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294 lines
7.3 KiB
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294 lines
7.3 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\")\n",
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"\n",
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"# Course Notes for Learning Intelligent Systems\n",
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"\n",
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias\n",
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"\n",
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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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"* [Reading Data](#Reading-Data)\n",
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"* [Iris flower dataset](#Iris-flower-dataset)\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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"# Reading 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 goal of this notebook is to learn how to read and load a sample dataset.\n",
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"\n",
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"Scikit-learn comes with some bundled [datasets](http://scikit-learn.org/stable/datasets/): iris, digits, boston, etc.\n",
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"\n",
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"In this notebook we are going to use the Iris dataset."
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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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"## Iris flower dataset"
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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 [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), available at [UCI dataset repository](https://archive.ics.uci.edu/ml/datasets/Iris), is a classic dataset for classification.\n",
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"\n",
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"The dataset consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features.\n",
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"\n",
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""
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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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"In ordert to read the dataset, we import the datasets bundle and then load the Iris dataset. "
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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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"# import datasets from scikit-learn\n",
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"from sklearn import datasets\n",
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"\n",
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"# load iris dataset\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": "markdown",
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"metadata": {},
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"source": [
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"A dataset is a dictionary-like object that holds all the data and some metadata about the data. This data is stored in the `.data` member, which is a 2D (`n_samples`, `n_features`) array. In the case of supervised problem, one or more response variables are stored in the `.target` member."
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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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"#type 'bunch' of a dataset\n",
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"type(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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"# print descrition of the dataset\n",
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"print(iris.DESCR)"
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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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"# names of the features (attributes of the entities)\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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"#names of the targets(classes of the classifier)\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": "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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"#type numpy array\n",
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"type(iris.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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"Now we are going to inspect the dataset. You can consult the NumPy tutorial listed in the references."
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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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"#Data in the iris dataset. The value of the features of the samples.\n",
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"print(iris.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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"# Target. Category of every sample\n",
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"print(iris.target)"
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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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"# Iris data is a numpy array\n",
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"# We can inspect its shape (rows, columns). In our case, (n_samples, n_features)\n",
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"print(iris.data.shape)"
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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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"#Using numpy, I can print the dimensions (here we are working with 2D matriz)\n",
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"print(iris.data.ndim)"
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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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"# I can print n_samples\n",
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"print(iris.data.shape[0])"
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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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"# ... n_features\n",
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"print(iris.data.shape[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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# names of the features\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": "markdown",
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"metadata": {},
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"source": [
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"In following sessions we will learn how to load a dataset from a file (csv, excel, ...) using the pandas library."
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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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"* [Iris flower data set](https://en.wikipedia.org/wiki/Iris_flower_data_set)\n",
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"* [How to load an example dataset with scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html#loading-example-dataset)\n",
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"* [Dataset loading utilities in scikit-learn](http://scikit-learn.org/stable/datasets/)\n",
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"* [How to plot the Iris dataset](http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)\n",
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"* [An introduction to NumPy and Scipy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)\n",
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"* [NumPy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.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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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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"version": "3.5.5"
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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"autocomplete": true,
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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