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sitc/ml1/2_3_0_Visualisation.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](files/images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
"* [Visualisation](#Visualisation)\n",
"* [Exploratory visualisation](#Exploratory-visualisation)\n",
"* [References](#References)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploratory visualisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section covers different ways to inspect the distribution of samples per feature.\n",
"\n",
"First of all, let's take a see how many samples of each class we have, using a [histogram](https://en.wikipedia.org/wiki/Histogram). \n",
"\n",
"A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable (quantitative variable). \n",
"\n",
"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",
"\n",
"In our case, since the values are not continuous and we have only three values, we do not need to bin them."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"iris = datasets.load_iris()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
"/usr/lib/python3/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"# library for displaying plots\n",
"import matplotlib.pyplot as plt\n",
"# display plots in the notebook\n",
"# if this is not set, you will not see the graphic here\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f04f62a1a90>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f04f626b710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot histogram, the default is 10 bins\n",
"plt.hist(iris.target, bins=10)\n",
"plt.xlabel('Number of species')\n",
"plt.ylabel('iris class')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see we have the same distribution of samples for every class.\n",
"The next step is to see the distribution of the features"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
]
}
],
"source": [
"# This is a reminder of the name and index of each feature\n",
"print(iris.feature_names)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['setosa' 'versicolor' 'virginica']\n"
]
}
],
"source": [
"# A reminder of feature names and indexes\n",
"print(iris.target_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f04f62a1630>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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92T7vAW5o5edUjTHV/TnlCwnNzCzJlBrCMjOzycMJxMzMkjiBmJlZEicQMzNL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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f05193cde10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# scatter makes a plot of x vs y\n",
"plt.scatter(iris.data[:,0], iris.target)\n",
"plt.ylabel(iris.feature_names[0])\n",
"plt.xlabel('species')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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Y88M2uxE4ICJnAg8DK6IJRimllLNsXTUUkY9DbwfSeTYffjp+OVARer8JmOFI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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f05193a9c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the distribution of the dataset\n",
"names = set(iris.target)\n",
"\n",
"# x and y are all the samples from column 0 (sepal_length) and 1 (sepal_width) respectively\n",
"x,y = iris.data[:,0], iris.data[:,1]\n",
"\n",
"for name in names:\n",
" cond = iris.target == name\n",
" plt.plot(x[cond], y[cond], linestyle='none', marker='o', label=iris.target_names[name])\n",
"\n",
"plt.legend(numpoints=1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, the Setosa class seems to be linearly separable with these two features.\n",
"\n",
"Another nice visualisation is given below."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f04f5a176a0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jnw+QnzZtGlnp6QzKzycKI76sM9DB7eb4vn0MLCgg0lfeDWidk8Nbr79eruek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\nMBgMljFGw2AwGAyW+f9Dg6SMpWb31AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f04f5a9c4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_index = 0\n",
"y_index = 1\n",
"formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
"plt.scatter(iris.data[:, x_index], iris.data[:, y_index], s=40,\n",
"c=iris.target)\n",
"plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
"plt.xlabel(iris.feature_names[x_index])\n",
"plt.ylabel(iris.feature_names[y_index])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This alternate visualisation also suggests that the Setosa class seems to be linearly separable.\n",
"\n",
"Students interested in practicing advanced visualisations can check [Advanced visualisation notebook](2_3_1_Advanced_Visualisation.ipynb).\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
"* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
"* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n",
"* [Matplotlib web page](http://matplotlib.org/index.html)\n",
"* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
"* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",
"* [Iris dataset visualisation notebook](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebook)\n",
"* [Tutorial plotting with Seaborn](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Licence\n",
"\n",
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
],
"metadata": {
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}