mirror of
https://github.com/gsi-upm/sitc
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290 lines
8.1 KiB
Plaintext
290 lines
8.1 KiB
Plaintext
{
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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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"![](files/images/EscUpmPolit_p.gif \"UPM\")"
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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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"# Course Notes for Learning Intelligent Systems"
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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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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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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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"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
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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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"# Table of Contents\n",
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"* [Visualisation](#Visualisation)\n",
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"* [Exploratory visualisation](#Exploratory-visualisation)\n",
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"* [References](#References)"
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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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"# Visualisation"
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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 goal of this notebook is to learn how to analyse a dataset. We will cover other tasks such as cleaning or munging (changing the format) the dataset in other sessions."
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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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"## Exploratory visualisation"
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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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"This section covers different ways to inspect the distribution of samples per feature.\n",
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"\n",
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"First of all, let's see how many samples of each class we have, using a [histogram](https://en.wikipedia.org/wiki/Histogram). \n",
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"\n",
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"A histogram is a graphical representation of the distribution of numerical data. It is an estimation of the probability distribution of a continuous variable (quantitative variable). \n",
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"\n",
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"For building a histogram, we need first to 'bin' the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. \n",
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"\n",
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"In our case, since the values are not continuous and we have only three values, we do not need to bin them."
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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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"outputs": [],
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"source": [
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"from sklearn import datasets\n",
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"iris = datasets.load_iris()"
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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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"outputs": [],
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"source": [
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"# library for displaying plots\n",
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"import matplotlib.pyplot as plt\n",
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"# display plots in the notebook\n",
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"# if this is not set, you will not see the graphic here\n",
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"%matplotlib inline"
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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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"outputs": [],
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"source": [
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"# Plot histogram, the default is 10 bins\n",
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"plt.hist(iris.target)\n",
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"plt.ylabel('Number of instances')\n",
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"plt.xlabel('iris class')\n",
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"plt.xticks(range(len(iris.target_names)), iris.target_names);"
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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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"As can be seen, we have the same distribution of samples for every class.\n",
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"The next step is to see the distribution of the features"
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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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"outputs": [],
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"source": [
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"# This is a reminder of the name and index of each feature\n",
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"print(iris.feature_names)"
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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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"outputs": [],
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"source": [
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"# A reminder of feature names and indexes\n",
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"print(iris.target_names)"
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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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"A [**scatter plot**](https://en.wikipedia.org/wiki/Scatter_plot) (*gráfico de dispersión*) displays the value of typically two variables for a set of 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# scatter makes a plot of x vs y\n",
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"plt.scatter(iris.data[:,0], iris.target)\n",
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"plt.yticks(range(len(iris.target_names)), iris.target_names);\n",
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"plt.xlabel(iris.feature_names[0])\n",
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"plt.ylabel('iris class')"
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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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"outputs": [],
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"source": [
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"# Plot the distribution of the dataset\n",
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"names = set(iris.target)\n",
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"\n",
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"# x and y are all the samples from column 0 (sepal_length) and 1 (sepal_width) respectively\n",
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"x,y = iris.data[:,0], iris.data[:,1]\n",
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"\n",
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"#if you want to understand better this code, see what happens when you replace name by 0, 1, 2 in the line\n",
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"# cond = iris.target == name. \n",
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"for name in names:\n",
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" cond = iris.target == name\n",
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" plt.plot(x[cond], y[cond], linestyle='none', marker='o', label=iris.target_names[name])\n",
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"\n",
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"plt.legend(numpoints=1)\n",
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"plt.show()"
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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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"As we can see, the Setosa class seems to be linearly separable with these two features.\n",
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"\n",
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"Another nice visualisation is given below."
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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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"outputs": [],
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"source": [
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"x_index = 0\n",
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"y_index = 1\n",
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"formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
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"plt.scatter(iris.data[:, x_index], iris.data[:, y_index], s=40,\n",
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"c=iris.target)\n",
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"plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
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"plt.xlabel(iris.feature_names[x_index])\n",
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"plt.ylabel(iris.feature_names[y_index]);"
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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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"This alternate visualisation also suggests that the Setosa class seems to be linearly separable.\n",
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"\n",
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"Students interested in practicing advanced visualisations can check [Advanced visualisation notebook](2_3_1_Advanced_Visualisation.ipynb).\n",
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"\n"
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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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"# References"
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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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"* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
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"* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
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"* [Mastering Pandas](https://learning.oreilly.com/library/view/mastering-pandas/9781789343236/), Femi Anthony, Packt Publishing, 2015.\n",
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"* [Matplotlib web page](http://matplotlib.org/index.html)\n",
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"* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
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"* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",
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"* [Iris dataset visualisation notebook](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook)\n",
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"* [Tutorial plotting with Seaborn](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html)"
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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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"## Licence\n",
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"\n",
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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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