Make notebook code match book examples more closely in chapter 1
Aurélien Geron
parent
88acd2b4b9
commit
1bc60fe315
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@ -2,7 +2,10 @@
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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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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"**Chapter 1 – The Machine Learning landscape**\n",
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"\n",
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@ -11,14 +14,20 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"# Setup"
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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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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
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]
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@ -28,6 +37,8 @@
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true,
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"slideshow": {
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"slide_type": "-"
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}
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@ -67,7 +78,10 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"# Load and prepare Life satisfaction data"
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]
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@ -76,7 +90,9 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -95,7 +111,9 @@
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -104,7 +122,10 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"# Load and prepare GDP per capita data"
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]
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@ -113,7 +134,9 @@
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -129,7 +152,9 @@
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -142,7 +167,9 @@
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -153,7 +180,9 @@
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -166,9 +195,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -195,7 +226,22 @@
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"sample_data.to_csv(\"life_satisfaction_vs_gdp_per_capita.csv\")"
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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": 10,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -204,9 +250,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -230,9 +278,11 @@
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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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"execution_count": 12,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -247,9 +297,11 @@
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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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"execution_count": 13,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -265,9 +317,11 @@
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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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"execution_count": 14,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -279,9 +333,11 @@
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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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"execution_count": 15,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -300,9 +356,11 @@
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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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"execution_count": 16,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -311,9 +369,11 @@
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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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"execution_count": 17,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -322,26 +382,40 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"backup = oecd_bli, gdp_per_capita\n",
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"\n",
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"def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
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" return sample_data\n",
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"\n",
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" return sample_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": 19,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# Code example\n",
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"########################################################################\n",
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"import sklearn\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import sklearn\n",
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"\n",
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"# Load the data\n",
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"oecd_bli = pd.read_csv(datapath+\"oecd_bli_2015.csv\", thousands=',')\n",
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"gdp_per_capita = pd.read_csv(datapath+\"gdp_per_capita.csv\", thousands=',',delimiter='\\t',\n",
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"oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
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"gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
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" encoding='latin1', na_values=\"n/a\")\n",
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"\n",
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"# Prepare the data\n",
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@ -354,24 +428,36 @@
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"plt.show()\n",
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"\n",
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"# Select a linear model\n",
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"lin_reg_model = sklearn.linear_model.LinearRegression()\n",
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"model = sklearn.linear_model.LinearRegression()\n",
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"\n",
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"# Train the model\n",
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"lin_reg_model.fit(X, y)\n",
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"model.fit(X, y)\n",
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"\n",
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"# Make a prediction for Cyprus\n",
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"X_new = [[22587]] # Cyprus' GDP per capita\n",
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"print(lin_reg_model.predict(X_new)) # outputs [[ 5.96242338]]\n",
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"########################################################################\n",
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"\n",
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"print(model.predict(X_new)) # outputs [[ 5.96242338]]"
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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": 20,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"oecd_bli, gdp_per_capita = backup"
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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": 18,
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"execution_count": 21,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -380,9 +466,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 22,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -399,9 +487,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -432,9 +522,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 24,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -458,9 +550,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -469,9 +563,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 26,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -480,9 +576,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 27,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -513,50 +611,79 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 28,
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"metadata": {
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"collapsed": false
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"backup = oecd_bli, gdp_per_capita\n",
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"\n",
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"def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
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" return sample_data\n",
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"\n",
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"# Code example\n",
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"########################################################################\n",
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"from sklearn import neighbors\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"# Load the data\n",
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"oecd_bli = pd.read_csv(datapath+\"oecd_bli_2015.csv\", thousands=',')\n",
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"gdp_per_capita = pd.read_csv(datapath+\"gdp_per_capita.csv\", thousands=',',delimiter='\\t',\n",
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" encoding='latin1', na_values=\"n/a\")\n",
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"\n",
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"# Prepare the data\n",
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"country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
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" return sample_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": 29,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# Replace this linear model:\n",
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"model = sklearn.linear_model.LinearRegression()"
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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": 30,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# with this k-neighbors regression model:\n",
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"model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
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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": 31,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"X = np.c_[country_stats[\"GDP per capita\"]]\n",
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"y = np.c_[country_stats[\"Life satisfaction\"]]\n",
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"\n",
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"# Visualize the data\n",
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"country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
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"plt.show()\n",
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"\n",
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||||
"# Select a k-neighboors regression model\n",
|
||||
"k_neigh_reg_model = neighbors.KNeighborsRegressor(n_neighbors=3)\n",
|
||||
"\n",
|
||||
"# Train the model\n",
|
||||
"k_neigh_reg_model.fit(X, y)\n",
|
||||
"model.fit(X, y)\n",
|
||||
"\n",
|
||||
"# Make a prediction for Cyprus\n",
|
||||
"X_new = [[22587]] # Cyprus' GDP per capita\n",
|
||||
"print(lin_reg_model.predict(X_new)) # outputs [[ 5.96242338]]\n",
|
||||
"########################################################################\n",
|
||||
"\n",
|
||||
"oecd_bli, gdp_per_capita = backup"
|
||||
"X_new = np.array([[22587.0]]) # Cyprus' GDP per capita\n",
|
||||
"print(model.predict(X_new)) # outputs [[ 5.76666667]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
@ -575,7 +702,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
"version": "3.5.3"
|
||||
},
|
||||
"nav_menu": {},
|
||||
"toc": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue