teoria
This commit is contained in:
Francesco Mecca
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.#todo.org
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.#todo.org
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user@thinkgentoo.29142:1592891722
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anno3/apprendimento_automatico/esercizi/1/ex1.org
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anno3/apprendimento_automatico/esercizi/1/ex1.org
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Modify the given Jupyter notebook on decision trees on Iris data and
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perform the following tasks:
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1. get an artificial inflation of some class in the training set by
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a given factor: 10 (weigh more the classes virginica e versicolor
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which are more difficult to discriminate). Learn the tree in these
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conditions.
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2. modify the weight of some classes (set to 10 the weights for
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misclassification between virginica into versicolor and vice versa)
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and learn the tree in these conditions. You should obtain similar
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results as for step 1.
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3. learn trees but avoid overfitting (by improving the error on the
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test set) tuning the parameters on: the minimum number of samples
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per leaf, max depth of the tree, min_impurity_decrease parameters,
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max leaf nodes, etc.
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4. build the confusion matrix of the created tree models on the
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test set and show them.
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5. build the ROC curves (or coverage curves in coverage space) and
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plot them for each tree model you have created: for each model you
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have to build three curves, one for each class, considered in turn
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as the positive class.
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258
anno3/apprendimento_automatico/esercizi/4/least_squares.ipynb
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258
anno3/apprendimento_automatico/esercizi/4/least_squares.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Experimenting with least squares and its variants"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from sklearn import datasets\n",
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"from scipy.optimize import fmin_bfgs\n",
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"import numpy as np\n",
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"from numpy.linalg import norm\n",
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"from numpy.linalg import inv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data preparation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 6.32000000e-03, 1.80000000e+01, 2.31000000e+00, ...,\n",
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" 1.53000000e+01, 3.96900000e+02, 4.98000000e+00],\n",
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" [ 2.73100000e-02, 0.00000000e+00, 7.07000000e+00, ...,\n",
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" 1.78000000e+01, 3.96900000e+02, 9.14000000e+00],\n",
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" [ 2.72900000e-02, 0.00000000e+00, 7.07000000e+00, ...,\n",
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" 1.78000000e+01, 3.92830000e+02, 4.03000000e+00],\n",
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" ..., \n",
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" [ 6.07600000e-02, 0.00000000e+00, 1.19300000e+01, ...,\n",
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" 2.10000000e+01, 3.96900000e+02, 5.64000000e+00],\n",
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" [ 1.09590000e-01, 0.00000000e+00, 1.19300000e+01, ...,\n",
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" 2.10000000e+01, 3.93450000e+02, 6.48000000e+00],\n",
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" [ 4.74100000e-02, 0.00000000e+00, 1.19300000e+01, ...,\n",
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" 2.10000000e+01, 3.96900000e+02, 7.88000000e+00]])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"boston = datasets.load_boston()\n",
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"data = np.array(boston.data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The boston dataset is one of the standard regression problems used to experiment with learning algorithms. Below you can find the dataset description"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Boston House Prices dataset\n",
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"\n",
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"Notes\n",
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"------\n",
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"Data Set Characteristics: \n",
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"\n",
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" :Number of Instances: 506 \n",
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"\n",
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" :Number of Attributes: 13 numeric/categorical predictive\n",
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" \n",
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" :Median Value (attribute 14) is usually the target\n",
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"\n",
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" :Attribute Information (in order):\n",
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" - CRIM per capita crime rate by town\n",
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" - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n",
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" - INDUS proportion of non-retail business acres per town\n",
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" - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
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" - NOX nitric oxides concentration (parts per 10 million)\n",
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" - RM average number of rooms per dwelling\n",
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" - AGE proportion of owner-occupied units built prior to 1940\n",
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" - DIS weighted distances to five Boston employment centres\n",
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" - RAD index of accessibility to radial highways\n",
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" - TAX full-value property-tax rate per $10,000\n",
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" - PTRATIO pupil-teacher ratio by town\n",
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" - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
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" - LSTAT % lower status of the population\n",
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" - MEDV Median value of owner-occupied homes in $1000's\n",
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"\n",
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" :Missing Attribute Values: None\n",
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"\n",
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" :Creator: Harrison, D. and Rubinfeld, D.L.\n",
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"\n",
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"This is a copy of UCI ML housing dataset.\n",
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"http://archive.ics.uci.edu/ml/datasets/Housing\n",
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"\n",
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"\n",
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"This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
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"\n",
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"The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
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"prices and the demand for clean air', J. Environ. Economics & Management,\n",
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"vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
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"...', Wiley, 1980. N.B. Various transformations are used in the table on\n",
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"pages 244-261 of the latter.\n",
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"\n",
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"The Boston house-price data has been used in many machine learning papers that address regression\n",
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"problems. \n",
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" \n",
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"**References**\n",
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"\n",
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" - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
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" - 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",
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" - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
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"\n"
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]
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}
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],
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"source": [
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"print(boston.DESCR)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"t = np.ones(len(data)).reshape(len(data),1)\n",
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"data = np.append(data, t, 1)\n",
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"target = np.array(boston.target)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now divide the data into a training set $X$ and a test set $X_\\textrm{test}$."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"X,y = data[0:400,:], target[0:400]\n",
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"X_test, y_test = data[400:,:], target[400:]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"1. Calculate the ridge regression solution (set lambda to 0.01) and evaluate its performances on the training set and on test set.\n",
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"1. Calculate the lasso regression solution and evaluate its performances on the training set and on the test set."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Notes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- 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",
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" - `transpose`: matrix transposition (e.g., `transpose(X)`)\n",
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" - `dot`: matrix multiplication (e.g., `X.dot(X2)`) \n",
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" - `inv`: matrix inversion (e.g., `inv(X)`)\n",
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"- 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",
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" ```python\n",
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" def f(w):\n",
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" return w[0]**2 + w[1]**2 + w[0] + w[1]\n",
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" \n",
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" w = fmin_bfgs(f, [0,0])\n",
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" ```\n",
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" note that the function may (and should) reference your data variables (i.e., $X$ and $y$).\n",
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"- to evaluate the performances of your solutions use the $S$ statistic:\n",
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" $$\n",
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" S = \\sqrt{ \\frac{1}{n} \\sum_{i=1}^n (y_i' - y_i)^2 }\n",
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" $$\n",
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" where $y'_i$ is your model prediction for the i-th example, and $n$ is the number of examples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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209
anno3/apprendimento_automatico/esercizi/4/svm.ipynb
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anno3/apprendimento_automatico/esercizi/4/svm.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# # Classifiers introduction\n",
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"\n",
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"In the following program we introduce the basic steps of classification of a dataset in a matrix"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the package for learning and modeling trees"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"from sklearn import tree "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Define the matrix containing the data (one example per row)\n",
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"and the vector containing the corresponding target value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"X = [[0, 0, 0], [1, 1, 1], [0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]\n",
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"Y = [1, 0, 0, 0, 1, 1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Declare the classification model you want to use and then fit the model to the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"clf = tree.DecisionTreeClassifier()\n",
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"clf = clf.fit(X, Y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Predict the target value (and print it) for the passed data, using the fitted model currently in clf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0]\n"
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]
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}
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],
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"source": [
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"print(clf.predict([[0, 1, 1]]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 0]\n"
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]
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}
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],
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"source": [
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"print(clf.predict([[1, 0, 1],[0, 0, 1]]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/svg+xml": [
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"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
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"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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" viewBox=\"0.00 0.00 484.54 358.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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"<text text-anchor=\"middle\" x=\"265.769\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\">X[1] <= 0.5</text>\n",
|
||||
"<text text-anchor=\"middle\" x=\"265.769\" y=\"-320.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.5</text>\n",
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"<text text-anchor=\"middle\" x=\"265.769\" y=\"-306.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 6</text>\n",
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"Instance # 70: \n",
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"Instance # 87: \n",
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"Instance # 36: \n",
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"Instance # 21: \n",
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"Instance # 9: \n",
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"Instance # 103: \n",
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"Predicted: virginica\t True: virginica\n",
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"Instance # 67: \n",
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"Instance # 117: \n",
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"Predicted: virginica\t True: virginica\n",
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"Instance # 47: \n",
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||||
"source": [
|
||||
"# Look at the specific examples"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"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<len(iris.feature_names)-1): s=s+\", \"\n",
|
||||
" print(s)\n",
|
||||
" print(\"Predicted: \"+iris.target_names[predicted_y_test[i]]+\"\\t True: \"+iris.target_names[iris_y_test[i]]+\"\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Obtain model performance results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Accuracy score: 0.9\n",
|
||||
"F1 score: 0.885714285714\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# print some metrics results\n",
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"from sklearn.metrics import f1_score\n",
|
||||
"acc_score = accuracy_score(iris_y_test, predicted_y_test)\n",
|
||||
"print(\"Accuracy score: \"+ str(acc_score))\n",
|
||||
"f1=f1_score(iris_y_test, predicted_y_test, average='macro')\n",
|
||||
"print(\"F1 score: \"+str(f1))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Use Cross Validation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.96666667 1. 0.9 0.86666667 1. ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_iris\n",
|
||||
"from sklearn.model_selection import cross_val_score # will be used to separate training and test\n",
|
||||
"iris = load_iris()\n",
|
||||
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:1,2:1})\n",
|
||||
"clf = clf.fit(iris.data, iris.target)\n",
|
||||
"scores = cross_val_score(clf, iris.data, iris.target, cv=5) # score will be the accuracy\n",
|
||||
"print(scores)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.96658312 1. 0.89769821 0.86666667 1. ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# computes F1- score\n",
|
||||
"f1_scores = cross_val_score(clf, iris.data, iris.target, cv=5, scoring='f1_macro')\n",
|
||||
"print(f1_scores)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Show the resulting tree "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Print the picture in a PDF file"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'my_iris_predictions.pdf'"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import graphviz \n",
|
||||
"dot_data = tree.export_graphviz(clf, out_file=None) \n",
|
||||
"graph = graphviz.Source(dot_data) \n",
|
||||
"graph.render(\"my_iris_predictions\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Generate a picture here"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
|
||||
"['setosa', 'versicolor', 'virginica']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(list(iris.feature_names))\n",
|
||||
"print(list(iris.target_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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"image/svg+xml": [
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"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
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"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
|
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"<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
|
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" -->\n",
|
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"<!-- Title: Tree Pages: 1 -->\n",
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"<svg width=\"653pt\" height=\"528pt\"\n",
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" viewBox=\"0.00 0.00 652.83 528.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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"<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 524)\">\n",
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"<title>Tree</title>\n",
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"<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-524 648.831,-524 648.831,4 -4,4\"/>\n",
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"<text text-anchor=\"start\" x=\"246.328\" y=\"-448.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n",
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"</g>\n",
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"<g id=\"edge1\" class=\"edge\"><title>0->1</title>\n",
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"Instance # 70: \n",
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"Instance # 36: \n",
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"Instance # 21: \n",
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"Instance # 9: \n",
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"Instance # 103: \n",
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"Instance # 67: \n",
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"Instance # 117: \n",
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"Predicted: virginica\t True: virginica\n",
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"Instance # 47: \n",
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"Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n",
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"sepal length (cm)=5.6, sepal width (cm)=3.0, petal length (cm)=4.1, petal width (cm)=1.3\n",
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"\n",
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"Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n",
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"sepal length (cm)=5.9, sepal width (cm)=3.2, petal length (cm)=4.8, petal width (cm)=1.8\n",
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|
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"\n",
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"Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n",
|
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"sepal length (cm)=6.3, sepal width (cm)=2.3, petal length (cm)=4.4, petal width (cm)=1.3\n",
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|
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"\n",
|
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"Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n",
|
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"sepal length (cm)=5.5, sepal width (cm)=3.5, petal length (cm)=1.3, petal width (cm)=0.2\n",
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"\n",
|
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"Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n",
|
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"sepal length (cm)=5.1, sepal width (cm)=3.7, petal length (cm)=1.5, petal width (cm)=0.4\n",
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|
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"\n",
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"Instance # [ 88 70 87 36 21 9 103 67 117 47]: \n",
|
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"sepal length (cm)=4.9, sepal width (cm)=3.1, petal length (cm)=1.5, petal width (cm)=0.1\n",
|
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"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<len(iris.feature_names)-1): s=s+\", \"\n",
|
||||
" print(s)\n",
|
||||
" print(\"Predicted: \"+iris.target_names[predicted_y_test[i]]+\"\\t True: \"+iris.target_names[iris_y_test[i]]+\"\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Obtain model performance results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Accuracy score: 0.9\n",
|
||||
"F1 score: 0.885714285714\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# print some metrics results\n",
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"from sklearn.metrics import f1_score\n",
|
||||
"acc_score = accuracy_score(iris_y_test, predicted_y_test)\n",
|
||||
"print(\"Accuracy score: \"+ str(acc_score))\n",
|
||||
"f1=f1_score(iris_y_test, predicted_y_test, average='macro')\n",
|
||||
"print(\"F1 score: \"+str(f1))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Use Cross Validation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.96666667 1. 0.9 0.86666667 1. ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_iris\n",
|
||||
"from sklearn.model_selection import cross_val_score # will be used to separate training and test\n",
|
||||
"iris = load_iris()\n",
|
||||
"clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=300,min_samples_leaf=5,class_weight={0:1,1:1,2:1})\n",
|
||||
"clf = clf.fit(iris.data, iris.target)\n",
|
||||
"scores = cross_val_score(clf, iris.data, iris.target, cv=5) # score will be the accuracy\n",
|
||||
"print(scores)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.96658312 1. 0.89769821 0.86666667 1. ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# computes F1- score\n",
|
||||
"f1_scores = cross_val_score(clf, iris.data, iris.target, cv=5, scoring='f1_macro')\n",
|
||||
"print(f1_scores)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Show the resulting tree "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Print the picture in a PDF file"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'my_iris_predictions.pdf'"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import graphviz \n",
|
||||
"dot_data = tree.export_graphviz(clf, out_file=None) \n",
|
||||
"graph = graphviz.Source(dot_data) \n",
|
||||
"graph.render(\"my_iris_predictions\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Generate a picture here"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
|
||||
"['setosa', 'versicolor', 'virginica']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(list(iris.feature_names))\n",
|
||||
"print(list(iris.target_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
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"outputs": [
|
||||
{
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"data": {
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" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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"<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
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" -->\n",
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224
anno3/apprendimento_automatico/esercizi/all_m/one_vs_rest.ipynb
Normal file
224
anno3/apprendimento_automatico/esercizi/all_m/one_vs_rest.ipynb
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18
todo.org
18
todo.org
|
|
@ -9,7 +9,23 @@
|
|||
- [ ] 02:
|
||||
+ [ ] Inductive Leap
|
||||
+ [ ] Biased hyp
|
||||
|
||||
- [ ] 04:
|
||||
+ [ ] growtree algorithm
|
||||
+ [ ] Bene tutto il discorso su entropia
|
||||
+ [ ] Ranking and regression trees
|
||||
- [ ] 05:
|
||||
+ [ ] Learning ordered rules da libro
|
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+ [ ] Subgroups discovery e fine
|
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- [ ] 07:
|
||||
+ [ ] Mahalanobis distance
|
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+ [ ] Meaning of variance in neighboors
|
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+ [ ] Sum of squared error
|
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+ [ ] Silhouttes
|
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+ [ ] Rivedi kernelization
|
||||
- [ ] Esercizi:
|
||||
- [ ] es1: perche` min_impurity decrease
|
||||
- [ ] chiedi a Galla`, Marco e Naz quali sono tutti gli es
|
||||
- [ ] linear models.zip?
|
||||
|
||||
* Tesi [8/33]
|
||||
- [ ] Rivedere inference rules di Gabriel e aggiustarle con le mie
|
||||
|
|
|
|||
Loading…
Reference in a new issue