf_knn2.score(X_test, y_test)\n", + " if best_accuracy" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure(figsize=(8,8))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "fig.subplots_adjust(top=1)\n", + "ax.set_title('Dataset n. 1 of data points')\n", + "\n", + "ax.set_xlabel(feature_names1[0])\n", + "ax.set_ylabel(feature_names1[1])\n", + "\n", + "#plot the dataset\n", + "plt.plot(data1[:,0], data1[:,1], '.',markersize=1)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "The following is the program for plotting the dataset n.2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AAAAAVmdhaQOqB6jpBtStqXr/685/9qn63NXdO2ZWffxn76yr+vjO3hmThaUN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\nu7ruvQOA0zK8Gxha9QDS2TuwTPW1q+u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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure(figsize=(20,10))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "fig.subplots_adjust(top=1)\n", + "ax.set_title('Dataset n. 2 of data points')\n", + "\n", + "ax.set_xlabel(feature_names2[0])\n", + "ax.set_ylabel(feature_names2[1])\n", + "\n", + "#plot the dataset\n", + "plt.plot(data2[:,0], data2[:,1], '.', markersize=2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "The following is the program for plotting the dataset n.3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MYCrleyTARTtwbbJlU9thtxY9yjDDc7aGGjwhAzhXrak1K9ZpjTK79nbvkW8N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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure(figsize=(8,8))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "fig.subplots_adjust(top=1)\n", + "ax.set_title('Dataset n. 3 of data points')\n", + "\n", + "ax.set_xlabel(feature_names3[0])\n", + "ax.set_ylabel(feature_names3[1])\n", + "\n", + "#plot the dataset\n", + "plt.plot(data3[:,0], data3[:,1], '.', markersize=0.5, markeredgecolor = 'none')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In the following program we cluster the dataset n.1 with K-means. \n", + "From the plot of dataset n.1 we see 3 separated clusters. Thus k=3. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example n.0=(10.0,10.0)\n", + "in cluster n.0\n", + "Example n.1=(11.0,11.0)\n", + "in cluster n.0\n", + "Example n.2=(12.0,12.0)\n", + "in cluster n.0\n", + "Example n.3=(13.0,13.0)\n", + "in cluster n.0\n", + "Example n.4=(14.0,14.0)\n", + "in cluster n.0\n", + "Example n.5=(15.0,15.0)\n", + "in cluster n.0\n", + "Example n.6=(16.0,16.0)\n", + "in cluster n.0\n", + "Example n.7=(17.0,17.0)\n", + "in cluster n.0\n", + "Example n.8=(18.0,18.0)\n", + "in cluster n.0\n", + "Example n.9=(19.0,19.0)\n", + "in cluster n.0\n", + "Example n.10=(20.0,20.0)\n", + "in cluster n.0\n", + "Example n.11=(21.0,21.0)\n", + "in cluster n.0\n", + "Example n.12=(22.0,22.0)\n", + "in cluster n.0\n", + "Example n.13=(23.0,23.0)\n", + "in cluster n.0\n", + "Example n.14=(24.0,24.0)\n", + "in cluster n.0\n", + "Example n.15=(25.0,25.0)\n", + "in cluster n.0\n", + "Example n.16=(26.0,26.0)\n", + "in cluster n.0\n", + "Example n.17=(27.0,27.0)\n", + "in cluster n.0\n", + "Example n.18=(28.0,28.0)\n", + "in cluster n.0\n", + "Example n.19=(29.0,29.0)\n", + "in cluster n.0\n", + "Example n.20=(30.0,30.0)\n", + "in cluster n.0\n", + "Example n.21=(31.0,31.0)\n", + "in cluster n.0\n", + "Example n.22=(32.0,32.0)\n", + "in cluster n.0\n", + "Example n.23=(33.0,33.0)\n", + "in cluster n.0\n", + "Example n.24=(34.0,34.0)\n", + "in cluster n.0\n", + "Example n.25=(35.0,35.0)\n", + "in cluster n.0\n", + "Example n.26=(36.0,36.0)\n", + "in cluster n.0\n", + "Example n.27=(37.0,37.0)\n", + "in cluster n.0\n", + "Example n.28=(38.0,38.0)\n", + "in cluster n.0\n", + "Example n.29=(39.0,39.0)\n", + "in cluster n.0\n", + "Example n.30=(40.0,40.0)\n", + "in cluster n.0\n", + "Example n.31=(41.0,41.0)\n", + "in cluster n.0\n", + "Example n.32=(42.0,42.0)\n", + "in cluster n.0\n", + "Example n.33=(43.0,43.0)\n", + "in cluster n.0\n", + "Example n.34=(44.0,44.0)\n", + "in cluster n.0\n", + "Example n.35=(45.0,45.0)\n", + "in cluster n.0\n", + "Example n.36=(46.0,46.0)\n", + "in cluster n.0\n", + "Example n.37=(47.0,47.0)\n", + "in cluster n.0\n", + "Example n.38=(48.0,48.0)\n", + "in cluster n.0\n", + "Example n.39=(49.0,49.0)\n", + "in cluster n.0\n", + "Example n.40=(50.0,50.0)\n", + "in cluster n.0\n", + "Example n.41=(51.0,51.0)\n", + "in cluster n.0\n", + "Example n.42=(52.0,52.0)\n", + "in cluster n.0\n", + "Example n.43=(53.0,53.0)\n", + "in cluster n.0\n", + "Example n.44=(54.0,54.0)\n", + "in cluster n.0\n", + "Example n.45=(55.0,55.0)\n", + "in cluster n.0\n", + "Example n.46=(56.0,56.0)\n", + "in cluster n.0\n", + "Example n.47=(57.0,57.0)\n", + "in cluster n.0\n", + "Example n.48=(58.0,58.0)\n", + "in cluster n.0\n", + "Example n.49=(80.0,80.0)\n", + "in cluster n.2\n", + "Example n.50=(81.0,81.0)\n", + "in cluster n.2\n", + "Example n.51=(82.0,82.0)\n", + "in cluster n.2\n", + "Example n.52=(83.0,83.0)\n", + "in cluster n.2\n", + "Example n.53=(84.0,84.0)\n", + "in cluster n.2\n", + "Example n.54=(85.0,85.0)\n", + "in cluster n.2\n", + "Example n.55=(86.0,86.0)\n", + "in cluster n.2\n", + "Example n.56=(87.0,87.0)\n", + "in cluster n.2\n", + "Example n.57=(88.0,88.0)\n", + "in cluster n.2\n", + "Example n.58=(89.0,89.0)\n", + "in cluster n.2\n", + "Example n.59=(90.0,90.0)\n", + "in cluster n.2\n", + "Example n.60=(91.0,91.0)\n", + "in cluster n.2\n", + "Example n.61=(92.0,92.0)\n", + "in cluster n.2\n", + "Example n.62=(93.0,93.0)\n", + "in cluster n.2\n", + "Example n.63=(94.0,94.0)\n", + "in cluster n.2\n", + "Example n.64=(95.0,95.0)\n", + "in cluster n.2\n", + "Example n.65=(96.0,96.0)\n", + "in cluster n.2\n", + "Example n.66=(97.0,97.0)\n", + "in cluster n.2\n", + "Example n.67=(98.0,98.0)\n", + "in cluster n.2\n", + "Example n.68=(99.0,99.0)\n", + "in cluster n.2\n", + "Example n.69=(100.0,100.0)\n", + "in cluster n.2\n", + "Example n.70=(101.0,101.0)\n", + "in cluster n.2\n", + "Example n.71=(102.0,102.0)\n", + "in cluster n.2\n", + "Example n.72=(103.0,103.0)\n", + "in cluster n.2\n", + "Example n.73=(104.0,104.0)\n", + "in cluster n.2\n", + "Example n.74=(105.0,105.0)\n", + "in cluster n.2\n", + "Example n.75=(106.0,106.0)\n", + "in cluster n.2\n", + "Example n.76=(107.0,107.0)\n", + "in cluster n.2\n", + "Example n.77=(108.0,108.0)\n", + "in cluster n.2\n", + "Example n.78=(109.0,109.0)\n", + "in cluster n.2\n", + "Example n.79=(110.0,110.0)\n", + "in cluster n.2\n", + "Example n.80=(111.0,111.0)\n", + "in cluster n.2\n", + "Example n.81=(112.0,112.0)\n", + "in cluster n.2\n", + "Example n.82=(113.0,113.0)\n", + "in cluster n.2\n", + "Example n.83=(114.0,114.0)\n", + "in cluster n.2\n", + "Example n.84=(115.0,115.0)\n", + "in cluster n.2\n", + "Example n.85=(116.0,116.0)\n", + "in cluster n.2\n", + "Example n.86=(117.0,117.0)\n", + "in cluster n.2\n", + "Example n.87=(118.0,118.0)\n", + "in cluster n.2\n", + "Example n.88=(119.0,119.0)\n", + "in cluster n.2\n", + "Example n.89=(120.0,120.0)\n", + "in cluster n.2\n", + "Example n.90=(121.0,121.0)\n", + "in cluster n.2\n", + "Example n.91=(122.0,122.0)\n", + "in cluster n.2\n", + "Example n.92=(123.0,123.0)\n", + "in cluster n.2\n", + "Example n.93=(124.0,124.0)\n", + "in cluster n.2\n", + "Example n.94=(125.0,125.0)\n", + "in cluster n.2\n", + "Example n.95=(126.0,126.0)\n", + "in cluster n.2\n", + "Example n.96=(127.0,127.0)\n", + "in cluster n.2\n", + "Example n.97=(128.0,128.0)\n", + "in cluster n.2\n", + "Example n.98=(129.0,129.0)\n", + "in cluster n.2\n", + "Example n.99=(120.0,10.0)\n", + "in cluster n.1\n", + "Example n.100=(121.0,11.0)\n", + "in cluster n.1\n", + "Example n.101=(122.0,12.0)\n", + "in cluster n.1\n", + "Example n.102=(123.0,13.0)\n", + "in cluster n.1\n", + "Example n.103=(124.0,14.0)\n", + "in cluster n.1\n", + "Example n.104=(125.0,15.0)\n", + "in cluster n.1\n", + "Example n.105=(126.0,16.0)\n", + "in cluster n.1\n", + "Example n.106=(127.0,17.0)\n", + "in cluster n.1\n", + "Example n.107=(128.0,18.0)\n", + "in cluster n.1\n", + "Example n.108=(129.0,19.0)\n", + "in cluster n.1\n", + "Example n.109=(130.0,20.0)\n", + "in cluster n.1\n", + "Example n.110=(131.0,21.0)\n", + "in cluster n.1\n", + "Example n.111=(132.0,22.0)\n", + "in cluster n.1\n", + "Example n.112=(133.0,23.0)\n", + "in cluster n.1\n", + "Example n.113=(134.0,24.0)\n", + "in cluster n.1\n", + "Example n.114=(135.0,25.0)\n", + "in cluster n.1\n", + "Example n.115=(136.0,26.0)\n", + "in cluster n.1\n", + "Example n.116=(137.0,27.0)\n", + "in cluster n.1\n", + "Example n.117=(138.0,28.0)\n", + "in cluster n.1\n", + "Example n.118=(139.0,29.0)\n", + "in cluster n.1\n", + "Example n.119=(140.0,30.0)\n", + "in cluster n.1\n", + "Example n.120=(141.0,31.0)\n", + "in cluster n.1\n", + "Example n.121=(142.0,32.0)\n", + "in cluster n.1\n", + "Example n.122=(143.0,33.0)\n", + "in cluster n.1\n", + "Example n.123=(144.0,34.0)\n", + "in cluster n.1\n", + "Example n.124=(145.0,35.0)\n", + "in cluster n.1\n", + "Example n.125=(146.0,36.0)\n", + "in cluster n.1\n", + "Example n.126=(147.0,37.0)\n", + "in cluster n.1\n", + "Example n.127=(148.0,38.0)\n", + "in cluster n.1\n", + "Example n.128=(149.0,39.0)\n", + "in cluster n.1\n", + "Example n.129=(150.0,40.0)\n", + "in cluster n.1\n", + "Example n.130=(151.0,41.0)\n", + "in cluster n.1\n", + "Example n.131=(152.0,42.0)\n", + "in cluster n.1\n", + "Example n.132=(153.0,43.0)\n", + "in cluster n.1\n", + "Example n.133=(154.0,44.0)\n", + "in cluster n.1\n", + "Example n.134=(155.0,45.0)\n", + "in cluster n.1\n", + "Example n.135=(156.0,46.0)\n", + "in cluster n.1\n", + "Example n.136=(157.0,47.0)\n", + "in cluster n.1\n", + "Example n.137=(158.0,48.0)\n", + "in cluster n.1\n", + "Example n.138=(159.0,49.0)\n", + "in cluster n.1\n", + "Example n.139=(160.0,50.0)\n", + "in cluster n.1\n", + "Example n.140=(161.0,51.0)\n", + "in cluster n.1\n", + "Example n.141=(162.0,52.0)\n", + "in cluster n.1\n", + "Example n.142=(163.0,53.0)\n", + "in cluster n.1\n", + "Example n.143=(164.0,54.0)\n", + "in cluster n.1\n", + "Example n.144=(165.0,55.0)\n", + "in cluster n.1\n", + "Example n.145=(166.0,56.0)\n", + "in cluster n.1\n", + "Example n.146=(167.0,57.0)\n", + "in cluster n.1\n", + "Example n.147=(168.0,58.0)\n", + "in cluster n.1\n", + "Example n.148=(169.0,59.0)\n", + "in cluster n.1\n", + "Example n.149=(170.0,60.0)\n", + "in cluster n.1\n" + ] + } + ], + "source": [ + "import csv\n", + "import sys\n", + "from os.path import join\n", + "\n", + "import numpy as np\n", + " \n", + "def load_data(file_path, file_name):\n", + " with open(join(file_path, file_name)) as csv_file:\n", + " data_file = csv.reader(csv_file,delimiter=',')\n", + " temp1 = next(data_file)\n", + " n_samples = int(temp1[0])\n", + " n_features = int(temp1[1])\n", + " temp2 = next(data_file)\n", + " feature_names = np.array(temp2[:n_features])\n", + "\n", + " data_list = [iter for iter in data_file]\n", + " \n", + " data = np.asarray(data_list, dtype=np.float64) \n", + " \n", + " return(data,feature_names,n_samples,n_features)\n", + "\n", + "# The main program reads the input file containing the dataset\n", + "# file_path is the file path where the file with the data to be read are located\n", + "# we assume the file contains an example per line\n", + "# each example is a list of real values separated by a comma (csv format)\n", + "# The first line of the file contains the heading with:\n", + "# N_samples,n_features,\n", + "# The second line contains the feature names separated by a comma \n", + "\n", + "#file_path=\"~/meo/Documents/Didattica/Laboratorio-15-16-Jupyter/\"\n", + "file_path=\"./\"\n", + "# all the three datasets contain data points on (x,y) \n", + "file_name1=\"3-clusters.csv\"\n", + "\n", + "data1,feature_names1,n_samples1,n_features1 = load_data(file_path, file_name1)\n", + "\n", + "from sklearn.cluster import KMeans\n", + "\n", + "np.random.seed(5)\n", + "\n", + "k=3\n", + "kmeans1 = KMeans(n_clusters=k, random_state=0).fit(data1)\n", + "\n", + "i=0\n", + "for i in range(n_samples1):\n", + " print(\"Example n.\"+str(i)+\"=(\"+str(data1[i,0])+\",\"+str(data1[i,1])+\")\")\n", + " print(\"in cluster n.\"+str(kmeans1.labels_[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following program we plot the clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure(figsize=(8,8))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "fig.subplots_adjust(top=1)\n", + "ax.set_title('Clustered points in dataset n. 1')\n", + "\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "\n", + "# set the list of colors to be selected when plotting the different clusters\n", + "color=['b','g','r','c','m','y','k','w']\n", + " \n", + "#plot the dataset\n", + "for clu in range(k):\n", + " # collect the sequence of cooordinates of the points in each given cluster (determined by clu)\n", + " data_list_x = [data1[i,0] for i in range(n_samples1) if kmeans1.labels_[i]==clu]\n", + " data_list_y = [data1[i,1] for i in range(n_samples1) if kmeans1.labels_[i]==clu]\n", + " plt.scatter(data_list_x, data_list_y, s=2, edgecolors='none', c=color[clu], alpha=0.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "In the following cell we perform clustering with K-means on dataset2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following we plot clustering results on dataset2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform clustering with K-means on the remaining dataset3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following we plot clustering results on dataset3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "In the following cells I propose you to write a program that computes with a statistical measure of your choice a quantitative evaluation of clusters in the three datasets.\n", + "It is advisable to execute K-means a certain number of times (let us try 10 times) and then select the clustering solution that gives the best value of the evaluation measure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As already done with classification by k-nn, plot in the cell below the quantitative measure of your choice (used above) with an increasing value of k (the number of clusters) so that the best value of k can be selected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:aaaid]", + "language": "python", + "name": "conda-env-aaaid-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/anno3/apprendimento_automatico/slides/00.1.Introduction.pdf b/anno3/apprendimento_automatico/slides/00.1.Introduction.pdf new file mode 100644 index 0000000..99a6443 Binary files /dev/null and b/anno3/apprendimento_automatico/slides/00.1.Introduction.pdf differ diff --git a/anno3/apprendimento_automatico/slides/00.2.Tasks.pdf b/anno3/apprendimento_automatico/slides/00.2.Tasks.pdf new file 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